nickel composites

nickel composites

Renewable Energy 143 (2019) 1782e1790 Contents lists available at ScienceDirect Renewable Energy journal homepage: www.elsevier.com/locate/renene L...

2MB Sizes 0 Downloads 9 Views

Renewable Energy 143 (2019) 1782e1790

Contents lists available at ScienceDirect

Renewable Energy journal homepage: www.elsevier.com/locate/renene

Lipid accumulation and biodiesel quality of Chlorella pyrenoidosa under oxidative stress induced by nutrient regimes Lei Zhang, Nan Wang, Mei Yang, Ke Ding, Yong-Zhong Wang*, Danqun Huo, Changjun Hou Key Laboratory of Biorheological Science and Technology (Chongqing University), Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400030, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 5 August 2018 Received in revised form 6 April 2019 Accepted 17 May 2019 Available online 31 May 2019

In this study, effects of media compositions on the suitability of Chlorella pyrenoidosa lipids for biodiesel production were investigated. The results indicate that sulfur deficiency and excessive phosphorus resulted in significant inhibition of cell growth. Moreover, the cellular lipid content increased under nitrogen-, phosphorus- or sulfur-deficient conditions, with the maximal lipid content of 48.90% and energy conversion efficiency of 8.89% being reached under nitrogen deficient conditions. In all runs, C16 to C18 components accounted for more than 95% of the total fatty acids. In addition, several biodiesel quality parameters were calculated according to the fatty acid profile, and principal component analysis and cluster dendrogram analysis were used to evaluate the quality of the corresponding biodiesel. A high similarity of biodiesel quality under nitrogen-, phosphorus- or sulfur-deficient cultivation was observed, and the biodiesel quality was better than those obtained under other culture conditions. Additionally, a strong correlation between lipid accumulation and reactive oxygen species level was confirmed. Overall, the work indicates that the lipid content of C. pyrenoidosa increased under nitrogen-, phosphorus- or sulfur-deficient conditions and the biodiesel quality was also improved. © 2019 Published by Elsevier Ltd.

Keywords: Chlorella pyrenoidosa Nutrient stress Lipid productivity Energy conversion efficiency Biodiesel quality Reactive oxygen species

1. Introduction Microalgae can synthesize many high-value compounds including starch, protein, cellulose and lipid, which can be used to produce a variety of promising biofuels, such as bioethanol, food additives, biohydrogen and biodiesel [1]. Biodiesel, one of the most promising biofuels, is produced by the transesterification of intracellular lipids with methanol or ethanol [2e4]. Microalgal-based biodiesel has attracted worldwide interest because of its rapid growth rates, high photosynthetic rates and good adaptability [5,6]. Nevertheless, the high cost of microalgae cultivation, uncontrollability of growing conditions, and lack of microalgal strains with high lipid yield have limited large-scale commercial application of

Abbreviations: SV, Saponification value; IV, Iodine value; CN, Cetane number; DU, Degree of unsaturation; LCSF, Long-chain saturated factor; CFPP, Cold filter plugging point; SFAs, Saturated fatty acids; UFAs, Unsaturated fatty acids; MUFA, Monounsaturated fatty acid; PUFA, Polyunsaturated fatty acid; FAMEs, Fatty acid methyl esters; ROS, Reactive oxygen species; SOD, Superoxide dismutase; CAT, Catalase; APX, Ascorbate peroxidase; MDA, Malondialdehyde; PCA, Principal component analysis; ECE, Energy conversion efficiency. * Corresponding author. E-mail address: [email protected] (Y.-Z. Wang). https://doi.org/10.1016/j.renene.2019.05.081 0960-1481/© 2019 Published by Elsevier Ltd.

algal biodiesel [7]. The greatest challenge is improve microalgal lipid productivity [8]. Lipid productivity is determined by the growth rate and lipid content of microalgae. Lipid content can be enhanced under various stress conditions, but at the expense of biomass productivity [9,10]. To overcome this dilemma, a two-stage cultivation strategy consisting of biomass growth (heterotrophy) and lipid accumulation (autotrophy) has been adopted [11,12]. Firstly, the algal strain grows under heterotrophic conditions to obtain sufficient robust seeds, then these cells are transferred to autotrophic conditions to increase the lipid content, and in this stage stress is often applied [13]. The two-stage culture mode has been considered an effective method for improving cellular lipid accumulation of microalgae. In addition to lipid productivity, it is essential to assess the quality of biodiesel. Biodiesel quality which is related to fatty acid composition can be evaluated according to saponification value (SV), iodine value (IV), cetane number (CN), degree of unsaturation (DU), long-chain saturated factor (LCSF) and cold filter plugging point (CFPP) [14]. These parameters are calculated according to the content of saturated fatty acids (SFAs) and unsaturated fatty acids (UFAs). The SV represents the milligram number of KOH required to saponify 1 g of lipid, while the IV is related to the oxidative stability

