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At least 559 records · Page 31

Valorization of bamboo biomass using combinatorial pretreatments

Current studies concerning liquid hot water pretreatment (LHWP) usually focus on cellulose saccharification or hemicellulose conversion, while the appropriate extraction and utilization of LHWP-induced lignin have not been addressed. To valorize whole biomass, here, in this study, a series of LHWP-based combinatorial pretreatments are proposed using advanced solvent systems. The first LHWP stage removed 73.74% of xylan at the optimized pretreatment severity (~4.0), at the same time preserving almost all the cellulose and lignin. As a result, 9.04 g L –1 xylo-oligosaccharides (XOSs) were obtained in the prehydrolyzate, representing 63.31% of the degraded xylan. The LHWP also caused the migration and partial degradation of the lignin which benefits the delignification in the second stage of pretreatment utilizing NaOH, deep eutectic solvent (DES) and 1,4-butanediol (BDO) under relatively mild conditions. The proposed combinatorial pretreatment significantly enhanced glucan conversion. The physiochemical structural properties of the untreated and pretreated substrates were investigated with XRD, FTIR, GPC, SEM, and Simons’ staining to understand the mechanisms of different combinatorial pretreatments for overcoming biomass recalcitrance and facilitating integrated biomass valorization. Finally, the physical and chemical properties of the regenerated lignins were analyzed. The results revealed that lignins from LHWP–NaOH pretreatment preserved most of the native β-O-4 structure (37.1%). Thus it could be used as an ideal method for the catalytic production of aromatic monomers. On the other hand, LHWP–DES pretreatment generated lignins with low molecular weight and high phenolic OH content, which is promising for synthesizing resins and antioxidants. In conclusion, the proposed combinatorial pretreatment established a reliable sequential pretreatment methodology for maximizing the output of bamboo biomass and may fit into different biorefinery configurations for various value-added products.

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Efficient Fast Fractionation of Biomass Using a Diol-Based Deep Eutectic Solvent for Facilitating Enzymatic Hydrolysis and Obtaining High-Quality Lignin

Current DES pretreatment is often performed under relatively severe conditions with high temperature, long time, and high DES usage. This work studied a short-time diol DES (deep eutectic solvent) pretreatment under mild conditions to fractionate the bamboo, facilitate enzymatic hydrolysis, and obtain high-quality lignin. At an optimized condition of 130 °C for only 10 min, lignin and xylan removal reached 61.34% and 84.15 %, with residual glucan showing a ~90% enzymatic hydrolysis yield. Equally important, the dissolved lignin could be readily recovered with 97.51% yield, exhibiting 96.65 % β-O-4 preservation. The fractionation and lignin protection mechanisms were unveiled by XRD, FTIR, cellulose-DP, 2D HSQC NMR, 31 P NMR and GPC analysis. Finally, this study highlighted that short-time fractionation of bamboo can be achieved by a diol-based DES which is an ideal strategy to upgrade the lignocellulose biomass for high enzymatic hydrolysis yields and high-quality lignin stream.

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Editorial: Lignocellulose valorization: Fractionation, conversion and applications

Lignocellulosic biomass is the most abundant form of renewable feedstock on the Earth. Chemicals, energies, and materials derived from lignocellulosic biomass are renewable and sustainable, and have the potential to replace fossil feedstocks. Biorefinery, a process to fractionate lignocellulose into the three major components, is considered the most pivotal step for bioenergy, biomaterials and biochemicals. However, due to the complex hierarchy and chemical structures, only limited lignocellulose is valorized into value-added products, and most of them are burnt or just discarded. Therefore, there is a need to investigate the heterogeneous structure of lignocellulose, improve its efficient fractionation into cellulose, hemicelluloses and lignin, and develop the technology that can convert the lignocellulose into high-value chemical products and high-performance functional materials in a sustainable and promising configuration. In short, the ultimate goal of biomass utilization is that the production of biofuels and biomaterials in industrial applications must be cost-, and performance-competitive with petroleum-derived equivalents.

