**1. Introduction: Food security, plant breeding, and high-throughput plant phenotyping**

Albeit the rate on population rise is slowing down, world population is still increasing; from the current figure slightly above 7.2 billion, it is expected to reach 9.6 billion by 2050 [1]. Therefore, there is a pressing need to increase the global food production ([2] and references therein). Mostly, the population increase is expected to occur in developing countries ([3] and referen‐ ces therein), where the current productivity of farms is far below the one in the developed world. For instance, in India, where the population is expected to surpass China's, the current productivity of wheat farming is only one third than that of France, while rice productivity accounts for less than half of China's [4]. Increasing food production and ensuring availability of safe and nutritious food at affordable prices to the population of developing countries are, therefore, a pressing urgency. As most of the best arable lands are already under production, expanding the agricultural areas would have negative impacts on important natural areas. Thereby, increasing the productivity of the current agricultural areas is the chief objective [5], which requires more productive and better adapted plant varieties [6]. There is a long tradi‐ tion of conventional plant breeding that deeply changed our world [7]. Moreover, being a costeffective tool for increasing nutritional value of forage and crops, plant breeding still can contribute to global food security [4]. Therefore, it is at the forefront of concern of both agrono‐ mists and plant biologists. The use of molecular biology techniques jointly with informatics capabilities—giving rise to the omics techniques—deeply accelerated plant breeding, provid‐ ing better plant varieties at an increased pace [8]. In fact, during the past 20 years, molecular profiling and classical sequencing technologies enabled significant advances toward the largescale characterization of plant genomes [9], yielding valuable tools for plant breeding such as marker-assisted selection [10]. However, integrating approaches across all scales, from molecular to field applications, are necessary to develop sustainable plant production with higher yield. Regrettably, the advances in genomics far by-passed the advances in phenom‐ ics, and therefore, there is a rising consensus among plant biologists that plant phenotyping is a bottleneck to advancing both fundamental research and plant breeding [11]. Therefore, a range of international initiatives in high-throughput plant phenotyping (HTPP) are at course, and new automated equipment is being developed [12]. Plant phenotyping is "the application of a set of methodologies and protocols used to measure plant growth, architecture, and composi‐ tion with a certain accuracy and precision at different scales of organization, from organs to canopies" [11]. Phenotyping, being a paradigm of the interdisciplinary character of modern plant physiology [13], is not a simple matter. To begin with, it has to be decided which param‐ eters to measure to extrapolate to the desired plant traits (resistance and productivity). That is, researchers need sound and robust knowledge about the traits that are indicative of the intended performance. Here, mechanistic understanding of plant physiology plays a role in identifying useful parameters and proxies to measure [11]. In any case, it seems plausible that effective HTPP platforms will involve the measurement of multiple parameters. Two classes of param‐ eters are of major importance: structural parameters and photosynthetic parameters. The structure of both the shoots [14] and the roots has been primarily phenotyped using a broad range of cameras sensitive in the visible spectral range. Conversely, photosynthetic activity has been phenotyped mainly by using *in vivo* chlorophyll (Chl) fluorescence techniques.

#### **2. Photosynthesis: a proxy of plant performance**

purpose. On the contrary, the techniques based on *in vivo* chlorophyll (Chl) a fluores‐ cence measurements are perfectly fit. In this chapter, an historical perspective on the development of *in vivo* Chl a measurement is briefly addressed. Then, the state of the art of the fluorescence-based techniques of photosynthesis assessment is presented, and their potential use in HTPP is evaluated. Finally, the current use of these techniques in the main

**Keywords:** photosynthesis, photochemistry, chlorophyll fluorescence, optical techni‐

**1. Introduction: Food security, plant breeding, and high-throughput plant**

Albeit the rate on population rise is slowing down, world population is still increasing; from the current figure slightly above 7.2 billion, it is expected to reach 9.6 billion by 2050 [1]. Therefore, there is a pressing need to increase the global food production ([2] and references therein). Mostly, the population increase is expected to occur in developing countries ([3] and referen‐ ces therein), where the current productivity of farms is far below the one in the developed world. For instance, in India, where the population is expected to surpass China's, the current productivity of wheat farming is only one third than that of France, while rice productivity accounts for less than half of China's [4]. Increasing food production and ensuring availability of safe and nutritious food at affordable prices to the population of developing countries are, therefore, a pressing urgency. As most of the best arable lands are already under production, expanding the agricultural areas would have negative impacts on important natural areas. Thereby, increasing the productivity of the current agricultural areas is the chief objective [5], which requires more productive and better adapted plant varieties [6]. There is a long tradi‐ tion of conventional plant breeding that deeply changed our world [7]. Moreover, being a costeffective tool for increasing nutritional value of forage and crops, plant breeding still can contribute to global food security [4]. Therefore, it is at the forefront of concern of both agrono‐ mists and plant biologists. The use of molecular biology techniques jointly with informatics capabilities—giving rise to the omics techniques—deeply accelerated plant breeding, provid‐ ing better plant varieties at an increased pace [8]. In fact, during the past 20 years, molecular profiling and classical sequencing technologies enabled significant advances toward the largescale characterization of plant genomes [9], yielding valuable tools for plant breeding such as marker-assisted selection [10]. However, integrating approaches across all scales, from molecular to field applications, are necessary to develop sustainable plant production with higher yield. Regrettably, the advances in genomics far by-passed the advances in phenom‐ ics, and therefore, there is a rising consensus among plant biologists that plant phenotyping is a bottleneck to advancing both fundamental research and plant breeding [11]. Therefore, a range of international initiatives in high-throughput plant phenotyping (HTPP) are at course, and new automated equipment is being developed [12]. Plant phenotyping is "the application of a set of methodologies and protocols used to measure plant growth, architecture, and composi‐

systems of phenotyping is surveyed.

4 Applied Photosynthesis - New Progress

**phenotyping**

ques, plant phenomics, high-throughput plant phenotyping

An in-depth knowledge of plant physiology is required for successful plant breeding [15], as most of plant stresses are under the control of complex traits [16]. In particular, as accumulating data did not fit into conventional theories, it becomes clear that metabolic engineering is in need of a better understanding of metabolic regulation and plasticity [8]. Photosynthesis has been considered as a good indicator of overall plant performance. It is the only significant energy input in plants and thereby impacts all aspects of plant metabolism and physiology. The cumulative rate of photosynthesis over the growing season is the primary determinant of crop biomass [17]. Photosynthesis largely determines the redox state of plant cells and therefore is at the core of regulatory networks [18]. Therefore, assessing photosynthesis and the photosynthetic apparatus plays a core role on plant phenotyping. Nevertheless, highthroughput phenotyping demands very rapid measurements, and consequently, the most common methods of photosynthesis measurement—the infra-red gas analysis and the polarographic measurement of oxygen evolution—are not well suited for this purpose. On the contrary, optical methods present considerable advantages for *in vivo* and *in vitro* assessment of the physiological condition of live tissues, as compared to chemical and physicochemical methods, because they are much faster, non-invasive, and non-destructive (e.g. [19, 20]). Among these, *in vivo* Chl fluorescence measurements are best suited for this purpose. Albeit the use of optical methods in plant phenotyping has been recently reviewed [21], to our knowledge, a review specifically addressing the use of Chl fluorescence techniques in HTPP is missing in the literature. In the following sections, an historical perspective on the develop‐ ment of *in vivo* Chl fluorescence measurement, from the seminal work of Kautsky and Hirsch [22] to the ground-breaking invention of pulse amplitude modulation (PAM) [23], is briefly addressed. Then, the current state of the art of the Chl fluorescence measurement techniques is presented, and their potential use in HTPP is evaluated.

