An example of microstructural optimization through heat treatments is the effective control of the corrosion behavior of the as-cast (F) Mg3Nd0.2Zn (wt. %) (NZ) and Mg3Nd0.2Zn0.4Zr (wt. %) (NZK) alloys through solution heat treatment (T4) and solution heat treated and artificially aged (T6) in 5 % NaCl solution. The T4 treatment is carried out at 540°C for 6 h followed by water quench at 25°C. After this solution treatment the alloys are artificially aged in an oil bath at 200°C for 16h (T6) [66]. Immersion tests indicate that the highest corrosion rates stand for the as-cast samples: 1.353 mg/cm2 /day and 0.203 mg/cm2 /day for NZ and NKZ alloys, respectively. The heat treatments increase the corrosion resistance in the following order: F < T6 < T4. The lowest corrosion rates values are obtained at T4 conditions (0.266 mg/cm2 /day for NZ alloy and 0.11 mg/cm2 /day for NZK alloy) and only increase slightly at T6 conditions. The change in the corrosion rate after the heat treatments is ascribed to micro‐ structural modifications. In the as-cast condition the microstructure of both alloys consist of α-Mg matrix and an eutectic Mg12Nd compound inhomogeneously distributed within the matrix. Because of the discontinuous distribution, the Mg12Nd acts as a microgalvanic cathode and, so, it accelerates the corrosion of the matrix. The authors reached this conclusion by comparing the role of Mg12Nd phase on the corrosion behavior of NZ and NZK alloys with the role of β phase (i.e., Mg17Al12) on the corrosion of AZ alloys [27]. Song and Atrens proposed that when the β phase is discontinuously distributed within the material, the corrosion rate of AZ alloys increases (see section 5.1.3) and thus the same behavior is expected for the Mg12Nd phase. However, when the NZ and NZK alloys are subjected to T4 or T6 treatments, the Mg12Nd compound dissolves into the matrix and microgalvanic couples are no longer present. The slightly higher corrosion rates detected at T6 to T4 is attributed to the precipitation of very small Nd-rich precipitates. Consistently, the corrosion morphologies reveal that the localized attack zones are more severe in the as-cast than in the T4 condition. Also, in the T6 condition the attack is slightly more severe than in T4 condition.

% Zr) alloy is processed by an integrated extrusion combined with ECAP, it is observed that Zn-Zr and Mg-Zn intermetallics become fractured and redistributed within the microstruc‐ ture. Electrochemical and immersion tests in NaCl electrolytes indicate that grain refinement

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The corrosion behavior also depends on microstructural effects such as twins, dislocations, etc., caused by deformation processing. For example, the corrosion resistance in 3.5 % NaCl of AZ31B magnesium alloy has been studied in the initially hard rolled condition and after heat treating at 200, 300, 400 and 500°C for 3 h in an inert atmosphere of argon and subsequent

The initial average grain size of 35 μm in the as-received condition increases with the heat treating temperature to 50 μm at 200°C (HT 200), 65 μm at 300°C (HT 300), 90 μm at 400°C (HT 400) and to 250 μm at 500°C (HT 500). In the HT300 conditions, the microstructure is untwined because a high density of twins are eliminated and so the intra-granular corrosion is the least. However, in the as-received and HT200 conditions the deformation twins and thus the dislocation density is higher. This can explain the more serious corrosion of the HT200 microstructure compared with the HT300 microstructure despite the fact that the HT200 microstructure is finer and thus the physical corrosion barrier is larger. In other words, twins accelerate the corrosion. From the physical metallurgy viewpoint, in the as rolled conditions (i.e., after plastic deformation), the amount of twins is the largest but they are progressively annihilated as temperature increases. For this reason potentiodynamic polarization curves

show that the corrosion rate increases as the microstructure becomes more twinned.

Not only the presence of twins but also the distribution and density of dislocations are correlated with the corrosion behavior. The AZ31 alloy plastically deformed by ECAP at 350°C with a pressing speed of 350 mm/min exhibit twins and a higher density of dislocations than after being extruded at 350°C with an extrusion ratio of 10.24 (in this case twins were not observed) [65]. From corrosion studies in 3.5 % NaCl saturated with Mg(OH)2 at pH 10.5, the authors concluded that the corrosion rate of AZ31 alloy decreases after extrusion but it increases after ECAP, suggesting that the twins and/or presence of higher density of disloca‐

The corrosion behavior of a AZ31 Mg alloy with different grain sizes immersed in two different solutions, NaCl and phosphate-buffer solution (PBS) has been studied by other researchers. The microstructure is refined by ECAP with a first pass of 250°C and successively heat treated to 300°C and rolled [72]. The best corrosion behavior is attained by the alloy having finest grains after long-term immersion in PBS [72]. This behavior is related to the formation of a mixed compact protective layer of P-containing compounds together with magnesium hydroxide that promotes protection against the chloride ions. The superior corrosion behavior of the fine-grained AZ31 alloy over the coarser one is attributed to the formation of a layer of corrosion products that provides better protection against the diffusion of aggressive ions to

Although these results suggest that the corrosion performance can be tuned by controlling the microstructure, other factors such as the chemical composition plays a more important role.

and redistribution of Zr and Zn solutes improve the corrosion resistance [70].

quenching in water to room temperature [71].

tions decisively affect the corrosion rate.

the surface of the material [72].

For a ZE41 alloy (4 wt. % Zn, 1 wt. % RE) the corrosion behavior improves after heat treating for 5 days at 500°C [67]. This improvement is again related to microstructural changes that occur during heat treatment. The Mg7Zn3RE phase present in the material before heating partly redissolves, which explains the increasing concentration of Zn and RE in the matrix. Similarly, the corrosion resistance of the as-cast Mg10Gd3Y0.4Zr (wt. %) alloy increases with solution treatments due to the dissolution into the α-Mg matrix of the (Gd+Y)-rich eutectic present in the as-cast condition [68]. The improvement of the corrosion resistance greatly depends on the thermal treatment. Namely, it is highest for a T4 solution treatment (500°C for 6 h) than for any of the T6 solution treatments (oil bath at 250°C for 0.5, 16, 193 and 500 h). The reason lies in that an increasing ageing time increases the volume fraction of secondary phase that act as cathodes and thereby ultimately increases the corrosion rate.

The microstructure of alloys can be optimized if the temperature is properly controlled during the dynamic recrystallization in an extrusion process. The corrosion behavior in SBF at 37°C of Mg3Nd0.2Zn0.4Zr (wt. %) NZK alloy initially solution-treated at T4 conditions (at 540°C for 10 h and then water quenched to room temperature) is effectively modified by controlling the extrusion temperature (250°C, 350°C and 450°C) [69]. Both immersion and electrochemical tests indicate that the corrosion rate in the extruded condition at 250°C, 350°C and 450°C is much slower than in the T4 state. Moreover, the corrosion resistance increases with the decrease of the extrusion temperature and so does the grain size.

Deformation processing can also have an effect on the redistribution of solutes within the microstructure and ultimately affect the corrosion behavior. When a ZK60 (6 wt.% Zn, 0.5 wt. % Zr) alloy is processed by an integrated extrusion combined with ECAP, it is observed that Zn-Zr and Mg-Zn intermetallics become fractured and redistributed within the microstruc‐ ture. Electrochemical and immersion tests in NaCl electrolytes indicate that grain refinement and redistribution of Zr and Zn solutes improve the corrosion resistance [70].

followed by water quench at 25°C. After this solution treatment the alloys are artificially aged in an oil bath at 200°C for 16h (T6) [66]. Immersion tests indicate that the highest corrosion

alloys, respectively. The heat treatments increase the corrosion resistance in the following order: F < T6 < T4. The lowest corrosion rates values are obtained at T4 conditions (0.266

conditions. The change in the corrosion rate after the heat treatments is ascribed to micro‐ structural modifications. In the as-cast condition the microstructure of both alloys consist of α-Mg matrix and an eutectic Mg12Nd compound inhomogeneously distributed within the matrix. Because of the discontinuous distribution, the Mg12Nd acts as a microgalvanic cathode and, so, it accelerates the corrosion of the matrix. The authors reached this conclusion by comparing the role of Mg12Nd phase on the corrosion behavior of NZ and NZK alloys with the role of β phase (i.e., Mg17Al12) on the corrosion of AZ alloys [27]. Song and Atrens proposed that when the β phase is discontinuously distributed within the material, the corrosion rate of AZ alloys increases (see section 5.1.3) and thus the same behavior is expected for the Mg12Nd phase. However, when the NZ and NZK alloys are subjected to T4 or T6 treatments, the Mg12Nd compound dissolves into the matrix and microgalvanic couples are no longer present. The slightly higher corrosion rates detected at T6 to T4 is attributed to the precipitation of very small Nd-rich precipitates. Consistently, the corrosion morphologies reveal that the localized attack zones are more severe in the as-cast than in the T4 condition. Also, in the T6 condition

For a ZE41 alloy (4 wt. % Zn, 1 wt. % RE) the corrosion behavior improves after heat treating for 5 days at 500°C [67]. This improvement is again related to microstructural changes that occur during heat treatment. The Mg7Zn3RE phase present in the material before heating partly redissolves, which explains the increasing concentration of Zn and RE in the matrix. Similarly, the corrosion resistance of the as-cast Mg10Gd3Y0.4Zr (wt. %) alloy increases with solution treatments due to the dissolution into the α-Mg matrix of the (Gd+Y)-rich eutectic present in the as-cast condition [68]. The improvement of the corrosion resistance greatly depends on the thermal treatment. Namely, it is highest for a T4 solution treatment (500°C for 6 h) than for any of the T6 solution treatments (oil bath at 250°C for 0.5, 16, 193 and 500 h). The reason lies in that an increasing ageing time increases the volume fraction of secondary phase that act as

The microstructure of alloys can be optimized if the temperature is properly controlled during the dynamic recrystallization in an extrusion process. The corrosion behavior in SBF at 37°C of Mg3Nd0.2Zn0.4Zr (wt. %) NZK alloy initially solution-treated at T4 conditions (at 540°C for 10 h and then water quenched to room temperature) is effectively modified by controlling the extrusion temperature (250°C, 350°C and 450°C) [69]. Both immersion and electrochemical tests indicate that the corrosion rate in the extruded condition at 250°C, 350°C and 450°C is much slower than in the T4 state. Moreover, the corrosion resistance increases with the

Deformation processing can also have an effect on the redistribution of solutes within the microstructure and ultimately affect the corrosion behavior. When a ZK60 (6 wt.% Zn, 0.5 wt.

/day and 0.203 mg/cm2

/day for NZK alloy) and only increase slightly at T6

/day for NZ and NKZ

rates stand for the as-cast samples: 1.353 mg/cm2

324 Biodegradation - Engineering and Technology

/day for NZ alloy and 0.11 mg/cm2

the attack is slightly more severe than in T4 condition.

cathodes and thereby ultimately increases the corrosion rate.

decrease of the extrusion temperature and so does the grain size.

mg/cm2

The corrosion behavior also depends on microstructural effects such as twins, dislocations, etc., caused by deformation processing. For example, the corrosion resistance in 3.5 % NaCl of AZ31B magnesium alloy has been studied in the initially hard rolled condition and after heat treating at 200, 300, 400 and 500°C for 3 h in an inert atmosphere of argon and subsequent quenching in water to room temperature [71].

The initial average grain size of 35 μm in the as-received condition increases with the heat treating temperature to 50 μm at 200°C (HT 200), 65 μm at 300°C (HT 300), 90 μm at 400°C (HT 400) and to 250 μm at 500°C (HT 500). In the HT300 conditions, the microstructure is untwined because a high density of twins are eliminated and so the intra-granular corrosion is the least. However, in the as-received and HT200 conditions the deformation twins and thus the dislocation density is higher. This can explain the more serious corrosion of the HT200 microstructure compared with the HT300 microstructure despite the fact that the HT200 microstructure is finer and thus the physical corrosion barrier is larger. In other words, twins accelerate the corrosion. From the physical metallurgy viewpoint, in the as rolled conditions (i.e., after plastic deformation), the amount of twins is the largest but they are progressively annihilated as temperature increases. For this reason potentiodynamic polarization curves show that the corrosion rate increases as the microstructure becomes more twinned.

Not only the presence of twins but also the distribution and density of dislocations are correlated with the corrosion behavior. The AZ31 alloy plastically deformed by ECAP at 350°C with a pressing speed of 350 mm/min exhibit twins and a higher density of dislocations than after being extruded at 350°C with an extrusion ratio of 10.24 (in this case twins were not observed) [65]. From corrosion studies in 3.5 % NaCl saturated with Mg(OH)2 at pH 10.5, the authors concluded that the corrosion rate of AZ31 alloy decreases after extrusion but it increases after ECAP, suggesting that the twins and/or presence of higher density of disloca‐ tions decisively affect the corrosion rate.

