*Article* **Comparative Investigation on the Soil Burial Degradation Behaviour of Polymer Films for Agriculture before and after Photo-Oxidation**

### **Francesco Paolo La Mantia 1,2,\*, Laura Ascione 2, Maria Chiara Mistretta 1, Marco Rapisarda 3 and Paola Rizzarelli 3,\***

Received: 26 February 2020; Accepted: 26 March 2020; Published: 30 March 2020

**Abstract:** Polymer films based on biodegradable polymers, polyethylene (PE) and modified PE with oxo-degradable additive were prepared by film blowing. Carbon black (1%) was added to all the films. Commercial biodegradable Ecovio® and Mater-Bi® samples were used. Mechanical properties, soil burial degradation and surface wettability were investigated, before and after UV irradiation. Chemical modifications induced by UV and soil degradation, or a synergic effect, were highlighted by Attenuated Total Reflection-Fourier Transform Infra-Red (ATR-FTIR). Photo-oxidized film samples with an elongation at break equal to 50% and 0.5 the initial value were selected for the soil burial degradation test at 30 ◦C. Weight loss measurements were used to follow biodegradation in soil. Predictably, the degradation in soil was higher for biodegradable polymer-based films than for the PE-based ones. UV irradiation increased surface wettability and encouraged the disintegration in soil of all the samples. In fact, photo-oxidation produced a molar mass reduction and hydrophilic end groups, thus increasing surface erosion and weight loss. This paper not only supplies new criteria to evaluate the performance of biodegradable films in agriculture, before and after lifetime, but also provides a comparative analysis on the soil burial degradation behaviour with traditional ones.

**Keywords:** biodegradable polymers; mulch films; soil burial test; Ecovio®; Mater-Bi®; polylactide; poly(butyleneadipate-*co*-butyleneterephtalate); polyethylene; polymer degradation; photo-oxidation

### **1. Introduction**

Polymer films are widely applied in agriculture [1]. The main applications of plastic films are for greenhouses and mulching. The more extensively used polymers for these applications are polyethylenes (LDPE and LLDPE) and the copolymer poly(ethylene-vinyl acetate) (EVA). The importance of these films consists in the protection of the cultures and creation of a microclimate that strongly improves the yield of the plants. The principal problem of these films is their short, or relatively short, life and the consequent frequent necessity to eliminate them in huge amounts from the ground. Indeed, the photo-oxidation strongly worsens the mechanical and optical properties of the polymers and, nevertheless the strong stabilizing systems used, the lifetime is no more than three-four years. Anyway, at the end of the service life, all these films must be collected to avoid the dissemination in the ground with a negative environmental impact [2]. Recycling of these films is relatively simple because of the easy differentiated collection for the similar composition. However, the properties of the

recycled materials are relatively poor for the photo-oxidation undergone during their use. The recycling is more complicated for the mulch films as their very low thickness (15–50 micron) implies many difficulties during their washing and the fragmentation. For these films, then, the use of biodegradable, compostable polymers could be a good opportunity [3,4]. The films are mixed with the soil and buried in the ground during the ploughing. The successive biodegradation process gives rise to compost that is a fertilizer for the ground.

Aliphatic polyesters are the most adopted biodegradable materials in food packaging as well as in agriculture and medical fields [5]. Diverse studies on commercial biodegradable polymers have been performed to verify their performance in mulching or irrigation pipes [4,6,7]. Nevertheless, both plastic mulch films and irrigation pipes, during their service life, are exposed to sunlight. Thus, the occurrence of photo-aging can modify their properties and performances as well as their biodegradation rates.

The degradation of plastic materials, traditional and biodegradable, involves different processes promoted by one or more environmental factors (i.e., heat, light, microorganisms) or chemicals. The degradation produces irreversible structural changes that are mostly unfavourable or, in some cases, necessary, as in biodegradation processes, or are further generated to determine polymer structure, such as in pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) studies [8,9].

The degradability in soil of poly(vinyl alcohol) [10], copolyesters [11] and poly(ester amide) [12] film samples has been investigated under controlled soil burial conditions. However, the influence of UV on degradation rates in soil has been limited investigated. In fact, despite extensive research on biodegradable materials, only some studies have been aimed on polymer degradation related to a synergistic effect of UV exposure and soil burial [6,7,13–16]. Briassoulis et al. evaluated the degradation behaviour of MaterBi®-based films and irrigation tubes under real field conditions [6,7]. Kijchavengkul et al. studied the degradation of an aliphatic-aromatic biodegradable polyester film under solar exposure and soil burial in a tropical area [13]. Recently, we investigated the photo-oxidative and soil burial degradation of irrigation tubes based on biodegradable polymer blends under controlled conditions [14]. Actually, laboratory tests supply more reproducible data for both degradation mechanisms and kinetics.

It should be always kept in mind that biodegradation is schematically a three step process: in the first one, macromolecular chains are depolymerized into monomers and oligomers; in the second stage, the monomers and oligomers are taken up as biomass; and finally in the third step, the respiration of biomass there consumes O2 and produces CO2 and H2O (under aerobic conditions). The measurement of the plastic material consumption does not allow to ascertain whether the process has actually been completed or has prevented, for example, at depolymerisation. As a result, all the standardized methods for determining biodegradation are focused on the measurement of respiration, i.e., the conversion into CO2 of the carbon initially present in the plastic through the use of the oxidant (O2). On the other hand, most of the papers in the literature concerning polymer and composite biodegradation are based on weight loss [17–19] that is considered a measurement of plastic film biodegradability.

Aim of this work is to study the degradation behaviour in soil of a couple of biodegradable polymer systems after a photo-oxidative aging that simulates the real conditions of the service life of the film samples to be used in agriculture. For comparison, films of conventional polyethylene (PE) and PE-modified with an oxo-biodegradable compound have been used. A very interesting issue of the present work is that the films have not been aged under UV irradiation for the same time before the soil burial test. In particular, they have been aged until the elongation at break (EB) reached a given value, i.e., when the EB arrived at one half of the initial value, according to an international rule for the use of much films [20], as well as at the value of 50%. The elongation at break has been chosen to monitor the degradation of these films because it is a property of the polymers very sensitive to the changes of structure and morphology. In addition, it is very important for the use of these films as they must remain flexible during their use.

By blow film extrusion, we have prepared film samples based on biodegradable polymers (Ecovio® and Mater-Bi®), PE and PE modified with oxo-degradable additive. Soil burial tests were carried out and weight loss used to monitor biodegradation rate. Mechanical properties determined. Remarkably, the films have been photo-oxidized up to the EB reached the value of 50% and one half of the initial value. We compared mechanical properties, weight loss due to burial degradation in soil and surface wettability, before and after UV irradiation. Structural modifications induced by UV and soil degradation, or a synergic effect, were checked by Attenuated Total Reflection (ATR)-FTIR.

### **2. Materials and Methods**

### *2.1. Materials*

The samples investigated in this work were three films produced in our laboratory and a commercial one. Sample codes of the films, thickness and extrusion temperature are reported in Table 1. PE is a film made by a blend of 80% LDPE (FC 30, Versalis, San Donato Milanese, Italy) and 20% LLDPE (FG20, Versalis); this composition has been chosen because is used in the manufacturing of mulch films. PE-OXO is made by the same polymer blend by adding 1% wt/wt of an oxo-biodegradable masterbatch, d2w (Symphony Environmental, Hertfordshire, UK). B1 is a film prepared by ECOVIO F23B1 (Bayer, Leverkusen, Germany), a biodegradable blend poly(butyleneadipate-*co*-butyleneterephtalate) (PBAT) with small amount of polylactide (PLA), containing about 12% of insoluble, inert particles. This amount has been determined after a Soxhlet extraction in tetrahydrofuran. B2 is a commercial film based on Mater-Bi EF04P by Novamont. This biodegradable polymer system is a blend constituted by corn starch and a biodegradable thermoplastic copolyester, Origo BI (Novamont, Novara, Italy), containing about 8% of insoluble, inert particles. Carbon black (1%) was added to all the films. The first three films were produced in a Brabender single screw extruder (D = 19 mm, L/D = 25) equipped with a head for film blowing.

**Table 1.** Sample code, extrusion temperature of the three samples produced in laboratory scale and thickness for all the investigated films.

### *2.2. Methods*

### 2.2.1. Accelerated Weathering Tests

The films were exposed to accelerated weathering tests in a Q-UV Panel system (Q-Labs Corp., Westlake, OH, USA) containing eight UVB lamps with a peak at 313 nm. The accelerated weathering tests were carried out using continuously the following cycle: a period of UV irradiation for 8 h at 70 ◦C followed by a steam condensation (deionized water spray) for 4 h at 55 ◦C. Film samples were removed from the chamber at different aging intervals.

### 2.2.2. Mechanical Properties

Stress-strain curves were measured using a universal testing machine mod. 3365 (Instron, Norwood, MA, USA). The elastic modulus was measured at a speed of 1 mm/min until the deformation was 10%. Then, the crosshead speed was increased to 100 mm/min until the specimen breaks. The values of elastic modulus, E, tensile strength, TS, and elongation at break, EB, were calculated as average of 10 experimental values.

### 2.2.3. Attenuated Total Reflection-Fourier Transform Infra-Red (ATR-FTIR)

ATR-FTIR spectra were measured by using a Spectrum Two spectrometer (Perkin-Elmer, Waltham, MA, USA) with the Spectrum software. Spectra were recorded with 8 scans and 4 cm−1 resolution. In order to compare the different effects on the structures of photo-oxidation and degradation in soil, the carbonyl index (CI) was evaluated for the virgin and degraded samples. In particular, the CI was calculated from the ratio of the total area of the absorption bands between 1808–1550 cm−1 or 1801–1487 cm−1 (carbonyl region) and 3029–2749 cm−1 (stretching of the CH2) respectively for the B1 and B2 samples, from the ratio of the total of the peaks in the range 1826–1550 cm−1 (carbonyl region) and that 2983–2662cm−1 (stretching of the CH2 and CH3 groups) for the PE based films.