#### **3. Chlorophyll fluorescence**

The emission of photons from excited molecules was named fluorescence by the Irish physicist George Gabriel Stokes [24], after fluorspar or fluoride, the mineral from calcium fluorite where he studied the phenomenon. When a molecule of Chl a from the antenna complex of a photosynthetic organism is hit by a photon, it absorbs its energy and an electron is raised to a higher energy level S1 (singlet 1, corresponding to the absorption of one red photon) or S3 (singlet 3, corresponding to the absorption of one blue photon; **Figure 1**).

**Figure 1.** Simplified diagram of the energy levels of the singlet excitation states of chlorophyll a molecules. *Source*: Adapted from Ref. [25].

The excited molecule is very unstable, and its excess energy is promptly released. There are three different competing ways of de-excitation: (1) heat dissipation; (2) photochemical utilization of energy; and (3) fluorescence emission. The relative contribution of each process is dependent on the physiological status of the photosynthetic systems [25]. In the last few decades, the measurement of Chl fluorescence has become a universal technique in the study of virtually all types of photosynthetic entities, including fruits [26–28], corals [29], seagrasses [30], macroalgae [31], microphytobenthos [32–34], and many types of higher plants, such as tobacco [35], maize [36], and tomato [37]. The use of Chl fluorescence has been recently proposed for detecting early responses to abiotic and biotic stresses, before a decline in growth can be observed [38–40]. Likewise, there are numerous applications of Chl fluorescence in the horticultural sectors (reviewed in [41]). With the advent of different instrumental techniques, Chl fluorometry developed into various types, with different timescales of signal capturing [42]. It is useful, however, to divide the currently available techniques for *in vivo* Chl fluores‐ cence measurements into passive and active [43]. While passive techniques measure fluores‐ cence emission under actinic light, active techniques stimulate fluorescence emission using dedicated light sources.

#### **3.1. Passive fluorescence**

**3. Chlorophyll fluorescence**

6 Applied Photosynthesis - New Progress

Adapted from Ref. [25].

dedicated light sources.

The emission of photons from excited molecules was named fluorescence by the Irish physicist George Gabriel Stokes [24], after fluorspar or fluoride, the mineral from calcium fluorite where he studied the phenomenon. When a molecule of Chl a from the antenna complex of a photosynthetic organism is hit by a photon, it absorbs its energy and an electron is raised to a higher energy level S1 (singlet 1, corresponding to the absorption of one red photon) or S3

**Figure 1.** Simplified diagram of the energy levels of the singlet excitation states of chlorophyll a molecules. *Source*:

The excited molecule is very unstable, and its excess energy is promptly released. There are three different competing ways of de-excitation: (1) heat dissipation; (2) photochemical utilization of energy; and (3) fluorescence emission. The relative contribution of each process is dependent on the physiological status of the photosynthetic systems [25]. In the last few decades, the measurement of Chl fluorescence has become a universal technique in the study of virtually all types of photosynthetic entities, including fruits [26–28], corals [29], seagrasses [30], macroalgae [31], microphytobenthos [32–34], and many types of higher plants, such as tobacco [35], maize [36], and tomato [37]. The use of Chl fluorescence has been recently proposed for detecting early responses to abiotic and biotic stresses, before a decline in growth can be observed [38–40]. Likewise, there are numerous applications of Chl fluorescence in the horticultural sectors (reviewed in [41]). With the advent of different instrumental techniques, Chl fluorometry developed into various types, with different timescales of signal capturing [42]. It is useful, however, to divide the currently available techniques for *in vivo* Chl fluores‐ cence measurements into passive and active [43]. While passive techniques measure fluores‐ cence emission under actinic light, active techniques stimulate fluorescence emission using

(singlet 3, corresponding to the absorption of one blue photon; **Figure 1**).

Albeit it has been known for a long time that Chl, like many other molecules, emits fluorescence after excitation, it was not until 1931 that Kautsky and Hirsch observed that the *in vivo* emission of fluorescence during a dark–light transition showed a typical variation [22], usually known as the Kautsky induction curve or simply Kautsky effect. Even though these authors have speculated on a possible relation between the observed fluorescence emission and carbon fixation, the molecular basis of photosynthesis was, at the time, poorly understood,1 and therefore, their observations did not significantly impact photosynthesis research. As the understanding of the molecular mechanisms progressed, however, prototypes of continuous fluorescence recording fluorometers were built and used in photosynthesis research [45]. These simple devices had limited use in stress physiology, but Lichtenthaler and Rinderle [46] developed the Vitality Index (Rfd = *F*v′/(*F*m–*F*v′)) and successfully used it to detect low temperature stress in higher plants. However, continuous fluorescence recording fluorometers acquired increase potential in photosynthesis research only when equipped with high-time resolution capacities. This allowed to explore the kinetics of the fast phase of fluorescence signal rise in a dark–light transition. Although the exploration of these signals begun earlier with experimental prototypes, it was the commercial availability of the Plant Efficiency Analyzer by the UK-based manufacturer Hansatech that made this technique widely available to plant physiologists and plant breeders. In Switzerland, Reto Strasser provided the theoret‐ ical basis for the interpretation of these signals [47]. His group at the University of Genève have developed the JIP test (termed after the main inflections in the fast fluorescence rise, called J, I, and P) to analyze the photosystem II (PS II) behavior [48] (**Figure 2**).

**Figure 2.** Chlorophyll fluorescence induction curve (OJIP transient).

1 Please note that the very concept of photosynthesis as being a redox process was only to be demonstrated 6 years later [44].

Based on the Chl a fast fluorescence transient measurement [49], numerous indexes and parameters quantifying the energy flow related to the different phases in the PS II photo‐ chemical reactions can be calculated. The JIP transient rise reflects the successive but overlap‐ ping reduction of the electron acceptor pool of PS II [50] and can be used to obtain information on the redox state of the photosynthetic electron transport chain, on the stoichiometry of its components and on the relative PS II antenna size [51]. This transient has been found to be very sensitive to stress caused by changes in different biotic and abiotic conditions, presenting alterations even before visible symptoms could be detected on the plants [52–57]. Albeit some information may be obtained from the application of the JIP test to pre-illuminated leaves, the most used protocols require a dark-adaptation period [58]. This may be achieved for many leaves simultaneously by using the leaf clips provided by the manufacturer Hansatech. Individual measurements are rapid as usually a 1-second light (saturating) pulse is applied, and the kinetics of fluorescence rise is immediately recorded. Therefore, the main hindrance to the use of the JIP test on high-throughput automated plant phenotyping is the need to previously dark-adapt the samples (**Table 1**). Albeit this technique has been proved useful in manual low-throughput plant phenotyping [57, 59], none of the commercial phenotyping platforms makes use of it. It is possible to envisage, however, a system where whole plants would be pre-adapted for dark conditions, and non-contact measurements of the fluorescence induction curve would be made. In fact, imaging of the JIP parameters is already possible and has been used to screen wild barley genotypes under heat stress [60]. Passive fluorescence spectra of leaves may also be obtained and provide information on the status of the photo‐ synthetic apparatus [61]. However, fluorescence emission spectra have been studied mainly with active fluorescence techniques, mostly with laser-induced fluorescence (LIF) [62] which is discussed in the next section.