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p HBMT1, a BAHD-family monolignol acyltransferase, mediates lignin acylation in poplar

Abstract Poplar (Populus) lignin is naturally acylated with p-hydroxybenzoate ester moieties. However, the enzyme(s) involved in the biosynthesis of the monolignol–p-hydroxybenzoates have remained largely unknown. Here, we performed an in vitro screen of the Populus trichocarpa BAHD acyltransferase superfamily (116 genes) using a wheatgerm cell-free translation system and found five enzymes capable of producing monolignol–p-hydroxybenzoates. We then compared the transcript abundance of the five corresponding genes with p-hydroxybenzoate concentrations using naturally occurring unrelated genotypes of P. trichocarpa and revealed a positive correlation between the expression of p-hydroxybenzoyl-CoA monolig-nol transferase (pHBMT1, Potri.001G448000) and p-hydroxybenzoate levels. To test whether pHBMT1 is responsible for the biosynthesis of monolignol–p-hydroxybenzoates, we overexpressed pHBMT1 in hybrid poplar (Populus alba × P. grandidentata) (35S::pHBMT1 and C4H::pHBMT1). Using three complementary analytical methods, we showed that there was an increase in soluble monolignol–p-hydroxybenzoates and cell-wall-bound monolignol–p-hydroxybenzoates in the poplar transgenics. As these pendent groups are ester-linked, saponification releases p-hydroxybenzoate, a precursor to parabens that are used in pharmaceuticals and cosmetics. This identified gene could therefore be used to engineer lignocellulosic biomass with increased value for emerging biorefinery strategies.

59 BASIC BIOLOGICAL SCIENCES↗

Probing Condensed-Phase Structure and Dynamics in Hierarchical Zeolites and Nanosheets for Catalytic Upgradation of Biomass (Final Report)

Understanding complex reaction pathways in systems governed by multi-scale collective interactions across time and length scales remains a central scientific challenge. This project was guided by the hypothesis that the interplay among oligomers, solvents, and active sites can be tuned by a suitable choice of solvation environment and pore architecture in solid-acid catalysts to direct chemical transformations relevant to biomass conversion. Zeolites and zeolite nanosheets were used as model platforms, allowing for the interaction of macromolecules with the surface of the zeolite nanosheets and with smaller pores that host catalytically active sites. To investigate these coupled phenomena, we employ a multi-scale computational framework that integrates molecular-level descriptions with advanced sampling approaches to capture key physical and chemical interactions. Our work through this project improved fundamental understanding of how reactants and solid-acid catalysts interact in solvent-rich environments, thereby enabling the rational design of catalytic systems that upgrade biomass with enhanced selectivity and energy efficiency. In addition, the project developed advanced sampling methodologies critical for disentangling complex, reactive processes in multi-component catalytic environments.

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Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗

Renewable Schiff-Base Ionic Liquids for Lignocellulosic Biomass Pretreatment

Growing interest in sustainable sources of chemicals and energy from renewable and reliable sources has stimulated the design and synthesis of renewable Schiff-base (iminium) ionic liquids (ILs) to replace fossil-derived ILs. In this study, we report on the synthesis of three unique iminium-acetate ILs from lignin-derived aldehyde for a sustainable “future” lignocellulosic biorefinery. The synthesized ILs contained only imines or imines along with amines in their structure; the ILs with only imines group exhibited better pretreatment efficacy, achieving >89% sugar release. Various analytical and computational tools were employed to understand the pretreatment efficacy of these ILs. This is the first study to demonstrate the ease of synthesis of these renewable ILs, and therefore, opens the door for a new class of “Schiff-base ILs” for further investigation that could also be designed to be task specific.

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Engineered Sorghum Bagasse Enables a Sustainable Biorefinery with p-Hydroxybenzoic Acid-Based Deep Eutectic Solvent

Integrating multidisciplinary research in plant genetic engineering and renewable deep eutectic solvents (DESs) can facilitate a sustainable and economic biorefinery. In this work, we leveraged a plant genetic engineering approach to specifically incorporate C6C1 monomers into the lignin structure. By expressing the bacterial ubiC gene in sorghum, p-hydroxybenzoic acid (PB)-rich lignin was incorporated into the plant cell wall while this monomer was completely absent in the lignin of the wild-type (WT) biomass. A DES was synthesized with choline chloride (ChCl) and PB and applied to the pretreatment of the PB-rich mutant biomass for a sustainable biorefinery. The release of fermentable sugars was significantly enhanced (~190 % increase) compared to untreated biomass by the DES pretreatment. In particular, the glucose released from the pretreated mutant biomass was up to 12 % higher than that from the pretreated WT biomass. Lignin was effectively removed from the biomass with the preservation of more than half of the β-Ο-4 linkages without condensed aromatic structures. Hydrogenolysis of the fractionated lignin was conducted to demonstrate the potential of phenolic compound production. In addition, a simple hydrothermal treatment could selectively extract PB from the same engineered lignin, showing a possible circular biorefinery. These results suggest that the combination of PB-based DES and engineered PB-rich biomass is a promising strategy to achieve a sustainable closed-loop biorefinery.