### 2.2.4. Soil Burial Test

Tests were carried out at 30 ± 0.1 ◦C, under moisture-controlled conditions. Triplicate specimens of film samples were placed in darkened vessels containing a multi-layer substrate [12]. Filter paper and polyethylene samples were used, respectively, as a positive and negative control. Film portions of 2 cm × 2 cm were cut. Specimens of film (initial weight 7–8 mg, filter paper ~29 mg; Mettler Toledo MX5, *d* = 1 μg) were sandwiched between two layers of a mixture of milled perlite (50 g) and commercial soil (200 g), moistened with 100 mL of distilled water. The bottom and top layers were filled with 60 g of perlite moistened with 120 mL of distilled water. Perlite was used for increasing aeration to the soil and the amount of water retained. A flow of moistened air was supplied from the bottom of each vessel every 24 h for 15 min. Film samples were removed after regular intervals, brushed softly, washed with distilled water several times and dried under vacuum in the presence of P2O5 at room temperature, to constant weight [21]. The degree of degradation was evaluated by weight loss (WL) by using the following equation:

$$\text{WIL (\%)}=(\text{Wi}-\text{Wt})/\text{Wi}\times100,\tag{1}$$

where Wi is the initial weight of the sample and Wt is the weight after the established time.

### 2.2.5. Contact Angle

The surface wettability values of samples were measured at room temperature using a contact angle goniometer (OCA15EC, Dataphysics, Filderstadt, Germany). Static contact angle (CA) were determined dropping 2 μL of water from a micro syringe onto the film surfaces and analysing by software (SCA 20) the images taken by the connect video camera. To eliminate interference, the sample was previously equilibrated for 30 min and then CA was measured. The films were kept flat using a film sample holder that allowed their correct positioning and stretching. Five measurements were carried out for each sample in order to ensure repeatability of the experiments.

#### **3. Results**

#### *3.1. Mechanical Properties*

The values of the elongation at break (EB0), tensile strength and elastic modulus of the virgin films are listed in Table 2. The dimensionless elongation at break vs. photo-oxidation time curves of all the samples are reported in Figure 1. The dimensionless values of the elongation at break were obtained by dividing the values of the elongation at break recorded for each photo-oxidation time by the corresponding initial value (EB0).

**Table 2.** Mechanical properties of the virgin films.

**Figure 1.** Dimensionless elongation at break vs. photo-oxidation time of all the samples.

Figure 1 highlights that the photo-oxidation affects the elongation at break of the two biodegradable polymers much more than that of the PE-based film samples. Of course, the presence of a pro-oxidant system strongly increases the kinetic of photo-oxidation of the PE matrix. As for the two biodegradable polymers, the photo-oxidation kinetics are slightly more rapid for the B1 samples.

In Figure 2 the photo-oxidation times at which all the films reach the elongation at break equal to 50% (EB1) and to one half (EB2) of the initial value (EB0) are reported.

**Figure 2.** Photo-oxidation times (hours) of different samples when the elongation at break reaches the value of 50% (EB1) and one half of the initial value (EB2).

From this plot, it is well evident as the useful accelerated lifetime of the biodegradable films is significantly shorter than that of the PE samples. In particular, considering the values of the elongation at break 50% as the lowest value compatible with the use, the lifetime—as measured in these accelerated aging experimental conditions—of the PE sample is about 2400 h, while it is 42 and 56 h for the two biodegradable polymer systems. The presence of the pro-oxidant additive in the PE matrix considerably reduces the useful lifetime to about 225 h.

### *3.2. Soil Burial Degradation*

Soil burial degradation tests were carried sandwiching the polymer films between two layers of a mixture of milled perlite and commercial soil to simulate soil degradation after their use lifetime. Perlite was used to increase the amount of water retained and accelerate degradation in soil.

Figure S1 summarizes the photo-oxidation and soil burial degradation intervals, together with some representative photographs of the film samples recovered after the burial test. Except for the B2 sample photo-oxidized (56 h), all the film portions appeared almost intact.

In Figure 3 the average weight losses of the virgin and photo-oxidized samples are reported vs. the soil burial time. Of course, the WL increases and kinetic of biodegradation decreases with burial degradation time. Additionally, the WL values increased because of UV exposure, as recently evidenced in soil burial degradation of photo-oxidized irrigation tubes based on biodegradable polymer blends [14]. Reasonably, UV exposure yielded a molecular weight (MW) reduction producing oligomeric chains, bearing hydrophilic chain ends, which are more easily removed from the film surface and susceptible to the attacks of microorganisms in the soil. In fact, it must be kept in mind that biodegradation proceeds in three stages. Whenever WL is used to monitor the degradation of polymer samples, just the first step is involved, i.e., macromolecular chain depolymerisation into monomers and oligomers that are eroded from the surface. UV irradiation enhances and accelerates the formation of monomers and oligomers, via random main chain scission, increasing consequently the WL rate.

**Figure 3.** Average weight loss values (%) vs. degradation time for all the film samples—virgin and photo-oxidized.

The positive control (paper) and B2 sample photo-oxidized for 56 h were totally disintegrated (WL = 100%) respectively after 65 and 125 days of soil burial test (Figure 3). The complete disintegration in soil of the B2 sample photo-oxidized for 56 h suggests that the photo-oxidation is not limited to the surface but occurred throughout the matrix bulk, in agreement with the literature [13]. WL values of B1 (Figure 4a) are remarkably lower than that of B2 (Figure 4b) samples. The photo-oxidized samples of all the films show a more pronounced WL than the corresponding virgin samples (Figures 4 and 5). This holds also for the PE-based films, but the final values of the WL are almost negligible although the photo-oxidized PE-OXO film (Figure 5a) shows WL values slightly larger than that of the pure PE (Figure 5b). On the contrary, the weight losses of the two biodegradable samples are quite similar for the virgin films. WL curves of B1 and B2 photo-oxidized samples shows a comparable trend with an induction period (40 days) and then a rapid increase.

**Figure 4.** Average weight loss values (%) vs. degradation time for the film samples (**a**) B1 and (**b**) B2, virgin and photo-oxidized.

**Figure 5.** Average weight loss values (%) vs. degradation time for the film samples (**a**) polyethylene (PE)-OXO and (**b**) PE, virgin and photo-oxidized.

### *3.3. Wettability of the Film Surfaces*

The wettability of the film surfaces submitted to UV irradiation was determined by contact angle measurements. In fact, chemical modifications on the surface of the thin films, induced by UV irradiation and/or soil degradation, increase plastic samples wettability, highlighted by CA decrease (Figures 6 and 7) and, consequently, the microbial susceptibility is encouraged. The UV induced increase in surface wettability is more pronounced for photo-oxidized and buried B1 (Figure 6) samples (CA ~68◦) as well as for photo-oxidized B2 films (CA ~70◦, Figure 6). CA decrease is reasonably related to the formation of hydrolytic chain ends. In photo-oxidised and buried B1 samples the CA is quite similar to that of B1 buried (Figure 6). This could be related to the surface erosion and the removal of the oligomeric species produced by hydrolysis.

In a similar way, the increase in the wettability of the irradiated PE (Figure 7) and PE-OXO (Figure 7) samples can be attributed to the formation of hydrophilic groups on the polymer surface by photo-oxidation, which occurs by the exposure of PE to UV irradiation in the presence of air [22,23]. The photo-degradation of PE samples, promoted by the oxo-degradable additive, induces a more marked decrease in the contact angle because of the introduction of carbonyl groups (Figure 7). However, wettability is higher in photo-oxidized and buried PE sample (CA ~79◦, Figure 7) than in photo-oxidized and buried PE-OXO (CA ~91◦, Figure 7). This can be reasonably due to the higher WL

and thus faster surface erosion of oligomers, bearing hydrophilic chain ends, in the PE-OXO (Figure 5a) than in PE sample (Figure 5b).

**Figure 6.** Average contact angle values for the film samples B1 and B2, virgin, buried (125 days) and photo-oxidized. After 125 days of soil burial test, photo-oxidized B2 samples were completely disintegrated and it was not possible to carry out the contact angle (CA) measurements.

**Figure 7.** Average contact angle values for the film samples PE-OXO and PE, virgin, buried (125 days) and photo-oxidized.

### *3.4. UV and Soil Burial Induced Modifications*

Both UV and soil burial degradation induced chemical modifications on the surface of all the polymeric films. Then, to check the structural changes caused by the different types of degradation, ATR-FTIR analysis was performed on virgin, photo-oxidized, soil buried and photo-oxidized + buried

film samples. In Figures S2 and S3, ATR-FTIR spectra are reported as examples of the trend of variations in functional groups on the film surface. Overall, degradation produced a broadening and a change of the peak area more noteworthy in B2 (Figure S2b) and PE (Figure S3b) samples compared respectively with B1 (Figure S2a) and PE-OXO (Figure S3a). A significant decrease in peak intensity between photo-oxidized and photo-oxidized + buried B2 sample was more evident (Figure S2b). Remarkably, it is in accordance with the higher surface erosion of oligomers produced by UV irradiation and the resultant weight loss values increase.

The increase of the carbonyl groups was a marker of the photo-oxidation evolvement as well as of hydrolytic reactions taking place during soil degradation. Unsurprisingly, in agreement with the literature [13,14], the spectra of the two biodegradable polymer-based films show that both photo-oxidation and degradation in soil increase the CI (Figure 8a). Remarkably, the area ratio of photo-oxidized + buried B1 and B2 sample is lower than that photo-oxidized and similar to the not photo-oxidized and buried ones. This result suggests that the oligomers, bearing carbonyl groups, have been partially removed from the surface during the soil burial test. However, B1 films were clearly more vulnerable to photo-oxidation than to hydrolysis; photo-oxidation products were removed by degradation in soil and CI of "B1 photox + buried" was lower than "B1 photox" sample (Figure 8a). Compared to the virgin samples, the increase of CO functional groups, due to the combined photo-oxidation and soil degradation, was significantly more evident in the B2 than in the B1 samples; in fact, the UV irradiation did not accelerate the WL rate as it did in the B2 films (Figure 4). These results were in agreement with the degradation process and the choice of monitoring degradation in soil through weight loss. Actually, the B2 films were much more prone than the B1 ones, since monomers and low-molecular-weight oligomers were originated and removed in the erosion step faster than in the B1 sample.