#### **3.2. Active fluorescence**

#### *3.2.1. Laser-induced fluorescence*

Since LIF measurements can be carried out remotely, allowing, for example, to inspect difficultto-access canopies, this technique is particularly interesting for HTPP in the field. In fact, large crop areas can be efficiently surveyed by scanning an instrument placed at a high viewpoint or by mounting it in an airplane or a drone [63]. LIF was applied for estimating the overall metabolic activity of plants during a defined period of time [64, 65]; differentiating plant species [66–68]; assessing potassium deficiency [69]; estimating the maturity of lettuce [70]; detecting mildew and rust fungal [71] and bacterial [72] infections; and studying the influence of water stress [73, 74], ambient light [68], UV radiation [75], atmospheric [76], and soil pollutants [76, 77] and excess of ammonium nitrate [78] and nickel [79] on plant physiology. In addition, LIF has been used to assess the productive biomass of benthic diatoms [62, 80] and to differentiate between groups of macroalgae [81]. LIF spectra of plant leaves present a local Chl emission maximum in the red region of the spectrum at 685 nm (F685) and an absolute maximum at the far-red region, circa 740 nm (F740) [65, 75]. The relative intensity, shape, and wavelength of these peaks are dependent on the physiological status of the photosynthetic apparatus. Changes in the Fr/Ffr ratio were suggested to be well correlated to Chl a concen‐ tration [69, 82, 83], which is altered, in most plant species by stress [84]. In addition to changes in the Chl concentration, other factors, related to membrane lipid composition and protein environment [85] as well as leaf ultrastructure and the accumulation of specific light absorbing metabolites, such as anthocyanins, are likely to affect Fr/Ffr ratios. It is known that water stress significantly impacts leaf lipid composition of thylakoid membranes, causing a decrease in the contents of polar lipids, namely chloroplast-specific glycolipids as well as changes in their fatty acid composition, likely to affect membrane fluidity [86]. In forest species subjected to severe drought, Lavrov and coworkers [73] showed that the red/far-red emission fluorescence ratio (Fr/Ffr) is very well correlated with the maximum potential photochemical efficiency of PS II estimated with a Plant Efficiency Analyzer (Hansatech, U.K.). Also, simultaneous measure‐ ment of Chl fluorescence in maize (*Zea mays*), sugar beet (*Beta vulgaris*), and kalanchoë (*Kalanchoë* sp.) by LIF and PAM indicated that the steady state of fluorescence is useful for water stress detection [87]. Albeit LIF has been extensively used in applied research, its possibilities have not been thoroughly explored in fundamental research. In fact, even though *Arabidopsis thaliana* had become the main model organism in plant biology, applications of LIF to this species are almost absent, the exceptions being a study on npq mutants, altered in the expression of the PS II PsbS protein [88] and, more recently, a study on the water stress effect on photosynthesis [74]. Advanced variations of this technique as is the case of laser-induced fluorescence transients (LIFT) allow the calculation of quantum efficiency [89], leading values in line with the ones obtained by the more established technique of PAM fluorometry (see below).

#### *3.2.2. Conventional pulse amplitude modulated fluorescence*

Based on the Chl a fast fluorescence transient measurement [49], numerous indexes and parameters quantifying the energy flow related to the different phases in the PS II photo‐ chemical reactions can be calculated. The JIP transient rise reflects the successive but overlap‐ ping reduction of the electron acceptor pool of PS II [50] and can be used to obtain information on the redox state of the photosynthetic electron transport chain, on the stoichiometry of its components and on the relative PS II antenna size [51]. This transient has been found to be very sensitive to stress caused by changes in different biotic and abiotic conditions, presenting alterations even before visible symptoms could be detected on the plants [52–57]. Albeit some information may be obtained from the application of the JIP test to pre-illuminated leaves, the most used protocols require a dark-adaptation period [58]. This may be achieved for many leaves simultaneously by using the leaf clips provided by the manufacturer Hansatech. Individual measurements are rapid as usually a 1-second light (saturating) pulse is applied, and the kinetics of fluorescence rise is immediately recorded. Therefore, the main hindrance to the use of the JIP test on high-throughput automated plant phenotyping is the need to previously dark-adapt the samples (**Table 1**). Albeit this technique has been proved useful in manual low-throughput plant phenotyping [57, 59], none of the commercial phenotyping platforms makes use of it. It is possible to envisage, however, a system where whole plants would be pre-adapted for dark conditions, and non-contact measurements of the fluorescence induction curve would be made. In fact, imaging of the JIP parameters is already possible and has been used to screen wild barley genotypes under heat stress [60]. Passive fluorescence spectra of leaves may also be obtained and provide information on the status of the photo‐ synthetic apparatus [61]. However, fluorescence emission spectra have been studied mainly with active fluorescence techniques, mostly with laser-induced fluorescence (LIF) [62] which

Since LIF measurements can be carried out remotely, allowing, for example, to inspect difficultto-access canopies, this technique is particularly interesting for HTPP in the field. In fact, large crop areas can be efficiently surveyed by scanning an instrument placed at a high viewpoint or by mounting it in an airplane or a drone [63]. LIF was applied for estimating the overall metabolic activity of plants during a defined period of time [64, 65]; differentiating plant species [66–68]; assessing potassium deficiency [69]; estimating the maturity of lettuce [70]; detecting mildew and rust fungal [71] and bacterial [72] infections; and studying the influence of water stress [73, 74], ambient light [68], UV radiation [75], atmospheric [76], and soil pollutants [76, 77] and excess of ammonium nitrate [78] and nickel [79] on plant physiology. In addition, LIF has been used to assess the productive biomass of benthic diatoms [62, 80] and to differentiate between groups of macroalgae [81]. LIF spectra of plant leaves present a local Chl emission maximum in the red region of the spectrum at 685 nm (F685) and an absolute maximum at the far-red region, circa 740 nm (F740) [65, 75]. The relative intensity, shape, and wavelength of these peaks are dependent on the physiological status of the photosynthetic apparatus. Changes in the Fr/Ffr ratio were suggested to be well correlated to Chl a concen‐

is discussed in the next section.