L. Zhang et al. / Renewable Energy 143 (2019) 1782e1790

of biodiesel. CN is a descriptor of the ignition delay time and combustion quality which are determined by branching and chain length of fatty acid methyl esters (FAMEs) [15]. The LCSF is related to chain length and will affect the value of the CFPP which represents the flow performance of biodiesel at low temperatures [16]. High SV, CN, LCSF and CFPP are associated with high chain length, while a high degree of unsaturation corresponds to a large IV [17]. It is well known that reactive oxygen species (ROS), known to be messengers of various cellular responses that facilitate organism adaptation to stressful conditions [18,19], can accumulate in cells under stress conditions. ROS accumulation can be counteracted by cellular antioxidant defense systems, including various antioxidative enzymes such as superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX), as well as some nonenzymatic biomarkers, such as malondialdehyde (MDA) [18]. The cellular ROS status can be evaluated according to antioxidative enzyme activities and MDA levels. Although many stress strategies have been adopted to improve lipid productivity, such as high salinity, high light intensity, nitrogen deficiency and unusual temperatures [20,21], there is lack of systematic investigation on the mechanism of lipid accumulation and the interaction between ROS and cellular lipid content under these stress conditions. Certainly, the growth and lipid accumulation of microalgae are primarily influenced by components of the culture medium such as nitrogen, phosphorus and sulfur. In this study, biomass growth, lipid accumulation, ROS and fatty acid compositions of Chlorella pyrenoidosa were investigated under different nitrogen, phosphorus and sulfur conditions, and biodiesel quality, energy conversion efficiency and interaction between ROS and increased cellular lipid content were also evaluated. Furthermore, the produced lipids were classified using principal component analysis (PCA) and cluster dendrogram analysis according to the obtained biodiesel parameter values of C. pyrenoidosa. The purpose of this work is to study the biofuel properties of C. pyrenoidosa in response to different nutrient statuses and to reveal the correlation of intracellular ROS levels with lipid accumulation. 2. Materials and methods 2.1. Strains and culture medium C. pyrenoidosa FACHB-9 were purchased from the Freshwater Algae Culture Collection of the Institute of Hydrobiology, Chinese Academy of Science (Wuhan, China) and purified by our laboratory. The BG-11 is as full culture medium for algae cultivation. It mainly consists of 1.5 g/L NaNO3, 0.04 g/L K2HPO4, 0.075 g/L MgSO4$7H2O, 0.036 g/L CaCl2$2H2O, 0.006 g/L citric acid and 1 mL/L of trace metal stock solution. The trace metal stock solution is composed of 6 g/L FeC6H5O7$NH4OH, 1 g/L Na2EDTA, 1.81 g/L MnCl2$4H2O, 0.222 g/L ZnSO4$7H2O, 0.39 g/L Na2MoO4$2H2O, 0.08 g/L CuSO4$5H2O and 2.86 g/L H3BO3. In this work, the contents of N, P and S in the BG-11 were adjusted according to the experimental requirement. The initial nitrate concentration was set at 0 g/ L (-N), 1.5 g/L (1N), 3 g/L (2N) and 4.5 g/L (3N). The initial phosphorus concentration was fixed at 0 g/L (-P), 0.04 g/L (1P), 0.08 g/L (2P) and 0.12 g/L (3P), and initial sulfur concentration at 0 g/L (-S), 0.075 g/L (1S), 0.15 g/L (2S) and 0.225 g/L (3S). 2.2. Experimental conditions Seeds were cultured heterotrophically in a 1 L Erlenmeyer flask containing 500 mL BG11 medium with 10 g glucose in a shaker (150 rpm) at 30  C for 72 h. After centrifugation, the cell suspension was inoculated into air-lift column laboratory-scale flat

1783

photobioreactors (chamber volume, 0.22 m  0.22 m  0.025 m) filled with 1 L culture medium for autotrophic growth. The cultivation temperature was maintained at 30  C. The light intensity was set at 10 W/m2 and applied from one side and the cultures were aerated with filter-sterilized air at 0.5 vvm. Light intensity was measured using a solar power meter (FZ-A, Photoelectric Instrument Factory of Beijing Normal University, China). Carbon dioxide from the air was the only carbon source provided to the algal cells in autotrophic mode. 2.3. Analytical methods 2.3.1. Biomass and lipid determination Aliquots (20 mL) of culture were dried using a vacuum freeze dryer (FD-1A-50, China) until a constant weight was attained, after which the samples were weighed using an electronic analytical balance (EL204, China) to determine the dried cell weight (DCW). Lipid content was determined gravimetrically according to the Bligh and Dyer method, with some modification [22]. Briefly, freeze-dried algal powder was suspended in 3 mL of chloroformmethanol (2:1 v/v). The mixture was then agitated for 30 min in an orbital shaker at 150 rpm and room temperature, then centrifuged and the solvent phase was collected. This process was repeated three times. The total solvent phase was transferred into a preweighed centrifuge tube and dried at 60  C until constant weight. Lipid productivity (mg/L/d) was calculated based on the following equation [23]:

Lipid productivity ¼ 1000 ðCt  Lt  Ct0  Lt0 Þ=T

(1)

where T is the culture time (day), Ct (g/L) and Lt (%) are the biomass concentration and the lipid content, respectively, and C0 (g/L) and L0 (%) are the initial biomass concentration and the initial lipid content, respectively. 2.3.2. Fatty acid methyl ester (FAME) determination For the transmethylation reactions, 20 mg freeze-dried algae biomass, 2.5 mL methanol and 0.05 mL H2SO4 were mixed well, after which the mixture was heated at 80  C in a water bath for 2.5 h. Next, the mixture was cooled to room temperature, 1 mL nhexane and 1 mL saturated NaCl aqueous solution (5.43 mol/L) were added and the samples were allowed to stand for 1 h. The upper liquid layer was subsequently analyzed using a gas chromatograph (GC-210, China) equipped with a flame ionization detector (FID). The injector temperature was set at 290  C, while the column temperature and the FID temperature were 190  C and 290  C, respectively. The standard curve for identifying fatty acids was established according to the FAMEs standard mixture (Supelco, USA), with C17:0 fatty acid used as an internal standard (Sigma, USA). 2.3.3. Biodiesel properties based on FAMEs profile SV, IV, CN, DU, LCSF and CFPP were calculated using Eqs. (2)e(7) [14,16,24].