The carbonyl index values show similar trends in PE-OXO and PE samples. Photo-oxidation provides an increase in the area ratio, significantly more marked for PE than for PE-OXO (Figure 8b), due to the formation of ketones, aldehydes and/or esters groups. The successive degradation in soil on photo-oxidized PE films produces a low CI decrease reasonably related to a limited surface erosion of oligomers highlighted by WL (Figure 5b). The small CI decrease in photo-oxidized PE-OXO and PE after soil burial test for 125 days is in agreement with the literature and was attributed to a minimal consumption of CO groups by microorganisms [23].

It has to be underlined that carbon back is a UV absorber and its photo-stabilizing action is due to the absorption of the ultraviolet energy that, then, is no more available for the formation of radicals and for the consequent propagation of the oxidation reactions [24]. This means that carbon black does not interfere with the photo-oxidative mechanisms of the different matrices and the effect is just to shift towards higher times the photo-oxidation increasing the induction time of the process. Carbon black slows down the molecular weight reduction and consequently the degradation process in soil. The shift towards higher time to starting photo-oxidation is relevant on a commercial point of view above all with biodegradable polymeric systems whose performance can be impaired by photo-oxidation processes more than the traditional ones [4].

**Figure 8.** Carbonyl index values for (**a**) the two biodegradable and (**b**) PE based film samples. CI (B1) = A(1808–1550 cm−1)/A(3029–2749 cm−1); CI (B2) = A(1801–1487 cm−1)/A(3029–2749 cm−1); CI (PE and PE-OXO) = A(1826–1550 cm−1)/A(2983– 2662 cm−1). B1 = Ecovio®-based and B2 = Mater Bi®-based films.

### **4. Conclusions**

A comparative investigation on the degradation behaviour in soil of traditional and biodegradable polymer films for agriculture, before and after photo-oxidation, have been accomplished. The two polyethylene-based samples showed the better mechanical properties, while the Ecovio®-based films, named B1, displayed a lower value of the tensile strength and the Mater-Bi®-based films, called B2, had the lowest values of the elastic modulus and elongation at break. However, all the samples showed high values of the elongation at break that strongly decreases with the photo-oxidation time, in particular for the biodegradable films. Interestingly, the film samples have been aged under UV irradiation until the elongation at break reached the value of 50%, according to an international rule for the use of much films, as well as at one half of the initial value.

Soil burial degradation tests were carried sandwiching the samples, virgin and photo-oxidized, between two layers of a mixture of milled perlite and commercial soil to simulate degradation in soil after their use lifetime as mulch films. Weight loss measurements showed that the Mater-Bi®-based

films were more susceptible to soil degradation than the Ecovio®-based ones were. Additionally, for all of the samples, soil degradation was accelerated by UV irradiation. The positive control (paper) was biodegraded in soil within 65 days, while photo-oxidized Mater-Bi®-based films were fully disintegrated in soil after 125 days at 30 ◦C. These results suggest that the photo-oxidation was not restricted to the surface but occurred throughout the bulk. Chemical structure changes induced by UV exposure affected wettability of films, as highlighted by contact angle decrease. A relevant increase intensity in carbonyl peaks and carbonyl index was highlighted in photo-oxidized biodegradable and PE film samples. Carbonyl index values of photo-oxidized biodegradable and PE film samples decreased after soil burial test because of surface erosion of monomers and oligomers.

This paper not only supplies new criteria to evaluate the performance of biodegradable films in agriculture, before and after lifetime, but also yields a comparative analysis on the soil burial degradation behaviour with traditional ones. However, accelerated weathering test does not provide real lifetime. Further studies will be addressed to establish a correspondence between accelerated weathering time and aging time in real conditions.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2073-4360/12/4/753/s1.

**Author Contributions:** Conceptualization, F.P.L.M. and P.R.; Data curation, L.A. and M.R.; Funding acquisition, F.P.L.M. and P.R.; Investigation, L.A., M.C.M. and M.R.; Project administration, F.P.L.M. and P.R.; Supervision, F.P.L.M. and P.R.; Visualization, F.P.L.M., L.A., M.R. and P.R.; Writing—original draft, F.P.L.M., L.A., M.R. and P.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Polieco (Italy) and Agriplast (Italy). POR FSE Sicily 2020 (project "Polymeric systems: innovative aspects and applications in the biomedical and agri-food fields – SPIN OFF of Polymers") and Call 11/2017 ("Strengthening employability in the R&D system and the emergence of research SPIN OFFS in Sicily") partially granted this work.

**Acknowledgments:** In this section you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).

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

### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**4. Conclusions**

In this work, alginate lyase Alg2951 was cloned from a novel marine bacterium *Alteromonas portus* HB161718T, expressed extracellularly, and characterized. The coldadapted Alg2951 performed its highest activity at 25 ◦C and performed more than 60% activity at 15–40 ◦C and was relatively stable at temperatures lower than 30 ◦C as well. Furthermore, it was a PL7 alginate lyase and could be powerfully promoted by sodium and potassium salts at a concentration of 0.2 M. Meanwhile, it specifically degraded polyG and sodium alginate but had almost no activity on polyM. The TLC and ESI-MS analyses indicated that it could hydrolyze sodium alginate to produce DEH and trisaccharide in an exolytic and endolytic manner. Moreover, the degradation products of alginate by Alg2951 exhibited good antioxidant activities by detecting the scavenging hydroxyl, ABTS radicals ability, and reducing power. This study hopefully provides a potential tool for pharmaceutical and industrial applications.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/1660-339 7/19/3/155/s1: Figure S1: Gellation reactions observed on the plate covered by the CaCl2 solution, Figure S2: Transmission electron micrograph of cells from a 12-hour-old culture on marine agar 2216 of strain HB161718T. Bar, 1 μm, Figure S3: Substrate specificity of the recombinant enzyme Alg2951. **Author Contributions:** Conceptualization, H.H., S.B., and Y.H.; data curation, H.H., S.L., and K.M.; formal analysis, H.H., S.L., and K.M.; funding acquisition, H.H. and Y.H.; investigation, S.B. and D.S.; methodology, D.S. and Y.H.; project administration, Y.H.; software, S.L.; supervision, S.B.; writing—original draft, H.H. and S.L.; and writing—review and editing, S.B. and Y.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by grants from the Key Research and Development Project of Hainan Province (ZDYF2020182 and ZDYF2019133) and the Financial Fund of the Ministry of Agriculture and Rural Affairs of China (NHYYSWZZZYKZX2020).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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


Preface

IntechOpen presents a new technical book, *Advanced Supercritical Fluids Technologies*, which covers modern and advanced applications of supercritical fluids in various industries. The editorial team consists of top international experts, mainly from academia, but with engineering and technological experience, and representing the following countries: 1) Canada; 2) China; 3) Italy; 4) Japan; 5) Romania; 6) Spain; 7) Sweden; and 8) USA.

The need for this book is based on the following rationale.

Using SuperCritical Fluids (SCFs) in various processes is not new, because Mother Nature has been processing minerals in aqueous solutions at critical and supercritical pressures for billions of years. In the late 1800s, scientists started to use this natural process in their laboratories to create various crystals. The first studies dedicated to the investigation of various properties and heat transfer in SCFs started possibly as early as the 1930s. It was found that heat transfer near a critical point was quite high, and this discovery was used in single-phase thermosyphons with an intermediate working fluid at the near-critical state. Therefore, solid knowledge of specifics of thermophysical properties and heat transfer is important for many applications of SCFs in industry.

In the 1950s, the idea of using supercritical water was rather attractive for coalfired thermal power plants. At supercritical pressures, there is no liquid-vapour phase transition; therefore, there is no such phenomenon as critical heat flux or dry-out. Only within a certain range of parameters may a deterioration of heat transfer occur. The objective of operating "steam" generators at supercritical pressures was to increase the total efficiency of a power plant. Currently, the use of supercritical water in power-plant "steam" generators is the largest application of a fluid at supercritical pressures in industry.

Also, at the end of the 1950s and the beginning of the 1960s, some studies were conducted to investigate the possibility of using SCFs in nuclear reactors. Several designs of nuclear reactors using water as the reactor coolant at supercritical pressures were developed in the USA and the former USSR. This idea was abandoned for almost 30 years but regained support in the 1990s.

Currently, six concepts of nuclear-power reactors/nuclear power plants of the next generation – Generation-IV – were proposed, which will have thermal efficiencies comparable with those of modern thermal power plants. The SuperCritical Water-cooled Reactor (SCWR) is one of these six concepts under development in a number of countries. Analysis of Generation-IV concepts shows that SCFs, such as helium and water, will be used as reactor coolants, and SCFs such as helium, nitrogen (or mixture of nitrogen (80%) and helium (20%)), carbon dioxide, and water will be used as Working Fluids (WFs) in power Brayton and Rankine cycles.

However, there are other areas where SCFs are used or will be implemented in the near future. The latest developments within these areas focus on:

**II**

**Chapter 8 181**

**Chapter 9 195**

Impregnation of Materials in Supercritical CO2 to Impart

*by Molla Tadesse Abate, Ada Ferri, Jinping Guan, Guoqiang Chen*

Foaming + Impregnation One-Step Process Using Supercritical CO2 *by Antonio Montes, Clara Pereyra and Enrique Martínez de la Ossa*

Various Functionalities

*and Vincent Nierstrasz*

• Production of liquid fuels from biomass in sub and supercritical water and carbohydrate up-conversion in ionic liquid and SCFs mixtures;

**Chapter 1**

*Igor L. Pioro*

**Abstract**

**1**

Supercritical-Fluids

and Heat Transfer in

Thermophysical Properties

Power-Engineering Applications

Researches on specifics of thermophysical properties and heat transfer at supercritical pressures (SCPs) started as early as the 1930s with the study on free-convection heat transfer to fluids at a near-critical point. In the 1950s, the concept of using SC "steam" to increase thermal efficiency of coal-fired thermal power plants became an attractive option. Germany, USA, the former USSR, and some other countries extensively studied heat transfer to SC fluids (SCFs) during the 1950s till the 1980s. This research was primarily focused on bare circular tubes cooled with SC water (SCW). However, some studies were performed with modeling fluids such as SC carbon dioxide and refrigerants instead of SCW. Currently, the use of SC "steam" in coal-fired thermal power plants is the largest industrial application of fluids at SCPs. Near the end of the 1950s and at the

beginning of the 1960s, several studies were conducted to investigate a possibility of using SCW as a coolant in nuclear reactors with the objective to increase thermal efficiency of nuclear power plants (NPPs) equipped with water-cooled reactors. However, these research activities were abandoned for some time and regained momentum in the 1990s. In support of the development of SCW-cooled nuclearpower reactor (SCWR) concepts, first experiments have been started in annular and various bundle flow geometries. At the same time, more numerical and CFD studies have been performed in support of our limited knowledge on specifics of heat transfer at SCPs in various flow geometries. As the first step in this process, heat transfer to SCW in vertical bare tubes can be investigated as a conservative approach (in general, heat transfer in fuel bundles will be enhanced with various types of appendages, that is, grids, end plates, spacers, bearing pads, fins, ribs, etc.).