8 Applied Photosynthesis - New Progress

*3.2.1. Laser-induced fluorescence*

**3.2. Active fluorescence**

Active fluorescence protocols exploiting PAM [23] can measure the potential and effective quantum efficiency of photosystem II, the electron transport rate, and the extent of nonphotochemical quenching. Based on the concepts used in the light-doubling technique [90], PAM fluorometry enables to distinguish between the photochemical (qP) and non-photo‐ chemical (i.e., dissipative; qN and NPQ) use of light energy. Notably, the quantum efficiency of photosystem II can be measured much more easily than the other parameters [91]. Kitajima and Butler [92] showed that the maximum potential quantum yield of PS II is characterized by the dimensionless parameter *F*v/*F*m (the ratio of variable and maximum fluorescence measured after saturating light pulses). A very constant value of 0.832 ± 0.004 was found for healthy leaves of a very wide variety of species [93] while stress due to disease or environ‐ mental conditions is indicated by lower values. The basal fluorescence (*F*o) is dependent on the tissues' Chl concentration [33], which, in turn, depends on the physiological condition of the photosynthetic system. However, severe stress may change the basal fluorescence yield, affecting the relation between *F*o and Chl concentration. In fact, a significant increase of *F*o due to heat stress, independent of the Chl concentration was reported by Havaux and Strasser [94]. The heat-induced increase of the basal fluorescence intensity probably reflects a disturbance on the organization of thylakoid membranes [95]. The *F*v/*F*m parameter appears to be relatively insensitive to severe water limitation but could be used to differentiate between responses during cold. On the basis of the calculation of the fluorescence index Δ*F*/*F*m′ (where Δ*F* is the difference between the maximal fluorescence [*F*m′] and the steady-state fluorescence [*F*] of light-adapted samples), which measures the effective quantum yield of PS II [96], PAM fluorometry allows the construction of rapid light curves (RLCs) relating the rate of photo‐ synthetic electron transport and incident photon irradiance [97, 98]. PAM was successfully applied to a wide range of plants, such as the olive tree, rosemary, and lavender [99]; *Paspalum dilatatum* [100], *Phillyrea angustifolia* [101], and other Mediterranean shrubs [102]; the tropical grass *Setaria sphacelata* [84] and several C4 turfgrasses [103]; maize [36, 104]; and *Arabidopsis thaliana* [105], among others.

#### *3.2.3. Imaging pulse amplitude modulated fluorescence*

The development of Chl fluorescence imaging systems by numerous research groups [106– 108] together with the emergence of commercially available models by PSI (Brno, Czech Republic), Walz Systems (Effeltrich, Germany), and Technologica Ltd. (Colchester, UK) has greatly increased the versatility of Chl fluorometry techniques (reviewed in [109]). Systems that image at the microscopic level allow to measure PS II photochemical efficiencies from chloroplasts within intact leaves and from individual cells within mixed populations [107, 110]. On the other hand, lower resolution imaging systems allow the mapping of fluorescence parameters over large areas, making it a unique technique to study the spatial heterogeneity of the photosynthetic activity across an autotrophic surface [111–113]. Conventional and imaging techniques use different technologies, namely in the detection processes of the fluorescence signal: a photodiode or phototube in conventional PAM fluorometry and a charge-coupled device (CCD) camera in imaging-PAM fluorometry. Consequently, caution is needed when comparing results from conventional and imaging fluorescence techniques [114]. Nevertheless, imaging-PAM fluorometry has proven to be a powerful technique and new technological developments, as the use of semi-automated systems equipped with fluores‐ cence cameras continuously assessing the photochemical activity of leaves [115] are in course.


**5. Conclusions**

SEM-AM is a combined analytical tool of upgrading an SEM. It was initially developed and adopted for the fast and effective characterisation of metal-bearing ores and their processing products. A main driving force of SEM-AM development was to control and improve the effectivity of mineral processing.

The SEM-AM is organised in several successive steps (1–3) which can be controlled and adopted by the users. A measurement begins by the collection of a BSE image and its processing by various image analysis software routines (step 1). In step 2, the electron beam is focussed to induce EDS spectra at selected points according to the adjustments of the BSE image analysis. Step 3 is the classification of the gained sample EDS spectra against a list of approved reference EDS spectra. Different algorithms of the EDS spectra classification are realised by the various SEM-AM software providers.

Combinations of steps 1 and 2 lead to the principal SEM-AM measurement routines (1) point counting modal analysis; (2) particle analysis; (3) sparse phase search, and (4) EDS spectral mapping. These can be applied for the characterisation of natural and artificial electron-beam stable particulate, granular and solid massive materials. The routine (1) follows the traditional point counting method for estimation of the modal contents of different phases in a sample and provides no further information on particle properties and geometries. The particle analysis (2) measurement routines are designed for the analyses of grain mounts with 104–106 particles for addressing the special tasks in mineral processing and mining. This measurement routines allows the quantification of modal mineralogy, calculated assay, sizes and geometries of particles and grains as well as association and liberation of particles with di fferent chemical compositions within a bulk sample and without previous phase separations. This is not possible by conventional sedimentological particle size and size distribution analysis methods. The sparse phase search measurement routines (3) were developed to find rare phases like small grains of gold and platinum group minerals which is hard and time-consuming in a manual search. BSE triggers allow for focusing the SEM-AM sparse phase search on other interesting phases. The EDS spectral mapping methods appear as the most versatile routines to resolve intra-particle details, chemical zonation and phase relationships in combination with the full particle sizes and geometries.

The application of SEM-AM requires a high-quality preparation of the samples. Grain mounts with epoxy raisins in blocks and on glass, epoxy-embedded hard material fragments and epoxy-glued thin (30 μm) and thick (100 μm) sections on glass are the mostly used compounds. The detection and selection of a suitable epoxy type and mixture which does not evaporate under high vacuum conditions and remains stable under the electron beam after hardening is one of the principal challenges in sample preparation. The other di fficulty is the development of sample-specific multi-step polishing procedures to produce relief-free planar surfaces in samples containing phases of very di fferent hardness.

As demonstrated by several case studies in this contribution, the EDS spectral mapping measurement methods appear to have the most promising potential for novel applications apart from metal-bearing ores and mineral processing. This especially concerns the SEM-AM applications to artificial materials such as ashes, slags and ceramics. It has to be clarified that the complete identification of minerals and their denotation through the classification of their EDS spectra remains incomplete. For this, a full and reliable mineral identification requires additional XRD data. It also appears that XRD data identifies phases which appear in microcrystalline aggregates and not in larger grains. Many of the materials which potentially could be characterised by SEM-AM consist of microcrystalline, amorphous and glassy phases. In such cases, the generic labelling of reference EDS spectra and their subsequent target component grouping allow an interesting utilisation of the SEM-AM for novel studies not feasible by other analytical methods.

**Author Contributions:** Conceptualisation, B.S., D.S.; methodology, D.S., S.G.; investigation, B.S., D.S., S.G.; resources, B.S.; data curation, B.S., DS, S.G.; writing—original draft preparation, B.S., D.S.; writing—review and editing, B.S., D.S., S.G.; visualisation, B.S., D.S.; project administration, B.S., D.S., S.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Helmholtz Institute Freiberg for Resource Technology, the Deutsche Forschungsgemeinschaft (DFG) Grant SCHU676/20 and through contract work for numerous enterprises. The Open Access Funding and APC was funded by the Publication Fund of the TU Bergakademie Freiberg.

**Acknowledgments:** The authors acknowledge the great expertise of Andreas Bartzsch, Roland Würkert and Michael Stoll in preparation of numerous standard and special geomaterials at Helmholtz Institute Freiberg for Resource Technology. The constructive comments of three anonymous reviewers considerably contributed to improvement of the manuscript.

