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Results for “Process Efficiency”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Implementation and Validation of a Generalized Actuator Disk Parameterization for Wind Turbine Simulations Within the FastEddy Model

ABSTRACT Fast and accurate large‐eddy simulation (LES) of the atmospheric boundary layer plays a crucial role in advancing wind energy research. Long‐duration wind farm studies at turbine‐resolving scales have become increasingly important to understand the intricate interactions between large wind farms and the atmospheric boundary layer. However, the prohibitive computational cost of these turbulence‐ and turbine‐resolving simulations has precluded such modeling to be exercised on a regular basis. To that end, we implement and validate the generalized actuator disk (GAD) model in the computationally efficient, graphics processing unit (GPU)–resident, LES model FastEddy. We perform single‐turbine simulations under three atmospheric stabilities (neutral, unstable, and stable) and compare them against observations from the Scaled Wind Farm Technology (SWiFT) facility and other LES codes from the recent Wakebench turbine wake model benchmark. Our idealized LES results agree well with observed wake velocity deficit and downstream recovery across stability regimes. Turbine response in terms of rotational speed, generated power, torque, and thrust coefficient are well predicted across stability regimes and are consistent with the LES results from the benchmark. The FastEddy simulations are found to be at least two orders of magnitude more efficient than the traditional CPU‐based LES models, opening the door for realistic LES simulations of full wind plants as a viable standard practice.

17 WIND ENERGY↗
DOE OSTI2025

Solventless Dual‐Cure Liquid Resins Via Circular Use of Phthalic Anhydride for Recyclable Composite Applications

Abstract Fiber‐reinforced composites (FRCs) possess a remarkable strength‐to‐weight ratio, making them ideal light‐weighing alternative materials of metals used in automotive, aerospace, and outdoor equipment applications, but their recycling is challenging. Chemically recyclable thermoset polymers can enable fiber recovery and reuse; however, challenges remain in the separation and purification of depolymerized small molecules for efficient polymer recycling. To this end, a series of liquid resins for chemically recyclable polymer networks is designed based on phthalic anhydride, a widely produced and inexpensive chemical. The straightforward sublimation of phthalic anhydride is leveraged to enable a simple and efficient separation process for polymer recycling. To liquefy phthalic anhydride, five mono‐acryloyl‐phthalates are synthesized to obtain stable liquid resins together with phthalic diglycidyl ester. These liquid resins undergo dual‐cure reactions that comprise photopolymerization of acrylate and, subsequently, heat‐mediated epoxy‐acid polymerization reactions. These liquid resins exhibit moderate viscosities (2600–6400 cP @ 22 °C), fast curing, and robust thermomechanical properties (T g s from 71 to 116 °C). It is demonstrated that hydrolysis of the dual‐cured polymers completes within 2 h at 80 °C, and direct sublimation produces phthalic anhydride with 82% yield. This resin system is expected to provide a cost‐competitive, highly efficient platform for recyclable FRCs.

Polymer Science↗
DOE OSTI2025

CHEMREASONER: Heuristic Search over a Large Language Model’s Knowledge Space using Quantum-Chemical Feedback

The discovery of new catalysts is essential for the design of new and more efficient chemical processes in order to transition to a sustainable future. We introduce an AI-guided computational screening framework unifying linguistic reasoning with quantum-chemistry based feedback from 3D atomistic representations. Our approach formulates catalyst discovery as an uncertain environment where an agent actively searches for highly effective catalysts via the iterative combination of large language model (LLM)-derived hypotheses and atomistic graph neural network (GNN)-derived feedback. Identified catalysts in intermediate search steps undergo structural evaluation based on spatial orientation, reaction pathways, and stability. Scoring functions based on adsorption energies and barriers steer the exploration in the LLM's knowledge space toward energetically favorable, high-efficiency catalysts. We introduce planning methods that automatically guide the exploration without human input, providing competitive performance against expert-enumerated chemical descriptor-based implementations. By integrating language-guided reasoning with computational chemistry feedback, our work pioneers AI-accelerated, trustworthy catalyst discovery.