SV ¼ Sð560  NÞ=M IV ¼

X

ð254  DNÞ=M

CN ¼ 46:3 þ ð5458=SVÞ  ð0:225  IVÞ

(2) (3) (4)

where M is the fatty acid molecular mass, N is the fatty acid percentage and D is the number of double bonds.

1784

L. Zhang et al. / Renewable Energy 143 (2019) 1782e1790

DU ¼ MUFA þ 2  ðPUFAÞ

(5)

where MUFA is the monounsaturated fatty acids weight percentage and PUFA is the polyunsaturated fatty acids weight percentage.

LCSF ¼ ð0:1  C16 Þ þ ð0:5  C18 Þ þ ð1  C20 Þ þ ð2  C24 Þ (6) where C16 and C18 are the content of the saturated fatty acids of respective chain length.

CFPP ¼ ð3:1417  LCSFÞ  16:477

(7)

2.3.4. Energy conversion analysis Energy conversion efficiency (ECE) was calculated based on Eq. (8) [25]. The heat value (HV) of lipids was 36.3 kJ g1.

fluorescence spectrometer (L55, USA). To measure SOD, CAT, APX and MDA, fresh algal pellets were ground in a glass homogenizer. The obtained mixture was then centrifuged and the supernatant was analyzed with assay kits for SOD (Beyotime, China), CAT (Suzhou Comin Biotechnology Corporation, China), APX (Suzhou Comin Biotechnology Corporation, China), and MDA (Nanjing Jiancheng Bioengineering Institute, China). 2.4. Statistical analysis All data shown were the mean values of three independent replicates ± the SD. The values were compared using the LSD test with a P < 0.05. Additionally, correlation between ROS and lipid content was analyzed using Pearson's correlation analysis. Each biodiesel property parameter (SFA, MUFA, PUFA, SV, IV, CN, DU, LCSF and CFPP) was evaluated by PCA and cluster dendrogram analysis to classify the different culture conditions. All statistical analyses were conducted using SPSS 19.0 (SPSS, Chicago, IL, USA).

ECE ð%Þ ¼ HV of extracted lipids ðkJ Þ = input light energy ðkJ Þ (8)

3. Results and discussion 3.1. Growth performance

2.3.5. ROS, antioxidative enzymes, and MDA determination Reactive oxygen species were measured using a Reactive Oxygen Species Assay Kit (Beyotime, China). Briefly, the algal biomass was collected by centrifuging 5e10 mL solution at 5159 g for 3 min, after which 2 mL 1 mmol/L 20 -70 -dichlorofluorescindiacetate (DCFHDA) was added to the sediment and stirred for 30 min at room temperature. Next, the solution was centrifuged at 10,008 g for 3 min, and the sediment was collected. 2 mL of distilled water was added into the sediment, stirred for 5 min at room temperature and then centrifuged again. This procedure was repeated three times. Then, 2 mL distilled water was added into the sediment and the fluorescence intensity of the solution was measured using a

As shown in Fig. 1, the algae grew autotrophically for 5 days in the flat photobioreactors using light and atmospheric CO2. The initial inoculum biomass concentration was 0.55 g/L, and the biomass concentrations at the end point were 1.32 (full-medium), 1.05 (-N), 1.29 (2N), 1.21 (3N), 1.18 (-P), 1.09 (2P), 0.37 (3P), 0.72 (-S), 1.48 (2S) and 1.43 g/L (3S). The full medium culture demonstrated 26.07% (P < 0.001), 12.34% (P < 0.01) and 83.49% (P < 0.001) higher biomass concentrations than the eN, -P and eS cultures, respectively. As expected, macronutrient (N, P and S) deprivation in C. pyrenoidosa had a negative effect on algal growth. However, algal cells died within 72 h in the simultaneous absence of nitrogen, phosphorus and sulfur (data not shown); therefore, we did not

Fig. 1. Changes in biomass concentration (g/L) of C. pyrenoidosa under different culture conditions.