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Exploring the Structural, Biochemical, and Functional Diversity of Glycoside Hydrolase Family 12 from Penicillium subrubescens

Glycoside hydrolases (GHs) play an essential role in plant biomass degradation and modification for the sustainable production of biochemicals. The filamentous Ascomycete fungus Penicillium subrubescens contains a higher number of GH12 candidates compared to related species. Therefore, we aimed to compare P. subrubescens GH12s for their ability and substrate specificity for plant cell wall polysaccharide degradation and species’ potential as a source of novel enzymes for plant biomass valorization. Our re-evaluated phylogenetic analysis of fungal GH12 members showed that the P. subrubescens GH12s were located in different (new) clades. Biochemical characterization marked PsEglA as an endoglucanase and four other P. subrubescens GH12s (i.e., PsXegA–D) as xyloglucanases. Interestingly, structural features of PsXegD and PsXegE were more comparable to those of Basidiomycete GH12 xyloglucanases with a unique open substrate-binding cleft. PsUegA displayed dual xyloglucanase and endoglucanase activity and also showed distinct structural features. Comparative transcriptome analysis supported the functional diversity of P. subrubescens GH12s in plant biomass degradation. The gene encoding PsUegA was expressed under diverse conditions, suggesting a scouting role for this enzyme.

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Deep Convolutional Neural Networks Exploit High-Spatial- and -Temporal-Resolution Aerial Imagery to Phenotype Key Traits in Miscanthus

Miscanthus is one of the most promising perennial crops for bioenergy production, with high yield potential and a low environmental footprint. The increasing interest in this crop requires accelerated selection and the development of new screening techniques. New analytical methods that are more accurate and less labor-intensive are needed to better characterize the effects of genetics and the environment on key traits under field conditions. We used persistent multispectral and photogrammetric UAV time-series imagery collected 10 times over the season, together with ground-truth data for thousands of Miscanthus genotypes, to determine the flowering time, culm length, and biomass yield traits. We compared the performance of convolutional neural network (CNN) architectures that used image data from single dates (2D-spatial) versus the integration of multiple dates by 3D-spatiotemporal architectures. The ability of UAV-based remote sensing to rapidly and non-destructively assess large-scale genetic variation in flowering time, height, and biomass production was improved through the use of 3D-spatiotemporal CNN architectures versus 2D-spatial CNN architectures. The performance gains of the best 3D-spatiotemporal analyses compared to the best 2D-spatial architectures manifested in up to 23% improvements in R2, 17% reductions in RMSE, and 20% reductions in MAE. The integration of photogrammetric and spectral features with 3D architectures was crucial to the improved assessment of all traits. In conclusion, our findings demonstrate that the integration of high-spatiotemporal-resolution UAV imagery with 3D-CNNs enables more accurate monitoring of the dynamics of key phenological and yield-related crop traits. This is especially valuable in highly productive, perennial grass crops such as Miscanthus, where in-field phenotyping is especially challenging and traditionally limits the rate of crop improvement through breeding.

47 OTHER INSTRUMENTATION↗

Comparison of Precision of Biomass Estimates in Regional Field Sample Surveys and Airborne LiDAR-Assisted Surveys in Hedmark County, Norway

Airborne scanning LiDAR (Light Detection and Ranging) has emerged as a promising tool to provide auxiliary data for sample surveys aiming at estimation of above-ground tree biomass (AGB), with potential applications in REDD forest monitoring. For larger geographical regions such as counties, states or nations, it is not feasible to collect airborne LiDAR data continuously ("wall-to-wall") over the entire area of interest. Two-stage cluster survey designs have therefore been demonstrated by which LiDAR data are collected along selected individual flight-lines treated as clusters and with ground plots sampled along these LiDAR swaths. Recently, analytical AGB estimators and associated variance estimators that quantify the sampling variability have been proposed. Empirical studies employing these estimators have shown a seemingly equal or even larger uncertainty of the AGB estimates obtained with extensive use of LiDAR data to support the estimation as compared to pure field-based estimates employing estimators appropriate under simple random sampling (SRS). However, comparison of uncertainty estimates under SRS and sophisticated two-stage designs is complicated by large differences in the designs and assumptions. In this study, probability-based principles to estimation and inference were followed. We assumed designs of a field sample and a LiDAR-assisted survey of Hedmark County (HC) (27,390 km2), Norway, considered to be more comparable than those assumed in previous studies. The field sample consisted of 659 systematically distributed National Forest Inventory (NFI) plots and the airborne scanning LiDAR data were collected along 53 parallel flight-lines flown over the NFI plots. We compared AGB estimates based on the field survey only assuming SRS against corresponding estimates assuming two-phase (double) sampling with LiDAR and employing model-assisted estimators. We also compared AGB estimates based on the field survey only assuming two-stage sampling (the NFI plots being grouped in clusters) against corresponding estimates assuming two-stage sampling with the LiDAR and employing model-assisted estimators. For each of the two comparisons, the standard errors of the AGB estimates were consistently lower for the LiDAR-assisted designs. The overall reduction of the standard errors in the LiDAR-assisted estimation was around 40-60% compared to the pure field survey. We conclude that the previously proposed two-stage model-assisted estimators are inappropriate for surveys with unequal lengths of the LiDAR flight-lines and new estimators are needed. Some options for design of LiDAR-assisted sample surveys under REDD are also discussed, which capitalize on the flexibility offered when the field survey is designed as an integrated part of the overall survey design as opposed to previous LiDAR-assisted sample surveys in the boreal and temperate zones which have been restricted by the current design of an existing NFI.