The corrosion behavior of a AZ31 Mg alloy with different grain sizes immersed in two different solutions, NaCl and phosphate-buffer solution (PBS) has been studied by other researchers. The microstructure is refined by ECAP with a first pass of 250°C and successively heat treated to 300°C and rolled [72]. The best corrosion behavior is attained by the alloy having finest grains after long-term immersion in PBS [72]. This behavior is related to the formation of a mixed compact protective layer of P-containing compounds together with magnesium hydroxide that promotes protection against the chloride ions. The superior corrosion behavior of the fine-grained AZ31 alloy over the coarser one is attributed to the formation of a layer of corrosion products that provides better protection against the diffusion of aggressive ions to the surface of the material [72].

Although these results suggest that the corrosion performance can be tuned by controlling the microstructure, other factors such as the chemical composition plays a more important role. For example, Liao et al. [73] observed that the fine grained AZ31B alloy exhibits a lower corrosion resistance than the AM60 alloy with coarser grains.

electrochemical techniques in pH 9.2 sodium borate [79]. These studies concluded that the corrosion rate of magnesium is decreased for larger contents of aluminium. Similarly, low concentrations of zinc and lithium decrease the corrosion rate below that of pure magnesium [79]. These results indicate that composition has an important influence on the corrosion rate

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As was explained on section 4.1, the corrosion rate of magnesium alloys depends on the nature and concentration of impurities. The corrosion resistance can be improved either by purifying Mg or through appropriate additions of alloying elements. Mg alloys are basically classified [34] in two groups: 1) those that contain Al as primary alloying element and 2) those that do not contain Al and have small amounts of Zr to refine the microstructure. Al is generally considered as a beneficial element to improve the corrosion resistance [80]. For small contents, Al remains in solid solution, but above certain concentration β-Mg17Al12 secondary particles

The β-phase is very stable in NaCl solutions and it is inert to corrosion due to the formation of a passive thin film on its surface. However, β-phase is also an effective cathode, which can explain the dual role of these precipitates in the corrosion of AZ alloys according to Song and Atrens [25]. A fine and continuous distribution of β-phase is recommended to increase the corrosion resistance. For example, Lunder et al. reported that Al additions higher than 8 wt. % increase the corrosion resistance of Mg-Al alloys [81]. An improvement of the corrosion resistance with the Al content is also found in AZ91, AZ61 and AZ31 alloys in 5 % NaCl [82]. However, Song et al. reported an increase of the corrosion rate in NaCl in the following order

For the second family of alloys, small additions of Zr refine the microstructure of Mg and improve the corrosion resistance [84]. Since Zr can easily combine with impurities, especially Fe and Ni, it can form insoluble precipitates that settle out during melting. This purification effect of Zr enhances the corrosion resistance of Mg [85]. Depending on the composition, a minimum concentration of Zr is required to observe such effect. For example, for Zr contents from 0 to 0.42 wt. % the corrosion resistance of Mg10Gd3Y (10 wt. % Gd, 3 wt. %) deteriorates, whereas for higher Zr contents, ranging from 0.42 to 0.93 %, the corrosion resistance improves. The distinct behavior is attributed to the differences in size and distribution of the Zr-rich particles [86]. An exceedingly larger amount of Zr can lead to precipitation of Zr within the

matrix, which becomes detrimental for the corrosion performance of the alloy [34].

The addition of 1 wt. % of Al, Ag, In, Mn, Sn, Zn and Zr elements decrease the volume of evolved hydrogen gas, and thus decrease the corrosion rate, of Mg when immersed either in SBF or in Hank´s solution [87]. On the contrary, the addition of 1 wt. % Y or Si have a deleterious

Ca is an essential element to the body since it is a major component of human bones. For this reason, it has been used over the years to fabricate biocompatible Mg-based alloys. The concentration of Ca has, though, to be carefully controlled to avoid the precipitation

of glassy Mg alloys, as it has also been observed in crystalline alloys.

*5.1.3. Influence of alloying elements on corrosion performance*

precipitate.

AZ501 < AZ21 < AZ91 [83].

effect on the corrosion performance.

#### *5.1.2. Amorphous and partly amorphous alloys*

As aforementioned, the grain size can be tuned by controlling the cooling rate. For certain compositions such as AZ91 alloy [74] rapid cooling is an effective technique to obtain fine grain sizes. For other Mg-based compositions a sufficiently fast cooling rate can lead to the formation of glassy materials. Moreover, rapid cooling allows to extend the solubility of alloying elements in Mg alloys and to form a homogeneous single-phase structure (i.e., metallic glass) with a very different corrosion behavior than that of crystalline Mg alloys [75]. Typically, amorphous materials possess stronger corrosion and chemical resistance than their crystalline counter‐ parts due to the absence of grain boundaries, segregated phases, secondary particles and also due to chemical homogeneity [76]. For this reason different Mg-based glassy materials have been studied over the years. For example, glassy Mg60+xZn35-xCa5 (0<x<7 at. %) ribbons of 50 μm in thickness can be successfully obtained by melt spinning [36]. Immersion tests of these ribbons in SBF lead to the formation of corrosion layers that are different from those found in Zn-poor and Zn-rich alloys. For the Zn-rich alloys(above 28 at. % Zn), the Zn-rich oxygencontaining passivating layer that is formed on the surface of the ribbon is responsible for the more noble behavior of these alloys as compared to the Zn-poor alloys [36]. Moreover, a high Zn content appears to reduce hydrogen evolution. In fact, due to the extended solubility of Zn in the amorphous structure of the Mg-Zn-Ca system, the Mg60Zn35Ca5 glass only exhibits marginal hydrogen evolution during in vitro and in vivo degradation [36].

Through the addition of different alloying elements to the Mg-Zn-Ca alloys family, the corrosion behavior can be tuned as well. Small Pd additions are enough to decrease the glass forming ability of glassy Mg72Zn23Ca5 alloys and to shift the corrosion potential towards more positive values [6]. Cytotoxic tests do not show the presence of death cells, which confirm that these alloys might have potential use as implants [77]. Cytocompatibility tests also show that metallic glass Mg66Zn30Ca4 and Mg70Zn25Ca5 samples have higher cell viability and exhibit more positive corrosion potential than that of as-rolled crystalline pure Mg [78].

It is well known that glassy materials can be used as precursors of crystalline phases by controlling the crystallization temperature and/or time. Since the corrosion behavior depends on the structure (i.e., amorphous vs. crystalline) of the material, the extent of crystallization can be controlled to tune the corrosion rate. For example, glassy Mg67Zn28Ca5 ribbons exhibit an increase of the corrosion resistance in simulated body fluid with the increase of annealing temperature up to a maximum and then the resistance decreases rapidly for higher tempera‐ tures. The best corrosion resistance of these ribbons is attained at 160°C, when the microstruc‐ ture is constituted by a metastable crystalline Mg102.08Zn39.6 phase embedded in an amorphous matrix [76]. This behavior was explained considering that the electrochemical activity of this phase is similar to that of its amorphous matrix. However, the newly formed phases at 225°C are more active and worsen the corrosion resistance of the alloy [76].

To determine the effect that alloying elements have on the corrosion resistance of rapidly solidified magnesium alloys, different binary Mg-based glassy alloys were studied by using electrochemical techniques in pH 9.2 sodium borate [79]. These studies concluded that the corrosion rate of magnesium is decreased for larger contents of aluminium. Similarly, low concentrations of zinc and lithium decrease the corrosion rate below that of pure magnesium [79]. These results indicate that composition has an important influence on the corrosion rate of glassy Mg alloys, as it has also been observed in crystalline alloys.

## *5.1.3. Influence of alloying elements on corrosion performance*

For example, Liao et al. [73] observed that the fine grained AZ31B alloy exhibits a lower

As aforementioned, the grain size can be tuned by controlling the cooling rate. For certain compositions such as AZ91 alloy [74] rapid cooling is an effective technique to obtain fine grain sizes. For other Mg-based compositions a sufficiently fast cooling rate can lead to the formation of glassy materials. Moreover, rapid cooling allows to extend the solubility of alloying elements in Mg alloys and to form a homogeneous single-phase structure (i.e., metallic glass) with a very different corrosion behavior than that of crystalline Mg alloys [75]. Typically, amorphous materials possess stronger corrosion and chemical resistance than their crystalline counter‐ parts due to the absence of grain boundaries, segregated phases, secondary particles and also due to chemical homogeneity [76]. For this reason different Mg-based glassy materials have been studied over the years. For example, glassy Mg60+xZn35-xCa5 (0<x<7 at. %) ribbons of 50 μm in thickness can be successfully obtained by melt spinning [36]. Immersion tests of these ribbons in SBF lead to the formation of corrosion layers that are different from those found in Zn-poor and Zn-rich alloys. For the Zn-rich alloys(above 28 at. % Zn), the Zn-rich oxygencontaining passivating layer that is formed on the surface of the ribbon is responsible for the more noble behavior of these alloys as compared to the Zn-poor alloys [36]. Moreover, a high Zn content appears to reduce hydrogen evolution. In fact, due to the extended solubility of Zn in the amorphous structure of the Mg-Zn-Ca system, the Mg60Zn35Ca5 glass only exhibits

corrosion resistance than the AM60 alloy with coarser grains.

marginal hydrogen evolution during in vitro and in vivo degradation [36].

positive corrosion potential than that of as-rolled crystalline pure Mg [78].

are more active and worsen the corrosion resistance of the alloy [76].

Through the addition of different alloying elements to the Mg-Zn-Ca alloys family, the corrosion behavior can be tuned as well. Small Pd additions are enough to decrease the glass forming ability of glassy Mg72Zn23Ca5 alloys and to shift the corrosion potential towards more positive values [6]. Cytotoxic tests do not show the presence of death cells, which confirm that these alloys might have potential use as implants [77]. Cytocompatibility tests also show that metallic glass Mg66Zn30Ca4 and Mg70Zn25Ca5 samples have higher cell viability and exhibit more

It is well known that glassy materials can be used as precursors of crystalline phases by controlling the crystallization temperature and/or time. Since the corrosion behavior depends on the structure (i.e., amorphous vs. crystalline) of the material, the extent of crystallization can be controlled to tune the corrosion rate. For example, glassy Mg67Zn28Ca5 ribbons exhibit an increase of the corrosion resistance in simulated body fluid with the increase of annealing temperature up to a maximum and then the resistance decreases rapidly for higher tempera‐ tures. The best corrosion resistance of these ribbons is attained at 160°C, when the microstruc‐ ture is constituted by a metastable crystalline Mg102.08Zn39.6 phase embedded in an amorphous matrix [76]. This behavior was explained considering that the electrochemical activity of this phase is similar to that of its amorphous matrix. However, the newly formed phases at 225°C

To determine the effect that alloying elements have on the corrosion resistance of rapidly solidified magnesium alloys, different binary Mg-based glassy alloys were studied by using

*5.1.2. Amorphous and partly amorphous alloys*

326 Biodegradation - Engineering and Technology

As was explained on section 4.1, the corrosion rate of magnesium alloys depends on the nature and concentration of impurities. The corrosion resistance can be improved either by purifying Mg or through appropriate additions of alloying elements. Mg alloys are basically classified [34] in two groups: 1) those that contain Al as primary alloying element and 2) those that do not contain Al and have small amounts of Zr to refine the microstructure. Al is generally considered as a beneficial element to improve the corrosion resistance [80]. For small contents, Al remains in solid solution, but above certain concentration β-Mg17Al12 secondary particles precipitate.

The β-phase is very stable in NaCl solutions and it is inert to corrosion due to the formation of a passive thin film on its surface. However, β-phase is also an effective cathode, which can explain the dual role of these precipitates in the corrosion of AZ alloys according to Song and Atrens [25]. A fine and continuous distribution of β-phase is recommended to increase the corrosion resistance. For example, Lunder et al. reported that Al additions higher than 8 wt. % increase the corrosion resistance of Mg-Al alloys [81]. An improvement of the corrosion resistance with the Al content is also found in AZ91, AZ61 and AZ31 alloys in 5 % NaCl [82]. However, Song et al. reported an increase of the corrosion rate in NaCl in the following order AZ501 < AZ21 < AZ91 [83].

For the second family of alloys, small additions of Zr refine the microstructure of Mg and improve the corrosion resistance [84]. Since Zr can easily combine with impurities, especially Fe and Ni, it can form insoluble precipitates that settle out during melting. This purification effect of Zr enhances the corrosion resistance of Mg [85]. Depending on the composition, a minimum concentration of Zr is required to observe such effect. For example, for Zr contents from 0 to 0.42 wt. % the corrosion resistance of Mg10Gd3Y (10 wt. % Gd, 3 wt. %) deteriorates, whereas for higher Zr contents, ranging from 0.42 to 0.93 %, the corrosion resistance improves. The distinct behavior is attributed to the differences in size and distribution of the Zr-rich particles [86]. An exceedingly larger amount of Zr can lead to precipitation of Zr within the matrix, which becomes detrimental for the corrosion performance of the alloy [34].