L. Zhang et al. / Renewable Energy 143 (2019) 1782e1790

1785

Fig. 2. Changes in lipids content of C. pyrenoidosa under different culture conditions.

investigate cell growth and lipid synthesis in this culture condition. In addition, high levels of nitrogen (2 N) and sulfur (2S) contributed to the increase in biomass to a threshold; however, further increases in initial nitrogen (3N) and sulfur (3S) concentrations to 4.5 g/L and 0.225/L, respectively, did not further increase cell growth. Especially, excessive phosphorus in the medium, such as 3P, caused significant inhibition (P < 0.001) of biomass growth, with the final biomass concentration decreasing to 0.37 g/L. This can be attributed to the inhibition of orthophosphate (Pi) to ADPglucose pyrophosphorylase (AGPase) which catalyzes starch synthesis in microalgae [26], affecting photosynthesis, respiration and other activities of cells. These results indicate that low nutrient concentrations resulted in limited biomass growth, while high nutrient concentrations caused growth inhibition. 3.2. Lipid accumulation and energy conversion analysis In this study, the maximal lipid content of 48.90% was obtained under eN, while eP and eS also resulted in increased lipid contents of 38.85% and 31.60% of the dry weight, respectively (Fig. 2). Nitrogen, phosphorus and sulfur are the most abundant elements in many macromolecules such as protein, ATP, chlorophyll, coenzyme factors and DNA [20]. Nitrogen-, phosphorus- or sulfur-deficiency will cause decreased protein synthesis and photophosphorylation, resulting in large amounts of carbon being channeled from proteins or other macromolecules into energy-storage molecules, such as starch and lipid [27]. As shown in Fig. 2, the contribution of phosphorus- or sulfur-deficiency to lipid production was not as significant as that of nitrogen deficiency. Lipid productivity is determined by the biomass concentration and lipid content of microalgae. As shown in Table 1, the mean lipid productivity increased significantly in the culture of 2N (P < 0.01), 3N (P < 0.01), 2S (P < 0.001) and 3S (P < 0.001), indicating that excessive nitrogen and sulfur were beneficial to the increase of the lipid productivity of C. pyrenoidosa. The high level of lipid

productivity can be attributed to the high biomass concentrations under 2N, 3N, 2S or 3S conditions. In addition, the maximal mean lipid productivity of 72.04 mg/L/d was achieved under eN, while the minimal value of 5.63 mg/L/d was obtained under 3P. Although the growth of algal biomass decreased under nitrogen deficient cultivation, the lipid productivity still increased due to the high intracellular lipid content. Moreover, 3P could improve the lipid accumulation of C. pyrenoidosa, but the corresponding lipid productivity was low due to the low biomass concentration. As shown in Table 1, eN, 2S and 3S are desirable for lipid production of C. pyrenoidosa. The energy conversion efficiency (ECE) in different microalgal culture modes is shown in Table 2. The effective area of the reactor irradiated by light was 0.0484 m2, the light intensity was 10 W/m2, and the algal cultivation time was 120 h. The input light energy can be calculated as follows: 10  0.0484  120  3600/1000 ¼ 209.1 kJ. ECE in different microalgal culture mode was calculated based on Eq. (8). The maximal ECE of 8.89% was obtained at eN, while the minimal value of 2.29% was obtained under 3P. These findings show that nitrogen deficiency facilitated the utilization of light energy during lipid synthesis in algal cells. 3.3. Fatty acid composition Fatty acid profiles of C. pyrenoidosa cultured under nutrient stress conditions at the end of the experiment are shown in Fig. 3. The main fatty acid components were palmitic acid (C16:0), palmitoleic acid (C16:1), stearic acid (C18:0), oleic acid (C18:1), linoleic acid (C18:2) and alpha-linolenic acid (C18:3), which accounted for more than 95% of the total fatty acids. The main UFAs were PUFAs (mainly C18:2 and C18:3) which accounted for about 47% of the total fatty acids. Interestingly, the accumulation of C18:0 and C18:3 increased when suffering nutritional stress, while C18:1 decreased under all treatments. This may be related to the transformation of microalgae cells from heterotrophic to autotrophic. In addition, the

1786

L. Zhang et al. / Renewable Energy 143 (2019) 1782e1790

Table 1 Cumulative lipid productivities (mg/L/d) of C. pyrenoidosa under different culture conditions. Cultivation mode

Day 1

Day 2

Day 3

Day 4

Day 5

Mean Productivity

Maximal Productivity

FM -N -P -S 2N 2P 2S 3N 3P 3S

49.78 47.58 27.99 21.54 67.50 39.57 69.32 68.09 9.98 83.49

45.25 64.14 38.18 16.29 55.60 44.77 63.31 60.02 11.40 71.48

44.44 84.59 41.27 14.22 52.63 47.36 67.83 53.54 9.88 66.68

46.40 83.37 58.45 21.48 50.37 44.84 67.28 51.66 12.43 63.72

41.75 80.50 69.40 23.57 50.18 44.33 63.55 46.73 4.43 56.90

45.53 72.04 47.06 19.42 55.26 44.17 66.26 56.01 5.63 68.45

49.78 84.59 69.40 23.57 67.50 47.36 69.32 68.09 12.43 83.49

3.4. Biodiesel quality analysis

Table 2 Energy conversion efficiency in different microalgal culture modes.

ECE (%)

FM

-N

-P

-S

2N

2P

2S

3N

3P

3S

5.75

8.89

7.92

3.95

6.26

5.75

7.42

5.96

2.29

6.84

fatty acid profiles varied among the various nutrient availabilities. SFAs and MUFAs are produced in the chloroplast, and they serve as substrates needed for PUFA biosynthesis. A lower ratio of (SFA þ MUFA)/PUFA was observed as nutrient availability increased, indicating a shift in SFA and MUFA synthesis towards PUFA production. However, the ratio increased under nitrogen-, phosphorus- or sulfur-deficient conditions (Table 3). The mechanism which nutrient deficiency leads to an increase of SFAs has still been unknown. However, this can be explained by the fact that the ratio of carbon to mineral substrate (N, P or S) increased under these nutrient-deficiency conditions, causing an increased CO2 availability being related to improving SFAs over UFAs [28]. In the present study, the only carbon source used in the autotrophic stage was CO2. Because CO2 remained constant, the present results suggest that the ratio of carbon to mineral substrate increased under nutrient deficient conditions, which enhanced SFA synthesis.

