Evaluation of the impact of compositional differences in switchgrass genotypes on pyrolysis product yield

Industrial Crops and Products 74 (2015) 957–968

Contents lists available at ScienceDirect

Industrial Crops and Products journal homepage: www.elsevier.com/locate/indcrop

Evaluation of the impact of compositional differences in switchgrass genotypes on pyrolysis product yield Michelle J. Serapiglia a , Charles A. Mullen a , Akwasi A. Boateng a,∗ , Laura M. Cortese b , Stacy A. Bonos b , Lindsey Hoffman b a Eastern Regional Research Center, Agricultural Research Service, United States Department of Agriculture, 600 East Mermaid Lane, Wyndmoor, PA 19038, USA b Department of Plant Biology and Pathology, Rutgers University, 59 Dudley Road, New Brunswick, NJ 08901, USA

a r t i c l e

i n f o

Article history: Received 13 March 2015 Received in revised form 27 May 2015 Accepted 7 June 2015 Keywords: Biomass composition Bio-oil G × E interactions Mineral content Pyrolysis Switchgrass

a b s t r a c t As a dedicated bioenergy crop, switchgrass is a potential feedstock within the United States for biofuels production. It can be converted to energy dense bio-oil through fast pyrolysis. Biomass compositional differences can influence the conversion efficiency and bio-oil product yield and quality. In order to understand how improvements in bio-oil quality can be achieved by manipulation of biomass composition, differences in switchgrass biomass composition were evaluated for their impacts on fast pyrolysis product yield. Nine genotypes of switchgrass were grown on one prime and two marginal sites in New Jersey. The results show that biomass composition was affected by genotype, the environment, and genotype × environment interactions. Non-catalytic pyrolysis product yields were largely affected by genotypic differences. The most significant impacts on non-catalytic pyrolysis products were from cellulose content and K content in the biomass. It was found that non-methoxylated phenolics were mainly produced from the breakdown of levoglucosan in the presence of K. Mineral content in the biomass was highly variable by environment and soil variability across the sites examined. These differences in mineral content largely impacted product distribution of HZSM-5-catalyzed pyrolysis, showing that lower mineral uptake in the biomass was beneficial for the production of aromatic hydrocarbons. Significant genotypic and environmental effects among the pyrolysis products demonstrate that breeding for improvements in pyrolysis product yield is conceivable but that growing conditions and soil conditions must also be taken into consideration. Published by Elsevier B.V.

1. Introduction The development of commercially viable alternative transportation fuels is steadily becoming more important due to the exploitation of the finite supply of non-renewable fossil fuels, the contribution of fossil fuel emissions to greenhouse gases, and the increasing rate of energy consumption worldwide (McKendry, 2002). Switchgrass (Panicum virgatum L.), a native North American prairie grass, is a dedicated bioenergy crop with high-biomass yield suitable for use in renewable energy applications. As an alternative energy source, switchgrass has many beneficial characteristics including long-term productivity, low water and nutrient requirements for its cultivation, and has been shown to positively impact

∗ Corresponding author at: Lead Scientist/Chemical Engineer, Sustainable Biofuels and Coproducts Unit, Eastern Regional Research Center, Agricultural Research Service, USDA, 600 E. Mermaid Lane, Wyndmoor, PA 19038, USA. Fax: 215 233 6406. E-mail address: [email protected] (A.A. Boateng). http://dx.doi.org/10.1016/j.indcrop.2015.06.024 0926-6690/Published by Elsevier B.V.

soils and the environment by reducing erosion and providing wildlife habitats (Sanderson et al., 2006). In addition, switchgrass can be cultivated on soils not suitable for agricultural crop production while continuing to produce high levels of biomass on a yearly basis (Casler & Vogel, 2014). Taken together, switchgrass has the potential to be a major component in the sustainable production of biofuels in the future. While biochemical technologies have been in the forefront of lignocellulosic biofuels (typically cellulosic ethanol), fast pyrolysis produces direct liquid hydrocarbon fuel precursors (i.e. bio-oils) with greater potential to blend into the existing infrastructure. Fast pyrolysis is the rapid heating of biomass in the absence of oxygen and uses the entire biomass component to produce liquid fuel intermediates which can be directly upgraded to petroleum-like products. Currently, research is underway to develop techniques that will allow for the use of fast pyrolysis in the conversion of agricultural residues, forest residues, and dedicated energy crops, including short-rotation woody crops and perennial grasses (Carpenter et al., 2014); however, bio-oil is an unstable, com-

958

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

plex mixture of water, aromatics, phenolics, organic acids, and other highly oxygenated compounds derived from the cell wall polymers in the biomass, comprising hemicellulose, cellulose, and lignin. Owing to these shortcomings, upgrading bio-oil to a usable petroleum equivalent is necessary but has been costly, and has inhibited the scale-up of this technology to the commercial level. One potential cost mitigation strategy is a proposal to manipulate biomass composition and its biofuel quality through plant breeding and crop management strategies; these constitute an upstream approach to tailoring the biomass to improve the downstream conversion to bio-oil with the potential to reduce the cost of upgrading. Research has shown that there is a strong relationship between biomass composition and pyrolysis product yield (Hodgson et al., 2011; Jeffrey et al., 2014; Kelkar et al., 2014; Serapiglia et al., 2015). The polysaccharide content, lignin, and ash content within the biomass along with their interactions with one another can affect fast pyrolysis product distribution (Carpenter et al., 2014; Fahmi et al., 2008; Hosoya et al., 2007; Patwardhan et al., 2010). The inorganic metals found in the ash portion of the biomass, in particular the alkali metals, can impact overall pyrolysis liquid yield by increasing char production and promoting depolymerization/fragmentation reactions forming lower molecular weight oxygenates (Carpenter et al., 2014). Alkali metals can also cause operational difficulties or change product yields by causing dehydration reactions (McKendry, 2002). A higher amount of alkali metals, such as potassium, has been reported to increase the yield of char and gas, while reducing the amount of bio-oil (Kelkar et al., 2014). Biomass composition is complex and is controlled by a number of factors, including genetics, various environmental conditions, genotypic × environment (G × E) interactions, time of harvest, fertilizer treatments, and other crop management strategies. Many studies have focused on identifying and understanding the effects of environment on composition among various switchgrass populations (Casler, 2012; Casler & Boe, 2003; Cassida et al., 2005; Hopkins et al., 1995a; Vogel & Jung, 2001); however, it is unclear how this variation impacts conversion to bio-oil produced by fast pyrolysis. In particular, a better understanding of G × E interactions on bio-oil end products is needed for fast pyrolysis conversion before the commercialization of the technology can be realized. The current study evaluated biomass compositional traits and condensable pyrolysis vapors from specific clones of nine genotypes of switchgrass grown in three different environments. Non-catalytic and HZSM-5 catalyzed fast pyrolysis (CFP) product yields were determined through pyrolysis – gas chromatography/mass spectroscopy (py–GC/MS). Relationships between cell wall composition, ash and mineral content in the biomass, and fast pyrolysis products were evaluated. The major objectives of this study were to (i) identify biomass traits impacted by genotype, environment, and G × E interactions and (ii) determine the effect of variation in biomass traits on product distribution from both non-catalytic fast pyrolysis products and HZSM-5 catalyzed fast pyrolysis.