artificial intelligence↗
DOE OSTI2017

Realizing the Power of Near-Term Quantum Technologies

This the final report of the LDRD project entitled "Realizing the Power of Near-Term Quantum Technologies", which was tasked with laying a theoretical foundation and computational framework for quantum simulation on quantum devices, to support both future Sandia efforts and the broader academic research effort in this area. The unifying theme of the project has been the desire to delineate more clearly the interface between existent classical computing resources that are vast and reliable with emerging quantum computing resources that will be scarce and unreliable for the foreseeable future. We seek to utilize classical computing resources to judge the efficacy of quantum devices for quantum simulation tasks and determine when they exceed the performance of classical devices, thereby achieving "quantum supremacy". This task was initially pursued by adapting the general concept of "parameter space compression" to quantum simulation. An inability to scale this analysis efficiently to large-scale simulations precipitated a shift in focus to assessing quantum supremacy of a specific quantum device, a 1D Bose gas trapped in an optical lattice, that was more amenable to large-scale analysis. We also seek to reconstruct unobserved information from limited observations of a quantum device to enhance their utility. This task was initially pursued as an application of maximum entropy reconstruction. Initial attempts to improve entropy approximations for direct reconstruction by free energy minimization proved to be more difficult than expected, and the focus shifted to the development of a quantum thermostat to facilitate indirect reconstruction by evolving a quantum Markov process. An efficient quantum thermostat is broadly useful for quantum state preparation in almost any quantum simulation task. In the middle of the project, a small opportunistic investment was made in a high-risk experiment to build an analog quantum simulator out of hole quantum dots in Ge/SiGe heterostructures. While a useful simulator was not produced, hole quantum dots at a Ge/SiGe interface have been successfully observed for the first time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A Graphics Processing Unit (GPU) Approach to Large Eddy Simulation (LES) for Transport and Contaminant Dispersion

Recent advances in the development of large eddy simulation (LES) atmospheric models with corresponding atmospheric transport and dispersion (AT&D) modeling capabilities have made it possible to simulate short, time-averaged, single realizations of pollutant dispersion at the spatial and temporal resolution necessary for common atmospheric dispersion needs, such as designing air sampling networks, assessing pollutant sensor system performance, and characterizing the impact of airborne materials on human health. The high computational burden required to form an ensemble of single-realization dispersion solutions using an LES and coupled AT&D model has, until recently, limited its use to a few proof-of-concept studies. An example of an LES model that can meet the temporal and spatial resolution and computational requirements of these applications is the joint outdoor-indoor urban large eddy simulation (JOULES). A key enabling element within JOULES is the computationally efficient graphics processing unit (GPU)-based LES, which is on the order of 150 times faster than if the LES contaminant dispersion simulations were executed on a central processing unit (CPU) computing platform. JOULES is capable of resolving the turbulence components at a suitable scale for both open terrain and urban landscapes, e.g., owing to varying environmental conditions and a diverse building topology. In this paper, we describe the JOULES modeling system, prior efforts to validate the accuracy of its meteorological simulations, and current results from an evaluation that uses ensembles of dispersion solutions for unstable, neutral, and stable static stability conditions in an open terrain environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

H I Gas and Star Formation in Major Galaxy Pairs from the FAST All Sky H I Survey (FASHI)