The addition of 1 wt. % of Al, Ag, In, Mn, Sn, Zn and Zr elements decrease the volume of evolved hydrogen gas, and thus decrease the corrosion rate, of Mg when immersed either in SBF or in Hank´s solution [87]. On the contrary, the addition of 1 wt. % Y or Si have a deleterious effect on the corrosion performance.

Ca is an essential element to the body since it is a major component of human bones. For this reason, it has been used over the years to fabricate biocompatible Mg-based alloys. The concentration of Ca has, though, to be carefully controlled to avoid the precipitation

of Mg2Ca particles (that takes place for Ca contents ranging from 0.8 to 5 wt. % [88] or from 1 to 3 wt. % [89] depending on the system under study). These Mg2Ca particles form micro-galvanic cells within the Mg matrix and accelerate preferentially the dissolution of the latter, worsening the corrosion resistance of the binary Mg-xCa alloy. For 1.5 wt. % Ca, a protective oxide layer of MgO and CaO is formed after heating to 500°C for 1h [90]. The influence of Ca on the corrosion behavior not only depends on its amount but also on the composition of the Mg-based alloy to which it is added. For example, the addition of 13 wt. % Ca increases the corrosion rate of AZ91D alloy (37 wt. % Al, 0.5 wt. % Zn) [91]. An improvement of the biocorrosion resistance is also detected when 0.2-0.4 wt. % Ca is added to a Mg-Si alloy since it refines the grain size and modifies the morphology of Mg2Si phase [92]. The same holds when 1.6 wt. % Zn is added to Mg-Si alloy due to the modifica‐ tions on the Mg2Si phase morphology derived from the addition; namely from a coarse eutectic structure to a small dot or short bar shape [92]. Zn is an essential element to the human body and capable of decreasing the corrosion rate of pure Mg in small amounts. For example, the corrosion rate (measured in terms of volume of evolved hydrogen) of CP-Mg decreases from 26 ml/cm2 /day to 0.280 ml/cm2 /day with the addition of 1 wt.% Zn [22]. The addition of 6 wt. % zinc shifts the corrosion potential toward more cathodic values and decreases the in-vitro degradation rate of high purity Mg in SBF [93]. However, concentrations above the equilibrium solid solubility of Zn in Mg (i.e., 6.2 wt. %) [94] can lead to an increase of the corrosion rate in 3 % NaCl due to the formation of β-Mg7Zn3 phase in the magnesium matrix [95]. The introduction of Mn can help to decrease the corrosion rate of Mg-Zn alloys. Ahmed et al. [96] reported that adding Mn to a Mgbased alloy containing 4 to 8 wt. %. Zn decreases the dissolution rate of Mg. The corro‐ sion rate of Mg2Zn0.2Mn (2 wt. % Zn, 0.2 wt. % Mn) in Hank´s solution is also smaller than that of Mg1Zn (1 wt. % Zn) [22].

The mechanical integrity can be evaluated using various mechanical tests (three-point bending, tensile tests, nanoindentation, etc). These tests can be performed under physiological

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From nanoindentation tests, Pellicer et al. [77] studied the evolution of the Young´s modulus (Er), hardness (H), and H/Er ratio (which is an indirect measure of the wear resistance) of amorphous Mg72Zn23Ca5 after different immersion times in HBSS as shown in Fig. 5. The same study was carried out on crystalline Mg70Zn23Ca5Pd2 alloy and the results were systematically compared. While the stiffness of both compositions decreases with the immersion time, hardness exhibits a more complex dependence, especially for the Mg72Zn23Ca5 alloy. Namely, an increase is observed after short-term immersion, which was mainly attributed to the fast dissolution of Mg and the concomitant enrichment in Zn (Zn is mechanically harder, so solution hardening takes place). For longer immersion times, the dissolution progresses and the alloy not only undergoes surface chemical change but the surface is also physically modified with to the formation of flaws such as pores and corrugations, which cause a decrease of hardness [77]. The values of H/Er increase from 0.053 for the as-cast Mg72Zn23Ca5 alloy to 0.1 for Mg72Zn23Ca5 after 2h immersion in HBSS at 37°C, thus indicating that the effect of porosity on the Young's modulus for short-immersion times is more noticeable than on hardness. These

results are consistent with those observed in many other metallic alloys [99].

**Figure 5.** Dependence of (a) reduced Young's modulus, Er, (b) hardness, H and (c) H/Er ratio on the immersion time in HBBS at 37°C for Mg72Zn23Ca5 alloy. Adapted and reprinted from Pellicer et al. [77], page 8, with permission from John

To evaluate the mechanical integrity of AZ91Ca alloy (i.e., a calcium-containing magnesium alloy) in SBF at 36.5°C, slow strain rate tensile tests at 1.2x10-7 s-1 were carried out [91]. The AZ91Ca alloy shows lower elongation (3.5±0.2 %) and lower ultimate tensile strength (106±10 MPa) in the SBF than in air (4.6±0.3 % and 126±5 MPa, respectively). The decrease of the mechanical performance is, however, small and thus the alloy is not highly susceptible to corrosion in SBF. From electrochemical experiments it is observed that the AZ91Ca alloy exhibits improved corrosion resistance compared to the AZ91 alloy, which can be attributed to the formation of calcium phosphate on the surface of the AZ91Ca alloy. This surface film has higher stability than the film formed on AZ91 alloy for this reason not only the general

corrosion resistance but also the pitting corrosion resistance improve.

conditions or in air.

Wiley&Sons.

Other atypical alloying elements such as Y, Ce, Ti and Sc were reported to improve the corrosion performance when alloyed with Mg at a level below the solubility limit [97].

## **6. Biodegradation and mechanical integrity**

The use of Mg alloys as weight-bearing implants requires that the material should have sufficient strength not only at the moment of being implanted but also when the alloy degrades over the time while remaining in contact with body fluids. It is important that implants keep their strength at least until the bone heals. For this reason different studies have been carried out to evaluate the mass loss and evolution of the strength over the implantation or immersion time [77].

According to Pietak et al. [98] the best technique to assess the degradation of Mg alloys is measuring the mechanical integrity as a function of the incubation time. Nevertheless, the measurement of the mass change [46] has been more frequently used. However, this procedure has several shortcomings due to the association of non-soluble degradation products that precipitate on the sample and obscure the mass loss [98].

The mechanical integrity can be evaluated using various mechanical tests (three-point bending, tensile tests, nanoindentation, etc). These tests can be performed under physiological conditions or in air.

of Mg2Ca particles (that takes place for Ca contents ranging from 0.8 to 5 wt. % [88] or from 1 to 3 wt. % [89] depending on the system under study). These Mg2Ca particles form micro-galvanic cells within the Mg matrix and accelerate preferentially the dissolution of the latter, worsening the corrosion resistance of the binary Mg-xCa alloy. For 1.5 wt. % Ca, a protective oxide layer of MgO and CaO is formed after heating to 500°C for 1h [90]. The influence of Ca on the corrosion behavior not only depends on its amount but also on the composition of the Mg-based alloy to which it is added. For example, the addition of 13 wt. % Ca increases the corrosion rate of AZ91D alloy (37 wt. % Al, 0.5 wt. % Zn) [91]. An improvement of the biocorrosion resistance is also detected when 0.2-0.4 wt. % Ca is added to a Mg-Si alloy since it refines the grain size and modifies the morphology of Mg2Si phase [92]. The same holds when 1.6 wt. % Zn is added to Mg-Si alloy due to the modifica‐ tions on the Mg2Si phase morphology derived from the addition; namely from a coarse eutectic structure to a small dot or short bar shape [92]. Zn is an essential element to the human body and capable of decreasing the corrosion rate of pure Mg in small amounts. For example, the corrosion rate (measured in terms of volume of evolved hydrogen) of

/day to 0.280 ml/cm2

[22]. The addition of 6 wt. % zinc shifts the corrosion potential toward more cathodic values and decreases the in-vitro degradation rate of high purity Mg in SBF [93]. However, concentrations above the equilibrium solid solubility of Zn in Mg (i.e., 6.2 wt. %) [94] can lead to an increase of the corrosion rate in 3 % NaCl due to the formation of β-Mg7Zn3 phase in the magnesium matrix [95]. The introduction of Mn can help to decrease the corrosion rate of Mg-Zn alloys. Ahmed et al. [96] reported that adding Mn to a Mgbased alloy containing 4 to 8 wt. %. Zn decreases the dissolution rate of Mg. The corro‐ sion rate of Mg2Zn0.2Mn (2 wt. % Zn, 0.2 wt. % Mn) in Hank´s solution is also smaller

Other atypical alloying elements such as Y, Ce, Ti and Sc were reported to improve the corrosion performance when alloyed with Mg at a level below the solubility limit [97].

The use of Mg alloys as weight-bearing implants requires that the material should have sufficient strength not only at the moment of being implanted but also when the alloy degrades over the time while remaining in contact with body fluids. It is important that implants keep their strength at least until the bone heals. For this reason different studies have been carried out to evaluate the mass loss and evolution of the strength over the implantation or immersion

According to Pietak et al. [98] the best technique to assess the degradation of Mg alloys is measuring the mechanical integrity as a function of the incubation time. Nevertheless, the measurement of the mass change [46] has been more frequently used. However, this procedure has several shortcomings due to the association of non-soluble degradation products that

/day with the addition of 1 wt.% Zn

CP-Mg decreases from 26 ml/cm2

328 Biodegradation - Engineering and Technology

than that of Mg1Zn (1 wt. % Zn) [22].

time [77].

**6. Biodegradation and mechanical integrity**

precipitate on the sample and obscure the mass loss [98].

From nanoindentation tests, Pellicer et al. [77] studied the evolution of the Young´s modulus (Er), hardness (H), and H/Er ratio (which is an indirect measure of the wear resistance) of amorphous Mg72Zn23Ca5 after different immersion times in HBSS as shown in Fig. 5. The same study was carried out on crystalline Mg70Zn23Ca5Pd2 alloy and the results were systematically compared. While the stiffness of both compositions decreases with the immersion time, hardness exhibits a more complex dependence, especially for the Mg72Zn23Ca5 alloy. Namely, an increase is observed after short-term immersion, which was mainly attributed to the fast dissolution of Mg and the concomitant enrichment in Zn (Zn is mechanically harder, so solution hardening takes place). For longer immersion times, the dissolution progresses and the alloy not only undergoes surface chemical change but the surface is also physically modified with to the formation of flaws such as pores and corrugations, which cause a decrease of hardness [77]. The values of H/Er increase from 0.053 for the as-cast Mg72Zn23Ca5 alloy to 0.1 for Mg72Zn23Ca5 after 2h immersion in HBSS at 37°C, thus indicating that the effect of porosity on the Young's modulus for short-immersion times is more noticeable than on hardness. These results are consistent with those observed in many other metallic alloys [99].

**Figure 5.** Dependence of (a) reduced Young's modulus, Er, (b) hardness, H and (c) H/Er ratio on the immersion time in HBBS at 37°C for Mg72Zn23Ca5 alloy. Adapted and reprinted from Pellicer et al. [77], page 8, with permission from John Wiley&Sons.

To evaluate the mechanical integrity of AZ91Ca alloy (i.e., a calcium-containing magnesium alloy) in SBF at 36.5°C, slow strain rate tensile tests at 1.2x10-7 s-1 were carried out [91]. The AZ91Ca alloy shows lower elongation (3.5±0.2 %) and lower ultimate tensile strength (106±10 MPa) in the SBF than in air (4.6±0.3 % and 126±5 MPa, respectively). The decrease of the mechanical performance is, however, small and thus the alloy is not highly susceptible to corrosion in SBF. From electrochemical experiments it is observed that the AZ91Ca alloy exhibits improved corrosion resistance compared to the AZ91 alloy, which can be attributed to the formation of calcium phosphate on the surface of the AZ91Ca alloy. This surface film has higher stability than the film formed on AZ91 alloy for this reason not only the general corrosion resistance but also the pitting corrosion resistance improve.