**Conflicts of Interest:** The authors declare no conflict of interest.


*2.4. Spinning of Fibers*

Polyacrylonitrile (PAN) fibers and both types of polyacrylonitrile-polyaniline fibers (PAN/PANI) were obtained using the wet method. A reference fiber spinning solution (PAN) was prepared by dissolving polyacrylonitrile (13.5%) in dimethylformamide (DMF) at 40 ◦C. After passing through the nozzle, the spinning solution was introduced into a solidifying bath, in which PAN coagulated in the form of fibers (Figure 2). In a solidifying bath (60% aqueous DMF solution) at 20 ◦C, and in a plasticizing bath (50% aqueous solution of DMF) at a temperature of 70 ◦C, intensive removal of the solvent and impurities from the formed fibers took place. The fibers underwent a two-stage stretching process—in the plasticizing bath and in water vapor at 135 ◦C. During these processes, the orientation of macromolecules took place and the fiber structure was ordered, which improved the mechanical properties of the fibers. The PAN/PANI in situ fibers were not subjected to stretching in water vapor because of tearing during the tensile test. The formed and stretched fibers were wound on reels.

**Figure 2.** Scheme of fiber spinning using the wet method.


**6.2.2 Concluding remarks - WLCG**

The experience from the first year and a half of the LHC data taking implies that the WLCG has built a truly working grid infrastructure. The LHC experiments have their own distributed models and have used the WLCG infrastructure to deliver Physics results within weeks after the data recording which has never been achieved before. The fact that a significant numbers of people are doing analysis on the Grid, that all the resources are being used up to the limits and the scientific papers are produced with an unprecedented speed is proving an evident success of the WLCG mission.

#### **6.3 ALICE performance**

To conclude this section, we will briefly summarize the experience and performance of the ALICE experiment. ALICE started extremely successfully the processing of the LHC data in 2009: the data collected during the first collisions delivered by the LHC on November 23rd (2009) got processed and analyzed so fast that within one week the article with the results from the first collisions was accepted for publication as the first ever scientific paper with the Physics results from the LHC collisions [65].


**7. Conclusions**

512 Recent Trends in Processing and Degradation of Aluminium Alloys

Fig. 32. Typical SEM micrographs showing the effects of homogenization parameters and Zr content on the sizes and size distributions of the Zr-containing dispersoids (a) alloy variant N3, T=470 °C for 8 h, (b) N3, T= 470 °C for 24 h, (c) alloy variant N3, T=550 °C for 24 h and

Fig. 34 (a) shows the effect of Zr content on the average diameter of dispersoids formed at 470 °C as a function of time. It can be seen that the average diameter of the dispersoids formed in the alloy with the highest Zr content are larger than those in the other two alloys. However, this effect is not very strong, as the average dispersoid diameters in the N1 and N2 variants do not differ much from each other. An increase in the average diameter of the dispersoid particles with increasing homogenization time towards a constant value is also observed for the alloy variants with different Zr contents. Fig. 34 (b) presents the effect of Zr content on the number density of the Zr-containing dispersoids homogenized at 470 °C. It is clear that the Zr-content has a strong effect on the number density of the dispersoids. The number density of the dispersoids for the alloy with a Zr content of 0.2 wt.% is almost two

(d) alloy variant N1, T= 470 °C for 24 h [63, 69]

times as much as that in the alloy with a Zr-content of 0.13 wt.%.

Main particles detected in the as-cast microstructure of AA7020 aluminum alloys were categorized to be grain boundary ones, low melting point particles and dispersoids. The evolution of these particles during the homogenization treatment of the AA7020 aluminum alloy was quantitatively analyzed and the following conclusions have been drawn.

Microstructural Evolution During the Homogenization of A**l**-Z**n**-M**g** Aluminum Alloys 515

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1) The particles distributed along the grain boundaries which constitute more than 70% of the secondary phases present in the as-cast structure of the AA7020 aluminum alloy are Al17(Fe3.2,Mn0.8)Si2 particles. The low melting point phases are indeed present in the as-cast microstructure of the AA7020 aluminum alloy, which may cause incipient melting at 576 °C. These phases contain Al-Cu-Mg-Zn and dissolve during homogenization at 550 °C for 2 h.

2) The width of the grain boundary particles remains unchanged during homogenization at low temperatures. It however decreases at higher temperatures. The extent of the dissolution is more dependent on homogenization temperature than on time. The evolution mechanisms of the GB particles during homogenization consist of spheroidization during homogenization at low temperatures and thinning, discontinuation and full dissolution (TDFD) at high temperatures.

3) Four different types of dispersoids are formed in the AA7020 aluminum alloy variants during homogenization. In addition to the well-known Al3Zr dispersoids, three other types of dispersoids are also present in the homogenized microstructure of the AA7020 aluminum alloy. The number densities of Zr- and Cr-containing dispersoids are large in the grain interior and very small in the grain boundary regions. These two types of dispersoids appear to be fully spherical and are formed at all the homogenization conditions. The Mncontaining dispersoids form only when the homogenization temperature is equal to or higher than 510 °C and holding time longer than 4 h. The number density of these dispersoids is close to zero in the grain interior but becomes high in the grain boundary regions. The number density and sizes of the Zr-containing dispersoids increase with increasing Zr content of the alloy and homogenization time.


**1. Introduction**

In this chapter, I described several studies on the lipid peroxidation of membrane phospholipids in retina. Particular emphasis is placed on the molecular modifications of very long chain polyunsaturated fatty acids associated with protein changes during peroxidation of photoreceptor membranes. Retina possesses membranes with high content of polyunsaturated fatty acids. Reactive oxygen species begins chain reactions of lipid peroxidation which damage the retina, especially the membranes that play important roles in visual function. Furthermore, biomolecules such as proteins or amino lipids can be covalently modified by lipid decomposition products. In retinal membranes, peroxidation of lipids is also usually accompanied by oxidation of membrane proteins. In consequence, lipid peroxidation may alter the arrangement of proteins in bilayers and by that it interferes with their physiological role on the membrane function.


*2.1. Materials*

The following materials were used for the experiments: the carbomer Ultrez 10 was provided by Antibiotice SA Iasi (Iasi, Romania); *N*-hydroxyphthalimide, medium-viscosity sodium alginate (extracted from *Macrocytis pyrifera*), and glycerin (99.9%) were purchased from Sigma-Aldrich (St. Louis, MO, USA); medium-viscosity carboxymethyl cellulose from Fluka (Buchs, Switzerland); and reagent-grade ethanol (EtOH) and Milli-Q ultrapure distilled water from Merck Chemicals (Darmstadt, Germany). The chemical reagents used in this study were of analytical-grade purity and were used without further purification. MDA-MB-231 cells were purchased from the American Type Culture Collection, Rockville (ATCC), VA, USA; 4T1 cells were from the American Type Culture Collection, Rockville, MD, USA; bovine serum was purchased from Sigma-Aldrich; HDMVECn (primary dermal microvascular endothelial cells) were from ATCC; Balb/c-5064 (mouse dermal microvascular endothelial cells) were from Cell Biologics (Chicago, IL, USA); and Matrigel Matrix from BD Biosciences (356234), Bedford, MA, USA. For biological tests, the Live and Dead Cell Assay (Abcam ab115347), CellTiter-Blue ® Cell Viability Assay (Promega, (Madison, WI, USA), MitoTracker ™ Red (Molecular Probe, Eugene, OR, USA), Matrigel (Corning, Bedford, MA, USA ), and Caspase-3/7 Assay (Promega, Madison, WI, USA ) were used according to the manufacturer's recommendations.