Table 1 Cultivar name and clone number, ecotype designation, and ploidy level of switchgrass evaluated in this study. Cultivar*

Ecotype

Ploidy

Alamo 1 Alamo 4 Cimmaron 2 Cimmaron 4 Timber 3 Northern Southern Lowland (NSL) 2 Cave-In-Rock 3 Carthage 3 High Tide 4

Lowland Lowland Lowland Lowland Lowland Lowland Upland Upland Upland

4× 4× 4× 4× 4× N/A 8× 8× N/A

N/A - Information not available. * Numbers after cultivar name indicate distinct genotypes of the cultivar.

2. Materials and methods 2.1. Trial establishment and source material Plant material consisted of nine clones of seven switchgrass cultivars representing both upland and lowland ecotypes (Table 1). The clones were selected based on previous research conducted by Cortese and Bonos (2013). Design and establishment of these field trials, along with yield data have been previously reported in Cortese (2014). Briefly, clones were vegetatively replicated and planted at three locations in Central New Jersey in early summer of 2009. The site located in Freehold, NJ is classified as class II prime land (USDA) with a Freehold sandy loam soil (fine-loamy, mixed, active, mesic Typic Hapludults). The site at Somerset, NJ is designated as class IV marginal land with a Klinesville Channery loam soil (loamy-skeletal, mixed, active mesic Lithic Dystrudepts), and the site at Jackson, NJ is class V marginal land with an Evesboro sandy soil (mesic, coated Lamellic Quartzipsamments). Soil analyses were completed for each site prior to planting (Table 2). At each location, individual plants were spaced 0.9 m apart with 10 plants per row in a randomized complete block design with six replications. All clones received 56.1 Kg N ha−1 upon transplant to the field and in June 2010. Individual plants were hand harvested in the spring of 2010 following an overwintering period in 2009. Following harvest, samples were dried at 43 ◦ C for 14 days and ground through a 1 mm screen on a Wiley mill (Thomas-Wiley Mill Co., Philadelphia, PA). Samples were stored in sealed plastic bags and kept at 15.6 ◦ C and relative humidity levels of 40% or less until analyzed. To ensure samples were moisture free, samples were dried at 105 ◦ C for four hours prior to all analyses. 2.2. Biomass compositional analysis All samples were analyzed for neutral detergent fiber (NDF) and acid detergent fiber (ADF) utilizing the filter bag system method described for use with an Ankom 2000 Fiber Analyzer (ANKOM Technology Corp., Fairport, NY) (Komarek, 1993) with the following modifications: samples were processed in a flask for 75 min

Table 2 Soil characteristics of three New Jersey locations where switchgrass clones were evaluated for lignocellulosic traits in 2009 and 2010. Location

pH

Pa

Ka

Mga

Caa

ECb

Sandc

Siltc

Clayc

Gravelc

SOMc,d

Freehold Somerset Jacksone

6.31 5.35 5.50

495 40 504

253 297 89

363 878 46

1234 2064 788

0.08 0.09 –

76 36 –

17 43 –

7 21 –

0.40 14.04 –

1.10 1.82 –

a b c d e

Kg ha−1 Electrical conductivity (mmho cm−1 ) (%) Soil organic matter Not all soil characteristics were measured

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

959

under constant agitation at 97–100 ◦ C for both NDF and ADF procedures. Acid detergent lignin (ADL) was determined using the procedure described by the ANKOM ADL procedure. Samples were run in duplicate and mean values were used to calculate percent cellulose and hemicellulose contents as follows: cellulose was calculated as the difference between ADF and ADL, and hemicellulose was calculated as the difference between NDF and ADF (Hopkins et al., 1995b). Percent lignin was reported as ADL. Ash content was determined by ashing 0.5 g of oven-dry biomass at 600 ◦ C in preweighed crucibles in a Barnstead Thermolyne Muffle Furnace for four hours.

studentized range test. Pearson correlations were performed using PROC CORR to identify any significant correlations among all variables. Principle component analysis (PCA) was performed using PROC PRINCOMP on two sets of data, the biomass traits alone examined across all genotypes at all locations, and non-catalytic pyrolysis products from each genotype averaged across the locations. A mixed analysis of co-variance (ANCOVA) was performed to identify the relationship between the response variables, the non-methoxylated phenolics, and the covariates, cellulose and K content, in the biomass. The model was unstructured and the subject was genotype.

2.3. Non-catalytic and catalytic pyrolysis by Py–GC/MS

3. Results

Micropyrolysis of all biomass samples was performed according to Mihalcik et al. (2011) using a Frontier Lab (Koriyama, Japan) Double-Shot micro pyrolyzer PY-3030iD with the Frontier Lab Auto-Shot Sampler AS-1020E attached to a gas chromatograph, Shimadzu GC-2010 (Columbia, MD) coupled with a Shimadzu GCMS-QP2010S mass spectrometer. Pyrolysis was performed on 350–450 ␮g of sample at 500 ◦ C.