New experiments in the 1990–2000s were triggered by several reasons: (1) thermophysical properties of SCW and other SCFs have been updated from the 1950s–1970s, for example, a peak in thermal conductivity in the critical/

pseudocritical points was "officially" introduced in 1990s; (2) experimental techniques have been improved; (3) in SCWRs, various bundle flow geometries will be used instead of bare-tube geometry; (4) in SC "steam" generators of thermal power plants, larger diameter tubes/pipes (20–40 mm) are used, however in SCWRs hydraulic-equivalent diameters of proposed bundles will be within 5–12 mm; (5) with Research and Development (R&D) of next-generation or Generation-IV nuclear-power-reactor concepts, new areas of application for SCFs have appeared for example, SCP helium was proposed to be used as a reactor coolant, SCP Brayton

This book on *Advanced Supercritical Fluids Technologies* provides engineers and specialists in various industries dealing with SCFs as well as researchers, scientists, and students of the corresponding departments with a comprehensive overview of the current status, latest trends and developments of these technologies.

**Igor Pioro, Ph.D., Dr. Tech. Sc., P.Eng., Fellow ASME, CSME & EIC** Professor, Founding Editor of the ASME Journal of Nuclear Engineering and Radiation Science, Faculty of Energy Systems and Nuclear Science, University of Ontario Institute of Technology, Canada

## **Chapter 1**


**6.1 Chaotic financial model**

We consider a dynamic finance model composed of three coupled first order differential equation. This model describes the temporal evolution of the state variables viz. the interest rate *X*, the investment demand *Y* and the price index *Z*. The model is described as [39]:

$$\begin{aligned} \frac{dX}{dt} &= Z + (Y - a)X, \\ \frac{dY}{dt} &= \mathbf{1} - bY - X^2, \\ \frac{dZ}{dt} &= -X - cZ. \end{aligned} \tag{23}$$

Here *a*, *b* and *c* are positive constants and represent the saving amount, cost per investment and elasticity of demand of the commercial markets. First equation appears, representing the changes in *X*, as a result of contradiction in the investment market and structural adjustment from the goods prices. Second equation representing the changes in *Y* appears due to proportionality to the rate of investment and also to an inversion of the cost of investment and interest rate. The third equation emerges due to contradiction between supply and demand in commercial markets which is influenced by interest rates.

Eq. (23) has been numerically integrated using fourth-order Runge-Kutta method to obtain the time series of the dynamic variables *X*, *Y* and *Z*, shown in **Figure 21** with *a* ¼ 3*:*0, *b* ¼ 0*:*1, *c* ¼ 1*:*0 and initial condition ð Þ¼ *X*0, *Y*0, *Z*0 ð Þ 2, 3, 2 . Similar choice of the parameter were made in [45].

*y* ð Þ*s j* ¼ 1 *s*

*Chaotic Dynamics and Complexity in Real and Physical Systems*

*DOI: http://dx.doi.org/10.5772/intechopen.96573*

the original time series.

1þ1 ≤*yn*þ *j*

2þ1 ≤⋯≤*yn*þ *j*

*n*þ1

*yn*þ *j*

**Figure 23.**

**53**

X *js*

*Xi* (24)

*p*ð Þ Π *ln* ð Þ Π *:* (25)

� �,

*i*¼ð Þ *j*�1 *τ*þ1

where 1≤*i* ≤ *N=s* and *s* defines the scale factor. Each time series length is of size that is an integral multiple of *N=s*. For *s* ¼ 1, the coarse-grained time series is just

The second step involves the computation of permutation entropy [47] for each of the coarse-grained time series. For a coarse-grained time series *y j* we first consider the series of vector of length *m*, and obtain *Sm*ð Þ¼ *n yn*, *yn*þ1, ⋯, *yn*þ*m*�1

1≤*n* ≤ð Þ� *N=s m* þ 1. Subsequently, *Sm*ð Þ *n* is arranged in an increasing order viz.,

h i. For *m* different numbers, there will be *m*! possible

order patterns/structures Π which are termed as permutations. If *f*ð Þ Π denotes the frequency of order pattern Π, then the relative frequency and hence the probability *p* ¼ *f*ð Þ Π *=*ð Þ *N=s* � *m* þ 1 . The permutation entropy *H m*ð Þ therefore is given by

Π¼1

The maximum value of *H m*ð Þ is *log m*ð Þ! thus showing all permutations to have equal probability. Also, the time series is termed as regular if minimum value of *H m*ð Þ is zero. Therefore *H m*ð Þ the permutation entropy provide a quantitative measure of dynamical complexity of a time series as it refers to its local structures. It may be noted that the permutation entropy depends on the chosen value of *m*. For *m* < 3 , there will be very few distinct states and the foregoing scheme does not work satisfactorily. In the present analysis we have considered sufficiently large time

For the financial model, Eq. (23), we have simulated the permutation entropy as

a function of the scale *s* for *m* ¼ 6. The simulated results have been shown in **Figure 23a** where we observe a saturation behavior with increased value of the scale factor *s*. It is also observed that the permutation entropy at any scale *s* for the interest rate *X* time series is higher than the investment demand time series *Y* which

*H m*ð Þ¼�X*m*!

series and chosen *m* ¼ 6 to estimate the complexity measure *MPE*.

*Multiscale permutation of (a) Financial model time series, (b) Rossler model.*

**Figure 21.** *Temporal evolution of Finance model.*

**Figure 22.** *(a) Phase portrait of Finance model, (b) 3-D attractor of the Finance model.*

The representation of two dimensional phase portrait ð Þ *X*, *Y* and the attractor are shown in **Figure 22**. Obviously they represent chaotic dynamics of the temporal behavior shown in **Figure 22**.


*2.2. Experimental Design*

A central composite design (CCD) was used combined with RSM methodology by Box and Wilson [28]. A flexible design structure was constructed to accommodate a custom model, with numeric and categorical independent factors and irregular constrained regions. Five numeric factors (A, B, C, D, and E) and one categorical factor (F) were analyzed by a quadratic design domain. A total of 38 runs were determined by a selection criterion chosen during the experimental design (Table 1).

**Table 1.** Independent experimental factors and design layout runs.

The responses of the volatile compounds expressed as chromatographic area (%) were used as dependent variables. For this purpose, a randomly selected sample was used for response prediction. The model's fitness was confirmed by analysis of variance (ANOVA) and the determination coefficient (R2) using *p*-values. Dependent variables were also confirmed by the Box-Cox, correlations, and normality of residuals statistical tests. All possible optimized solutions, for (a) volatile profile and (b) each volatile molecule separately were evaluated by maximizing desirability indices. The robustness of the model was validated with response data of 24 samples according to the optimum SPME solution.

Statistical analysis was carried out using Desing-Expert <IP_ADDRESS> (Stat-Ease, Inc.,Minneapolis, MN, USA).

### *2.3. Isolation and Analysis of Volatile Compounds*

Isolation of the volatile fraction was done based on experimental design layout run (Table 1) using a manual holder with triple-phase divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber 50/30 μm (Supelco, Bellefonte, PA, USA) with length of 1 cm. Before each analysis, fibers were conditioned at 270 ◦C. Moreover, a blank sample was performed for cleaning from previous volatile residues. Then, a predetermined volume ratio of water: Honey (*v*/*w*) was transferred in 15 mL screw top (22.7 × 86 mm) vials with PTFE/silicone septa and a portion of 20 μL (300 μg mL−1 in methanol) of benzophenone (Alfa Aesar, Kandal, Germany) was added as an internal standard.

RSM experiments were performed using a Trace Ultra gas chromatograph (GC) (Thermo Scientific Inc., Waltham, MA, USA), coupled with a mass spectrometer (MS) (DSQII, Thermo Scientific Inc., Waltham, MA, USA). GC-MS was performed with a Restek Rtx-5MS (30 m × 0.25 mm i.d., 0.25 μm film thickness) chromatography column with helium as carrier gas at a 1 mL min−1 rate. The chromatography conditions and temperature program have been previously described [29]. In brief, the GC inlet temperature 260 ◦C in the splitless mode for 3 min, with a 0.8 mm injector liner (SGE International Pty Ltd., Ringwood, Australia). Oven temperature was adapted to 40 ◦C for 6 min, then increased to 120 ◦C at a rate of 5 ◦C min−1, followed by an increment of 3 ◦C min−1 up to 160 ◦C and up to 250 ◦C with a step of 15 ◦C min−1. Finally, the temperature of 250 ◦C was kept constant for 1 min. The transfer line and injector temperatures were maintained at 290 and 220 ◦C, respectively. Electron impact was 70 eV, and mass spectra were recorded at the 35–650 mass range.

The peak identification was achieved with the Wiley 275 mass spectra library, and the arithmetic index provided by Adams [30]. Retention Index (RI) values of volatile compounds were calculated using n-alkane (C8–C20) standards (Supelco, Bellefonte, PA, USA). The isolated compounds were semi-quantified against the internal standard (benzophenone) and expressed as mg kg−1 of honey. All samples were analyzed in triplicate.

### **3. Results and Discussion**

### *3.1. Evaluation of Isolated Volatile Compounds*

In total, 49 volatile compounds were identified, including esters, hydrocarbons, alcohols, aldehydes, ketones, acids, terpenoids, and others (Table 2).

Esters have been encountered almost always in all blossom and honeydew honeys with some of them being dominant volatiles [18]. In our results, most esters were detected in small amounts except for methyl nonanoate. However, methyl nonanoate has been reported at much higher concentrations in honeydew honey, like fir and pine [29]. This presence could occur in the collection period of pine honey by the bees in October. Moreover, methyl octanoate and methyl dodecanoate can be related to the above conjecture [29]. Ethyl 4-methoxybenzoate was a derivative coming from *p*-anisic acid which has been reported in *Erica arborea* L. honey [10] by a Likens-Nickerson steam distillation (L-N) isolation technique. However, ethyl 4-methoxybenzoate was worth studying as it has not been detected in other botanical sources yet.