**Abstract**

The promise of algae to address the renewable energy and green-product production demands of the globe has yet to be realized. Over the past ten years, however, there has been a substantial investment and interest in realizing the potential of algae to meet these needs. Tremendous progress has been achieved. Ten years ago, the price of gasoline produced from algal biomass was 20-fold greater than it is today. Technoeconomic models indicate that algal biocrude produced in an optimized cultivation, harvesting, and biomass conversion facility can achieve economic parity with petroleum while reducing carbon-energy indices substantially relative to petroleum-based fuels. There is also an emerging recognition that algal carbon capture and sequestration as lipids may offer a viable alternative to direct atmospheric CO2 capture and sequestration. We review recent advances in basic and applied algal biomass production from the perspectives of algal biology, cultivation, harvesting, energy conversion, and sustainability. The prognosis is encouraging but will require substantial integration and field testing of a variety of technology platforms to down select the most economical and sustainable systems to address the needs of the circular economy and atmospheric carbon mitigation.

**Keywords:** algae, biofuel, biomass, carbon sequestration, carbon index

## **1. Introduction**

Over the last ten years (since 2010) there has been accelerated investment in research for the development of commercially viable algal biomass and coproduct production systems [1–5]. The challenge for algae biomass production systems has been that unlike crop biomass production systems having thousands of years of development history, algae until very recently were not the target of integrated research and development (R&D) strategies focused on efficient production of food, fuel, and coproducts [6]. Recent estimates indicate that there are globally more than 150,000 species of single cell and multicellular algae having polyphyletic origins, complex and diverse metabolic machinery, occupying vast environmental niches with immense ranges of biotic and abiotic stress tolerance, and having growth or biomass production rates that range over two magnitudes in yield compared to traditional agricultural production [7]. The challenge for the industry has been to identify the best algal production systems that are suitable for commercially viable industrial applications. Beginning with algal biology much effort has focused on identifying the best performing algal strains. The criteria for down-selecting the best performing strains have included, identifying algae with the greatest biomass production rates, optimizing algal growth media, CO2 exchange and culture

conditions, identifying algal strains that are the most resistant to pathogens and herbivory (minimizing pond crashes), and developing strains having enhanced performance characteristics through application of genetic engineering, breeding and genome editing tools [6]. Research and development for improved biomass production has also focused on developing enhanced cultivation, harvesting and biomass conversion technologies with the objective to achieve the lowest carbon emissions, recycle inorganic nutrients as efficiently as possible, minimize energy inputs at each stage in production, and integrate the algal biomass production systems into the existing energy infrastructure as seamlessly as possible.

In 2010, the US Department of Energy launched the largest government-funded integrated algal biomass, biofuels and bioproducts program carried out to date. The National Alliance for Advanced Biofuels and Bioproducts (NAABB) achieved notable advances in reducing the cost of producing biomass and making biofuels from microalgae. In three years NAABB developed and modeled a pathway to move the price point for producing a gallon gasoline equivalent (GGE) of fuel from microalgae from \$150 to \$8 a GGE [1–3]. More recently, the price point for a GGE produced from algal biomass has been reduced to < \$5. Based on Reliance's demonstration scale studies, the technoeconomic modeling (TEM) for a 10 k barrels/ day (bpd) scale production of crude oil from microalgae was estimated to be at 100\$/ barrel without any subsidy. The major factors contributing to the substantial cost reductions in producing fuel from algal biomass included, the discovery and development of more robust, high biomass producing algal strains for year-round consistent performance, identification of the best geographies to produce algal biomass, advance pond designs and improved culture mixing for effective light utilization, effective crop control methods that prevent pond crash and biomass loss, innovative harvesting techniques and effective water and nutrient recycling to maximize resource utilization. Also, advancements in biomass to biocrude conversion technologies including continuous flow hydrothermal liquefaction (HTL), the demonstration that algal biocrude coming from HTL could be used as a direct feedstock in existing oil refineries to produce fuels with performance characteristics similar to petroleum-based fuels, and the production of high value coproducts to offset the cost of producing fuels.

Stepping back, however, there remain many critical considerations that must be addressed if microalgal biomass is to be a commercial success in competition with other biomass sources in the world where the carbon energy index (g CO2 emitted/ kJ energy produced) and the environmental impacts of any biomass production system must also be considered along with economics [6]. Beginning with first principles it is critical to identify what the thermodynamically most efficient biological mechanisms are for producing algal biomass that also have the highest carbon capture efficiency. Recent thermodynamic models suggest that the greatest energy efficiency for carbon capture and biomass production is achieved in algae that utilize light most efficiently and accumulate chemical energy in the form of carbohydrate polymers, e.g., starch rather than those that store oils [8, 9]. Additionally, algae with rapid division rates and/ or the ability to grow substantially in volume are likely to be greater biomass producers [10]. While most algal biofuel programs have focused on producing biomass from high lipid accumulating strains due to ease of conversion of lipids into biocrude it is becoming apparent that algae accumulating starch as a metabolic storage end product have the highest biomass production rates and thermodynamic efficiency [8–10]. While lipids have greater energy density and are more readily converted into fuels, starches have a greater chemical energy density per carbon per photon captured during photosynthesis [8]. One of the microalgal strains achieving the highest known biomass yields in cultivation is *Pseudoneochloris* which stores starch as an energy reserve, achieves high cell numbers at stationary phase of growth, and can increase its cellular volume as it grows by greater than 100-fold [8].

**453**

*Recent Advances in Algal Biomass Production DOI: http://dx.doi.org/10.5772/intechopen.94218*

bioreactors may be more difficult to eradicate.

over traditional agriculture for feedstock production [12, 13].

Many microalgal species are good source of proteins, carbohydrates, lipids and other high value bioactive molecules, such as enzymes, pigments and vitamins. By altering the cultivation conditions or through metabolic engineering approaches, composition of algae can be manipulated to accumulate the specific biomolecule(s) of interest. Considering the higher growth rate and ability to accumulate high lipid content (≥30%), it is reported that microalgae can yield 58,700 L of oil/ ha as opposed to 172 L/ha for corn, 446 L/ha for soybean, 1892 L/ha for Jatropha and 5950 L/ha for oil palm [14]. Thus, the projected ability to produce oil from algae is ~10 times more compared to highest oil producing crop plant. Likewise, algae biomass can be a potential feedstock for bioethanol production because of its ability to accumulate starch even higher than 50% (w/w) of biomass under optimal conditions. Absence of lignin in algal cell wall makes its processing easier compared to lignocellulosic agricultural waste and woody biomass, where lignin removal is an additional step before processing for bioethanol production [13]. Moreover, lack of structural parts like leaves and roots in algae makes algal biomass more homogenous and might be less energy intensive to process compared to crop plants [13]. In an estimate, net energy from sugarcane ethanol and bagasse was 143 GJ/ha/year as opposed to 928 GJ/ha/year from microalgae, indicating microalgae to be significantly more efficient feedstock [15]. Protein is another commercially important component of algae biomass. Algae protein is comparable to other high-quality plant and animal protein sources, however, protein yield from algae happen to be between 4 and 15 tons/ha/year, which is significantly higher than 0.6–1.2 tons/ha/year,

Cultivation systems are also a major cost factor in producing algal biomass. It is generally recognized that to produce low value algal biomass open pond production systems have the lowest capital and operating expenses and require less maintenance (to prevent fouling) than closed bioreactor cultivation systems. However, open pond systems require greater amounts of water to operate due to evaporation, have higher energy costs associated with concentrating more dilute cultures, and are more susceptible to contamination although biological contaminants in closed