Krause et al. [100] compared the evolution of the mechanical behavior of Mg0.8Ca (8 wt.% Ca), LAE442 (4 wt. % Li, 4 wt. % Al, 2 wt. % RE) and WE43 (4 wt. % Y, 3 wt. % RE) alloys implanted in rabbits for 3 and 6 months by three-point bending tests. All the samples exhibit biodegra‐ dation as can be deduced from the loss in volume with implantation period. The MgCa0.8 alloy degrades slowly during the first 3 months but its corrosion rate accelerates during the following 3 months. The LA442 alloy exhibits slower degradation rate than the Mg0.8Ca and WE43 alloys. The difference of degradation rate is responsible for the distinct mechanical performance of the alloys over the time. Thee-point bending test results indicate the following trend in the initial strength: LAE442 (255.67±5.69 N) > WE43 (238.05 ±21.68 N) > Mg0.8Ca (178.76±25.15 N). However, after 3 months the strength trend changes so that it decreases in the following order: WE43 (185.59±15.64 N) > LAE442 (153.21±18.45 N) > Mg0.8Ca (115.42±9.66 N). After 6 months the strength follows this sequence: LAE442 (134.68±14.68 N) > WE43 (122.23±23.65 N) > Mg0.8Ca (52.90±5.96 N). From the results of the maximal applied force it can be deduced that the LAE442 alloy degrades faster during the first 3 months and slower between 3 and 6 months. The degradation rate of Mg0.8Ca and WE43 alloys is different; it decreases in a linear manner over the time [100]. The ductility of the alloys was also assessed from three-point bending tests by measuring the bending displacement but concluding results could not be obtained due to high scattering. The Mg0.8Ca alloy exhibits the highest loss and the LAE442 the lowest loss in volume after 6 months.

Figure 6 shows a comparative summary of the mechanical properties (compressive yield stress, σy,C, and Young's modulus, E) of various families of materials that can be used as bioabsorbable implants, such as metallic alloys, biodegradable polymers and ceramics. From the correlation H ≈ C σy,C (where C is the so-called constraint factor and is normally close to 3 for crystalline metallic alloys and slightly higher for metallic glasses [103]) the mechanical hardness is

Biodegradation and Mechanical Integrity of Magnesium and Magnesium Alloys Suitable for Implants

http://dx.doi.org/10.5772/55584

331

The yield stress of Mg-Zn-Ca bulk metallic glasses is relatively large (Figure 6), thus indicating that they are one of the hardest biodegradable materials reported in the literature. Moreover, the values of Young's modulus of glassy Mg-Zn-Ca are closer to that of cortical bone (Ebone = 3– 20 GPa) than most crystalline Mg-based alloys (they are also mechanically softer) and synthetic hydroxyapatites. Considering that the Mg70Zn23Ca5Pd2 alloy is fully crystalline, its hardness is also generally larger than that of most Mg-Zn-Ca crystalline alloys. Compared with most Fe-based biodegradable alloys, Mg-based bulk metallic glasses are generally harder. Moreover, Fe-based alloys are typically ferromagnetic at room temperature (except the antiferromagnetic FeMn), which precludes their use in nuclear resonance imaging techniques for diagnostics purposes. The Young's modulus of Mg72Zn23Ca5 becomes closer to that of Cabased or Sr-based biodegradable metallic glasses after long-term immersion in HBSS as well as to that of polymeric materials reinforced with glassy fibers. At the same time, the hardness

**Figure 6.** Comparison of the mechanical properties (compressive yield stress, σy,C, versus Young's modulus, E) in linear scale for different families of biodegradable implant materials, including metallic alloys (Mg-based, Ca-based, Srbased, or Fe-based), ceramics (e.g., synthetic hydroxyapatites) and polymers. Adapted and reprinted from Pellicer et al.

observed to be directly proportional to σy,C.

[77], page 13, with permission from John Wiley&Sons.

of Mg72Zn23Ca5 alloy is higher than that of all these materials.

The evolution of the bending strength of Mg6Zn (6 wt. % Zn) alloy with the immersion time in physiological saline solution (0.9 % NaCl) [88] is similar to that of implanted LAE442 alloy [100]. For short immersion times (i.e., 3 days) the degradation rate of Mg6Zn is very fast (0.20±0.05 mm/year) and exhibits a large weight loss but it becomes slower (0.07±0.02 mm/ year) for longer immersion times (i.e., 30 days). The bending strength of the alloy decreases rapidly with an initially small weight loss but then decelerates as the percentage weight loss increases. This behavior was attributed to the formation of surface defects such as corrosion holes during degradation.

Plates of ZEK100 (1 wt. % Zn, 0.1 wt. % Zr, 0.1 wt. % RE (rare earth)) were mechanically tested in vitro after 14 (2 weeks), 28 (4 weeks) and 42 days (6 weeks) of immersion with a constant laminar flow rate in HBSS via four-point bending tests [101]. The bending strength decreases from immersion week 2 to week 4 but increases again after 6 weeks. The lowering of the bending strength is attributed to dissolution of the plate whereas the increase at longer times can be explained by precipitation of calcium phosphates from the solution on the surface of the plate. This behavior was supposed to be caused by a decrease of the implant volume during the first 4 weeks and an increase for longer times up to 8 weeks.

The mechanical behavior of ZEK100 alloy was also tested via three-point bending after being implanted in rabbit tibiae for 3 and 6 months [102]. The corrosion rate increases from 0.067 mm/year after 3 months to 0.154 mm/year after 6 months. The volume of the implant tends to reduce with the increase of the implantation time. This can explain why the initial maximum force of 241 N (the maximum force at breakage) decreases to 153 and 100 N after 3 and 6 months, respectively.

Figure 6 shows a comparative summary of the mechanical properties (compressive yield stress, σy,C, and Young's modulus, E) of various families of materials that can be used as bioabsorbable implants, such as metallic alloys, biodegradable polymers and ceramics. From the correlation H ≈ C σy,C (where C is the so-called constraint factor and is normally close to 3 for crystalline metallic alloys and slightly higher for metallic glasses [103]) the mechanical hardness is observed to be directly proportional to σy,C.

Krause et al. [100] compared the evolution of the mechanical behavior of Mg0.8Ca (8 wt.% Ca), LAE442 (4 wt. % Li, 4 wt. % Al, 2 wt. % RE) and WE43 (4 wt. % Y, 3 wt. % RE) alloys implanted in rabbits for 3 and 6 months by three-point bending tests. All the samples exhibit biodegra‐ dation as can be deduced from the loss in volume with implantation period. The MgCa0.8 alloy degrades slowly during the first 3 months but its corrosion rate accelerates during the following 3 months. The LA442 alloy exhibits slower degradation rate than the Mg0.8Ca and WE43 alloys. The difference of degradation rate is responsible for the distinct mechanical performance of the alloys over the time. Thee-point bending test results indicate the following trend in the initial strength: LAE442 (255.67±5.69 N) > WE43 (238.05 ±21.68 N) > Mg0.8Ca (178.76±25.15 N). However, after 3 months the strength trend changes so that it decreases in the following order: WE43 (185.59±15.64 N) > LAE442 (153.21±18.45 N) > Mg0.8Ca (115.42±9.66 N). After 6 months the strength follows this sequence: LAE442 (134.68±14.68 N) > WE43 (122.23±23.65 N) > Mg0.8Ca (52.90±5.96 N). From the results of the maximal applied force it can be deduced that the LAE442 alloy degrades faster during the first 3 months and slower between 3 and 6 months. The degradation rate of Mg0.8Ca and WE43 alloys is different; it decreases in a linear manner over the time [100]. The ductility of the alloys was also assessed from three-point bending tests by measuring the bending displacement but concluding results could not be obtained due to high scattering. The Mg0.8Ca alloy exhibits the highest loss and

The evolution of the bending strength of Mg6Zn (6 wt. % Zn) alloy with the immersion time in physiological saline solution (0.9 % NaCl) [88] is similar to that of implanted LAE442 alloy [100]. For short immersion times (i.e., 3 days) the degradation rate of Mg6Zn is very fast (0.20±0.05 mm/year) and exhibits a large weight loss but it becomes slower (0.07±0.02 mm/ year) for longer immersion times (i.e., 30 days). The bending strength of the alloy decreases rapidly with an initially small weight loss but then decelerates as the percentage weight loss increases. This behavior was attributed to the formation of surface defects such as corrosion

Plates of ZEK100 (1 wt. % Zn, 0.1 wt. % Zr, 0.1 wt. % RE (rare earth)) were mechanically tested in vitro after 14 (2 weeks), 28 (4 weeks) and 42 days (6 weeks) of immersion with a constant laminar flow rate in HBSS via four-point bending tests [101]. The bending strength decreases from immersion week 2 to week 4 but increases again after 6 weeks. The lowering of the bending strength is attributed to dissolution of the plate whereas the increase at longer times can be explained by precipitation of calcium phosphates from the solution on the surface of the plate. This behavior was supposed to be caused by a decrease of the implant volume during

The mechanical behavior of ZEK100 alloy was also tested via three-point bending after being implanted in rabbit tibiae for 3 and 6 months [102]. The corrosion rate increases from 0.067 mm/year after 3 months to 0.154 mm/year after 6 months. The volume of the implant tends to reduce with the increase of the implantation time. This can explain why the initial maximum force of 241 N (the maximum force at breakage) decreases to 153 and 100 N after 3 and 6 months,

the LAE442 the lowest loss in volume after 6 months.

the first 4 weeks and an increase for longer times up to 8 weeks.

holes during degradation.

330 Biodegradation - Engineering and Technology

respectively.

The yield stress of Mg-Zn-Ca bulk metallic glasses is relatively large (Figure 6), thus indicating that they are one of the hardest biodegradable materials reported in the literature. Moreover, the values of Young's modulus of glassy Mg-Zn-Ca are closer to that of cortical bone (Ebone = 3– 20 GPa) than most crystalline Mg-based alloys (they are also mechanically softer) and synthetic hydroxyapatites. Considering that the Mg70Zn23Ca5Pd2 alloy is fully crystalline, its hardness is also generally larger than that of most Mg-Zn-Ca crystalline alloys. Compared with most Fe-based biodegradable alloys, Mg-based bulk metallic glasses are generally harder. Moreover, Fe-based alloys are typically ferromagnetic at room temperature (except the antiferromagnetic FeMn), which precludes their use in nuclear resonance imaging techniques for diagnostics purposes. The Young's modulus of Mg72Zn23Ca5 becomes closer to that of Cabased or Sr-based biodegradable metallic glasses after long-term immersion in HBSS as well as to that of polymeric materials reinforced with glassy fibers. At the same time, the hardness of Mg72Zn23Ca5 alloy is higher than that of all these materials.

**Figure 6.** Comparison of the mechanical properties (compressive yield stress, σy,C, versus Young's modulus, E) in linear scale for different families of biodegradable implant materials, including metallic alloys (Mg-based, Ca-based, Srbased, or Fe-based), ceramics (e.g., synthetic hydroxyapatites) and polymers. Adapted and reprinted from Pellicer et al. [77], page 13, with permission from John Wiley&Sons.

## **7. Summary and conclusion**

Magnesium and its alloys are suitable materials for biomedical applications due to their low weight, high specific strength, stiffness close to bone and good biocompatibility. Specifically, because magnesium exhibits a fast biodegradability, it has attracted an increasing interest over the last years for its potential use as "biodegradable implants". However, the main limitation is that Mg degrades too fast and that the corrosion process is accompanied by hydrogen evolution. In these conditions, magnesium implants lose their mechanical integrity before the bone heals and hydrogen gas accumulates inside the body. To overcome these limitations different methods have been pursued to decrease the corrosion rate of magnesium to accept‐ able levels, including the growth of coatings (conversion and deposited coatings), surface modification treatments (ion implantation, plasma surface modification, etc) or via the control of the composition and microstructure of Mg alloys themselves.

**Author details**

, E. Pellicer1

neering C 2004; 24: 753-760.

, S. Suriñach1

\*Address all correspondence to<EMAIL_ADDRESS>
, M.D. Baró1

1 Departament de Física, Facultat de Ciències, Universitat Autònoma de Barcelona, Barcelona,

2 Institució Catalana de Recerca i Estudis Avançats (ICREA) and Departament de Física,

[1] Bhat SV. Biomaterials. Kluwer Adademic Publishers. Boston, MIT, USA; 2002. p265.

[2] Sumita M, Hanawa T, Teoh SH. Development of nitrogen-containing nickel-free aus‐ tenitic stainless steels for metallic biomaterials-review. Matererials Science and Engi‐

[3] Puleo DA. Biochemical surface modification of Co-Cr-Mo. Biomaterials 1996; 17:

[4] Geetha M, Singh AK, Asokamani R, Gogia AK. Ti-based biomaterials, the ultimate choice for orthopaedic implants. A review. Progress in Materials Science 2009; 54:

[5] Moravej M, Mantovani D. Biodegradable metals for cardiovascular stent application: interests and new opportunities. International Journal of Molecular Sciences 2011; 12:

[6] González S, Pellicer E, Fornell J, Blanquer A, Barrios L, Ibañez E, Solsona P, Suriñach S, Baró MD, Nogués C, Sort J. Improved mechanical perfomance and delayed corro‐ sion phenomena in biodegradable Mg-Zn-Ca alloys through Pd-alloying. Journal of

[7] Hanbook of Biomaterials Properties. In: Black J, Hasting GW (ed.). Chapman and

[8] Zhou Z, Liu X, Liu Q, Liu L. Evaluation of the potential cytotoxicity of metals associ‐ ated with implanted biomaterials (I). Preparative Biochemistry and Biotechnology

[9] Li XN, Gu ZJ, Lou SQ, Zheng YF. The development of binary Mg–Ca alloys for use as

biodegradable materials within bone. Biomaterials 2008; 29: 1329–1344.

the Mechanical Behavior of Biomedical Materials 2012; 6: 53-62.