**Table 2.** Volatile compounds isolated from headspace of autumn heather honey.

**Table 2.** *Cont.*

**Table 2.** *Cont.*

aRT: Retention time (min); b RI: Experimental retention index; c NIST#.

Hydrocarbons were detected in most samples with undecane having the highest average compared to the rest. This class of volatiles is very common among honeys [18].

The chemical group of alcohols including 5-(3,3-dimethyloxiran-2-yl)-3-methylpent-1-en-3-ol (syn: *cis*-Linalool oxide) [11,17] and 2-phenylethan-1-ol [10,13,19] have been previously identified as dominant volatiles compounds of citrus, acacia, chestnut, and thyme honeys [18]. The compound, 3,4,5-trimethylphenol, has been previously described as one of the major volatile compounds of heather honey from Poland [14]. Furthermore, 4,6,10,10-tetramethyl-5-oxatricyclo[<IP_ADDRESS>,4]dec-2-en-7-ol and 6,6-dimethyl-5-methylenebicyclo[2.2.1]heptan-2-ol (6-camphenol) have been reported in *Erica* spp. honeys from Iberian Peninsula [17]. Nevertheless, the latter was not identified in all of our samples.

Aldehydes were detected in all samples, in smaller or larger amounts. Octanal, nonanal, and decanal were present in small quantities and are considered as important components of honeydew honey volatile profile [18]. Benzaldehyde [11,19] and 2-phenylacetaldehyde [13,16,17] were detected in all samples at a remarkable concentration. In addition, 2,6,6-trimethylcyclohexa-1,3-diene-1-carbaldehyde (safranal) [7]; 4- methoxybenzaldehyde (p-anisaldehyde) [10], and furan-2-carbaldehyde (furfural) [13] have been attributed to heather honey.

Ketones include many degraded carotenoids related to heather honey. Some of these compounds, such as 3,5,5-trimethylcyclohex-2-en-1-one (a-isophorone); 2,6,6- trimethylcyclohex-2-ene-1,4-dione (4-oxoisophorone); 2-hydroxy-3,5,5-trimethylcyclohex-2- en-1-one (2-hydroxyisophorone); (E)-1-(2,6,6-trimethylcyclohexa-1,3-dien-1-yl)but-2-en-1-one (β-damascenone); (E)-1,6,6-trimethyl-7-(3-oxobut-1-en-1-yl)-3,8-dioxatricyclo[<IP_ADDRESS>,4]octan-5-one; and (E)-4-(2,6,6-trimethylcyclohexa-1,3-dien-1-yl)but-3-en-2-one [10,13,14,31] are known heather honey compounds, all of which have been detected in our samples. Notably, 1-(furan-2-yl)ethan-1-one was found in all samples.

Terpenoids, acids, and other compounds do not include significant volatile compounds of heather honey, except for 1,1,5-trimethyl-1,2-dihydronaphthalene and 8-isopropyl-1-methyl-1,2,3,4-tetrahydronaphthalene, that have been identified in another study as well [17].

Other studies refer to hotrienol, *cis*-linalool oxide, and 2-phenylacetaldehyde as the main volatile compounds of *Erica* spp. honey [13,17,19]. However, this is not confirmed by our samples. Hotrienol was not detected in any of our samples. Oxide of cis-linalool was linked with hive atmospheres or combustion of wood/vegetation during beekeeping activity [32], and 2-phenylacetaldehyde also had been reported in relevant concentrations, while some studies attribute this molecule to long-term storage by enzymatic catalysis of phenylalanine or heat treatment [33]. Furan derivatives identified in some of our samples, emanate from thermal processing and/or prolonged storage [34–36] and cannot be related to honey botanical origin.

### *3.2. Optimization of Each Dominant Volatile Compound*

Several SPME conditions (A: Temperature; B: Equilibration time; C: Extraction time; D: Magnetic stirrer velocity; E: Sample volume; F: water: honey ratio) were investigated to determine the most suitable conditions for each volatile compound. A total of 13 volatile compounds were chosen for optimization (responses R1-R13) (Table 3). These compounds were selected as they constitute dominant and characteristic responses of autumn heather honey.

### **Table 3.** Dominant volatile compounds (responses R1-R13).

Prior to undertaking the processing steps, data for each volatile compound were confirmed by normal distribution, Box-Cox test, determination of coefficient (R2) and ANOVA (Table 4). The condition number of coefficient matrix (<10) did not indicate multicollinearity. Additionally, all responses followed the normal distribution. Box-Cox test provides a guideline for selecting the correct power law transformation. If the 95% confidence interval around this lambda includes 1.00, it does not require a specific transformation. Table 4 shows the lambda values at the 95% confidence range, as well as the current lambda. R-square (R2) constitutes a measure of the amount of variation around the mean explained by the model. The ANOVA in this case confirms the adequacy of the model (*p*-value < 0.05) and indicated whether the model terms were significant. Significant model terms may have a real effect on the response.

ANOVA results showed many considerable independent SPME conditions, while some of them could contribute in combination. At the same time, equations were developed in terms of coded factors that can be used to make predictions about the response for given levels of each factor. By default, the high levels of the factors are coded as +1 and the low levels are coded as −1. The coded equation is useful for identifying the relative impact of the factors by comparing the factor coefficients. However, these equations should be considered with caution because it is not safe to use them as panacea for modeling future responses. In this case, all these results are presented in Table 5.

**Table 4.** ANOVA, Box-Cox and determination of coefficient (R2) of each response subjected to the model.

a 95% confidence interval level. \* A: Temperature; B: Equilibration time; C: Extraction time; D: Magnetic stirrer velocity; E: Sample volume; F: water: honey ratio.

**Table5.***Cont.*

a MT: model term; b CE: coded equation.

After these steps, optimization models were developed based on each volatile molecule. The results are presented in Table 6 and were evaluated by desirability indices. A high level of ideal cases is coded as 1 and low level as zero. A predicted mean for each volatile response is also included.

\* A: Temperature; B: Equilibration time; C: Extraction time; D: Magnetic stirrer velocity; E: Sample volume; F: water: honey ratio.

> Experimental findings showed that optimum conditions of some volatiles required the maximum value of model terms. However, this conclusion is overturned by extraction time and magnetic stirrer velocity. As previously described, extraction time was a significant parameter, along with magnetic stirrer velocity, which in some cases, allowed better isolation of some compounds [20], whilst usually shortened the equilibration time.

### *3.3. Optimization and Validation of Dominant Volatile Compounds*

The optimum conditions proposed for dominant volatile compounds of autumn heather honey were A: 60 ◦C B: 30 min C: 15 min D: 100 rpm E: 6 mL F: 1:3 (*v*/*w*). Predicted mean (% Area) was estimated for benzaldehyde (4.53%), 3,5,5-trimethylcyclohex-2-en-1-one (0.88%), 2,6,6-trimethylcyclohex-2-ene-1,4-dione (1.64%), 2-hydroxy-3,5,5- trimethylcyclohex-2-en-1-one (0.80%), 2,6,6-trimethylcyclohexa-1,3-diene-1-carbaldehyde (1.29%), 4-methoxybenzaldehyde (12.21%), 3,4,5-trimethylphenol (0.12%), 1,1,5-trimethyl-1,2-dihydronaphthalene (0.66%), (E)-1-(2,6,6-trimethylcyclohexa-1,3-dien-1-yl)but-2-en-1- one (0.15%), (E)-1,6,6-trimethyl-7-(3-oxobut-1-en-1-yl)-3,8-dioxatricyclo[<IP_ADDRESS>,4]octan-5-one (0.71%), ethyl 4-methoxybenzoate (2.88%), (E)-4-(2,6,6-trimethylcyclohexa-1,3-dien-1yl)but-3-en-2-one (0.77%), and 4,6,10,10-tetramethyl-5-oxatricyclo[<IP_ADDRESS>,4]dec-2-en-7-ol (0.97%). Moreover, the desirability of optimized model was calculated at 1.000.

The validation of the above results was carried out with a test set of 24 samples. All responses (R1-R13) were isolated in all samples with the confirmed optimum conditions of the proposed method. Data mean (% Area) was estimated for benzaldehyde (2.62%), 3,5,5-trimethylcclohex-2-en-1-one (6.14%), 2,6,6-trimethylcyclohex-2-ene-1,4-dione (1.90%), 2-hydroxy-3,5,5-trimethylcyclohex-2-en-1-one (1.29%), 2,6,6- trimethylcyclohexa-1,3-diene-1-carbaldehyde (1.75%), 4-methoxybenzaldehyde (3.62%), 3,4,5-trimethylphenol (0.98%), 1,1,5-trimethyl-1,2-dihydronaphthalene (1.21%), (E)-1- (2,6,6-trimethylcyclohexa-1,3-dien-1-yl)but-2-en-1-one (0.84%), (E)-1,6,6-trimethyl-7-(3- oxobut-1-en-1-yl)-3,8-dioxatricyclo[<IP_ADDRESS>,4]octan-5-one (0.60%), ethyl 4-methoxybenzoate (0.43%), (E)-4-(2,6,6-trimethylcyclohexa-1,3-dien-1-yl)but-3-en-2-one (0.37%), and 4,6,10,10- tetramethyl-5-oxatricyclo[<IP_ADDRESS>,4]dec-2-en-7-ol (0.37%).

The extraction temperature indicated a notable effect on total volatility (Figure 1). The ideal temperature for the isolation of compounds with lower molecular weight and high volatility was 30 ◦C. Contrariwise, 60 ◦C was better for molecules with lower volatility (Table 6). In our case, the temperature of 60 ◦C was selected. Considering the above results, the higher the extraction temperature the larger the partition coefficients of compounds [14]. Nonetheless, this relation was not linear because higher temperatures may lead to the formation of by-products or thermal decomposition [20].