Regardless of the constraints and challenges mentioned above and the necessity to input higher capital investment in cultivation and downstream processing, production of microalgae biomass still stands out advantageous on many fronts in comparison to agriculture crops for food and fuel. Microalgae have high photosynthetic efficiency and short division time, making them highly suitable candidates for generating more biomass in less time. Growth rates of several microalgae have been reported to be 5–10 times higher than agriculture crops [9]. Moreover, microalgae can grow on low economic and ecological value lands and can utilize marine, brackish or fresh water for cultivation, depending on the species being used. CO2 from industrial exhaust can be used for cultivation and nutrients from waste streams can be utilized for growth. Excess nutrients lost during harvesting process can be recycled back in the cultivation system, ensuring minimal wastage and maximum utilization [10, 11]. In contrast, agriculture depends on limited natural resources, like arable land and fresh water, with fresh water consumption being highest globally in agriculture. Over 80% of all water consumed globally is used for agricultural production. Agriculture also needs extensive application of fertilizers and pesticides to improve biomass productivity and prevent crop losses. However, nitrogen utilization is inefficient in crop plants, resulting in ~50% of nitrogen loss through leaching, soil erosion and gaseous evaporation [12]. Considering these facts, use of agricultural crops to meet growing biomass demands for food and energy will lead to land use change, environment pollution, loss of forest cover and biodiversity. Thus, from environment standpoint algal cultivation is much favored

#### *Recent Advances in Algal Biomass Production DOI: http://dx.doi.org/10.5772/intechopen.94218*

*Biotechnological Applications of Biomass*

value coproducts to offset the cost of producing fuels.

conditions, identifying algal strains that are the most resistant to pathogens and herbivory (minimizing pond crashes), and developing strains having enhanced performance characteristics through application of genetic engineering, breeding and genome editing tools [6]. Research and development for improved biomass production has also focused on developing enhanced cultivation, harvesting and biomass conversion technologies with the objective to achieve the lowest carbon emissions, recycle inorganic nutrients as efficiently as possible, minimize energy inputs at each stage in production, and integrate the algal biomass production systems into the existing energy infrastructure as seamlessly as possible.

In 2010, the US Department of Energy launched the largest government-funded integrated algal biomass, biofuels and bioproducts program carried out to date. The National Alliance for Advanced Biofuels and Bioproducts (NAABB) achieved notable advances in reducing the cost of producing biomass and making biofuels from microalgae. In three years NAABB developed and modeled a pathway to move the price point for producing a gallon gasoline equivalent (GGE) of fuel from microalgae from \$150 to \$8 a GGE [1–3]. More recently, the price point for a GGE produced from algal biomass has been reduced to < \$5. Based on Reliance's demonstration scale studies, the technoeconomic modeling (TEM) for a 10 k barrels/ day (bpd) scale production of crude oil from microalgae was estimated to be at 100\$/ barrel without any subsidy. The major factors contributing to the substantial cost reductions in producing fuel from algal biomass included, the discovery and development of more robust, high biomass producing algal strains for year-round consistent performance, identification of the best geographies to produce algal biomass, advance pond designs and improved culture mixing for effective light utilization, effective crop control methods that prevent pond crash and biomass loss, innovative harvesting techniques and effective water and nutrient recycling to maximize resource utilization. Also, advancements in biomass to biocrude conversion technologies including continuous flow hydrothermal liquefaction (HTL), the demonstration that algal biocrude coming from HTL could be used as a direct feedstock in existing oil refineries to produce fuels with performance characteristics similar to petroleum-based fuels, and the production of high

Stepping back, however, there remain many critical considerations that must be addressed if microalgal biomass is to be a commercial success in competition with other biomass sources in the world where the carbon energy index (g CO2 emitted/ kJ energy produced) and the environmental impacts of any biomass production system must also be considered along with economics [6]. Beginning with first principles it is critical to identify what the thermodynamically most efficient biological mechanisms are for producing algal biomass that also have the highest carbon capture efficiency. Recent thermodynamic models suggest that the greatest energy efficiency for carbon capture and biomass production is achieved in algae that utilize light most efficiently and accumulate chemical energy in the form of carbohydrate polymers, e.g., starch rather than those that store oils [8, 9]. Additionally, algae with rapid division rates and/ or the ability to grow substantially in volume are likely to be greater biomass producers [10]. While most algal biofuel programs have focused on producing biomass from high lipid accumulating strains due to ease of conversion of lipids into biocrude it is becoming apparent that algae accumulating starch as a metabolic storage end product have the highest biomass production rates and thermodynamic efficiency [8–10]. While lipids have greater energy density and are more readily converted into fuels, starches have a greater chemical energy density per carbon per photon captured during photosynthesis [8]. One of the microalgal strains achieving the highest known biomass yields in cultivation is *Pseudoneochloris* which stores starch as an energy reserve, achieves high cell numbers at stationary phase of growth, and can increase its cellular volume as it grows by greater than 100-fold [8].

**452**

Cultivation systems are also a major cost factor in producing algal biomass. It is generally recognized that to produce low value algal biomass open pond production systems have the lowest capital and operating expenses and require less maintenance (to prevent fouling) than closed bioreactor cultivation systems. However, open pond systems require greater amounts of water to operate due to evaporation, have higher energy costs associated with concentrating more dilute cultures, and are more susceptible to contamination although biological contaminants in closed bioreactors may be more difficult to eradicate.

Regardless of the constraints and challenges mentioned above and the necessity to input higher capital investment in cultivation and downstream processing, production of microalgae biomass still stands out advantageous on many fronts in comparison to agriculture crops for food and fuel. Microalgae have high photosynthetic efficiency and short division time, making them highly suitable candidates for generating more biomass in less time. Growth rates of several microalgae have been reported to be 5–10 times higher than agriculture crops [9]. Moreover, microalgae can grow on low economic and ecological value lands and can utilize marine, brackish or fresh water for cultivation, depending on the species being used. CO2 from industrial exhaust can be used for cultivation and nutrients from waste streams can be utilized for growth. Excess nutrients lost during harvesting process can be recycled back in the cultivation system, ensuring minimal wastage and maximum utilization [10, 11]. In contrast, agriculture depends on limited natural resources, like arable land and fresh water, with fresh water consumption being highest globally in agriculture. Over 80% of all water consumed globally is used for agricultural production. Agriculture also needs extensive application of fertilizers and pesticides to improve biomass productivity and prevent crop losses. However, nitrogen utilization is inefficient in crop plants, resulting in ~50% of nitrogen loss through leaching, soil erosion and gaseous evaporation [12]. Considering these facts, use of agricultural crops to meet growing biomass demands for food and energy will lead to land use change, environment pollution, loss of forest cover and biodiversity. Thus, from environment standpoint algal cultivation is much favored over traditional agriculture for feedstock production [12, 13].