Facultat de Ciències, Universitat Autònoma de Barcelona, Barcelona, Spain

and J. Sort2

Biodegradation and Mechanical Integrity of Magnesium and Magnesium Alloys Suitable for Implants

http://dx.doi.org/10.5772/55584

333

S. González1

**References**

217-222.

397-425.

4250-4270.

Hall. London; 1998.

2009; 39: 81-91.

Spain

For tuning efficiently the composition and microstructure it is first necessary to understand two of the most common types of corrosion that Mg and Mg alloys exhibit: galvanic and pitting corrosion. Galvanic corrosion develops because magnesium almost always behaves anodically in contact with other metals. Galvanic couples are usually encountered when the concentration of the alloying element surpasses their corresponding maximum solid solubility in magnesi‐ um. The alloying element then segregates during solidification or annealing and an inhomo‐ geneous microstructure is formed. The extent of the galvanic effect depends on a number of factors such as the crystal orientation of the magnesium matrix, the type of secondary phases and impurity particles, the solution in which the alloy is immersed and the grain size. The concentration and distribution of secondary phases is also important for the corrosion behavior. A fine and continuous distribution of secondary phases typically improves the corrosion performance.

Mg alloys are susceptible to form a passivation layer of Mg(OH)2 or a mixture of Mg(OH)2 and MgO in aqueous solutions. Due to the presence of chloride ions in physiological fluids, the protective coating may be destroyed and localized attack (i.e., pitting corrosion) initiates. Physiological environments also contain carbonates, phosphates, sulfates and other ingredients that have different effects on the corrosion behavior of magnesium. Before magnesium alloys can be used as real implants it is necessary to evaluate the biodegrada‐ bility and mechanical performance over the immersion (in-vitro) or implantation (in vivo) time.

## **Acknowledgements**

This work has been partially financed by the 2009-SGR-1292 and MAT2011-27380-C02-01 research projects. S. G. acknowledges the Juan de la Cierva Fellowship from the Spanish Ministry of Science and Innovation. M.D.B. was partially supported by an ICREA Academia award.

## **Author details**

**7. Summary and conclusion**

332 Biodegradation - Engineering and Technology

corrosion performance.

**Acknowledgements**

time.

award.

Magnesium and its alloys are suitable materials for biomedical applications due to their low weight, high specific strength, stiffness close to bone and good biocompatibility. Specifically, because magnesium exhibits a fast biodegradability, it has attracted an increasing interest over the last years for its potential use as "biodegradable implants". However, the main limitation is that Mg degrades too fast and that the corrosion process is accompanied by hydrogen evolution. In these conditions, magnesium implants lose their mechanical integrity before the bone heals and hydrogen gas accumulates inside the body. To overcome these limitations different methods have been pursued to decrease the corrosion rate of magnesium to accept‐ able levels, including the growth of coatings (conversion and deposited coatings), surface modification treatments (ion implantation, plasma surface modification, etc) or via the control

For tuning efficiently the composition and microstructure it is first necessary to understand two of the most common types of corrosion that Mg and Mg alloys exhibit: galvanic and pitting corrosion. Galvanic corrosion develops because magnesium almost always behaves anodically in contact with other metals. Galvanic couples are usually encountered when the concentration of the alloying element surpasses their corresponding maximum solid solubility in magnesi‐ um. The alloying element then segregates during solidification or annealing and an inhomo‐ geneous microstructure is formed. The extent of the galvanic effect depends on a number of factors such as the crystal orientation of the magnesium matrix, the type of secondary phases and impurity particles, the solution in which the alloy is immersed and the grain size. The concentration and distribution of secondary phases is also important for the corrosion behavior. A fine and continuous distribution of secondary phases typically improves the

Mg alloys are susceptible to form a passivation layer of Mg(OH)2 or a mixture of Mg(OH)2 and MgO in aqueous solutions. Due to the presence of chloride ions in physiological fluids, the protective coating may be destroyed and localized attack (i.e., pitting corrosion) initiates. Physiological environments also contain carbonates, phosphates, sulfates and other ingredients that have different effects on the corrosion behavior of magnesium. Before magnesium alloys can be used as real implants it is necessary to evaluate the biodegrada‐ bility and mechanical performance over the immersion (in-vitro) or implantation (in vivo)

This work has been partially financed by the 2009-SGR-1292 and MAT2011-27380-C02-01 research projects. S. G. acknowledges the Juan de la Cierva Fellowship from the Spanish Ministry of Science and Innovation. M.D.B. was partially supported by an ICREA Academia

of the composition and microstructure of Mg alloys themselves.

S. González1 , E. Pellicer1 , S. Suriñach1 , M.D. Baró1 and J. Sort2

\*Address all correspondence to<EMAIL_ADDRESS>
1 Departament de Física, Facultat de Ciències, Universitat Autònoma de Barcelona, Barcelona, Spain

2 Institució Catalana de Recerca i Estudis Avançats (ICREA) and Departament de Física, Facultat de Ciències, Universitat Autònoma de Barcelona, Barcelona, Spain

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**Chapter 13**

**Biodegradation of Nitrogen in a Commercial**

**1.1. Need for biodegradation of nitrogen species in aquaculture systems**

Commercial production of fish involves high levels of feeding. While digestive breakdown of lipids and carbohydrates yields water and carbon dioxide as waste products, digestion of proteins also yields nitrogenous compounds. In teleost (i.e., bony) fishes, these nitrogenous wastes are excreted predominately as ammonia. Total ammonia-nitrogen (TAN) consists of

toxic to fish. The fraction of TAN in the unionized form is dependent upon the pH and temperature of the water (Losordo 1997, Lekang 2007) and to a lesser degree its salinity (Diaz et al. 2012). At pH values less than 7.5, most ammonia is in the ionized form, and high levels of TAN can be tolerated. At higher pH, however, levels of un-ionized ammonia become problematic. Hence, biodegradation of ammonia is critical for the success of fish culture. Nitrifying bacteria, including *Nitrosomonas* sp., utilize NH3-N as the energy source for growth, producing nitrite, NO2-N. While nitrite-nitrogen is not as toxic as un-ionized ammonianitrogen, it can prove harmful to fish. The most common mode of toxicity is anoxia, as nitritenitrogen crosses the gills into the circulatory system and converts hemoglobin to methemoglobin, rendering it unable to bind and transport oxygen to the tissues (Palachek and Tomasso 1984, Svobodova et al. 2005). Other nitrifying bacteria, including *Nitrobacter* sp., utilize nitrite as their energy source, producing nitrate, NO3-N. Nitrate-nitrogen concentra‐ tions are not generally of concern to aquaculturists, as most species can tolerate levels as high as 200 mg/L (Russo and Thurston 1991). Nitrate rarely reaches such high levels, as it is removed from the system by water exchanges and by passive denitrification in anaerobic pockets within the production or filtration systems (van Rijn 1996, Tal et al. 2006) or in denitrification reactors

© 2013 Sandu and Hallerman; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Sandu and Hallerman; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

**Recirculating Aquaculture Facility**

Additional information is available at the end of the chapter

S. Sandu and E. Hallerman

http://dx.doi.org/10.5772/55841

**1. Introduction**

ionized ammonia (NH4

+

(Hamlin et al. 2008, Sandu et al. 2011).

## **Biodegradation of Nitrogen in a Commercial Recirculating Aquaculture Facility**

S. Sandu and E. Hallerman

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55841

## **1. Introduction**

[98] Pietak A, Mahoney P, Dias GJ, Staigner MP. Bone-like matrix formation on magnesi‐ um and magnesium alloys. Journal of Materials Science: Materials in Medicine 2008;

[99] Tancret F, Osterstock F. Modelling the toughness of porous sintered glass beads with

[100] Krause A, von der Höh N, Bormann D, Krause C, Bach F-W, Windhagen H, Meyer-Lindenberg A. Degradation behavior and mechanical properties of magnesium im‐

[101] Waizy H, Weizbauer A, Modrejewski C, Witte F, Windhagen H, Lucas A, Kieke M, Denkena B, Behrens P, Meyer-Lindenberg A, Bach F-W, Thorey F. In vitro corrosion of ZEK100 plates in Hank´s Balanced Salt Solution. BioMedical Engineering OnLine

[102] Huehnerschulte TA, Angrisani N, Rittershaus D, Bormann D, Windhagen H, Meyer-Lindenberg A. In vivo corrosion of two novel magnesium alloys ZEK100 and AX30 and their mechanical suitability as biodegradable implants. Materials 2011; 4:

[103] Fornell J, Concustell A, Suriñach S, Li WH, Cuadrado N, Gebert A, Baró MD, Sort J. Yielding and intrinsic plasticity of Ti-Zr-Ni-Cu-Be bulk metallic glass. Int. J. Plast.

various fracture mechanisms. Philosophical Magazine 2003; 83: 125–136.

plants in rabbits tibiae. Journal of Materials Science 2010; 45: 624-632.

19: 407-415.

340 Biodegradation - Engineering and Technology

2012; 11: 12.

1144-1167.

2009; 25: 1540–1559.

## **1.1. Need for biodegradation of nitrogen species in aquaculture systems**

Commercial production of fish involves high levels of feeding. While digestive breakdown of lipids and carbohydrates yields water and carbon dioxide as waste products, digestion of proteins also yields nitrogenous compounds. In teleost (i.e., bony) fishes, these nitrogenous wastes are excreted predominately as ammonia. Total ammonia-nitrogen (TAN) consists of ionized ammonia (NH4 + -N) and un-ionized ammonia (NH3-N), the latter of which can prove toxic to fish. The fraction of TAN in the unionized form is dependent upon the pH and temperature of the water (Losordo 1997, Lekang 2007) and to a lesser degree its salinity (Diaz et al. 2012). At pH values less than 7.5, most ammonia is in the ionized form, and high levels of TAN can be tolerated. At higher pH, however, levels of un-ionized ammonia become problematic. Hence, biodegradation of ammonia is critical for the success of fish culture. Nitrifying bacteria, including *Nitrosomonas* sp., utilize NH3-N as the energy source for growth, producing nitrite, NO2-N. While nitrite-nitrogen is not as toxic as un-ionized ammonianitrogen, it can prove harmful to fish. The most common mode of toxicity is anoxia, as nitritenitrogen crosses the gills into the circulatory system and converts hemoglobin to methemoglobin, rendering it unable to bind and transport oxygen to the tissues (Palachek and Tomasso 1984, Svobodova et al. 2005). Other nitrifying bacteria, including *Nitrobacter* sp., utilize nitrite as their energy source, producing nitrate, NO3-N. Nitrate-nitrogen concentra‐ tions are not generally of concern to aquaculturists, as most species can tolerate levels as high as 200 mg/L (Russo and Thurston 1991). Nitrate rarely reaches such high levels, as it is removed from the system by water exchanges and by passive denitrification in anaerobic pockets within the production or filtration systems (van Rijn 1996, Tal et al. 2006) or in denitrification reactors (Hamlin et al. 2008, Sandu et al. 2011).

© 2013 Sandu and Hallerman; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Sandu and Hallerman; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Controlled degradation of nitrogenous wastes in filtration units is a major consideration in design and operation of commercial recirculating aquaculture systems. Among the technolo‐ gies available (Crab et al. 2007), biological filtration is most commonly used. Biological filters are designed to provide abundant surface area for the attachment of complex microbial communities (Schreier et al. 2010) rich in *Nitrosomonas* and *Nitrobacter* species (Chen et al. 2006, Itoi et al. 2007, van Kessel et al. 2010). The nitrification capacity of the water treatment system is often the factor that limits production in a recirculating aquaculture system (Lemarie et al. 2004, Eschar et al. 2006, Diaz et al. 2012).