Comparison↗

Overview of the Magnetic Resonance Capabilities at INL

The aim of this presentation is to give a broad overview of the magnetic resonance capabilities at Idaho National Laboratory and how the technique is implemented within the different directorates at the lab. Historically, the high-field NMR instrumentation has been used by our Energy & Environment Science and Technology (EES&T) directorate primarily as a characterization tool for synthetic chemistry work, but we have been actively trying to expand the use of the instrumentation into our Nuclear Science and Technology (NS&T) and National and Homeland Security (NHS) directorates. The expanded scope of work has ranged from solid-state characterization of biomass, analysis of electrolyte materials from lithium ion batteries, quantification of extracted critical materials, and measurement of metal-ligand complexes using ligands proposed for use in the nuclear fuel cycle.

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Production of renewable alcohols from maple wood using supercritical methanol hydrodeoxygenation in a semi-continuous flowthrough reactor

Biomass conversion to alcohols using supercritical methanol depolymerization and hydrodeoxygenation (SCM-DHO) with CuMgAl mixed metal oxide is a promising process for biofuel production. Here, we demonstrate how maple wood can be converted at high weight loadings and product concentrations in a batch and a semi-continuous reactor to a mixture of C 2 –C 10 linear and cyclic alcohols. Maple wood was solubilized semi-continuously in supercritical methanol and then converted to a mixture of C 2 –C 9 alcohols and aromatics over a packed bed of CuMgAlO x catalyst. Up to 95 wt% of maple wood can be solubilized in the methanol by using four temperature holds at 190, 230, 300, and 330 °C. Lignin was solubilized at 190 and 230 °C to a mixture of monomers, dimers, and trimers while hemicellulose and cellulose solubilized at 300 and 330 °C to a mixture of oligomeric sugars and liquefaction products. The hemicellulose, cellulose, and lignin were converted to C 2 –C 10 alcohol fuel precursors over a packed bed of CuMgAlO x catalyst with 70–80% carbon yield of the entire maple wood. The methanol reforming activity of the catalyst decreased by 25% over four beds of biomass, which corresponds to 5 turnovers for the catalyst, but was regenerable after calcination and reduction. In batch reactions, maple wood was converted at 10 wt% in methanol with 93% carbon yield to liquid products. The product concentration can be increased to 20 wt% by partially replacing the methanol with liquid products. The yield of alcohols in the semi-continuous reactor was approximately 30% lower than in batch reactions likely due to degradation of lignin and cellulose during solubilization. These results show that solubilization of whole biomass can be separated from catalytic conversion of the intermediates while still achieving a high yield of products. However, close contact of the catalyst and the biomass during solubilization is critical to achieve the highest yields and concentration of products.

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Catalyzing the future: recent advances in chemical synthesis using enzymes

Biocatalysis has the potential to address the need for more sustainable organic synthesis routes. Pro-tein engineering can tune enzymes to perform in cascade reactions and for efficient synthesis of en-antiomerically enriched compounds, using both natural and new-to-nature reaction pathways. This review highlights recent achievements in biocatal-ysis, especially the development of novel enzymatic syntheses to access versatile small molecule inter-mediates and complex biomolecules. Biocatalytic strategies for the degradation of persistent pollu-tants and approaches for biomass valorization are also discussed. Here, the transition of chemical synthesis to a greener future will be accelerated by imple-menting enzymes and engineering them for high performance and new activities.