Table 3 shows the estimated biodiesel properties of C. pyrenoidosa grown under different culture conditions at the end of the experiment. The results revealed that PUFAs decreased while SFAs increased under nitrogen-, phosphorus- or sulfur-deficient conditions. The CN is an indicator of the ignition delay time and combustion quality. The higher CN value means the better ignition

Table 3 Estimated biodiesel properties of C. pyrenoidosa under different culture conditions.

FM -N -P -S 2N 2P 2S 3N 3P 3S

SFA

MUFA

PUFA

SV

IV

CN

DU

LCSF

CFPP

32.44 38.60 36.25 40.35 30.92 28.40 29.17 31.08 31.76 29.68

15.42 18.90 20.96 16.73 19.18 15.29 16.22 20.06 14.75 14.20

50.78 40.55 41.81 41.83 45.77 56.42 54.21 48.38 53.48 55.21

202.90 201.87 202.92 203.06 197.10 205.33 204.82 203.63 205.70 203.50

123.32 103.07 109.10 106.11 113.69 130.42 127.14 118.96 124.12 131.36

45.45 50.15 48.65 49.30 48.41 43.54 44.34 46.34 44.91 43.56

116.98 99.99 104.59 100.40 110.73 127.92 124.64 116.83 121.72 124.63

7.72 6.71 7.73 8.37 6.08 5.91 6.19 6.14 6.70 7.30

7.79 4.62 7.82 9.83 2.63 2.08 2.98 2.81 4.58 6.47

Fig. 3. Changes in fatty acid profiles of C. pyrenoidosa under different culture conditions.

L. Zhang et al. / Renewable Energy 143 (2019) 1782e1790

1787

delay time and combustion quality. The CN value is positively correlated with the SFA content [16]. Owing to the higher SFA content, a higher CN value was also obtained under nitrogen-, phosphorus- or sulfur-deficient conditions. According to global standards of biodiesel quality, the minimal CN value should be 47. In the present study, the CN value was over 47 when algae were cultivated under nutrient-deficient conditions. The DU and IV describe oxidation stability, with lower values being more likely to lead to production of biodiesel with better oxidation stability [29]. Therefore, C. pyrenoidosa under nutrient-deficient conditions with lower DU and IV values would provide better oxidation stability (Table 3). According to the European standard for biodiesel quality, the maximal IV was 120 g I2 100 g1. In the present study, IV decreased from 123.32 (full-medium) to 103.07 (-N), 109.10 (-P) and 106.11 (-S) I2 100 g1. The results show that the lipid content and CN value ascended while IV and DU descended under nitrogen, phosphorus- or sulfur-deficient conditions. It can be considered that the cellular lipid content, as well as ignition delay time, combustion quality and oxidation stability of the resulting biodiesel were simultaneously improved under nitrogen-, phosphorus- or sulfur-deficient conditions. Another important biodiesel quality parameter, CFPP, is usually employed to predict the flow performance of biodiesel at low temperatures. A low CFPP value expresses better low-temperature flow properties [17]. As indicated in Eq. (7), the CFPP is correlated with the LCSF. Here, low CFPP values were obtained at 2N, 3N, 2P or 2S. 3.5. Multivariate analysis The FAME profile can be used to distinguish algal strains or culture conditions. However, it cannot be used for the interpretation of biodiesel quality. The measured parameters (SFAs, MUFAs, PUFAs, SV, IV, CN, DU, LCSF and CFPP) shown in Table 3 could cover a wide range of biodiesel quality. Here, PCA and multivariate cluster analysis were used to compare the biodiesel quality of the products generated under different nutrient conditions. As shown in Fig. 4A, the first and second components covered 66.76% and 23.05% of the observed variations, respectively. Principal component analysis could explain 89.81% of the total variations, indicating that the prediction models for these parameters can be used for the interpretation of biodiesel quality. Multivariate cluster analysis can help understand the biodiesel quality of the products generated under different culture conditions. Fig. 4B presents a dendrogram of C. pyrenoidosa according to these parameters. There were four distinct sets of clusters, cluster 1 (-N, -P, eS); cluster 2 (2N, 3N); cluster 3 (2P, 2S); and cluster 4 (3P, 3S, full-medium), which were in agreement with the PCA plot. We found that the resemblance of biodiesel quality was high under nitrogen-, phosphorus- or sulfurdeficient conditions, indicating high similarity among the produced biodiesels. This can be ascribed to the fact that under nitrogen-, phosphorus- or sulfur-deficient conditions, cells tended to synthesize SFAs rather than PUFAs; thus, the biodiesel quality was obviously improved. In summary, biodiesel parameters such as SFAs, MUFAs, PUFAs, SV, IV, CN and DU were similar under different nutrient-deficient conditions, suggesting that a high similarity of biodiesel quality was obtained in the experiments. 3.6. ROS and antioxidant defense system analysis As well known, ROS can be accumulated when the cells suffer from stress conditions. Fig. 5 shows changes in ROS under different culture conditions. The level of ROS increased greatly in the eN (P < 0.001), 3P (P < 0.01) and eS (P < 0.01) cultures. Here, Pearson's correlation analysis was used to reveal the correlation between ROS and lipid content (Fig. 6A). The x axis shows the ROS fluorescence