3.1. Biomass composition and trace metal analysis

2.4. Trace metal analysis Acid digestions were performed on 300 mg of oven-dried biomass using a modified closed tube method according to Wheal et al. (2011). In short, 2 mL of concentrated nitric acid (70%) and 0.5 mL of 30% hydrogen peroxide were added to each sample tube, capped and well mixed using a vortexer. Samples were then stored at room temperature overnight. Next, the samples were vortexed again and then placed in a digestion block and heated to 80 ◦ C for 30 min, followed by 125 ◦ C for 120 min. After digestion, samples were cooled and diluted with double-distilled water to a volume of 25 mL. After the settling of particulates, 10 mL of sample was transferred to autosampler tubes for analysis by inductively-coupled plasma optical emission spectroscopy (ICP-OES) on a Thermo Scientific iCAP 6300 (Waltham, MA). Quantitation of Al, Ca, Cu, Fe, K, Mg, Na, P, S, and Zn in the biomass was determined by external standards using the instrument software. 2.5. Statistical analysis All measured biomass traits, mineral analysis, and pyrolysis data were analyzed in SAS ® version 9.3 (SAS Institute Inc., Cary, NC) at a significance of 0.05. Kolmogorov D and Shapiro-Wilk’s statistic W were used to check for normality of distribution for each trait using PROC UNIVARIATE. No transformation of the data was required. A two-way mixed model analysis of variance (ANOVA) was performed using PROC MIXED to detect the effects of genotype, environment, and the interaction between genotype and environment using restricted maximum likelihood (REML) as the method. The blocks at each location were random effects nested within genotype and environment. The model for this analysis is as follows:  ijk =  + ˛i + ˇj + (˛ˇ)ij + c k(ij) + ijk where  ijk is the measurement in the kth block, for the ith genotype, at the jth location, ␮ is the overall mean, ˛i is the effect of genotype (a fixed effect), ˇj is the effect of environment (a fixed effect), (˛ˇ)ij is the interaction between genotype and environment, ck(ij) is the effect of block nested within genotype and environment (a random effect), and ijk is the experimental error. Least-squares means estimates were obtained for genotype and environment and statistical significance (at P < 0.05) of differences between least-squares means of genotypes and environment were tested using Tukey’s

Biomass composition and mineral content were significantly impacted by both differences by genotype and by growing location. Cellulose and lignin content were significantly different by genotype but not by location (Fig. 1A and B, respectively). Timber 3 had the highest cellulose content averaging 50.5% and Cave-In-Rock 3 had the highest lignin content with an average of 14.1% compared to all other genotypes analyzed in this study. The upland ecotypes, Cave-In-Rock 3, Carthage 3, and High Tide 4 had lower cellulose content and greater lignin content compared to the lowland ecotypes. Hemicellulose content was significantly different by location but not by genotype, with genotypes grown at Freehold and Jackson having the greatest hemicellulose content (Fig. 1C). Ash content in the biomass was significantly different by both location and genotype with an interaction between the two (Fig. 1D). The genotype Cave-In-Rock 3 had the greatest ash content at the Somerset site, while Timber 3 and Cimmaron 2 had the lowest ash content. Those grown at Somerset had the greatest ash content while those at the Jackson had the lowest ash content. Analysis of the mineral content in the biomass by ICP-OES determined that all ten nutrients measured were significantly different by location and genotype (Fig. 2). Out of the 10, six had an interaction between location and genotype (Al, Cu, Fe, K, Mg, P, and Zn). The nutrient with the greatest concentration in the biomass was Ca with a concentration of 2858 ppm found in Carthage 3 grown at Somerset (Fig. 2A). For most genotypes, the macronutrients (Ca, Mg, S, K, and P) were significantly lower for those grown at Jackson than those at Freehold or Somerset (Fig. 2A–E, respectively). For Al, Fe, and Cu, genotypes at Somerset had greater concentrations than those at Freehold and Jackson and the greatest amount was found in Cave-In-Rock 3 (Fig. 2G, F, and J, respectively. The concentration of Na in the biomass was not significantly different across the locations, except in Alamo1 and Cimarron 2 where those on the Jackson location had the lowest concentration (Fig. 2H). Concentrations of Zn were the lowest in genotypes grown on the Freehold location, compared to Jackson and Somerset (Fig. 2I). Analysis of the biomass traits by PCA showed all the genotypes grouping by their location of growth (Fig. 3) with PC1 accounting for 50% of the variation. The most significant variable contributing to PC1 was total ash content, which was followed by several macronutrients. 3.2. Non-catalytic and catalytic pyrolysis products Of the fast pyrolysis products quantified, 12 were significantly different by both environment and genotype, two (hydroxymethylfurfural (HMF) and levoglucosan) were significantly different by environment only with no effect by genotype, and 14 were significantly different by genotype only (Supplemental Table 1). The non-methoxylated phenolics were only affected by differences due to genotype with no effect by environment. Of the non-condensable

960

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

Fig. 1. Compositional traits for switchgrass genotypes; % Cellulose (A), %Lignin (B), %Hemicellulose (C), and %Ash (D) content for switchgrass genotypes. Bars represent the mean and standard error for three field replicates.

gas (NCG) products produced from fast pyrolysis, CO2 was the only gaseous product significantly different for environment and genotype with an interaction. The polysaccharide derived products, levoglucosan, HMF, and fufural had significantly greater yield from genotypes grown at Jackson compared to those at Freehold and Somerset (Fig. 4). The majority of the methoxylated phenolics (syringol, vanillin, acetovanillin, guaiacylacetone, syringaldehyde, and acetosyringone) had low yield from genotypes at the Somerset location and greater yield from those at Jackson (Fig. 4). The PCA of the fast pyrolysis products by genotype showed that Cave-In-Rock 3 and Carthage 3 produced the greatest amounts of syringols and guaiacols (Fig. 5) and Cimarron 4 produced the greatest amounts of non-methoxylated phenolics, furans, and ketones regardless of environment. The analysis of co-variance showed a significant relationship between phenol and K and cellulose content in the biomass (Fig. 6). Other non-methoxylated phenolics were found to have a significant relationship with K and cellulose as well (data not shown). Of the products from HZSM-5 catalyzed pyrolysis, 10 were significantly different by both environment and genotype, 12 were significantly different by environment only, and two were significantly different by genotype only (isoeugenol and vanillin; Supplemental Table 1). Aromatic hydrocarbons were not significantly different by genotype; they were only affected by environment, with the highest production of aromatic hydrocarbons derived from genotypes grown at Jackson and Freehold (Fig. 7A). The majority of the phenolics were significantly different by environment with the greatest production from genotypes at the Jackson location (Fig. 7B). Sugar derived products as well as the syringols and guaiacols decreased in yield from non-catalytic to catalytic (Fig. 8A); however, aromatic hydrocarbons and nonmethoxylated phenolics increased in yield from non-catalytic to catalytic (Fig. 8B). For the non-condensable gases, CO2 , CO, ethylene and propene all had greater yield from catalytic pyrolysis (Fig. 8C).