**Figure 1.** Chromatograms of the same sample at different temperatures (30, 45, and 60 ◦C).

The equilibration time, also known as "thermostating time", showed an uncommon high value at 30 min. Figure 2 presents the desirability surface area of all responses. Due to the nature and molecular structure variability of autumn heather honey volatiles, compounds with short equilibration time can be displaced from the headspace of the vial gradually, by compounds with higher equilibration time. However, it is not a factor that significantly affects the efficiency of the system [14], since it reacts with other parameters at a relative moderate impact, as observed from the ANOVA test (Table 5).

**Figure 2.** Desirability contour of equilibration time as a function of temperature of all responses.

Extraction time is critical for the sample to establish equilibrium with the SPME fiber coating. A typically 15 min extraction time showed a good performance. This parameter depends on the interactions of the molecules and can be lower when using the headspace technique and interact with high concentration samples. At the same time, it should be considered that many volatiles encumber the overall sensitivity and drive the specific compounds out of the fiber, which is easier to happen at prolonged extraction. On the other side, some studies indicated that the efficiency increases together with the extension of extraction time [14]. Obviously, the assessment is very difficult due to the intrinsic variability of honeys.

The magnetic stirrer speed, as previously referred to by Xagoraris [20], was confirmed to interact with most of the responses. However, this contribution is not always important as shown by the coded equation. Although one can assume that compounds with lower volatility require greater velocities, and in our case, 100 rpm gave satisfactory results. Thus, maintaining consistent agitation improves the accuracy and precision of the system.

The optimum sample volume was 6 mL, while this parameter had minimum impact on the isolation. Nevertheless, sensitivity is better when the headspace volume is small and fiber extracts faster compounds with higher volatility [37].

Finally, the ratio of water: Honey was proven a significant parameter. Honey is highly viscous and the addition of water enables the sample agitation, while also water evaporation drifts more easily the volatiles from honey. However, the excessive addition of water tends to dilute the honey reducing the concentration of specific molecules. The most favorable water: Honey ratio was 1:3 (*v*/*w*).

Reviewing the literature, in other botanical origin honey samples, different optimization conditions are reported. Ceballos [38] suggested the analytical conditions of the optimized SPME method by RSM as 60 μm PDMS/DVB fiber, 6 g honey, 3 mL water, and

20% *w*/*w* sodium chloride, 20 min for thermostatic time, 30 min for extraction at 60 ◦C. Plutowska [14] referenced the following conditions: CAR/PDMS/DVB fiber, 5:1 *w*/*w* honey to water ratio, 3 g sample, equilibration time 10 min, extraction time 30–60 min, and temperature at 60 ◦C. Bianchi [39] examined four different sets of conditions for volatile fraction isolation from thistle honey. Bianchin [40] proposed a new optimization strategy based on the use of three different extraction temperatures (60, 40, and 30 ◦C) followed by equilibration time (60, 36, and 6 min), respectively, in a single assay. Robotti, [41] reported as optimal extraction conditions for multi-floral honeys (extraction temperature: 70 ◦C; extraction time: 60 min; salt percentage: 27.50% *w*/*w*). Da Costa [42] reported the optimum condition for extraction of volatile compounds were as follows: equilibration time of 15 min, extraction time of 45 min, and extraction temperature of 45 ◦C.

On the basis of the above considerations, it is difficult to predict the factors that can affect the isolation of each volatile compound of honey. In a previous study, the optimized combination of isolation conditions of thyme honey was different with temperature (60 ◦C), equilibration time (15 min), extraction time (30 min), magnetic velocity speed (700 rpm), sample volume (6 mL) and water honey ratio (1:3 *v*/*w*) [20]. Each honey has its unique composition thus requiring different optimization conditions regarding volatile compounds.


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**2. Materials and Methods**

Table 1 shows the composition of the austenitic stainless steels (exception #16 and #17) which were used to prepare the samples for the corrosion evaluation tests.

**Table 1.** Chemical composition (wt.%) of the grades of austenitic steels used in corrosion tests.

According to the classification of Fontana [26], the evaluation of their behavior in uniform, pitting, crevice, intergranular corrosion was presented, as well as in galvanic couplings.

#### *2.1. Salt Fog Test*

To illustrate uniform corrosion, four austenitic steels—#2-1.4427, #3-1.4435/316L, #4-316L/1.4435Ugim and #5-1.4435/316LVal—were tested by using the salt fog test, according to ASTM B117-97 [27].

The samples were of cylindrical shape—10 mm diameter, 5 cm long. To create a reference state, as-received samples were first annealed and recrystallized at 1050 ◦C. The thermal treatment was carried out in a continuous industrial oven under hydrogen protection with gas cooling.

Half of the annealed cylinders were cold-worked to diameter 7.7 mm. The purpose of this cold-working operation was to increase the samples' sensitivity to corrosion [28,29]. The entire surface of the samples was "mirror" polished. The test was carried out over a period of 12 days, 6 days in 5% NaCl medium and 6 days in artificial sweat medium diluted 40 times (Table 2). Artificial sweat medium ISO 3160-2 has the following composition: NaCl 0.5 g/L; NH4Cl 0.4375 g/L; Acetic Acid 0.063 g/L; Urea 0.125 g/L; Lactic Acid 0.375 g/L; NaOH solid, necessary quantity to induce a pH of 4.7.

The test in question was an adaptation of the ASTM B117-97 standard [27] which is in current use for metallic objects in contact with the skin (ISO 3160-2). Three samples were used for each state, making a total of 48 samples.

**Table 2.** The salt fog test conditions.


**3. Model Setting and Data Description**

*3.1. Model Construction*

3.1.1. Superefficient EBM Model

The hybrid distance function model (EBM) can be compatible with the radial ratio of input frontier values to actual values and realize the effective combination of radial and nonradial methods in data envelopment analysis. The model makes up for the deficiencies of DEA and SBM, giving more consideration to the efficiency level [24–28]. The following superefficient EBM model with undesired outputs and nondirectional and constant payoffs of scale is used to measure the green economic efficiency of 280 cities across China, and the obtained combined efficiency value (GEE) is used as the core explanatory variable of the spatial econometric model.

$$\begin{array}{lcl} \mathbf{r}^\* = & \min \frac{\boldsymbol{\Phi} - \boldsymbol{\varepsilon}\_\mathbf{x} \sum\_{\mathbf{i}=1}^m \mathbf{w}\_i^- - \mathbf{s}\_\mathbf{i}^- / \mathbf{x}\_{i0}}{\boldsymbol{\Phi} + \boldsymbol{\varepsilon}\_\mathbf{y} \sum\_{\mathbf{i}=1}^n \mathbf{w}\_\mathbf{r}^+ + \mathbf{y}\_\mathbf{r}^- / \mathbf{y}\_{i0} + \boldsymbol{\varepsilon}\_\mathbf{z} \sum\_{\mathbf{p}=1}^n \mathbf{w}\_\mathbf{p}^- \mathbf{x}^- - \mathbf{y}\_\mathbf{p}^- / \mathbf{z}\_\mathbf{p}}} \\ \text{s.t.} & \begin{cases} \sum\_{\mathbf{i}=1}^n \mathbf{x}\_{i\mathbf{j}} \lambda\_\mathbf{j} + \mathbf{s}\_\mathbf{i}^- = \boldsymbol{\Phi} \mathbf{x}\_{i0} \ (\mathbf{i} = 1, 2, \cdots, \mathbf{m}) \\ \sum\_{\mathbf{j}=1}^n \mathbf{y}\_\mathbf{r} \lambda\_\mathbf{j} - \mathbf{s}\_\mathbf{r}^+ = \boldsymbol{\Phi} \mathbf{y}\_\mathbf{r} \mathbf{0} \ (\mathbf{r} = 1, 2, \cdots, \mathbf{s}) \\ \sum\_{\mathbf{i}=1}^n \mathbf{z}\_\mathbf{p} \lambda\_\mathbf{j} + \mathbf{s}\_\mathbf{p}^- = \boldsymbol{\Phi} \mathbf{z}\_\mathbf{p} \mathbf{0} \ (\mathbf{p} = 1, 2, \cdots, \mathbf{q}) \\ \lambda\_\mathbf{j} \geqslant 0, \mathbf{s}\_\mathbf{i}^-, \mathbf{S} \mathbf{r}^+, \mathbf{s}\_\mathbf{p}^- \ge 0 \end{array} \tag{1} \\ \end{array} \tag{1}$$

Regarding the specific meaning of these variables, r\* denotes the combined efficiency value, and x, y and z denote the input, desired output and undesired output elements, respectively. m, s and q denote their quantities. λ denotes the relative importance of the reference unit, ε is the core parameter representing the importance of the nonradial component, and θ is the efficiency value in the radial condition. wi, wr and wp denote the i-th input, r-th desired output and p-th nonweights of the expected output indicators.


**2. Classifications and epidemiology**

The recent WHO classification 2010 of all pancreatic tumors is more extensively used. This classification encompasses epithelial tumors (benign, premalignant lesions, malignant lesions, and neuroendocrine neoplasms), mesenchymal tumors, lymphomas, and secondary tumors [1].

Based on pathological, clinical and radiologic assessments, some not recent but valuable [2], several classifications of cystic pancreatic neoplasms and lesions have been proposed. We believe interesting to note the proposed classification that fully includes all cystic lesions of the pancreas [3]. This classification includes the following cystic lesions: neoplastic epithelial (benign, borderline, and malignant), non‐neoplastic epithelial, neoplastic non‐epithelial (very rare), and non‐epithelial non‐neoplastic (very rare).

The frequency of each cystic lesions is not defined with precision, may be for observers diversity (surgeons, radiologists, and pathologists) and for the assessment of different developmental stages of lesions.

Pancreatic cystic tumors comprise a variety of neoplasms with a wide range of malignant potential: benign, borderline, and malignant. The classification proposed by Kosmahl encom‐ passes the majority of the recently now described lesions (**Table 1**).

Many cystic neoplasms listed in the classifications are infrequent or rare pathological varieties, with minimal and not evident clinical characterization [4]. The simplified classification can be proposed that comprises two groups of lesions:

**Table 1.** Classification of cystic neoplasms and lesions of pancreas [3].

**1. Introduction**

94 Challenges in Pancreatic Pathology

of the diagnosis.

**2. Classifications and epidemiology**

rare), and non‐epithelial non‐neoplastic (very rare).

proposed that comprises two groups of lesions:

passes the majority of the recently now described lesions (**Table 1**).

developmental stages of lesions.

role.