Fig. 4. PCA results (a) and cluster dendogram analysis (b) comparing the estimated biodiesel properties of C. pyrenoidosa under different culture conditions.

intensity under all culture conditions versus the corresponding mean lipid content (mg.cell1). The Pearson correlation coefficient was 0.777, demonstrating a high correlation between ROS and lipid content. By plotting individual ROS fluorescence intensities against the corresponding mean lipid content (mg.cell1), a higher correlation coefficient under individual culture condition was found (Fig. 6B). These findings further confirmed that the ROS levels were positively related to the lipid contents under diverse conditions. Antioxidant defense systems, such as SOD, CAT, APX, and biomarker MDA, are often considered to be associated with increased cellular ROS caused by various stress conditions including high light, high salinity, and nutrient-deficiency. Superoxide dismutase, the first line of the defense system, neutralized ROS by catalyzing O 2 with two hydrogen ions to H2O2 and O2. CAT catalyzed H2O2 into water and oxygen, while APX catalyzed H2O2 and ascorbate into dehydroascorbate and water. As shown in Table 4, SOD activity was enhanced under eN (P < 0.01), -P (P < 0.01), eS (P > 0.05), and 3P (P < 0.01), corresponding to the high ROS content. Similarly, a significantly increased enzyme activity of APX and CAT was also observed under eN, -P, eS, and 3P conditions, which suggested that high intracellular ROS content existed under these conditions. The lipid peroxidation caused by ROS can also be validated by the non-enzymatic stress biomarker MDA which is produced during lipid peroxidation and reflects the

1788

L. Zhang et al. / Renewable Energy 143 (2019) 1782e1790

Fig. 5. Changes in reactive oxygen species of C. pyrenoidosa under different culture conditions.

Fig. 6. Relationship between the reactive oxygen species and corresponding mean lipid content (mg.cell1) of C. pyrenoidosa under different culture conditions (A), and under individual culture condition (B). Lines are linear fit with Pearson correlation coefficient (r).

L. Zhang et al. / Renewable Energy 143 (2019) 1782e1790 Table 4 SOD, CAT, and APX activities, and MDA contents in C. pyrenoidosa under different culture conditions.

FM -N 2N 3N -P 2P 3P -S 2S 3S

SOD (u mg1 pro)

CAT (nmol min1 g1 APX (nmol min1 g1 MDA (mM g1 FW) FW) FW)

19.83 ± 0.21 42.62 ± 4.81 20.54 ± 0.97 24.37 ± 4.96 32.82 ± 2.27 28.18 ± 6.68 32.43 ± 3.26 33.8 ± 7.34 20.82 ± 0.57 24.99 ± 3.70

140.79 ± 39.81 286.8 ± 23.20 157.87 ± 26.39 144.59 ± 12.46 231.39 ± 34.51 172.29 ± 11.31 158.28 ± 17.30 260.65 ± 4.59 126.23 ± 29.83 102.46 ± 8.69

69.25 ± 23.55 252.30 ± 3.40 76.54 ± 12.71 57.05 ± 14.55 203.20 ± 4.70 182.11 ± 5.83 233.17 ± 20.75 167.63 ± 6.58 62.85 ± 5.05 51.64 ± 2.07

0.68 ± 0.09 1.41 ± 0.15 0.68 ± 0.04 0.63 ± 0.04 1.05 ± 0.23 1.09 ± 0.11 1.13 ± 0.20 1.17 ± 0.05 0.69 ± 0.11 0.69 ± 0.08

cell damage caused by ROS [18,20]. In the present work, the MDA content increased significantly in the eN (P < 0.01), eS (P < 0.01), and 3P (P < 0.05) treatments, consistent with the ROS content. Furthermore, the biomass growth was found to be inversely proportional to those of SOD, CAT, APX activity and MDA content, demonstrating that the decreased growth may be caused by ROS toxicity (Fig. 1). Yilancioglu et al. (2014) reported that oxidative stress may act as a mediator for increased lipid accumulation, while Shi et al. (2017) reported that ROS may be the second messenger of many stress factors that regulate cellular responses to extracellular stress [30,31]. Recent evidence has indicated that ROS play an important role in autophagy, which causes cells to degrade macromolecules, resulting in large amounts of carbon being channeled from protein or other macromolecules into energy-storage molecules, such as lipid [27]. Suzuki et al. (2011) found that the occurrence of autophagy in eukaryotic cells was triggered by ROS accumulation [32]. Thus, our results further confirm that ROS may induce lipid accumulation through autophagy under stress conditions. 4. Conclusions In this work, the maximal lipid content of 48.90% was obtained under nitrogen deficiency, while phosphorus- or sulfur-deficiency also resulted in the increased lipid content of 38.85% and 31.60% of the dry weight, respectively, with high CN, low IV and DU. The results reveal that both the lipid content and the biodiesel quality were improved under nitrogen-, phosphorus- or sulfur-deficient conditions. In addition, it was found that the resemblance of biodiesel quality under nitrogen-, phosphorus- or sulfur-deficient conditions was high through PCA and cluster dendrogram analysis. The results presented herein will help improve algal biofuel production. Acknowledgements The authors would like to acknowledge the financial support of the National Natural Science Foundation of China (No. 51376200). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.renene.2019.05.081. References [1] L.G. Speranza, A. Ingram, G.A. Leeke, Assessment of algae biodiesel viability based on the area requirement in the European Union, United States and Brazil, Renew. Energy 78 (2015) 406e417. doi.org/10.1016/j.renene.2014.12. 059.