3.3. Correlations among the biomass traits Very few non-catalytic pyrolysis products correlated with any of the cell wall compositional traits (Table 3). Among those that did, the methoxylated phenols, guaiacol, creosol, isoeugenol, acetovanillin, and guaiacylacetone all had an expected positive correlation with lignin content. Somewhat unexpectedly, levoglucosan did not have a significant correlation with cellulose or hemicellulose content. Although only produced in small amounts from the non-catalytic process, both toluene and benzene production were positively correlated with ash content and many of the minerals individually. Levoglucosan yield was negatively correlated with ash content (r = −0.549; p = 0.003) and many of the minerals, the strongest of which was with K (r = −0.779; p < 0.0001). Acetol, propanoic acid, and all of the non-methoxylated phenols were positively correlated with K content. Most of the quantified products from HZSM-5 catalyzed fast pyrolysis were negatively correlated with ash, K content and the majority of the other minerals measured except for methane production and trimethylbenzene yield (Table 4). Methane production was positively correlated with ash content and all minerals. Trimethylbenzene production was positively correlated with ash and K content. Carbon monoxide production and the yields of benzene, acetic acid, the non-methoxylated phenols, and the naphthalenes were all positively correlated with levoglucosan content from non-catalytic pyrolysis. Methane and trimethylbenzene were negatively correlated with non-catalytic levoglucosan. 4. Discussion Environmental effects on the biomass composition of bioenergy crops are of significant importance for the development of renewable energy technologies. The results of this study demonstrated that the growing location for switchgrass not only affects biomass

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

961

Fig. 2. Mineral content in nine genotypes from all three growing locations. (A) Calcium, (B) Magnesium, (C) Sulfur, (D) Potassium, (E) Phosphorus, (F) Iron, (G) Aluminum, (H) Sodium, (I) Zinc, (J) Copper. All bars represent the mean and standard error of three replicates.

962

Table 3 Pearson correlation coefficients for non-catalytic fast pyrolysis products with significant correlations with switchgrass biomass compositional traits (bold indicates significance at 0.05). Hemicellulose (%)

Carbon monoxide Methane Carbon dioxide* Ethylene Benzene Acetic Acid Propanoic Acid Acetol Furfural HMF Levoglucosan Toluene

0.170 0.245 −0.089 −0.142 −0.356 0.192 −0.207 −0.170 −0.017 0.298 0.370 −0.490

Lignin (%)

Ash (%)

Al (ppm)

0.106 −0.205 0.081 0.149 −0.273 0.402 0.122 0.239 0.204 0.326 0.364 −0.313

−0.079 0.232 −0.181 0.118 0.160 −0.425 −0.125 −0.139 −0.153 −0.296 −0.289 0.175

−0.499 −0.303 −0.006 −0.199 0.437 −0.382 0.170 0.118 −0.126 −0.415 −0.549 0.630

−0.328 −0.247 −0.034 −0.009 0.294 −0.384 0.074 0.044 −0.034 −0.323 −0.353 0.434

−0.390 −0.280 −0.104 −0.002 0.350 −0.487 0.044 0.141 −0.450 −0.524 −0.713 0.620

Non-methoxylated phenols −0.111 Phenol −0.145 o-Cresol p-Cresol −0.079 −0.087 m-Cresol 0.036 Ethylphenol

0.046 0.077 −0.017 −0.243 0.184

−0.175 −0.034 −0.275 0.270 −0.360

0.199 0.269 0.272 0.323 0.037

0.055 0.169 0.032 0.204 −0.160

Guaiacols Guaiacol Creosol Isoeugenol Vanillin Acetovanillin Guaiacylacetone

0.067 0.152 0.176 0.133 0.182 0.094

−0.287 −0.416 −0.298 −0.266 −0.282 −0.280

0.524 0.607 0.618 0.343 0.508 0.452

0.145 0.085 0.059 -0.031 -0.073 -0.008

Syringols Syringol Syringaldehyde Acetosyringone

0.182 0.279 0.372

0.389 0.444 0.321

−0.328 −0.262 −0.201

−0.246 −0.435 −0.382

*

Cellulose (%)

Ca (ppm)

Fe (ppm)

K (ppm)

Mg (ppm)

Na (ppm)

−0.266 −0.239 −0.030 0.008 0.310 −0.328 0.068 −0.022 −0.031 −0.313 −0.333 0.459

−0.331 −0.237 −0.037 −0.008 0.307 −0.407 0.075 0.037 −0.033 −0.325 −0.369 0.442

−0.487 −0.277 −0.012 −0.369 0.502 −0.140 0.417 0.461 −0.292 −0.512 −0.779 0.790

−0.303 −0.227 0.096 −0.372 0.202 −0.214 0.118 0.288 −0.449 −0.530 −0.535 0.501

−0.311 −0.422 −0.007 −0.410 0.084 0.087 0.169 0.246 −0.193 −0.235 −0.246 0.244

−0.418 −0.133 −0.053 −0.181 0.471 −0.403 0.127 0.078 −0.390 −0.491 −0.682 0.734

−0.434 −0.234 0.033 −0.116 0.466 −0.395 0.195 0.236 −0.401 −0.579 −0.778 0.803

−0.015 −0.058 −0.056 0.236 0.134 −0.195 −0.152 −0.357 0.129 0.028 0.058 0.111

0.063 0.083 0.163 0.343 −0.054

0.052 0.044 0.052 0.077 −0.122

0.052 0.162 0.032 0.223 −0.172

0.551 0.553 0.545 0.606 0.429

0.176 0.281 0.215 0.265 0.176

0.170 0.225 0.198 0.061 0.221

0.382 0.290 0.474 0.569 0.248

0.263 0.263 0.329 0.413 0.132

−0.157 −0.319 −0.068 −0.243 −0.212

0.142 0.118 0.116 -0.026 0.008 0.053

−0.075 −0.143 −0.033 −0.248 −0.280 −0.329

−0.100 −0.096 −0.145 −0.139 −0.153 −0.050

0.159 0.145 0.137 0.000 0.037 0.064

−0.018 −0.314 −0.242 −0.296 −0.470 −0.283

−0.133 −0.371 −0.136 −0.422 −0.478 −0.327

−0.185 −0.412 −0.238 −0.376 −0.486 −0.224

−0.162 −0.228 −0.255 −0.314 −0.395 −0.356

−0.115 −0.261 −0.199 −0.334 −0.419 −0.342

−0.240 0.007 −0.190 0.010 0.051 0.031

−0.358 −0.463 −0.383

−0.298 −0.564 −0.521

−0.381 −0.457 −0.485

−0.386 −0.496 −0.397

0.201 −0.160 −0.268

0.127 −0.080 −0.172

0.237 0.127 −0.106

−0.114 −0.433 −0.427

−0.138 −0.411 −0.498

−0.444 −0.329 −0.346

Carbon dioxide was included even though there were no significant correlations with any of the biomass traits.