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Three-Dimensional Modeling of the Explicit Chemistry of Organic Aerosols: Formation, Removal, and Comparison with Observations

The major goals of the project were to further our understanding of the formation of secondary organic aerosols (SOA) from major anthropogenic and biogenic precursors, and to evaluate the GECKO-A chemistry model in the context of three DOE-led field campaigns (GoAmazon, BBOP, and HI-SCALE) with the long-term objective of improving the treatment of organic gases and particles in three-dimensional regional and global models. The three field campaigns were selected to evaluate and improve specific aspects of GECKO-A: (i) GoAmazon to help quantify interactions of biogenic and anthropogenic emissions; (ii) BBOP to predict the UV-visible absorption properties of biomass burning aerosols (brown carbon) from explicit molecular properties; and (iii) HI-SCALE to evaluate new model components that include aqueous (cloud) processing of organic gases and aerosols.

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Evidence for Lignin–Carbohydrate Complexes from Studies of Transgenic Switchgrass and a Model Lignin–Pectin Composite

Lignin–carbohydrate complexes (LCCs) form through interactions of lignin with plant cell wall polysaccharides and are thought to be a significant source of biomass recalcitrance. In this work, we investigated LCCs formed between lignin and pectin homogalacturonan (HG). The structural changes in HG deficient transgenic switchgrass (GAUT4-knockdown, GAUT4-KD) after hot water pretreatment were compared to wild-type plants using small-angle neutron scattering (SANS), which showed that there were ~2.2-fold more lignin aggregates in GAUT4-KD biomass compared to the wild type. This demonstrated that decreased pectin resulted in more lignin redistribution and suggested that interactions between lignin and HG restrict lignin mobility in plant cell walls. To better understand the types of interactions between lignin and pectin, a model composite was prepared by polymerizing either protiated or partially deuterated coniferyl alcohol to form a dehydrogenation polymer (DHP) in the presence of HG. Small-angle X-ray scattering (SAXS) showed that the DHP and HG form a highly interconnected network structure that is not observed in a physical mixture of the individual polymers. Contrast matching SANS revealed the structure of DHP and HG in the composite and showed that the HG forms a swollen interconnected polymer network (power-law exponent, P = 1.5) interspersed with DHP particles (radius of gyration, Rg, 264 Å) that are composed of solvent-accessible DHP polymers (P = 2.3). Fourier transform infrared spectroscopy showed a unique ester absorption band in the DHP/HG composites. Solid-state nuclear magnetic resonance (NMR) analysis also supports interactions between DHP and HG. Overall, this study provides new insights into the relationship between primary and secondary cell wall polymers during cell wall synthesis and how LCCs formed between pectin and lignin could represent a previously unrecognized source of biomass recalcitrance. This knowledge may help develop new approaches to modulate cell wall properties to improve biofuel and bioproduct production.

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Solid–liquid separation of lignocellulosic sugars from biomass by rotating ceramic disc filtration

In many biomass conversion processes, the separation of cellulosic sugars from residual, lignin-rich solids is a critical step, and achieving high recovery yields of sugars by conventional tangential crossflow and vacuum filtration is challenged by the presence of biomass solids, which rapidly foul filters, resulting in decreased throughput. Considering the performance limitations of traditional filtration methods, dynamic filtration, which generates high shear at the membrane surface to decrease fouling, is emerging as a viable alternative for demanding solid–liquid separations. For high solids separations, there is little available information regarding the performance, limitations, and energy consumption of dynamic filtration. To that end, here we characterized the performance of a dynamic filtration module, specifically a rotating ceramic disc (RCD) filter, for the aseptic recovery of cellulosic sugars from biomass solids following pretreatment and enzymatic hydrolysis. We show how RCD rotational velocity and percent biomass solids impact the filter throughput. Additionally, we used computational fluid dynamics (CFD) simulations to estimate the shear rate at the membrane surface and to visualize hydrodynamic profiles within the module. With the combined CFD simulations and experimental results, we estimated the energy demand and operating expenses for a viable dynamic filtration system operating with a lignocellulosic feed slurry. Our results indicate that an RCD filter can achieve ≥95% recovery of sugars and produce a retentate slurry containing 12 wt% insoluble solids with low energy consumption (a 2.2-fold improvement over cross-flow filtration) and low operating costs ($\$$0.06 per kg sugars). These results show a viable path towards operationally reliable, energy efficient, and cost-effective separations of sterilized cellulosic sugars from biomass solids and highlight the potential of dynamic filtration systems for challenging solid–liquid separations.

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