1789

[2] R.O. Dunn, Effects of minor constituents on cold flow properties and performance of biodiesel, Prog. Energ. Combust. 35 (2009) 481e489. doi.org/10. 1016/j.pecs.2009.07.002. [3] B. Likozar, J. Levec, Effect of process conditions on equilibrium, reaction kinetics and mass transfer for triglyceride transesterification to biodiesel: experimental and modeling based on fatty acid composition, Fuel Process. Technol. 122 (2014) 30e41, in: doi.org/10.1016/j.fuproc.2014.01.017. [4] B. Likozar, A. Pohar, J. Levec, Transesterification of oil to biodiesel in a continuous tubular reactor with static mixers: modelling reaction kinetics, mass transfer, scale-up and optimization considering fatty acid composition, Fuel Process. Technol. 142 (2016) 326e336, in: doi.org/10.1016/j.fuproc.2015. 10.035. [5] X.B. Tan, X.C. Zhao, L.B. Yang, J.Y. Liao, Y.Y. Zhou, Enhanced biomass and lipid production for cultivating Chlorella pyrenoidosa in anaerobically digested starch wastewater using various carbon sources and up-scaling culture outdoors, Biochem. Eng. J. 135 (2018) 105e114. doi.org/10.1016/j.bej.2018.04. 005. [6] G.B. Leite, K. Paranjape, P.C. Hallenbeck, Breakfast of champions: fast lipid accumulation by cultures of Chlorella and Scenedesmus induced by xylose, Algal Res. 16 (2016) 338e348. doi.org/10.1016/j.algal.2016.03.041. [7] P.J. Lammers, M. Huesemann, W. Boeing, D.B. Anderson, R.G. Arnold, X. Bai, J.K. Brown, Review of the cultivation program within the national alliance for advanced biofuels and bioproducts, Algal Res. 22 (2017) 166e186. doi.org/10. 1016/j.algal.2016.11.021. [8] N.S. Hosseini, H. Shang, J.A. Scott, Increasing microalgal lipid productivity for conversion into biodiesel by using a non-energy consuming light guide, Biochem. Eng. J. 134 (2018) 60e68. doi.org/10.1016/j.bej.2018.03.006. [9] B.G. Terigar, C.S. Theegala, Investigating the interdependence between cell density, biomass productivity, and lipid productivity to maximize biofuel feedstock production from outdoor microalgal cultures, Renew. Energy 64 (2014) 238e243. doi.org/10.1016/j.renene.2013.11.010. [10] C.G. Jerez, J.R. Malapascua, M. Sergejevov a, F.L. Figueroa, J. Masojídek, Effect of nutrient starvation under high irradiance on lipid and starch accumulation in Chlorella fusca (chlorophyta), Mar. Biotechnol. 18 (2015) 24e36. doi.org/10. 1007/s10126-015-9664-6. [11] F. Han, J. Huang, Y. Li, W. Wang, J. Wang, J. Fan, G. Shen, Enhancement of microalgal biomass and lipid productivities by a model of photoautotrophic culture with heterotrophic cells as seed, Bioresour. Technol. 118 (2012) 431e437. doi.org/10.1016/j.biortech.2012.05.066. [12] C.H. Ra, C.H. Kang, K.K. Na, C.G. Lee, S.K. Kim, Cultivation of four microalgae for biomass and oil production using a two-stage culture strategy with salt stress, Renew. Energy 80 (2015) 117e122. doi.org/10.1016/j.renene.2015.02.002. [13] G. Mujtaba, W. Choi, C.G. Lee, K. Lee, Lipid production by Chlorella vulgaris after a shift from nutrient-rich to nitrogen starvation conditions, Bioresour. Technol. 123 (2012) 279e283. doi.org/10.1016/j.biortech.2012.07.057. [14] A. Gismondi, F.D. Pippo, L. Bruno, S. Antonaroli, R. Congestri, Phosphorus removal coupled to bioenergy production by three cyanobacterial isolates in a biofilm dynamic growth system, Int. J. Phytoremediation 18 (2016) 869e876. doi.org/10.1080/15226514.2016.1156640.  Francisco, D.B. Neves, E. Jacob-Lopes, T.T. Franco, Microalgae as feedstock [15] E.C. for biodiesel production: carbon dioxide sequestration, lipid production and biofuel quality, J. Chem. Technol. Biotechnol. 85 (2010) 395e403. doi.org/10. 1002/jctb.2338. [16] M.J. Ramos, C.M. Fernandez, A. Casas, L. Rodriguez, A. Perez, Influence of fatty acid composition of raw materials on biodiesel properties, Bioresour. Technol. 100 (2009) 261e268. doi.org/10.1016/j.biortech.2008.06.039. [17] A.F. Talebi, S.K. Mohtashami, M. Tabatabaei, M. Tohidfar, A. Bagheri, M. Zeinalabedini, M.H. Hadavand, M. Mirzajanzadeh, S.S. Malekzadeh, S. Bakhtiari, Fatty acids profiling: a selective criterion for screening microalgae strains for biodiesel production, Algal Res. 2 (2013) 258e267. doi.org/10. 1016/j.algal.2013.04.003. [18] K. Chokshi, I. Pancha, A. Ghosh, S. Mishra, Salinity induced oxidative stress alters the physiological responses and improves the biofuel potential of green microalgae Acutodesmus dimorphus, Bioresour. Technol. 244 (2017) 1376e1383. doi.org/10.1016/j.biortech.2017.05.003. [19] K. Chokshi, I. Pancha, K. Trivedi, B. George, R. Maurya, A. Ghosh, S. Mishra, Biofuel potential of the newly isolated microalgae Acutodesmus dimorphus under temperature induced oxidative stress conditions, Bioresour. Technol. 180 (2015) 162e171. doi.org/10.1016/j.biortech.2014.12.102. [20] J. Fan, Y. Cui, M. Wan, W. Wang, Y. Li, Lipid accumulation and biosynthesis genes response of the oleaginous Chlorella pyrenoidosa under three nutrition stressors, Biotechnol. Biofuels 7 (2014) 17. https://doi.org/10.1186/17546834-7-17. [21] S. Srinuanpan, B. Cheirsilp, P. Prasertsan, Y. Kato, Y. Asano, Strategies to increase the potential use of oleaginous microalgae as biodiesel feedstocks: nutrient starvations and cost-effective harvesting process, Renew. Energy 122 (2018) 507e516, https://doi.org/10.1016/j.renene.2018.01.121. [22] E.G. Bligh, W.J. Dyer, A rapid method of total lipid extraction and purification, Can. J. Biochem. Physiol. 37 (1959) 911e917, https://doi.org/10.1139/o59-099. [23] W. Wang, F. Han, Y. Li, Y. Wu, J. Wang, R. Pan, G. Shen, Medium screening and optimization for photoautotrophic culture of Chlorella pyrenoidosa with high lipid productivity indoors and outdoors, Bioresour. Technol. 170 (2014) 395e403. doi.org/10.1016/j.biortech.2014.08.030. [24] L. Zhang, Y.Z. Wang, S.W. Wang, K. Ding, Effect of carbon dioxide on biomass and lipid production of Chlorella pyrenoidosa in a membrane bioreactor with