Cu (ppm)

P (ppm)

S (ppm)

Zn (ppm)

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

Pyrolysis Product

Table 4 Pearson correlation coefficients for catalytic fast pyrolysis products with significant correlations with switchgrass biomass traits and levoglucosan yields from non-catalytic pyrolysis (bold indicates significance at 0.05). Catalytic pyrolysis product

Hemicellulose (%)

Cellulose (%)

Lignin (%)

%Ash

Al (ppm)

Ca (ppm)

Cu (ppm)

Fe (ppm)

K (ppm)

Mg (ppm)

Na (ppm)

P (ppm)

S (ppm)

Zn (ppm)

Levoglucosan (non-catalytic)

0.332 −0.652 0.151 0.120 0.087 0.479 0.290 0.426

0.304 −0.319 0.063 0.003 −0.098 0.037 0.158 0.049

−0.069 0.262 −0.101 −0.096 0.062 −0.174 −0.228 −0.053

−0.439 0.956 −0.266 −0.241 −0.132 −0.682 −0.412 −0.561

−0.260 0.882 −0.182 −0.172 −0.043 −0.585 −0.434 −0.461

−0.495 0.751 −0.263 −0.302 −0.031 −0.519 −0.079 −0.384

−0.339 0.741 −0.167 −0.105 −0.076 −0.392 −0.436 −0.443

−0.257 0.890 −0.170 −0.157 −0.022 −0.577 −0.438 −0.454

−0.647 0.530 −0.402 −0.375 −0.150 −0.426 −0.036 −0.470

−0.661 0.404 −0.404 −0.373 −0.301 −0.278 0.207 −0.221

−0.570 0.094 −0.541 −0.498 −0.426 −0.060 0.108 −0.230

−0.533 0.528 −0.172 −0.154 0.135 −0.394 −0.165 −0.402

−0.661 0.737 −0.277 −0.274 −0.074 −0.565 −0.142 −0.467

0.078 0.354 0.193 0.200 0.121 −0.101 −0.464 −0.215

0.622 −0.493 0.335 0.370 0.088 0.451 −0.050 0.281

Aromatics Benzene Toluene Ethylbenzene p-Xylene Trimethylbenzene Naphthalene Methylnaphthalene

0.496 0.441 0.341 0.456 −0.049 0.433 0.405

−0.097 −0.141 −0.208 −0.211 −0.246 0.148 0.166

0.277 0.401 0.467 0.457 0.342 0.047 0.101

−0.324 −0.249 −0.062 −0.152 0.406 −0.470 −0.447

−0.156 −0.120 0.005 −0.083 0.328 −0.257 −0.226

−0.436 −0.374 −0.122 −0.194 0.324 −0.677 −0.647

−0.301 −0.324 −0.213 −0.344 0.140 −0.321 −0.328

−0.139 −0.105 0.026 −0.067 0.347 −0.251 −0.220

−0.608 −0.479 −0.195 −0.291 0.437 −0.727 −0.670

−0.726 −0.604 −0.365 −0.416 0.149 −0.869 −0.855

−0.543 −0.457 −0.308 −0.385 −0.008 −0.536 −0.547

−0.354 −0.355 −0.106 −0.204 0.377 −0.551 −0.556

−0.601 −0.528 −0.245 −0.366 0.304 −0.794 −0.767

0.006 −0.168 −0.249 −0.318 −0.173 0.084 0.020

0.499 0.292 0.049 0.089 −0.530 0.685 0.594

Non-methoxylated phenols Phenol o-Cresol p-Cresol m-Cresol Dimethylphenol Ethylphenol

0.305 0.305 0.474 0.407 0.415 0.374

0.148 0.119 0.126 0.032 −0.057 0.238

−0.188 −0.008 −0.191 0.041 0.218 −0.351

−0.426 -0.389 −0.606 −0.426 −0.440 −0.396

−0.349 −0.241 −0.561 −0.297 −0.296 −0.427

−0.611 −0.580 −0.641 −0.586 −0.526 −0.476

−0.195 −0.220 −0.412 −0.287 −0.337 −0.334

−0.352 −0.239 −0.555 −0.290 −0.283 −0.438

−0.428 −0.566 −0.503 −0.620 −0.661 −0.194

−0.566 −0.666 −0.507 −0.699 −0.710 −0.348

−0.217 −0.346 −0.208 −0.389 −0.466 −0.077

−0.270 −0.432 −0.427 −0.461 −0.492 −0.120

−0.481 −0.587 −0.613 −0.650 −0.639 −0.364

0.201 0.138 0.017 0.137 0.070 −0.025

0.599 0.615 0.569 0.597 0.558 0.353

Guaiacols Guaiacol Creosol Isoeugenol Vanillin Acetovanillin Guaiacylacetone

0.208 0.160 0.314 0.237 0.121 0.198

−0.549 −0.626 −0.578 −0.517 −0.537 −0.548

0.608 0.671 0.483 0.498 0.648 0.666

−0.069 0.001 0.050 0.089 −0.054 −0.125 −0.172 −0.163 −0.054 0.012 −0.076 −0.008

−0.171 0.003 0.047 −0.204 −0.081 −0.136

−0.119 −0.048 −0.204 −0.189 −0.093 −0.142

0.028 0.117 −0.098 −0.130 0.044 0.018

−0.447 −0.352 −0.302 −0.384 −0.367 −0.406

−0.372 −0.280 −0.100 −0.212 −0.266 −0.316

−0.413 −0.377 −0.199 −0.298 −0.376 −0.369

−0.275 −0.133 −0.130 −0.266 −0.277 −0.279

−0.339 −0.181 −0.187 −0.316 −0.215 −0.291

0.068 0.094 −0.033 −0.007 0.027 0.007

0.172 0.018 −0.070 0.049 −0.016 0.078

Syringols Syringol Syringaldehyde Acetosyringone

0.474 0.121 0.445

0.173 0.240 0.142

−0.119 −0.110 −0.126

−0.542 −0.634 −0.438 −0.433 −0.584 −0.575

−0.491 −0.531 −0.560

−0.607 −0.251 −0.573

−0.656 −0.454 −0.586

−0.209 −0.226 −0.347

−0.233 −0.217 −0.185

0.035 0.085 −0.084

−0.309 −0.448 −0.494

−0.399 −0.342 −0.521

−0.341 0.010 −0.339

0.181 0.230 0.336

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

Carbon monoxide Methane Carbon dioxide Propene Propane Acetic Acid Acetol Levoglucosan