Atomic hydrogen (H I ) plays a fundamental role in fueling star formation in galaxies. However, the behavior of H I gas in interacting systems, particularly galaxy pairs, remains elusive. In this work, we investigate the H I content of major mergers by crossmatching the extragalactic H I catalog from the Five-hundred-meter Aperture Spherical radio Telescope (FAST) All-Sky H I Survey with a previously established sample of isolated galaxy pairs. With the superior sensitivity of FAST, we have constructed the largest sample of major mergers with H I detections, consisting of 440 galaxy pairs: 364 spiral-spiral (S+S) and 76 spiral-elliptical (S+E) systems. We examine the H I gas fraction (f H I ), star formation rate (SFR), and H I star formation efficiency (SFE H I = SFR/M H I ) for individual galaxies in pairs. The control sample is matched in both stellar mass and redshift. We find that paired galaxies, particularly those in pairs with small projected separations (d p < 50 h −1 kpc), exhibit systematically lower (by 8.8%) H I gas fractions compared to the control galaxies. The SFR is enhanced for galaxies in the S+S pairs. The SFE H I is ∼15% higher for galaxies in the S+S pairs than in the control galaxies, while spiral galaxies in the S+E pairs show no significant difference in SFE H I compared to the control sample. These findings suggest that the merger process triggers efficient H I gas depletion and enhances star formation, especially in close S+S pairs. Notably, our sample includes 26 red spirals in paired systems. These galaxies exhibit H I deficiency and suppressed star formation activity compared to the isolated galaxies, indicating that interactions may affect quiescent spirals differently, potentially due to mechanisms similar to those of ellipticals.

Yan, Shulan 淑澜鄢 [Xiamen University (China); SDSS C↗
DOE OSTI2014

Generation of magnetic field on the accretion disk around a proto-first-star

The generation process of a magnetic field around a proto-first-star is studied. Utilizing the recent numerical results of proto-first-star formation based on radiation hydrodynamics simulations, we assess the magnetic field strength generated by the radiative force and the Biermann battery effect. We find that a magnetic field of ∼10{sup –9} G is generated on the surface of the accretion disk around the proto-first-star. The field strength on the accretion disk is smaller by two orders of magnitude than the critical value, above which the gravitational fragmentation of the disk is suppressed. Thus, the generated seed magnetic field hardly affect the dynamics of on-site first star formation directly, unless an efficient amplification process is taken into consideration. We also find that the generated magnetic field is continuously blown out from the disk on the outflows to the poles, that are driven by the thermal pressure of photoheated gas. The strength of the diffused magnetic field in low-density regions is ∼10{sup –14}-10{sup –13} G at n {sub H} = 10{sup 3} cm{sup –3}, which could play an important role in the next generation star formation, as well as the seeds of the magnetic field in the present-day universe.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2013

FROM PLANETESIMALS TO DUST: LOW-GRAVITY EXPERIMENTS ON RECYCLING SOLIDS AT THE INNER EDGES OF PROTOPLANETARY DISKS

Transporting solids of different sizes is an essential process in the evolution of protoplanetary disks and planet formation. Large solids are supposed to drift inward; high-temperature minerals found in comets are assumed to have been transported outward. From low-gravity experiments on parabolic flights, we studied the light-induced erosion of dusty bodies caused by a solid-state greenhouse effect and photophoresis within a dust bed's upper layers. The gravity levels studied were 0.16g, 0.38g, 1g, and 1.7g. The light flux during the experiments was 12 {+-} 2 kW m{sup -2} and the ambient pressure was 6 {+-} 0.9 mbar. Light-induced erosion is strongly gravity dependent, which is in agreement with a developed model. In particular for small dusty bodies ((sub)-planetesimals), efficient erosion is possible at the optically thin inner edges of protoplanetary disks. Light-induced erosion prevents significant parts of a larger body from moving too close to the host star and being subsequently accreted. The small dust produced continues to be subject to photophoresis and is partially transported upward and outward over the surface of the disk; the resulting small dust particles are observed over the disk's lifetime. The fraction of eroded dust participates in subsequent cycles of growth during planetesimal formation. Another fraction of dust might be collected by a body of planetary size if this body is already present close to the disk edge. Either way, light-induced erosion is an efficient recycling process in protoplanetary disks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Basic Research Needs for Catalysis Science to Transform Energy Technologies: Report from the U.S. Department of Energy, Office of Basic Energy Sciences Workshop May 8–10, 2017, in Gaithersburg, Maryland