*A*TAN = maximum allowable concentration of total ammonia nitrogen (mg/L)

*C*TANe= total ammonia nitrogen concentration in the effluent from filters (mg/L) *C*TANi = total ammonia nitrogen concentration in the supply water (mg/L)

*E*a = efficiency of rotating biological contactor for removal of ammonia nitrogen (percent)

Nitrogen Biodegradation in a Recirculating Aquaculture System

http://dx.doi.org/10.5772/55841

343

*LC50* = lethal concentration of a compound to 50% of the individuals in a population

*C*TAN = total ammonia nitrogen concentration in fish tank (mg/L)

BRA = Blue Ridge Aquaculture

*FA* = amount of feed (kg) *FB* = fish biomass (kg)

*FC* = feed conversion factor (decimal fraction) *FP* = protein content of feed (decimal fraction)

*FR*MTAN = maximum feeding rate (kg/d)

*LN* = nitrogen load (g N/kg fish produced)

*N*DIN = dissolved inorganic nitrogen (mg/L) *N*feed = nitrogen fixed in feed (g/kg feed)

= nitrite nitrogen (mg/L)

= nitrate nitrogen (mg/L)

*N*TAN = total ammonia nitrogen (mg/L) *N*TON = total organic nitrogen (mg/L)

*S* = surface area (m2)

*PC* = protein content of feed (decimal fraction) *P*NO3-N = partitioning of nitrate nitrogen (g/kg) *P*TAN = production rate of ammonia nitrogen (g/kg) *Q* = flow rate through system (m3/min or L/h) *Q*f = recirculation flow rate (m3/min or L/h) RAS = recirculating aquaculture system *R*TAN = ammonia removal rate (g/h)

*N*fish = nitrogen fixed in fish (g/kg fish produced) *N*mort = nitrogen fixed in dead fish (g/kg fish removed)

*N*DENIT = nitrogen gas removed by denitrification (mg/L)

*N*NH3vol = nitrogen removed by ammonia volatilization (mg/L) *NO3-N*pass = nitrate removed passively by denitrification (mg/L) *NO3-N*exch = nitrate removed by exchange of water (mg/L)

*L*TAN = ammonia loading (g/hr)

*N*NO2-

*N*NO3-

## **1.2. Utility of a nitrogen budget**

The production efficiency of an aquaculture system can be evaluated through analysis of the conversion of nitrogen to fish biomass and to biodegradation pathways (Thoman et al. 2001). Nitrogen dynamics can be quantified by a mass balance equation, most simply as the difference between nitrogen in the feed supply and nutrients subsequently fixed as fish biomass. A nitrogen budget can quantify nitrogen fixation in fish biomass at various fish stocking densities (Suresh and Kwei 1992; Siddiqui and Al-Harbi 1999), nutrient release into the water column as dissolved and particulate excretion of fish (Krom and Neori 1989), and deposition of nitrogen into pond sediment (Acosta-Nassar et al. 1994). By estimating total nitrogen budgets for a particular species and culture system, we can evaluate the efficacy of water treatment processes (Porter et al. 1987). Hence, a nitrogen budget provides information crucial for the design and optimization of a production system, feeding strategies, and water and effluent treatment processes.

#### **1.3. Biodegradation of nitrogenous wastes in a tilapia production system**

Blue Ridge Aquaculture (BRA) in Martinsville, Virginia, USA is a large commercial facility that produces 1300 metric tons of hybrid tilapia *Oreochromis sp.* per year. To our knowledge, it is the largest recirculating aquaculture facility in existence. Before our study, little informa‐ tion existed about nitrogen budgets in commercial-scale fish production facilities, especially those using freshwater recirculating systems. By deriving a nitrogen budget, we can quantify the forms and proportions of nitrogen ingested as food as it becomes bound in tilapia biomass, excreted as metabolites, biodegraded by microorganisms, lost as gas by denitrification, or released in effluent. Knowledge of the nitrogen budget can help optimize operations, improv‐ ing facility efficiency and maximizing production. Using Blue Ridge Aquaculture as our study system, our objectives were to: (1) examine nitrogen dynamics for the grow-out systems, (2) relate the nitrogen budget to water quality, (3) evaluate biofilter loading and nitrogen removal efficiency, and (4) predict maximum system carrying capacity. All abbreviations used in this chapter are shown in Table 1.

*a* = mole fraction of unionized ammonia nitrogen (decimal fraction) *ACR* = areal conversion rate (mg/m2-d) *A*NH3-N = concentration of unionized ammonia nitrogen (mg/L)

*A*TAN = maximum allowable concentration of total ammonia nitrogen (mg/L)

BRA = Blue Ridge Aquaculture

Controlled degradation of nitrogenous wastes in filtration units is a major consideration in design and operation of commercial recirculating aquaculture systems. Among the technolo‐ gies available (Crab et al. 2007), biological filtration is most commonly used. Biological filters are designed to provide abundant surface area for the attachment of complex microbial communities (Schreier et al. 2010) rich in *Nitrosomonas* and *Nitrobacter* species (Chen et al. 2006, Itoi et al. 2007, van Kessel et al. 2010). The nitrification capacity of the water treatment system is often the factor that limits production in a recirculating aquaculture system (Lemarie et al.

The production efficiency of an aquaculture system can be evaluated through analysis of the conversion of nitrogen to fish biomass and to biodegradation pathways (Thoman et al. 2001). Nitrogen dynamics can be quantified by a mass balance equation, most simply as the difference between nitrogen in the feed supply and nutrients subsequently fixed as fish biomass. A nitrogen budget can quantify nitrogen fixation in fish biomass at various fish stocking densities (Suresh and Kwei 1992; Siddiqui and Al-Harbi 1999), nutrient release into the water column as dissolved and particulate excretion of fish (Krom and Neori 1989), and deposition of nitrogen into pond sediment (Acosta-Nassar et al. 1994). By estimating total nitrogen budgets for a particular species and culture system, we can evaluate the efficacy of water treatment processes (Porter et al. 1987). Hence, a nitrogen budget provides information crucial for the design and optimization of a production system, feeding strategies, and water and effluent

Blue Ridge Aquaculture (BRA) in Martinsville, Virginia, USA is a large commercial facility that produces 1300 metric tons of hybrid tilapia *Oreochromis sp.* per year. To our knowledge, it is the largest recirculating aquaculture facility in existence. Before our study, little informa‐ tion existed about nitrogen budgets in commercial-scale fish production facilities, especially those using freshwater recirculating systems. By deriving a nitrogen budget, we can quantify the forms and proportions of nitrogen ingested as food as it becomes bound in tilapia biomass, excreted as metabolites, biodegraded by microorganisms, lost as gas by denitrification, or released in effluent. Knowledge of the nitrogen budget can help optimize operations, improv‐ ing facility efficiency and maximizing production. Using Blue Ridge Aquaculture as our study system, our objectives were to: (1) examine nitrogen dynamics for the grow-out systems, (2) relate the nitrogen budget to water quality, (3) evaluate biofilter loading and nitrogen removal efficiency, and (4) predict maximum system carrying capacity. All abbreviations used in this

**1.3. Biodegradation of nitrogenous wastes in a tilapia production system**

2004, Eschar et al. 2006, Diaz et al. 2012).

**1.2. Utility of a nitrogen budget**

342 Biodegradation - Engineering and Technology

treatment processes.

chapter are shown in Table 1.

*ACR* = areal conversion rate (mg/m2-d)

*a* = mole fraction of unionized ammonia nitrogen (decimal fraction)

*A*NH3-N = concentration of unionized ammonia nitrogen (mg/L)

*C*TAN = total ammonia nitrogen concentration in fish tank (mg/L)

*C*TANe= total ammonia nitrogen concentration in the effluent from filters (mg/L)

*C*TANi = total ammonia nitrogen concentration in the supply water (mg/L)

*E*a = efficiency of rotating biological contactor for removal of ammonia nitrogen (percent)

*FA* = amount of feed (kg)

*FB* = fish biomass (kg)

*FC* = feed conversion factor (decimal fraction)

*FP* = protein content of feed (decimal fraction)

*FR*MTAN = maximum feeding rate (kg/d)

*LC50* = lethal concentration of a compound to 50% of the individuals in a population

*LN* = nitrogen load (g N/kg fish produced)

*L*TAN = ammonia loading (g/hr)

*N*DENIT = nitrogen gas removed by denitrification (mg/L)

*N*DIN = dissolved inorganic nitrogen (mg/L)

*N*feed = nitrogen fixed in feed (g/kg feed)

*N*fish = nitrogen fixed in fish (g/kg fish produced)

*N*mort = nitrogen fixed in dead fish (g/kg fish removed)

*N*NO2- = nitrite nitrogen (mg/L)

*N*NO3- = nitrate nitrogen (mg/L)

*N*NH3vol = nitrogen removed by ammonia volatilization (mg/L)

*NO3-N*pass = nitrate removed passively by denitrification (mg/L)

*NO3-N*exch = nitrate removed by exchange of water (mg/L)

*N*TAN = total ammonia nitrogen (mg/L)

*N*TON = total organic nitrogen (mg/L)

*PC* = protein content of feed (decimal fraction)

*P*NO3-N = partitioning of nitrate nitrogen (g/kg)

*P*TAN = production rate of ammonia nitrogen (g/kg)

*Q* = flow rate through system (m3/min or L/h)

*Q*f = recirculation flow rate (m3/min or L/h)

RAS = recirculating aquaculture system

*R*TAN = ammonia removal rate (g/h)

*S* = surface area (m2)

for solids removal, a rotating biological contactor (RBC) for microbial biodegradation includ‐ ing nitrification, and an oxygenation unit. Each fish production system is rectangular in shape,

in the fish tank, sedimentation and RBC compartments, and water passes freely from one section into another through large pipes or apertures. A pump receives water from the rotating biological contactor compartment and pushes it through U-tubes and then to the far end of the

**Figure 1.** Commercial-scale tilapia grow-out systems at Blue Ridge Aquaculture. The grow-out units are to the right of

(A) (B)

**Figure 2.** A) Conceptual diagram and (B) and engineering drawing of a single recirculating tilapia grow-out system at

BRA practices partial water exchange daily for controlling solids, dissolved organics and nutrient accumulation in fish grow-out tanks. Water is exchanged daily from the system in the

Blue Ridge Aquaculture. Diagram courtesy of Blue Ridge Aquaculture.

the catwalk and sedimentation basins to the left. Photograph courtesy of Blue Ridge Aquaculture.

of water, and consists of a fish-rearing tank (119 m3

surface area per shaft), and an underground U-tube oxygenation

), an air-driven rotating biological contactor (59 m3

Nitrogen Biodegradation in a Recirculating Aquaculture System

. The water surface is at the same level

http://dx.doi.org/10.5772/55841

), a multi-

345

/min, and the system

built from concrete, holds 215 m3

basin volume, 13,366 m2

tube clarifier sedimentation basin (37 m3

turnover time is about once per hour.

system. The total volume of the grow-out unit is 9030 m3

fish production tank, driving the recirculation. The filtration rate is 3.8 m3

**Table 1.** Abbreviations and associated units.

Tilapias are a group of fishes of great importance to world aquaculture (Costa-Pierce and Rakocy 1997, Fitzsimmons 1997, Lim and Webster 2006). Tilapias adapt readily to a range of production systems ranging from traditional extensive pond systems to high-input intensive pond systems to super-intensive recirculating aquaculture systems. Like all fishes, tilapias are sensitive to concentrations of nitrogenous wastes. The 48-hour LC50 value for NH3 for Jordan tilapia *Oreochromis aureus* was 2.40 mg/L (Redner and Stickney 1979). The 48-hour LC50 value for hybrid red tilapia *O. mossambicus x O. niloticus* fry was 6.6 mg/L (Daud et al. 1988), although the threshold lethal concentration was 0.24 mg/L. The 24-hour LC50 value for un-ionized ammonia for *O. niloticus* was 1.46 mg/L (Evans et al. 2006) Sublethal effects of NH3-N include tissue damage, decreased growth, increased feed conversion ratio, acute stress response, increased disease susceptibility, and reduced reproductive capacity (Russo and Thurston 1991, Yildiz et al. 2006, El-Sherif and El-Feky 2008, Benli et al. 2008). Tilapias also exhibit sensitivity to elevated nitrite concentrations. The 96-hour LC50 for nitrite-nitrogen for *O. aureus* was 16.2 mg/L at pH 7.2 and 22 mg/L chloride (Palachek and Tomasso 1984). Acute nitrite toxicity for *O. niloticus* varied with chloride levels and with fish size, with smaller fish proving more tolerant (Atwood et al. 2001, Wang et al. 2006). Nitrite-nitrogen levels should be kept below 5 mg/L within tilapia culture vessels (Losordo 1997). Knowledge of these toxicity values is useful for setting criteria for the design or evaluating the performance of filters for biodegradation of nitrogenous wastes in aquaculture systems.

#### **2. Methods**

#### **2.1. Culture systems**

The BRA facility includes systems for broodstock holding, fish breeding, egg incubation/ hatching, fingerling rearing, and food-fish production. The main building houses 42 recircu‐ lating aquaculture systems for grow-out to market size (Figure 1) that were the focus of our study. Each grow-out system (Figure 2) includes a fish production tank, a sedimentation basin for solids removal, a rotating biological contactor (RBC) for microbial biodegradation includ‐ ing nitrification, and an oxygenation unit. Each fish production system is rectangular in shape, built from concrete, holds 215 m3 of water, and consists of a fish-rearing tank (119 m3 ), a multitube clarifier sedimentation basin (37 m3 ), an air-driven rotating biological contactor (59 m3 basin volume, 13,366 m2 surface area per shaft), and an underground U-tube oxygenation system. The total volume of the grow-out unit is 9030 m3 . The water surface is at the same level in the fish tank, sedimentation and RBC compartments, and water passes freely from one section into another through large pipes or apertures. A pump receives water from the rotating biological contactor compartment and pushes it through U-tubes and then to the far end of the fish production tank, driving the recirculation. The filtration rate is 3.8 m3 /min, and the system turnover time is about once per hour.