963

964

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

Fig. 3. Principle component analysis of biomass compositional traits for all nine switchgrass genotypes across all three locations. Principle component one (PC1) explains 50% of the variation in the data set, while PC2 explains only 17%.

compositional traits and mineral content but that these changes in the biomass can impact the conversion of the biomass to fast pyrolysis products. Cell wall composition among the genotypes was impacted to a lesser extent by location of growth than were ash and mineral content, with hemicellulose being the only cell wall trait affected by the environment (Supplemental Table 1). Biomass from Freehold and Somerset had similar levels of Mg, K, and P, but the significantly greater levels of micronutrients in the biomass at Somerset impacted the overall ash content in the biomass. The greater levels of Al, Fe, and Cu in the biomass at Somerset are a result of the greater availability of these nutrients due to the lower soil pH at this location. Soil at Jackson had a lower pH compared to Freehold

but overall nutrient content in the soil was lower than the other two locations. Cave-In-Rock 3 and Carthage 3 at Somerset had the ability to uptake greater levels of Al and Fe compared to the other genotypes, impacting the overall ash content in the biomass as well. Cave-In-Rock, Carthage, and High Tide are upland ecotypes of switchgrass known to accumulate more ash in their biomass compared to lowland ecotypes (El-Nashaar et al., 2009), which was true for the Somerset location, but not Freehold or Jackson. Freehold, with its more alkaline soil, and Jackson, with its sandier, nutrient poor soils, both influenced nutrient availability and uptake. The lower amount of total ash content and alkali metals present in the biomass from Jackson could be advantageous for most thermo-

Fig. 4. Non-catalytic pyrolysis product yields for switchgrass biomass across all three field locations. Bars represent the mean and standard error of 27 values (9 genotypes × 3 field replicates). Letters over the bars indicate significant differences by environment.

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

965

Fig. 5. Principle component analysis of non-catalytic pyrolysis products for all nine genotypes (averaged across all three locations).

chemical conversion processes, including fast pyrolysis examined in this study. Understanding the G × E interactions or lack thereof is critical for bioenergy crop development and the breeding of traits that can be largely affected by genetics as well as environmental conditions. It is important to note that while most of the variability in mineral and total ash content observed was due to genotype × environment interactions; this was not the case for the condensable products from both non-catalytic and HZSM-5 catalyzed pyrolysis, demonstrating that pyrolysis products were only impacted by the main effects of environment and/or genotype. Fur-

thermore, the variation in non-catalytic pyrolysis products was largely due to genotype effects, whereas CFP products were largely impacted by environment effects. These results demonstrate that non-catalytic pyrolysis products were affected by the genetics of the genotypes and breeding efforts could alter downstream conversion products from fast pyrolysis. The lack of an environmental effect will be discussed in further detail in the latter part of this section. While the make-up of condensable products from fast pyrolysis are directly related to the cell wall components in the biomass, interactions with K and other minerals in the biomass can interfere

Fig. 6. Analysis of co-variance results showing phenol yield from non-CFP as a function of K content and cellulose content in the switchgrass biomass. Both K and cellulose are significant predictors in the model with p-values < 0.05.

966

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

Fig. 7. Catalytic pyrolysis product yields for switchgrass biomass across all three field locations. (A) Total aromatics – benzene, toluene, ethylbenzene, xylene, and nappthalene (BTEXN). (B) Total phenolics. Bars represent the mean and standard error of 27 values (9 genotypes × 3 field replicates). Letters over the bars indicate significant differences by environment.

with many of these relationships, making the connection between the biomass and pyrolysis products more complex. Potassium has a clear effect on the production of pyrolysis products and can have a direct impact on the thermochemical breakdown of cellulose in particular indicated by the significantly strong negative correlation between levoglucosan and HMF yield and K content in the switchgrass biomass. The impact of K on the pyrolysis of cellulose can significantly alter the pyrolysis reaction products lowering the yields of levoglucosan and increasing lower molecular weight deoxygenated compounds (Patwardhan et al., 2010).

The amount of potassium accumulated in the biomass is not only related to the environmental conditions and available K in the soil but also to the genetics of that particular genotype and its ability to uptake K, indicated by the significant G × E interaction; however, it was differences by genotype that had the most effect on pyrolysis product yield. The only two non-CFP products that were not as affected by genotypic differences were levoglucosan and HMF, and were mainly influenced by the large differences in K content by environment. These are key pyrolysis products that are directly impacted by K content in the biomass (Boateng et al., 2015; Mullen et al., 2014; Patwardhan et al., 2010). While it

Fig. 8. Key pyrolysis products from non-catalytic to catalytic pyrolysis. (A) Condensable products that decreased in yield, (B) Condensable products that increased in yield, (C) Non-condensable gases. Bars represent the mean and standard error of all 54 observations.