Energy technologies affect virtually every aspect of life in modern societies—including transportation, utilities, agriculture, medicine, and the availability of a myriad of consumer products—and depend on human ability to accelerate and to guide chemical transformations. Controlling these transformations, which occur in the microscopic world of atoms and molecules, forms the basis of countless technologies such as production of fuels, fertilizers, plastics, pharmaceuticals and much more. At the very core of these chemical transformations are catalysts—specialized and often highly complex types of matter that allow chemical reactions to occur rapidly and produce specific products. Catalysts also have the remarkable ability to perform their tasks millions of times without themselves being consumed. The discovery of inexpensive and widely-deployable energy and chemical technologies, and their underpinning catalysis science, is critical to ensure the economic viability of U.S. energy and chemical industries. Over the past decade, remarkable new tools have been discovered that allow the observation of catalytic transformations in exquisite detail, and assembly of novel and elaborate catalytic architectures with atomic precision. Furthermore, increasingly sophisticated theoretical and computational tools allow understanding of the essential details of the catalytic processes, and this overall progress has led to the discovery of catalysts with superior performance and the associated economic benefit. In the next decade and beyond, science promises to revolutionize how catalysts and catalytic processes are designed, to enable the introduction of new energy resources, to provide routes to sustainable synthesis of chemicals and other valuable materials, and to create novel approaches to chemical energy storage. This report is the result of the Basic Energy Sciences Workshop on Basic Research Needs for Catalysis Science to Transform Energy Technologies that was held in May 2017, and was attended by more than 100 leading national and international scientific experts. The attendees were organized into four panels: 1. Diversified Energy Feedstocks and Carriers, 2. Novel Approaches to Energy Transformations, 3. Advanced Chemical Conversion Approaches, and 4. Crosscutting Capabilities and Challenges: Synthesis, Theory, and Characterization. The workshop identified five priority research directions (PRDs) that are aimed at harnessing complexity in catalysis to create next-generation energy technologies and realizing efficient catalytic processes to increase the diversity of resources for production of chemicals and energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach

Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme generalizes to multiple tissue classes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Solutions for Enhanced Legacy Probabilistic Risk Assessment Tools and Methodologies: Improving Efficiency of Model Development and Processing via Innovative Human Reliability Dependency Analysis

Probabilistic risk assessments (PRAs) are integral to nuclear power plant (NPP) operations, having tremendously benefitted the safety of the U.S. reactor fleet for decades. Insights obtained from the models have provided perspectives on a variety of applications, both at the plant and for the regulator. While these models are very useful, they are now being asked to represent and analyze aspects of the plant that were never envisioned by the initial PRA practitioners. Furthermore, heightened demands on the PRA models have led to increased computing power requirements. Additionally, as the complexity of the PRA models increased, the difficulty experienced by non-PRA experts in trying to understand these models, grasp the insights they provide, and effectively use that information has become problematic. The need for research to address key issues regarding PRA tools and methods has never been greater. Although the nuclear power industry has largely been well-served by these tools and methods, the underlying science is dated, remaining mostly unchanged for over two decades. Three areas were identified as most beneficial to address to maintain and improve the usefulness of the current practice legacy PRA tools: improved quantification speed, increased ability to efficiently model multi-hazard models, and improved modeling human action dependency in PRA. This report is focused on the third critical area, improvements in dependency analysis of human actions conducted as part of a typical human reliability assessment.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Selective Extraction and Recovery of Nd and Dy from Nd-Fe-B Magnet Scrap by Utilizing Molten MgCl{sub 2}

Fundamental experiments are conducted with the aim of developing an efficient recycling process for rare earth elements (REEs) from neodymium-iron-boron (Nd-Fe-B) permanent magnet scrap. Molten magnesium dichloride (MgCl{sub 2}) was chosen as an extraction medium, which can selectively chlorinate and extract REEs in magnet alloys. Dysprosium-containing Nd-Fe-B magnet alloy was immersed in molten MgCl{sub 2} at 1273 K (1000 °C) for 3 to 12 hours. The results of the experiments clearly show that the REEs in the magnetic alloy were successfully extracted into the molten salt, while the Fe-B alloy remained in a solid form. The extraction ratios of Nd and Dy were at most 87 and 78 mass pct, respectively. After the extraction experiment, excess MgCl{sub 2} and Mg were removed by vacuum distillation and the rare earth chlorides were recovered. Thus, the feasibility of this method for efficient recovery of rare earths using molten MgCl{sub 2} is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Long-term performance of a UASB reactor treating acid mine drainage: effects of sulfate loading rate, hydraulic retention time, and COD/SO42- ratio