Many microalgal species are good source of proteins, carbohydrates, lipids and other high value bioactive molecules, such as enzymes, pigments and vitamins. By altering the cultivation conditions or through metabolic engineering approaches, composition of algae can be manipulated to accumulate the specific biomolecule(s) of interest. Considering the higher growth rate and ability to accumulate high lipid content (≥30%), it is reported that microalgae can yield 58,700 L of oil/ ha as opposed to 172 L/ha for corn, 446 L/ha for soybean, 1892 L/ha for Jatropha and 5950 L/ha for oil palm [14]. Thus, the projected ability to produce oil from algae is ~10 times more compared to highest oil producing crop plant. Likewise, algae biomass can be a potential feedstock for bioethanol production because of its ability to accumulate starch even higher than 50% (w/w) of biomass under optimal conditions. Absence of lignin in algal cell wall makes its processing easier compared to lignocellulosic agricultural waste and woody biomass, where lignin removal is an additional step before processing for bioethanol production [13]. Moreover, lack of structural parts like leaves and roots in algae makes algal biomass more homogenous and might be less energy intensive to process compared to crop plants [13]. In an estimate, net energy from sugarcane ethanol and bagasse was 143 GJ/ha/year as opposed to 928 GJ/ha/year from microalgae, indicating microalgae to be significantly more efficient feedstock [15]. Protein is another commercially important component of algae biomass. Algae protein is comparable to other high-quality plant and animal protein sources, however, protein yield from algae happen to be between 4 and 15 tons/ha/year, which is significantly higher than 0.6–1.2 tons/ha/year,

reported for soybean [16]. Clearly, microalgae supersede traditional agriculture on multiple aspects, however, biomass harvesting is an area which is well established in case of crop plants but highly energy intensive in case of algae due to its small size and low biomass density [10].

Regarding algal biomass harvesting systems the general objective has been to develop algae harvesting and concentrating systems that have parasitic energy consumption values of less than 10% of the total algal biomass energy content [6]. To reduce the costs of fuel production, recent efforts have focused on the direct conversion of harvested algal biomass into separate fuel and coproduct fractions in a continuous flow system while efficiently recycling water and nutrients. One of the more promising technology developments in this sector has been the development of two-stage HTL which allows for the separate recovery of coproducts and biocrude feedstock while recycling water and nutrients back to the pond thus avoiding the energy intensive step of drying the algal biomass before biomass to fuel conversion. The appropriate selection of what high value coproduct(s) to produce from algal biomass is critical for economic viability when coproduct production is coupled with fuel production. From this perspective the coproduct should have sufficient value based on biomass yields to be economically sustainable without saturating markets to the point of driving coproduct prices so low as to be economically untenable. As modeled by the US-DOE PACE algal biofuels consortium a fully integrated algal cultivation, harvesting, co-product and fuel production system with integrated water and nutrient recycling has the potential to recover over 60% of the energy content of the algae as biocrude while producing valuable coproducts that have a large global market demand (**Figure 1**).

Optimizing algal biomass production and carbon sequestration also has the potential to address the existential threat of global climate change associated with greenhouse gas emissions. Currently, biological carbon capture and sequestration (BCCS) is one of the more feasible means to remediate the earth's atmosphere. As a BCCS system, algae are particularly attractive not only for their high areal

#### **Figure 1.**

*PACE consortium working model for the integrated co-production of biofuels and co-products (green chemicals, polysaccharides (guar), and methane) from algae. Inorganic nutrients and wastewater are recycled. Algae are preloaded with nutrients (nutrient pulse) and grown in minimal media to reduce weedy species competition and continuously harvested at mid-log phase growth. HTL, hydrothermal liquefaction; CHG, catalytic gasification.*

**455**

*Recent Advances in Algal Biomass Production DOI: http://dx.doi.org/10.5772/intechopen.94218*

mass and has a density of 0.91 g/cm3

as a solid has a density of 1.96 g/ cm3

chemical, and environmental challenges.

to other biomass production systems.

**2. Algal strains**

rates of carbon capture but also for their potential storage of carbon as lipids while recycling inorganic nutrients and water [17]. While not generally considered as a carbon sequestration material, lipids have several advantages over solid CO2 as a carbon sequestration material [17]. Triacylglycerol (C55H98O6) is 77% carbon by

that is 32% greater than solid CO2. Furthermore, being a liquid and not readily convertible to a gas, the ability of lipids to escape from deep geological sequestration is substantially less than CO2 reducing potential long-term risk to aerobic organisms [17]. Overall, algae have great potential to address simultaneously fuel, food, green

In the following sections we will review recent advances in the sustainable production of algal biomass and coproducts for fuels and economic competitiveness with petroleum and non-algal coproduct production systems. Substantial achievements have been realized from an industry that has a truly short history compared

Substantial efforts have focused on the identification of algal strains having maximum biomass yields under cultivation. Ideal biomass production strains must not only have fast growth rates but also must be robust and tolerate well abiotic (temperature, salinity, light) and biotic (pathogen, herbivore and weedy algae) stress conditions to minimize pond crashes and downtime in algal cultivation. There have been several large-scale algal surveys of wild algal species to identify those strains that perform well in cultivation [18]. In addition, screening systems for identifying strains with elevated performance characteristics in high light environments among others have led to some success in the identification of high performing algal strains [19]. Given that there as many as 150,000 species of algae have been identified and that limited resources have been available to screen algae for high biomass production, there remains a significant number of algae that remain to be assessed for biomass productivity in select environments [7]. In addition, substantial potential to improve algal productivity may also be achieved in traditional and molecular assisted breeding practices. Algae breeding efforts, except for laboratory strains such as *Chlamydomonas*, have been limited, however. This is because the means to induce gametogenesis to identify sexual mating types in most algae is not well understood. If the increased yield achieved through plant breeding are to serve as a prognosticator of the potential to enhance algal productivity it can be anticipated that algal breeding programs may enhance yields in the field by as much as ten-fold.

**2.1 Modulating cultivation conditions to impact oil and carbohydrate yields**

Given the fast rates of cell division and the absence of dedicated higher-order cellular structures including tissue and organs it is not unexpected that microalgae have an enhanced capability to metabolically remodel cellular functions under different growth conditions. Algae frequently live boom and busts cycles in the nutrient deserts of lakes and the open oceans. Thus, it is imperative that algae have flexible metabolic systems to survive in unpredictable and ever-changing environments and be unencumbered by programmed cell fates associated with the differentiation and organization of cells into higher order tissues and organs. One of the manifestations of this metabolic flexibility is the ability to shift the biochemistry of the major cellular energy storage products from low energy

. In contrast, CO2 is 27% carbon by mass and

. Thus, lipids have a volumetric carbon density

*Recent Advances in Algal Biomass Production DOI: http://dx.doi.org/10.5772/intechopen.94218*

rates of carbon capture but also for their potential storage of carbon as lipids while recycling inorganic nutrients and water [17]. While not generally considered as a carbon sequestration material, lipids have several advantages over solid CO2 as a carbon sequestration material [17]. Triacylglycerol (C55H98O6) is 77% carbon by mass and has a density of 0.91 g/cm3 . In contrast, CO2 is 27% carbon by mass and as a solid has a density of 1.96 g/ cm3 . Thus, lipids have a volumetric carbon density that is 32% greater than solid CO2. Furthermore, being a liquid and not readily convertible to a gas, the ability of lipids to escape from deep geological sequestration is substantially less than CO2 reducing potential long-term risk to aerobic organisms [17]. Overall, algae have great potential to address simultaneously fuel, food, green chemical, and environmental challenges.

In the following sections we will review recent advances in the sustainable production of algal biomass and coproducts for fuels and economic competitiveness with petroleum and non-algal coproduct production systems. Substantial achievements have been realized from an industry that has a truly short history compared to other biomass production systems.