[1].

Cystic lesions of the pancreas are less frequent in relation to solid neoplasies. These lesions have attracted new and great interest. In the last several decades, the knowledge of the cystic neoplasms has enlarged and the management has changed dramatically. The wide adoption in the diagnostic procedures of routine and advanced imaging such as ultrasonography (US), computed tomography (CT), magnetic resonance imaging (MRI), magnetic resonance chol‐ angiopancreatography (MRCP), and endoscopic ultrasound (EUS) has become the cornerstone

EUS‐guided fine needle aspiration (FNA) can allow the assessment of tumor markers, chemistries, cytology, and DNA analysis. Also pathological study has played a very important

The recent WHO classification 2010 of all pancreatic tumors is more extensively used. This classification encompasses epithelial tumors (benign, premalignant lesions, malignant lesions, and neuroendocrine neoplasms), mesenchymal tumors, lymphomas, and secondary tumors

Based on pathological, clinical and radiologic assessments, some not recent but valuable [2], several classifications of cystic pancreatic neoplasms and lesions have been proposed. We believe interesting to note the proposed classification that fully includes all cystic lesions of the pancreas [3]. This classification includes the following cystic lesions: neoplastic epithelial (benign, borderline, and malignant), non‐neoplastic epithelial, neoplastic non‐epithelial (very

The frequency of each cystic lesions is not defined with precision, may be for observers diversity (surgeons, radiologists, and pathologists) and for the assessment of different

Pancreatic cystic tumors comprise a variety of neoplasms with a wide range of malignant potential: benign, borderline, and malignant. The classification proposed by Kosmahl encom‐

Many cystic neoplasms listed in the classifications are infrequent or rare pathological varieties, with minimal and not evident clinical characterization [4]. The simplified classification can be

**•** Non‐mucinous cystic lesions: inflammatory pseudocysts without a true epithelial lining (in the setting of acute and chronic pancreatitis), serous cystic neoplasms (SCNs), solid pseudopapillary neoplasms (SPPNs), and cystic pancreatic endocrine neoplasms (CPENs). **•** Mucinous cystic lesions (epithelial lining produces mucinous cyst fluid): intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs).

This classification highlights as criterion of differentiation the presence of mucinous epitheli‐ um characterized by malignant potential.

Another criterion of classification of cystic lesions of pancreas is based on the epithelium lining of the cyst (**Table 2**).

In summary, the most common neoplasms include: serous cystic neoplasms (SCNs)/serous cystadenoma, mucinous cystic neoplasms (MCNs), intraductal papillary mucinous neoplasms (IPMNs), solid pseudopapillary neoplasms (SPPNs), and cystic pancreatic endocrine neo‐ plasms (CPENs). There are others rare or very rare tumors: acinar cells cystadenoma, cysta‐ denocarcinoma, cystic teratoma (dermoid cyst), and cystic pancreatoblastoma. Pancreatic cystic tumors are rare and less frequent than others pancreatic tumors. Image‐based studies show prevalence of pancreatic cystic lesions ranging from 1.2 to 19% [6, 7].

```
No lining → Pseudocysts (pancreatitis associated)
Lining
Mucinous epithelium
                   MCNs
                   IPMNs
Serous epithelium
                   SCNs
                   VHL‐associated pancreatic cysts
Squamous epithelium
                   Lymphoepithelial cysts
Acinar cells
                   Acinar cell cystadenocarcinomas
Endothelial lined cysts
                   Lymphangiomas
Degenerative necrotic changes in a neoplasm
SPPNs
CPENs
Cystic ductal adenocarcinomas
```
**Table 2.** Classification of cystic lesions of the pancreas [5].

In the autopsy series, the prevalence reaches 24% [8]. All cystic tumors of the pancreas reach about 10–15% of all cystic pancreatic lesions [7]. The exact prevalence of cystic pancreatic tumors is not defined. Autopsy study shows a prevalence of 24.3%; however, imaging studies have found the prevalence of 1.2–2.4%. On the other hand, pseudocystic lesions reach 90% of all pancreatic cystic lesions but only 45% of these patients had previous pancreatitis [6]. The economic impact that is necessary to follow these patients by imaging studies should be evaluated.

The epidemiologic and demographic features are different in the several types of cystic tumors, and they will be presented in specific sections.


*2.4. Field Tests*

2.4.1. Test Equipment and Materials

To determine the working parameters for toothed rollers, a V-shaped toothed roller stalk-pulling device bench was developed, and field tests were conducted (Figure 13). The test site was the Demonstration Base of New Cotton Variety K836 for Simplified and High-Yield Cultivation in Binzhou City, Shandong Province. The test was conducted in spring. The cotton variety is China Cotton Institute—50 (CCRI50). The cotton stalk rows were spaced at 76 cm, and the target plant spacing was 20 cm, with an average of 30 cm. The height of the cotton stalks typically ranged between 95 and 105 cm, and the diameter of the cotton stalks ranged from 10 to 22 cm. The average soil firmness is 672.3 KPa and the average soil moisture content is 27.4%.

**Figure 13.** Field test photo: 1. Reel wheel 2. V-shaped tooth roller 3. Torque transducer 4. Cotton stalks.


**6. DUREDAS™ technology**

It is a dual drug delivery form consisting of two layers where one provides immediate release and sustained release of the same drug (**Figure 4**). An immediaterelease layer is composed of granules with a matrix layer for the modified release of the drug. One or more hydrophilic polymers are used in such cases.


**2.5 The development of the right to nature since the sentences issued by the Constitutional Court of Ecuador**

In Ecuador, a jurisprudential line has been developed regarding nature as a subject of rights through the Constitutional Court. Thus, several rulings have been handed down in the recent years, for example: a) in Ruling No. 012–18-SIS-CC Case No. 0032–12-IS, the violation of the rights of nature is recognised due to effects on the

### *Reconstruction of Ethics: Nature as a Subject of Rights DOI: http://dx.doi.org/10.5772/intechopen.112336*

Vilcabamba river; b) in Ruling No. 22–18-IN−/21 Case No. 22–18, it is stated that mangrove ecosystems are holders of the recognised rights to nature and have the right to have their existence, maintenance, and regeneration of their life cycles, structure, functions, and evolutionary processes fully respected; c) in Ruling No. 1149–19-JP/21, the affectation of the rights of nature by allowing mining activity in the Protective Park "Los Cedros" is recognised. It was determined that nature has intrinsic value as a holder of rights and not necessarily as a value linked to human beings. Therefore, it has legal protection for itself; d) in judgement No.1185-20-JP/21 (The Aquepi River) case No. 1185-20-JP, the legal valuation of the river as a subject of rights individually and simultaneously as part of an ecosystem, recognised that rivers are dynamic, complex, and integrating ecosystems with multiple connections with other ecosystems.

Therefore, the Constitutional Court has established a jurisprudential line that recognises nature as a complex subject that must be understood from a systemic perspective. This comprises an interrelated, interdependent, and indivisible set of biotic and abiotic elements, in which, when one part is affected, the functioning of the entire system will be altered. Thus, Ruling No.253-20-JH (habeas corpus action in favour of a Chorongo monkey named Estrellita) recognised wild animals as subjects of protection rights by being part of nature.


*2.7. NIR-Chemometric Analyses*

Chemometric techniques for the calibration of the main physicochemical parameters of honey were applied. Spectral data of samples corresponding to the samples of the calibration set were analyzed by principal component analysis (PCA) [41]. Anomalous spectra were detected by applying the Mahalanobis distance (H-statistic). Considering an H-value greater than 3 (the spectra not belonging to the population), the equations are not used to make any prediction. The modified partial least squares (MPLS) regression method was used to obtain the NIR equations. Partial least squares (PLS) regression is similar to principal component regression (PCR) but uses both reference data and spectral information to form the factors useful for fitting purposes. Using the T ≥ 2.5 criterion, samples that presented high residual values when they were predicted were eliminated from the set. Therefore, statistical parameters of the calibration were obtained for each of the components after removing the samples for spectral (H criterion) or chemical (T criterion) reasons. To optimize the multivariate regression equations, the spectral scattering effects were taken into account with several mathematical treatments: multiplicative scatter correction (MSC), standard normal variate (SNV), D-trend (DT), and SNV-DT [42]. A nomenclature using 4 digits was used (1,4,4,1), in which the first digit is the number of the derivate, the second is the gap over which the derivative is calculated, the third is the number of data points in a running average or smoothing, and the fourth is the second smoothing.

Cross-validation is recommended to select the optimal number of factors and to avoid over fitting [43]. The calibration set is divided into several groups for the cross-validation. Each group is then validated using a calibration developed on the other group of samples. Validation errors generated are combined into a root mean square error of cross-validation (RMSECV). This statistic is considered the best single estimate for the prediction capability of the equations [44]. Cross-validation was performed by splitting the population into eight groups for all cases.

The performance of the models was determining by the squared correlation coefficient for predicted versus measured quantified in cross-validation and the ratio of standard deviation (SD) to SECV of the data set. RPD (ratio of performance to deviation) is the relation between SD and RMSEC, and it is desired to be larger than 2 for a good calibration, and an RPD ratio less than 1.5 indicates poor predictions and the model cannot be used for further prediction [44]. The statistics used to select the best equation for each physicochemical parameter were the highest RSQ (multiple correlation coefficients) and the lowest SECV (standard error of cross-validation) [25]. The software used for chemometric analysis was WinISI II version 1.50 (Infrasoft International, LLC, Silver Spring, Maryland, MD, USA).

### *2.8. Linear Discriminant Analysis*

Linear discriminant analysis (LDA) is a supervised classification technique, which uses a class member known for the analysis. In this case, the known variable was honey type determined by palynological analysis. Considering the pollen profile, six honey groups were characterized: heather, chestnut, eucalyptus, blackberry, honeydew, and multifloral. LDA was applied to the collected reference data set (physicochemical and botanical data) to determine a linear combination of these groups of subjects. LDA is considered as a dimensional reduction method to determine a lower dimension hyperplane on which the points will be projected from the higher dimension space [10]. A linear function of the variables is sought which maximizes the ratio of between-class variance and minimizes the ratio of within-class variance. STATGRAPHICS Centurion XVI software (Statpoint Technologies, Inc., The Plains, VA, USA) was used for treatment of data.