Acid mine drainage (AMD) is among the most serious threats to water and the typical alkali-based treatment costs are high. This study's main objective was the establishment of a highly efficient biological process using an upflow anaerobic sludge blanket (UASB) reactor to treat AMD based on a shorter hydraulic retention time (HRT) and lower organic matter input. The process was evaluated for a long-term operation (739days) in terms of the influence of HRT (14-24h), metal addition, sulfate loading rate (0.5-2.6gSO(4)(2-)l(-1)d(-1)), and the COD/SO42- ratio (0.67-1.0) using ethanol as the only electron donor at a pH of 4.0. Neutral effluent pH was achieved throughout the time apart from operational modifications. The reduction in HRT from 24 to 16h and an increase in the sulfate loading rate (SLR) up to 2.25gSO(4)(2-)l(-1)d(-1) improved the sulfate removal to (92.1 +/- 1.8)% with 80% chemical oxygen demand (COD) removal. However, the sulfate reduction was less than 80% when the HRT and SLR was changed to 14h and 2.6gSO(4)(2-)l(-1)d(-1), respectively. The oxidation of organic matter by sulfate reduction was greater than 50% regardless of the conditions imposed but the use of ethanol to treat AMD was more efficient when either the HRT was 16h (1.5gSO(4)(2-)l(-1)d(-1)) in the presence of Fe, Zn, and Cu or the HRT was 14h (2.6gSO(4)(2-)l(-1)d(-1)) but the COD/SO42- ratio was reduced to 0.67. The fully optimized conditions of the UASB reactor were set at an HRT of 16h, SLR of 1.5gSO(4)(2-)l(-1)d(-1), and a COD/SO42- ratio of 1.0.

Acid mine drainage↗
DOE OSTI2025

Crossing the Finish Line: Integration of Data-Driven Process Control for Maximization of Energy and Resource Efficiency in Advanced Water Resource Recovery Facilities

Improvements in process monitoring and control at water resource recovery facilities (WRRFs) could result in reductions in electricity consumption, chemical inputs, and greenhouse gas emissions, as well as improved energy recovery. Many current WRRF data collection, monitoring, and control approaches use 20th century process monitoring and control systems, which require large design safety factors to ensure reliability in the absence of more advanced, precise controls. Implementation of more modern data-driven control tools could lead to more efficient operations that provide intrinsic reliability with better overall process performance at full-scale. This project (1) developed and demonstrated data-driven process controls at full-scale facilities for five promising WRRF process technologies that provide whole-plant approaches and offer substantial energy and resource recovery benefits, and (2) created a Machine Learning (ML) Toolkit and an implementation guide of new process control approaches that walks users through each step of the ML workflow and illustrates the steps through case study examples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