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

was expected that there would be a positive correlation between cellulose content and its depolymerized product, levoglucosan, a correlation was not detected in the current study. Similarly, Kelkar et al. (2014) was unable to identify a correlation between cellulose and levoglucosan, among the grasses examined. Perhaps, the relationship between levoglucosan and cellulose is masked by the strong effects of K content on the mechanism of cellulose pyrolytic depolymerization and hence levoglucosan yield. Nowakowski and Jones (2008) demonstrated that cellulose depolymerization catalyzed by K will lead to the greater production of acetic and propanoic acid along with cyclopentene derivatives and non-methoxylated phenols such as phenol, cresols, and dimethylphenol. Yield of all of the non-methoxylated phenols from the current set of switchgrass samples were only affected by differences by genotype. At first glance, this is rather counterintuitive since there was a greater variation in K content across the environments than among the genotypes. However, further analysis of the data in this study did identify a relationship with cellulose, which was only significantly different by genotype, not by environment. The three genotypes with the greatest K content averaged across all environments were Cimarron 4, High Tide 4, and NSL 2, which were also separated out by PC1 by their non-methoxylated phenolic content (Figs. 2 D and 5). It was determined through this study that the greater yields of phenolics in these three genotypes were unrelated to total lignin content but due to the K catalyzed conversion of cellulose derived products (i.e., non-methoxylated phenolics are secondary products resultant of further conversion of levoglucosan promoted by K). An ANCOVA was performed to test the effects of K and cellulose as continuous variables on the yield of the non-methoxylated phenolics. As an example, Fig. 6 demonstrates this effect for phenol but the analysis holds true for other non-methoxylated phenols (e.g. cresols). The production of phenol in non-catalytic pyrolysis was a function of both K and cellulose content. While there is a clear positive relationship between K content and phenol yield, lower levels of cellulose content reduce levels of levoglucosan substrate for K to catalytically convert to phenol. This effect has more of an impact at higher levels of K, shown by the lower slope for the “low cellulose” regression line in Fig. 6. Therefore, genotypes with greater cellulose content and greater K uptake will produce higher levels of non-methoxylated phenolics regardless of environment, compared to genotypes that have lower levels of cellulose within the biomass. Cave-In-Rock 3 and Carthage 3 were among the lowest in cellulose content and were not affected by K content to the same extent that Cimarron 4, High Tide 4, and NSL 2 were. While High Tide 4 also had low cellulose being an upland ecotype, it had greater levels of K than Cave-In-Rock 3 and Carthage 3. Both Cave-In-Rock 3 and Carthage 3 had the greatest lignin content among all of the genotypes and produced the greatest levels of methoxylated guaiacols and syringols, which explains why they were separated from all the other genotypes by the PCA (Fig. 5). It can be assumed that the majority of these phenolics are derived from the guaiacyl and syringyl units present in the lignin. The effects of the mineral content due to the differences by environment were more pronounced when examining the product yields from catalytic fast pyrolysis over HZSM-5 compared to the non-catalytic pyrolysis products, with the majority of the CFP products only significantly different by environment. The CFP converted many of the primary pyrolysis products to a mixture high in deoxygenated aromatic hydrocarbons, with the majority of the aromatics derived from the cellulose portion of the biomass (Karanjkar et al., 2014; Mihalcik et al., 2011; Mullen et al., 2011; Zhang et al., 2014). During the CFP process, the levoglucosan, other carbohydrate derived products and some of the guaiacols and syringols underwent catalyzed reactions to produce aromatic compounds such as benzene, toluene, and naphthalene, as evidenced by the

967

decrease in these products from non-catalytic to catalytic pyrolysis (Fig. 8C). The percent yield of aromatics (BTEXN; benzene, toluene, ethylbenzene, xylene, naphthalene; Fig. 7A) was greatest from genotypes grown at the Jackson location and consistent with the greater yields of levoglucosan produced during non-catalytic pyrolysis. The plants grown at Jackson had lower mineral content to decrease the production of levoglucosan. The effects of K content in the biomass impacted the yields of CFP products including the aromatics. Similar results were reported in Mullen et al. (2014). These results indicate that soil variability associated with different marginal sites may significantly impact the yields of CFP products. We also observed an increase in the amount of nonmethoxylated phenolics from non-catalytic to catalytic pyrolysis. For fast pyrolysis, it was found that the production of these compounds resulted from the breakdown of the products of levoglucosan in the presence of K; these products had a positive correlation with K content in the biomass. Conversely, in catalytic pyrolysis, all of the non-methoxylated phenols were negatively correlated with K content in the biomass, producing the greatest amounts on non-methoxylated phenols on the Jackson location and differences by genotype were mostly gone after catalytic pyrolysis. It is most likely that demethoxylation reactions were taking place during the catalytic pyrolysis process converting guaiacols and syringols to non-methoxylated phenolics, similar to what has been observed in other studies (Agblevor et al., 2010; Wang et al., 2012). This means that non-methoxylated phenolics in the CFP product mixture could ultimately be derived from both cellulose and lignin from two different catalytic processes (K and HZSM-5 catalysis) which is why differences by genotype were no longer observed and any correlations with cellulose content were lost. Differences among the syringols and guaiacols were still influenced by genotype, even after CFP, indicating that lignin content in the biomass will influence the production of guaiacols and syringols from both non-CFP and CFP.

5. Conclusion Overall, ash content, and in particular K content, was significantly impacted by the location of growth for all switchgrass genotypes in this study. Soil variability across the growing locations had a large impact on mineral content and uptake in the biomass impacting fast pyrolysis and the yields of various conversion products. Based on experimental data, the switchgrass grown at the Somerset location produced lower levels of levoglucosan and other oxygenated products in response to the greater levels of ash and K in the biomass. The switchgrass genotypes grown at the Jackson location have potential to be most suitable for catalytic pyrolysis over HZSM-5 due to the lower levels of ash yielding more aromatic hydrocarbons in the condensable pyrolysis products. The large differences in ash content across the locations further support the catalytic effects K and ash have on the fast pyrolysis conversion process. Based on these results, growing locations for switchgrass should be carefully evaluated for soil conditions and nutrient availability and the downstream impacts on fast pyrolysis conversion.

Acknowledgments Funding for this project was provided by a USDA-NIFA grant (#2012-68005-19703) to the Northeast Woody/Warm Season Biomass Consortium (NEWBio). The authors would like to thank Mary Cheetham for her excellent technical assistance and data collection and analysis on this project.