*SBM*MTAN = maximum biomass that could be sustained by system (kg fish)

*TAN*pass+vol = ammonia removed by passive nitrification and ammonia volatilization (mg/L)

biodegradation of nitrogenous wastes in aquaculture systems.

*TAN*RBC nitrification = ammonia removed by nitrification in rotating biological contactor (mass/volume)

Tilapias are a group of fishes of great importance to world aquaculture (Costa-Pierce and Rakocy 1997, Fitzsimmons 1997, Lim and Webster 2006). Tilapias adapt readily to a range of production systems ranging from traditional extensive pond systems to high-input intensive pond systems to super-intensive recirculating aquaculture systems. Like all fishes, tilapias are sensitive to concentrations of nitrogenous wastes. The 48-hour LC50 value for NH3 for Jordan tilapia *Oreochromis aureus* was 2.40 mg/L (Redner and Stickney 1979). The 48-hour LC50 value for hybrid red tilapia *O. mossambicus x O. niloticus* fry was 6.6 mg/L (Daud et al. 1988), although the threshold lethal concentration was 0.24 mg/L. The 24-hour LC50 value for un-ionized ammonia for *O. niloticus* was 1.46 mg/L (Evans et al. 2006) Sublethal effects of NH3-N include tissue damage, decreased growth, increased feed conversion ratio, acute stress response, increased disease susceptibility, and reduced reproductive capacity (Russo and Thurston 1991, Yildiz et al. 2006, El-Sherif and El-Feky 2008, Benli et al. 2008). Tilapias also exhibit sensitivity to elevated nitrite concentrations. The 96-hour LC50 for nitrite-nitrogen for *O. aureus* was 16.2 mg/L at pH 7.2 and 22 mg/L chloride (Palachek and Tomasso 1984). Acute nitrite toxicity for *O. niloticus* varied with chloride levels and with fish size, with smaller fish proving more tolerant (Atwood et al. 2001, Wang et al. 2006). Nitrite-nitrogen levels should be kept below 5 mg/L within tilapia culture vessels (Losordo 1997). Knowledge of these toxicity values is useful for setting criteria for the design or evaluating the performance of filters for

The BRA facility includes systems for broodstock holding, fish breeding, egg incubation/ hatching, fingerling rearing, and food-fish production. The main building houses 42 recircu‐ lating aquaculture systems for grow-out to market size (Figure 1) that were the focus of our study. Each grow-out system (Figure 2) includes a fish production tank, a sedimentation basin

*TAN*exchange = ammonia removed by water exchange (mg/L)

*t* = time

**2. Methods**

**2.1. Culture systems**

*TKN* = total Kjeldall nitrogen (g) *TNI* = total nitrogen input (kg/day) *TNR* = total nitrogen recovered (kg/day)

344 Biodegradation - Engineering and Technology

*TNUA* = total nitrogen unaccounted for (kg/day)

**Table 1.** Abbreviations and associated units.

**Figure 1.** Commercial-scale tilapia grow-out systems at Blue Ridge Aquaculture. The grow-out units are to the right of the catwalk and sedimentation basins to the left. Photograph courtesy of Blue Ridge Aquaculture.

**Figure 2.** A) Conceptual diagram and (B) and engineering drawing of a single recirculating tilapia grow-out system at Blue Ridge Aquaculture. Diagram courtesy of Blue Ridge Aquaculture.

BRA practices partial water exchange daily for controlling solids, dissolved organics and nutrient accumulation in fish grow-out tanks. Water is exchanged daily from the system in the interval between 2:00 p.m. and 8:00 a.m. Management practice is to completely flush the sedimentation basin after each instance that 227 kilograms of feed has been administered to a particular production unit. The exchange rate averages 22.3% per day, but the daily percentage varies among production units as a function of the size of fish, water quality requirements, and the amount of feed delivered to the system. The exchange water originates from wells, and is supplemented with municipal tap water when necessary. Exchange water replaces that used to remove settled particulate material, and thereby dilutes dissolved organic materials, dissolved nutrients, and salts.

nitrogen input and total nitrogen recovered constituted pool 5, the mass fraction of total

Analyses of fish and of feed for protein content followed Thiex et al. (2002), who indicated that by dry weight, 16% of protein is nitrogen. Samples were processed at the Forage Testing Laboratory, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. Analyses

using a Hach DR2400 spectrophotometer (Hach Company, Loveland, Colorado). Total Kjeldall nitrogen (TKN) was determined using macro-Kjeldall Standard Method 4500 – Norg B (APHA et al., 1998). Samples were acidified below pH 2 using H2SO4, refrigerated with ice, and transported to the Department of Civil and Environmental Engineering at Virginia Polytechnic Institute and State University, Blacksburg, Virginia, for analysis. Temperature and pH were measured directly on site using an Acorn Meter (Kit Model pH 6, Oakton, Vernon Hills, Illinois). Alkalinity was determined on-site using the Hach Permachem® Method. Dissolved oxygen (DO) was measured using a YSI (Model 550, Yellow Springs, Ohio) instrument. We calculated total organic nitrogen as the difference between TKN and total ammonia nitrogen

Under steady-state conditions, fish biomass does not fluctuate significantly over time (i.e., harvest equals growth), and the daily rations of feed are constant. Under these assumptions, we derived the nitrogen budget by determining the nitrogen input with feed and the output of nitrogenous compounds in known pools. We quantified daily amounts of nitrogen in feed, fish, and mortalities using information on feed consumption, fish production, and mortalities provided by BRA management. We measured the components of dissolved inorganic nitrogen and total organic nitrogen pools directly. We extrapolated mean values to the entire exchange volume from a day to determine the mass of nitrogen recovered in these forms. We assumed that the amount of nitrogen missing from the balance was lost by passive denitrification and

We considered both types of feed used in the system (with 36% or 40% standard protein content) to determine nitrogen fixed in feed, *N*feed. We collected samples from three different points in storage silos for nitrogen content determination. We calculated *N*feed as a composite

where *FA* = amount of feed, *PC* = protein content of the feed, and 0.16 = concentration of nitrogen in protein (Thiex et al. 2002). We determined *PC* by laboratory analyses because protein content may differ from that claimed by the feed producer. We obtained the total mass of nitrogen originating from the feed input, *TNI*, by multiplying *N*feed by the amount fed, *FA*.

( ) feed *N FA PC* = S x x0.16 (1)

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347

nitrogen unaccounted for (*TNUA*).

**2.5. Nitrogen budget determination**

by ammonia volatilization.

using the equation:

for inorganic dissolved nitrogen forms (TAN, NO2

**2.4. Analytical techniques**

(TAN).

Fish are fed commercially-prepared pelleted diets containing 36 or 40% minimum crude protein and 8-16% lipid levels, varying with the age of the fish. The feed is distributed hourly to the tanks over the 24-hour period. Fish production is managed so that 21-27 metric tons of 600-g fish reaches marketable size each week for shipment to a live market.

## **2.2. System boundaries**

For the purpose of this study, the 42 recirculating aquaculture systems for grow-out were delimited as a unique system for purposes of quantifying the nitrogen budget. In certain contexts as set out below, N dynamics were quantified in greater detail in four individual systems. Broodstock holding and spawning facilities, a hatchery, and two greenhouses for fingerling production contain only a small part of the facility fish biomass, volume and exchange flow (i.e., they handle 3.0% of the fish biomass and 4.4% of the total nitrogen input). Because of their small contribution, the nitrogen budgets for these systems are not presented here, but can be found in Sandu (2004).

## **2.3. Inputs, outputs and nitrogen pools**

The nitrogen budget is expressed as a mass-balance equation of all nitrogen forms, with total inputs plus generation equal to total outputs plus consumption. We found no measurable amounts of dissolved inorganic nitrogen in the replacement water. Hence, feed provided to fish was the sole nitrogen source in the form of organic nitrogen (*N*feed). Multiplication of *N*feed by the total amount of feed provided the mass of total nitrogen input (*TNI*). The nitrogen budget was accounted for in five known pools:

All transformations among pools were assumed to be in a dynamic equilibrium over a defined period of time. We accounted for the mass fractions of nitrogen from Pools 1 to 4 (i.e., the measurable pools) as total nitrogen recovered (*TNR*), while the difference between total nitrogen input and total nitrogen recovered constituted pool 5, the mass fraction of total nitrogen unaccounted for (*TNUA*).

#### **2.4. Analytical techniques**

interval between 2:00 p.m. and 8:00 a.m. Management practice is to completely flush the sedimentation basin after each instance that 227 kilograms of feed has been administered to a particular production unit. The exchange rate averages 22.3% per day, but the daily percentage varies among production units as a function of the size of fish, water quality requirements, and the amount of feed delivered to the system. The exchange water originates from wells, and is supplemented with municipal tap water when necessary. Exchange water replaces that used to remove settled particulate material, and thereby dilutes dissolved organic materials,

Fish are fed commercially-prepared pelleted diets containing 36 or 40% minimum crude protein and 8-16% lipid levels, varying with the age of the fish. The feed is distributed hourly to the tanks over the 24-hour period. Fish production is managed so that 21-27 metric tons of

For the purpose of this study, the 42 recirculating aquaculture systems for grow-out were delimited as a unique system for purposes of quantifying the nitrogen budget. In certain contexts as set out below, N dynamics were quantified in greater detail in four individual systems. Broodstock holding and spawning facilities, a hatchery, and two greenhouses for fingerling production contain only a small part of the facility fish biomass, volume and exchange flow (i.e., they handle 3.0% of the fish biomass and 4.4% of the total nitrogen input). Because of their small contribution, the nitrogen budgets for these systems are not presented

The nitrogen budget is expressed as a mass-balance equation of all nitrogen forms, with total inputs plus generation equal to total outputs plus consumption. We found no measurable amounts of dissolved inorganic nitrogen in the replacement water. Hence, feed provided to fish was the sole nitrogen source in the form of organic nitrogen (*N*feed). Multiplication of *N*feed by the total amount of feed provided the mass of total nitrogen input (*TNI*). The nitrogen

**5.** Nitrogen gas removed from the system by passive denitrification, *N*denit, and by ammonia

All transformations among pools were assumed to be in a dynamic equilibrium over a defined period of time. We accounted for the mass fractions of nitrogen from Pools 1 to 4 (i.e., the measurable pools) as total nitrogen recovered (*TNR*), while the difference between total

600-g fish reaches marketable size each week for shipment to a live market.

dissolved nutrients, and salts.

346 Biodegradation - Engineering and Technology

**2.2. System boundaries**

here, but can be found in Sandu (2004).

**2.3. Inputs, outputs and nitrogen pools**

budget was accounted for in five known pools:

**4.** Total organic nitrogen in effluent, *N*TON, and

volatilization, *N*NH3 vol.

**1.** Nitrogen fixed in fish biomass as organic nitrogen, *N*fish,

**2.** Nitrogen fixed in dead fish biomass as organic nitrogen, *N*mort,

**3.** Dissolved inorganic nitrogen, *N*DIN, which included *N*TAN, *N*NO2, and *N*NO3,

Analyses of fish and of feed for protein content followed Thiex et al. (2002), who indicated that by dry weight, 16% of protein is nitrogen. Samples were processed at the Forage Testing Laboratory, Virginia Polytechnic Institute and State University, Blacksburg, Virginia. Analyses for inorganic dissolved nitrogen forms (TAN, NO2 - -N, and NO3 - -N) were conducted on site using a Hach DR2400 spectrophotometer (Hach Company, Loveland, Colorado). Total Kjeldall nitrogen (TKN) was determined using macro-Kjeldall Standard Method 4500 – Norg B (APHA et al., 1998). Samples were acidified below pH 2 using H2SO4, refrigerated with ice, and transported to the Department of Civil and Environmental Engineering at Virginia Polytechnic Institute and State University, Blacksburg, Virginia, for analysis. Temperature and pH were measured directly on site using an Acorn Meter (Kit Model pH 6, Oakton, Vernon Hills, Illinois). Alkalinity was determined on-site using the Hach Permachem® Method. Dissolved oxygen (DO) was measured using a YSI (Model 550, Yellow Springs, Ohio) instrument. We calculated total organic nitrogen as the difference between TKN and total ammonia nitrogen (TAN).

## **2.5. Nitrogen budget determination**

Under steady-state conditions, fish biomass does not fluctuate significantly over time (i.e., harvest equals growth), and the daily rations of feed are constant. Under these assumptions, we derived the nitrogen budget by determining the nitrogen input with feed and the output of nitrogenous compounds in known pools. We quantified daily amounts of nitrogen in feed, fish, and mortalities using information on feed consumption, fish production, and mortalities provided by BRA management. We measured the components of dissolved inorganic nitrogen and total organic nitrogen pools directly. We extrapolated mean values to the entire exchange volume from a day to determine the mass of nitrogen recovered in these forms. We assumed that the amount of nitrogen missing from the balance was lost by passive denitrification and by ammonia volatilization.