1790

[25]

[26]

[27]

[28]

L. Zhang et al. / Renewable Energy 143 (2019) 1782e1790 gas-liquid separation, Algal Res. 31 (2018) 70e76. doi.org/10.1016/j.algal. 2018.01.014. H.Y. Ren, B.F. Liu, F. Kong, L. Zhao, G.L. Xie, N.Q. Ren, Energy conversion analysis of microalgal lipid production under different culture modes, Bioresour. Technol. 166 (2014) 625e629. doi.org/10.1016/j.biortech.2014.05.106. S. Zhu, Y. Wang, J. Xu, C. Shang, Z. Wang, J. Xu, Z. Yuan, Luxury uptake of phosphorus changes the accumulation of starch and lipid in Chlorella sp. under nitrogen depletion, Bioresour. Technol. 198 (2015) 165e171. doi.org/ 10.1016/j.biortech.2015.08.142. S.A. Scott, M.P. Davey, J.S. Dennis, I. Horst, C.J. Howe, D.J. Leasmith, A.G. Smith, Biodiesel from algae: challenges and prospects, Curr. Opin. Biotechnol. 21 (2010) 277e286, https://doi.org/10.1016/j.copbio.2010.03.005. T. Fernandes, I. Fernandes, C.A.P. Andrade, N. Cordeiro, Changes in fatty acid biosynthesis in marine microalgae as a response to medium nutrient availability, Algal Res. 18 (2016) 314e320. doi.org/10.1016/j.algal.2016.07.005.

[29] G. Knothe, Improving biodiesel fuel properties by modifying fatty ester composition, Energy Environ. Sci. 2 (2009) 759e766. doi.org/10.1039/ B903941D. [30] K. Yilancioglu, M. Cokol, I. Pastirmaci, B. Erman, S. Cetiner, Oxidative stress is a mediator for increased lipid accumulation in a newly isolated Dunaliella salina strain, PLoS One 9 (2014), e91957, https://doi.org/10.1371/ journal.pone.0091957. [31] K. Shi, Z. Gao, T.Q. Shi, P. Song, L.J. Ren, H. Huang, X.J. Ji, Reactive oxygen species-mediated cellular stress response and lipid accumulation in oleaginous microorganisms: the state of the art and future perspectives, Front. Microbiol. 8 (2017) 793. 10.3389/fmicb.2017.00793. [32] N. Suzuki, G. Miller, J. Morales, V. Shulaev, M.A. Torres, R. Mittler, Respiratory burst oxidases: the engines of ROS signaling, Curr. Opin. Plant Biol. 14 (2011) 691e699, https://doi.org/10.1016/j.pbi.2011.07.014.