968

M.J. Serapiglia et al. / Industrial Crops and Products 74 (2015) 957–968

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.indcrop.2015.06. 024 References Agblevor, F.A., Beis, S., Mante, O., Abdoulmoumine, N., 2010. Fractional catalytic pyrolysis of hybrid poplar wood. Ind. Eng. Chem. Res. 49, 3533–3538. Boateng, A.A., Serapiglia, M.J., Mullen, C.A., Dien, B.S., Hashem, F.M., Dadson, R.B., 2015. Bioenergy crops grown for hyperaccumulation of phosphorous in the Delmarva Peninsula and their biofuels potential. J. Environ. Manage. 150, 39–47. Carpenter, D., Westover, T.L., Czernik, S., Jablonski, W., 2014. Biomass feedstocks for renewable fuel production: a review of the impacts of feedstock and pretreatment on the yield and product distribution of fast pyrolysis bio-oils and vapors. Green Chem. 16, 384. Casler, M.D., 2012. Switchgrass Breeding, Genetics, and Genomics. In: Monti, A. (Ed.), Switchgrass, Green Energy, and Technology. Springer, London, pp. 9–53. Casler, M.D., Boe, A.R., 2003. Cultivar × environment interactions in switchgrass. Crop Sci. 43, 2226–2233. Casler, M.D., Vogel, K.P., 2014. Selection for biomass yield in upland, lowland, and hybrid switchgrass. Crop Sci. 54, 626. Cassida, K.A., Muir, J.P., Hussey, M.A., Read, J.C., Venuto, B.C., Ocumpaugh, W.R., 2005. Biofuel component concentrations and yields of switchgrass in south central. U.S. environ. Crop Sci. 45, 682–692. Cortese, L., Bonos, S., 2013. Bioenergy traits of ten switchgrass populations grown in the northeastern/mid-atlantic USA. BioEnerg. Res. 6, 580–590. Cortese, L.M., 2014. Evaluation of switchgrass (Panicum virgatum L.) as a bioenergy feedstock for the Northeastern and Mid-Atlantic USA, Vol. PhD, Rutgers, The State University of New Jersey. El-Nashaar, H.M., Banowetz, G.M., Griffith, S.M., Casler, M.D., Vogel, K.P., 2009. Genotypic variability in mineral composition of switchgrass. Bioresour. Technol. 100, 1809–1814. Fahmi, R., Bridgwater, A.V., Donnison, I., Yates, N., Jones, J.M., 2008. The effect of lignin and inorganic species in biomass on pyrolysis oil yields, quality and stability. Fuel 87, 1230–1240. Hodgson, E.M., Nowakowski, D.J., Shield, I., Riche, A., Bridgwater, A.V., Clifton-Brown, J.C., Donnison, I.S., 2011. Variation in Miscanthus chemical composition and implications for conversion by pyrolysis and thermo-chemical bio-refining for fuels and chemicals. Bioresour. Technol. 102, 3411–3418. Hopkins, A.A., Vogel, K.P., Moore, K.J., Johnson, K.D., Carlson, I.T., 1995a. Genotypic effects and genotype by environment interactions for traits of elite switchgrass populations. Crop Sci. 35, 125–132. Hopkins, A.A., Vogel, K.P., Moore, K.J., Johnson, K.D., Carlson, I.T., 1995b. Genotypic variability and genotype × environment interactions among switchgrass accessions from the Midwestern USA. Crop Sci. 35, 565–571.

Hosoya, T., Kawamoto, H., Saka, S., 2007. Cellulose–hemicellulose and cellulose–lignin interactions in wood pyrolysis at gasification temperature. J. Anal. Appl. Pyrolysis 80, 118–125. Jeffrey, B., Nettleton, D., Kuzhiyil, N., Rover, M., Brown, R.C., Lamkey, K., Lübberstedt, T., 2014. Significant variation for bio-oil compounds after pyrolysis/gas chromatography–mass spectrometry of cobs and stover among five near-isogenic brown midrib hybrids in Maize. Bioenergy Res. 7, 1–693. Karanjkar, P.U., Coolman, R.J., Huber, G.W., Blatnik, M.T., Almalkie, S., de Bruyn Kops, S.M., Mountziaris, T.J., Conner, W.C., 2014. Production of aromatics by catalytic fast pyrolysis of cellulose in a bubbling fluidized bed reactor. AIChE J. 60, 1320–1335. Kelkar, S., Li, Z., Bovee, J., Thelen, K.D., Kriegel, R.M., Saffron, C.M., 2014. Pyrolysis of North-American grass species: effect of feedstock composition and taxonomy on pyrolysis products. Biomass Bioenergy 64, 152–161. Komarek, A.R., 1993. An improved filtering technique for the analysis of neutral detergent fiber and acid detergent fiber utilizing the filter bag technique. Publication No. 101, Ankom Technology. Fairport, NY. McKendry, P., 2002. Energy production from biomass (part 1): overview of biomass. Bioresour. Technol. 83, 37–46. Mihalcik, D.J., Mullen, C.A., Boateng, A.A., 2011. Screening acidic zeolites for catalytic fast pyrolysis of biomass and its components. J. Anal. Appl. Pyrolysis 92, 224–232. Mullen, C.A., Boateng, A.A., Dadson, R.B., Hashem, F.M., 2014. Biological mineral range effects on biomass conversion to aromatic hydrocarbons via catalytic fast pyrolysis over HZSM-5. Energy Fuels 28, 7014–7024. Mullen, C.A., Boateng, A.A., Mihalcik, D.J., Goldberg, N.M., 2011. Catalytic fast pyrolysis of white oak wood in a bubbling fluidized bed. Energy Fuels 25, 5444–5451. Nowakowski, D.J., Jones, J.M., 2008. Uncatalysed and potassium-catalysed pyrolysis of the cell-wall constituents of biomass and their model compounds. J. Anal. Appl. Pyrolysis 83, 12–25. Patwardhan, P.R., Satrio, J.A., Brown, R.C., Shanks, B.H., 2010. Influence of inorganic salts on the primary pyrolysis products of cellulose. Bioresour. Technol. 101, 4646–4655. Sanderson, M.A., Adler, P.R., Boateng, A.A., Casler, M.D., Sarath, G., 2006. Switchgrass as a biofuels feedstock in the USA. Can. J. Plant Sci. 86, 1315–1325. Serapiglia, M.J., Mullen, C.A., Boateng, A.A., 2015. Variability in pyrolysis product yield from novel shrub willow genotypes. Biomass Bioenergy 8, 259–269. Vogel, K.P., Jung, H.G., 2001. Genetic modification of herbaceous plants for feed and fuel. Crit. Rev. Plant Sci. 20, 15–49. Wang, L., Lei, H., Ren, S., Bu, Q., Liang, J., Wei, Y., Liu, Y., Lee, G.-S.J., Chen, S., Tang, J., Zhang, Q., Ruan, R., 2012. Aromatics and phenols from catalytic pyrolysis of Douglas fir pellets in microwave with ZSM-5 as a catalyst. J. Anal. Appl. Pyrolysis 98, 194–200. Wheal, M.S., Fowles, T.O., Palmer, L.T., 2011. A cost-effective acid digestion method using closed polypropylene tubes for inductively coupled plasma optical emission spectrometry (ICP-OES) analysis of plant essential elements. Analytical Methods 3, 2854–2863. Zhang, M., Resende, F.L.P., Moutsoglou, A., 2014. Catalytic fast pyrolysis of aspen lignin via Py-GC/MS. Fuel 116, 358–369.