We considered both types of feed used in the system (with 36% or 40% standard protein content) to determine nitrogen fixed in feed, *N*feed. We collected samples from three different points in storage silos for nitrogen content determination. We calculated *N*feed as a composite using the equation:

$$N\_{\text{feed}} = \Sigma \left( FA \mathbf{x} \mathbf{P} \mathbf{C} \mathbf{x} \mathbf{0}. 16 \right) \tag{1}$$

where *FA* = amount of feed, *PC* = protein content of the feed, and 0.16 = concentration of nitrogen in protein (Thiex et al. 2002). We determined *PC* by laboratory analyses because protein content may differ from that claimed by the feed producer. We obtained the total mass of nitrogen originating from the feed input, *TNI*, by multiplying *N*feed by the amount fed, *FA*.

To determine fixation of nitrogen in fish, *N*fish, we analyzed protein content in triplicate samples of muscle tissue from fish from three size-classes. We estimated the proportions of fish in each size-class as 5% juveniles (i.e., newly introduced to the system from the hatchery), 60% intermediate, and 35% marketable size. With data on protein content of each fish size-class, we determined *N*fish as a composite using the equation:

$$N\_{\rm fish} = \Sigma \left( FB \mathbf{x} F P \mathbf{x} 0.16 \right) \tag{2}$$

counted for, *TNUA*, by subtracting total nitrogen recovered, *TNR*, from total nitrogen input,

We used a simplified version of a model proposed by Losordo and Westers (1994) to determine the carrying capacity of the production system; that is, we considered only the parts of the model concerning maximum system carrying capacity with respect to TAN. Modeling of the flow rate through biofilters was unnecessary because the flow rate was fixed among all

/min. Four recirculating aquaculture systems chosen for intensive study held different age-groups of fish from juvenile to marketable size in order to represent the overall population in the facility. We knew total fish biomass, fish size, feeding rate, crude protein content of feed, daily percent body weight fed, flow rate through the system, and daily rate of exchange for each

ature, and dissolved oxygen, using standard methods (APHA et al., 1998). We performed these analyses on composite samples collected from the fish-rearing tanks or from the rotating biological contactor's influent and effluent at four-hour intervals. By sampling from appro‐ priate locations, we determined the effects of fish tanks, biofilters or sedimentation basins on each parameter. The experiments extended between consecutive water exchanges. We scaled the data to 24-hour intervals and determined mean and variance for each water quality

We determined the maximum system carrying capacity with respect to TAN as follows. We

where *a* = the mole fraction of unionized ammonia nitrogen as determined by pH and temperature (Huguenin and Colt, 1989). We calculated maximum feed rate, *FR*mTAN, by

where *Qf* = the recirculating flow rate, or flow rate to the RBC, known to be 227,100 L/hr, and 0.092 = model constant coefficient. We determined the efficiency of the rotating biological

We estimated the maximum biomass that could be sustained within the system, *SBM*mTAN as:

*FR A xQ E Q C C mTAN TAN f a TAN TANi* x – / 0.092x ( ) ( *PC*) = + <sup>é</sup> <sup>ù</sup> ë û (6)

( ) <sup>a</sup> – / x 100 *TAN TANe TAN EC C C* é ùû <sup>=</sup> <sup>ë</sup> (7)

**-**

**-N removal**

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349

3 / *AA a TAN NH N*- = (5)

**2.6. RAS carrying capacity, RBC design, TAN and NO3**

selected system. We measured other parameters, such as TAN, NO3

calculated the maximum allowable TAN concentration, *A*TAN, as:

assuming that the TAN concentration of a fish tank equals *A*TAN, as:

contactor for removal of ammonia nitrogen, *E*a as:

recirculating aquaculture systems at 3.78 m3

*TNI*.

parameter.

where *FB* = biomass of fish, and *FP* = protein content of the fish.

About 3.5% of the fish production (by number) was lost as mortalities. We assumed that nitrogen fixed in dead fish, *N*mort, had the same nitrogen content as *N*fish. In order to determine the biomass of *N*mort, we collected mortalities daily from the production system for a two-week period, sorted them by size, and weighed them. We used these data to determine *N*mort using equation 2.

Nitrogen load, *LN*, entered the water column as ammonia and as organic nitrogen bound in feces. We quantified *LN* as all nitrogen from feed that was not accounted for as living or dead fish as using the equation:

$$L\_N = \left[ N\_{\text{feed}} - \left( N\_{f\text{sh}} + N\_{\text{mort}} \right) \right] \text{ / FB} \tag{3}$$

Hence, *L*<sup>N</sup> quantified the amount of nitrogen that sustained the nitrogen cycle throughout the system, supplying all effluent nitrogen pools.

We quantified total organic nitrogen as the difference between TKN and TAN from the effluent. We obtained values for TKN, TAN, NO2 --N, and NO3 - -N by analyzing seven samples collected from the effluent discharge pipe at 3-hour intervals between 2:00 p.m. and 8:00 a.m. because effluent originated from the production system only during that interval. We repeated the tests twice (on different days) and averaged the results. We estimated daily production of these nitrogen forms by multiplying the average concentration (mg/L) by the volume of wastewater released from the system during a one-day period.

All nitrogen in feed that was not recovered as living or dead fish or as total organic nitrogen represented the dissolved inorganic fraction that entered the water as TAN. Hence, we determined ammonia production as:

$$P\_{TAN} = N\_{\text{feed}} - \left(N\_{\text{fish}} + N\_{\text{mort}} + N\_{TON}\right) \tag{4}$$

The sum of TAN, NO2 —N, and NO3 - -N found in the effluent represented the fraction of nitrogen recovered as dissolved inorganic nitrogen, *NDIN*. The summation of *NDIN*, *N*fish, *N*mort, and *N*TON provided the value for total nitrogen recovered, *TNR*. We determined total nitrogen unac‐ counted for, *TNUA*, by subtracting total nitrogen recovered, *TNR*, from total nitrogen input, *TNI*.

#### **2.6. RAS carrying capacity, RBC design, TAN and NO3 - -N removal**

To determine fixation of nitrogen in fish, *N*fish, we analyzed protein content in triplicate samples of muscle tissue from fish from three size-classes. We estimated the proportions of fish in each size-class as 5% juveniles (i.e., newly introduced to the system from the hatchery), 60% intermediate, and 35% marketable size. With data on protein content of each fish size-class,

About 3.5% of the fish production (by number) was lost as mortalities. We assumed that nitrogen fixed in dead fish, *N*mort, had the same nitrogen content as *N*fish. In order to determine the biomass of *N*mort, we collected mortalities daily from the production system for a two-week period, sorted them by size, and weighed them. We used these data to determine *N*mort using

Nitrogen load, *LN*, entered the water column as ammonia and as organic nitrogen bound in feces. We quantified *LN* as all nitrogen from feed that was not accounted for as living or dead

Hence, *L*<sup>N</sup> quantified the amount of nitrogen that sustained the nitrogen cycle throughout the

We quantified total organic nitrogen as the difference between TKN and TAN from the

collected from the effluent discharge pipe at 3-hour intervals between 2:00 p.m. and 8:00 a.m. because effluent originated from the production system only during that interval. We repeated the tests twice (on different days) and averaged the results. We estimated daily production of these nitrogen forms by multiplying the average concentration (mg/L) by the volume of

All nitrogen in feed that was not recovered as living or dead fish or as total organic nitrogen represented the dissolved inorganic fraction that entered the water as TAN. Hence, we

recovered as dissolved inorganic nitrogen, *NDIN*. The summation of *NDIN*, *N*fish, *N*mort, and *N*TON provided the value for total nitrogen recovered, *TNR*. We determined total nitrogen unac‐

– feed ish mort ( ) / *N f L N N N FB* é ù

( ) fish *N FB FP* = S x x0.16 (2)

ë û = + (3)

( ) feed fish mort – / *TAN TON P N N N N FA* = ++ (4)

we determined *N*fish as a composite using the equation:

equation 2.

fish as using the equation:

348 Biodegradation - Engineering and Technology

system, supplying all effluent nitrogen pools.

determined ammonia production as:

The sum of TAN, NO2

effluent. We obtained values for TKN, TAN, NO2

wastewater released from the system during a one-day period.

—N, and NO3

where *FB* = biomass of fish, and *FP* = protein content of the fish.

We used a simplified version of a model proposed by Losordo and Westers (1994) to determine the carrying capacity of the production system; that is, we considered only the parts of the model concerning maximum system carrying capacity with respect to TAN. Modeling of the flow rate through biofilters was unnecessary because the flow rate was fixed among all recirculating aquaculture systems at 3.78 m3 /min.

Four recirculating aquaculture systems chosen for intensive study held different age-groups of fish from juvenile to marketable size in order to represent the overall population in the facility. We knew total fish biomass, fish size, feeding rate, crude protein content of feed, daily percent body weight fed, flow rate through the system, and daily rate of exchange for each selected system. We measured other parameters, such as TAN, NO3 - -N, NO2 - -N, pH, temper‐ ature, and dissolved oxygen, using standard methods (APHA et al., 1998). We performed these analyses on composite samples collected from the fish-rearing tanks or from the rotating biological contactor's influent and effluent at four-hour intervals. By sampling from appro‐ priate locations, we determined the effects of fish tanks, biofilters or sedimentation basins on each parameter. The experiments extended between consecutive water exchanges. We scaled the data to 24-hour intervals and determined mean and variance for each water quality parameter.

We determined the maximum system carrying capacity with respect to TAN as follows. We calculated the maximum allowable TAN concentration, *A*TAN, as:

$$A\_{\rm TAN} = A\_{\rm NH3-N} / \mathfrak{a} \tag{5}$$

where *a* = the mole fraction of unionized ammonia nitrogen as determined by pH and temperature (Huguenin and Colt, 1989). We calculated maximum feed rate, *FR*mTAN, by assuming that the TAN concentration of a fish tank equals *A*TAN, as:

$$FR\_{\rm mTAN} = \left[A\_{\rm TAN} \mathbf{x} \mathbf{Q}\_f \mathbf{x} E\_a + \mathbf{Q} \left(\mathbf{C}\_{\rm TAN} - \mathbf{C}\_{\rm TAN}\right)\right] \\ \tag{6.092xPC} \\ \tag{6}$$

where *Qf* = the recirculating flow rate, or flow rate to the RBC, known to be 227,100 L/hr, and 0.092 = model constant coefficient. We determined the efficiency of the rotating biological contactor for removal of ammonia nitrogen, *E*a as:

$$E\_{\mathbf{a}} = \left[ \left( \mathbf{C}\_{TAN} - \mathbf{C}\_{TAN\epsilon} \right) / \mathbf{C}\_{TAN} \right] \ge 100 \tag{7}$$

We estimated the maximum biomass that could be sustained within the system, *SBM*mTAN as:

$$\text{SSM}\_{\text{mTAN}} = \text{FR}\_{\text{mTAN}} \;/\; \% \text{BW} \tag{8}$$

where *S*, the surface area of an RBC, was 13,336 m2

We used a similar approach to determine NO3

**2.7. Statistical analysis**

**3. Results**

**3.1. Nitrogen budget**

= 341.381 kg/day.

The mass balance quantifying the partitioning of *P*TAN removal was:

transformed by passive denitrification in the four systems tested.

.

We used linear regressions to determine the relationship between daily TAN production (*P*TAN) and TAN removal efficiency per pass (*Ea*), and between fish biomass and percent *P*TAN

We derived the nitrogen budget for the entire production system for mean conditions of 28.4ºC, pH 7.14, and alkalinity 119.0 mg/L as CaCO3. For annual production of 1300 metric tons of fish biomass, BRA administers 2210 metric tons of feeds. These amounts correspond to 6054.8 kg feed consumed per day and 3561.6 kg fish weight gain per day. Of the feed utilized, 95% (5752.0 kg) was nominally 36% protein and 5% (302.8 kg) 40% protein content. However, laboratory analyses showed that the actual protein contents of the two feeds were somewhat lower, 35.0±0.2% and 39.8±0.2%, respectively. The estimated percentages of feed types and the laboratory-determined protein concentrations were used to determine the nitrogen fixed in feed, *N*feed = 56.38 g/kg feed. By extrapolating *N*feed, we determined a total nitrogen input of *TNI*

Laboratory analyses showed that the three size-classes of fish from small to large had 18.04±0.16, 20.75±0.02 and 22.26±0.74% protein content, respectively. From these data, we determined that the nitrogen fixed in fish was *Nfish* = 33.83 g/kg produced. Extrapolating to the daily biomass of fish produced, the total nitrogen assimilated in fish was 120.49 kg/day.