**2. Heat transfer mechanism in solidification**

To comprehend the heat transfer mechanism we need to know the behavior of solidification. The heat transfer from the liquid hot temperature cast to the mold is a very complex phenomenon and different modes of heat transfer can be observed while solidification in the cast. While heat transfer is predominant the resistance

**40**

*Casting Processes and Modelling of Metallic Materials*

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[11] Kurz,Willfried; 'Plane Front Solidification' ;Book Chapter, ASM Handbooks, On Line, 2008, vol-15

[12] Trivedi, R. and Kurz, W; 'Dendritic Growth', International Materials Reviews, 1994, Vol. 39 (2), p. 49-74

[13] Flemings, M.C., 'Solidification Processing', McGraw-Hill, New York,

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[16] Mohapatra,S., Sarangi,H. and Mohanty,U.K, 'Effect Of Process Factors On The Characteristics Of Centrifugal Casting', Manufacturing Review, 2020,

p. 189-238

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[1] Neil W Ashcroft; N David Mermin, Solid state physics, New York : 1976,

[2] Smith, William F.; Hashemi, Javad, *Foundations of Materials Science and Engineering* (4th ed.), McGraw-Hill, 2006, ISBN 978-0-07-295358-9

Holt, Rinehart and Winston

[3] Degarmo,E.Paul, Black,J-T, Kohser,Ronald.A, Materials and Processing in Manufacturing', (9th edition) Willy Publication, ,2003, p. 277

[4] Campbell,J., Metal Casting

[5] Eskin,D.G. and Katgerman,L., Thermal Contraction During Solidification Of Alluminium Alloy, Materials Science Forum, 2006, Vols-

[6] Oxtobody,D.W., 'Homogenous Nucleation: Theory and Experiment, Journal of Physics: Condensed Matter, 1992, Volume 4, issue 38, p. 7627

[7] Mahata,Avik;Asle Zaeem Mohsen and Baskes,I.Michael, 'Understanding

[8] De Moor.P-Pe,Bleeten,T.A. and Van Sanken,R.A., In Situ Observation Of Nucleation And Crystal Growth In Zeolite Synthesis. A Small Angle X-Ray Scattering Investigation On Si-TPA-MFI, J.Phys.Chem B, 1999, Vol. 103(10),

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Processes, "Metallurgy,Techniques and Design", Complete Casting Handbook (Butterworth-Heinemann), 2015, 2nd

p. 326-327

**References**

edition

to the heat flow also has different dimensions to this solidification. This resistance mainly depends on liquid cast metal, latent heat release, interface, solidified cast, the type of mold and the ambient conditions. General solidification of an alloy is discussed in the **Figure 1** and specific cooling curve for Al6061 is shown in **Figure 2**.

Initially on pouring the liquid metal cast into the mold cavity the whole metal fluid flows and occupies the mold cavity, the liquid metal flowing with the velocity, mixes thoroughly and releases heat to the mold due to the very high temperature difference. Complete thermal contact is observed between the cast and the mold which causes the heat transfer to be purely conduction, where the resistance offered by this liquid metal is negligible since the entire fluid flow is the superheated cast metal. Once the cast metal reaches the liquidus point on cooling, the cast shrinks and releases latent heat and also a number of metal oxides are released which causes an air gap between the cast and the mold. Due to this air gap the heat transfer phenomenon now changes to a complex one where all modes of heat transfer can be observed simultaneously. This air gap is characterized with an Interfacial Heat transfer Coefficient (IHTC) "h" across the metal-mold interface. The rate of heat at the interface is found using the surface heat flux as q (W/m2 ) and given by the Eq. 1.

$$q = h(Tc - Tm) \tag{1}$$

**43**

continue.

**Figure 2.**

*Heat Transfer Studies on Solidification of Casting Process*

the turbulence in the liquid metal. This rate of cooling is linear and a minimum amount of heat is transferred from the cast to the mold as it is having a complete

As the solidification progresses with time it reaches the liquidus point at the same time where the mold temperature increases significantly to a maximum temperature. Further the solid skin forms on the outer cast surface, the metal shrinks and an air gap starts forming between the metal and the mold. When the cast solidifies further the air gap separates the two surfaces. This is a common phenomenon in most of the alloys. The rate of heat transfer from the cast to the mold is very high as it releases larger quantity of latent heat to the mold and the cast temperature gradually reaches a solidus (TS) temperature of the alloy. The air gap plays a significant

Further solidification reduces the cast surface temperature, however the inner cast metal shrinks and it further releases the heat to the mold and there is rise in the mold temperature as shown in **Figure 2**. Thereafter further reduction in the cast temperature after the solidus point (Ts) was found as the third stage of solidification. The air gap size is further increased as the solidification time increase and its effects are felt till the end of solidification. However there is still a temperature difference between the cast and mold for the further heat transfer to

Once the complete air gap is formed between the cast and the mold, the gap will contain almost all kinds of gaseous except air that contradicts the air gap term. The sand mold which is used for the casting application, generates the mold gases which are often high in hydrogen, containing typically 50 percent which fills the air gap. The hydrogen gas thermal conductivity increases the heat transfer by 7 times more as the mold temperature rises to a high temperature of 500°C due to radiation. Therefore it is very essential to know or analyze the interface during the solidifica-

On comparing the green sand mold with dry sand mold the green sand mold expand homogeneously and release heat to the surrounding which leads to a lesser resistance for the heat flow whereas dry sand mold offers more resistance than the green sand mold. The high thermal conductivity die mold material has uniform

role in varying IHTC with various factors influencing solidification.

tion process as it is further discussed in the next section.

temperature variation and assumes homogeneous expansion.

*DOI: http://dx.doi.org/10.5772/intechopen.95371*

contact with the mold surface.

*Aluminum alloy (Al6061) solidification curve.*

Tc and Tm are the cast and mold surface temperatures at the interface in K or deg. C.

The dynamics of solidification of cast metal, mold temperature and the cast temperature can be clearly understood from the cooling curves shown in **Figure 2**. Once the molten metal fills the cavity the alloy cast reaches the maximum temperature. Generally the heat transfer analysis starts from this point onwards as the temperature drops from the liquid cast metal to the liquidus temperature (TL), the point at which the solidification begins and this freezing is called liquid cooling. The loss of superheat temperature of the cast metal after pouring is found due to

**Figure 1.** *Solidification curve for alloy.*

*Heat Transfer Studies on Solidification of Casting Process DOI: http://dx.doi.org/10.5772/intechopen.95371*

*Casting Processes and Modelling of Metallic Materials*

the interface is found using the surface heat flux as q (W/m2

to the heat flow also has different dimensions to this solidification. This resistance mainly depends on liquid cast metal, latent heat release, interface, solidified cast, the type of mold and the ambient conditions. General solidification of an alloy is discussed in the **Figure 1** and specific cooling curve for Al6061 is shown in **Figure 2**. Initially on pouring the liquid metal cast into the mold cavity the whole metal fluid flows and occupies the mold cavity, the liquid metal flowing with the velocity, mixes thoroughly and releases heat to the mold due to the very high temperature difference. Complete thermal contact is observed between the cast and the mold which causes the heat transfer to be purely conduction, where the resistance offered by this liquid metal is negligible since the entire fluid flow is the superheated cast metal. Once the cast metal reaches the liquidus point on cooling, the cast shrinks and releases latent heat and also a number of metal oxides are released which causes an air gap between the cast and the mold. Due to this air gap the heat transfer phenomenon now changes to a complex one where all modes of heat transfer can be observed simultaneously. This air gap is characterized with an Interfacial Heat transfer Coefficient (IHTC) "h" across the metal-mold interface. The rate of heat at

Tc and Tm are the cast and mold surface temperatures at the interface in K

The dynamics of solidification of cast metal, mold temperature and the cast temperature can be clearly understood from the cooling curves shown in **Figure 2**. Once the molten metal fills the cavity the alloy cast reaches the maximum temperature. Generally the heat transfer analysis starts from this point onwards as the temperature drops from the liquid cast metal to the liquidus temperature (TL), the point at which the solidification begins and this freezing is called liquid cooling. The loss of superheat temperature of the cast metal after pouring is found due to

) and given by the Eq. 1.

*q h Tc Tm* = − ( ) (1)

**42**

**Figure 1.**

*Solidification curve for alloy.*

or deg. C.

**Figure 2.** *Aluminum alloy (Al6061) solidification curve.*

the turbulence in the liquid metal. This rate of cooling is linear and a minimum amount of heat is transferred from the cast to the mold as it is having a complete contact with the mold surface.

As the solidification progresses with time it reaches the liquidus point at the same time where the mold temperature increases significantly to a maximum temperature. Further the solid skin forms on the outer cast surface, the metal shrinks and an air gap starts forming between the metal and the mold. When the cast solidifies further the air gap separates the two surfaces. This is a common phenomenon in most of the alloys. The rate of heat transfer from the cast to the mold is very high as it releases larger quantity of latent heat to the mold and the cast temperature gradually reaches a solidus (TS) temperature of the alloy. The air gap plays a significant role in varying IHTC with various factors influencing solidification.

Further solidification reduces the cast surface temperature, however the inner cast metal shrinks and it further releases the heat to the mold and there is rise in the mold temperature as shown in **Figure 2**. Thereafter further reduction in the cast temperature after the solidus point (Ts) was found as the third stage of solidification. The air gap size is further increased as the solidification time increase and its effects are felt till the end of solidification. However there is still a temperature difference between the cast and mold for the further heat transfer to continue.

Once the complete air gap is formed between the cast and the mold, the gap will contain almost all kinds of gaseous except air that contradicts the air gap term. The sand mold which is used for the casting application, generates the mold gases which are often high in hydrogen, containing typically 50 percent which fills the air gap. The hydrogen gas thermal conductivity increases the heat transfer by 7 times more as the mold temperature rises to a high temperature of 500°C due to radiation. Therefore it is very essential to know or analyze the interface during the solidification process as it is further discussed in the next section.

On comparing the green sand mold with dry sand mold the green sand mold expand homogeneously and release heat to the surrounding which leads to a lesser resistance for the heat flow whereas dry sand mold offers more resistance than the green sand mold. The high thermal conductivity die mold material has uniform temperature variation and assumes homogeneous expansion.