KSHV vIRF4 enhances BCL6 transcription via downregulation of IRF4 expression

Highlights: • KSHV vIRF4 enhances the BCL6 expression by inhibiting IRF4 expression. • vIRF4 binds to the promoter region of IRF4 and suppresses IRF4 expression. • vIRF4-mediated deregulation of BCL6-IRF4 axis requires efficient KSHV lytic gene expression. Primary effusion lymphoma (PEL), strongly linked with latent infection of Kaposi's sarcoma-associated herpesvirus (KSHV), constitutively expresses cellular interferon regulatory factor 4 (IRF4) while suppressing the expression of B cell lymphoma 6 (BCL6). Recently, it was shown that IRF4, a key transcriptional repressor of BCL6, might be a pivotal regulator of KSHV for balancing between latency and its reactivation in PEL cells. However, the action of the BCL6-IRF4 transcription factor axis during KSHV's life cycle is not clear. Herein we found that the KSHV lytic protein viral interferon regulatory factor 4 (vIRF4) dramatically enhanced the transcriptional activity of the BCL6 through the inhibition of its negative regulator IRF4. Using a chromatin immunoprecipitation (ChIP) assay, we further showed that vIRF4 bound to the specific promoter region of IRF4, contributing to a dramatic suppression of IRF4 gene expression. Correspondingly, we also found BCL6 expression to be positively and inversely correlated with vIRF4 and IRF4 expression, respectively, during KSHV reactivation. Finally, we observed that these processes require efficient KSHV lytic replication. Thus, our findings suggest a crucial role of the BCL6-IRF4 axis in triggering the transition between KSHV latency and lytic reactivation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Quantum Efficiency of Charge Transfer Competing against Nonexponential Processes: The Case of Electron Transfer from CdS Nanorods to Hydrogenase

Photoexcited charge transfer from semiconductor nanocrystals to charge acceptors is a key step for photon energy conversion in semiconductor nanocrystal-based light-harvesting systems. Charge transfer competes against relaxation processes within the nanocrystals, and this competition determines the quantum efficiency of charge transfer. The quantum efficiency is a critical design element in photochemistry, but in nanocrystal-acceptor systems its extraction from experimental data is complicated by sample heterogeneity and intrinsically nonexponential excited-state decay pathways. In this manuscript, we systematically explore these complexities using TA spectroscopy over a broad range of timescales to probe electron transfer from CdS nanorods to the redox enzyme hydrogenase. To analyze the experimental data, we build a model that quantifies the quantum efficiency of charge transfer in the presence of competing, potentially nonexponential, relaxation processes. Our approach can be applied to calculate the efficiency of charge or energy transfer in any donor-acceptor system that exhibits nonexponential donor decay and any ensemble distribution in the number of acceptors, provided that donor relaxation and charge transfer can be described as independent, parallel decay pathways. We apply this analysis to our experimental system and unveil the connections between particle morphology and quantum efficiency. Our model predicts a finite quantum efficiency even when the mean recombination time diverges, as it does in CdS nanostructures with spatially separated electron-hole pairs that recombine with power-law dynamics. We contrast our approach to the widely used expressions for the quantum efficiency based on average lifetimes, which for our system overestimate the quantum efficiency. Furthermore, the approach developed here is straightforward to implement and should be applicable to a wide range of systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Double in-situ lignin modification in surfactant-assisted glycerol organosolv pretreatment of sugarcane bagasse towards efficient enzymatic hydrolysis

Organosolv pretreatment effectively fractionates lignocellulosic biomass for efficient enzymatic hydrolysis, yielding fermentable sugars. However, substantial lignin redeposition on the substrate surface and residual lignin content exacerbate lignin’s inhibitory effects on subsequent enzymatic hydrolysis. Here, an in-situ lignin modification is proposed to address this challenge by incorporating polyethylene glycol (PEG) series into glycerol organosolv (GO) pretreatment. According to the results obtained, PEG-assisted GO pretreated substrate exhibited significantly increased sugar yield during enzymatic hydrolysis, particularly with PEG 4000, showing 35.4% higher glucose yield over 72 h. The improved glucose yield with PEG could be attributed to changes in the physicochemical structure and properties of residual lignin, mitigating its non-productive interaction with cellulase enzymes. PEG's presence in GO pretreatment also increased β-O-4 linkages preservation by 55% and reduced phenolic hydroxyl groups by 28% in lignin fragments versus no surfactants. This outcome resulted from introducing glycerol and PEG hydroxyl tails into the lignin structure, forming α-etherified lignin and rendering it more hydrophilic. Simulations confirmed strong Van Der Waals and hydrogen bonding interactions between lignin units and PEG, hindering lignin fragments repolymerization. Incorporating PEG in the organosolv pretreatment proves highly beneficial for cellulase-mediated lignocellulosic biorefinery, resembling a lignin-first strategy. The optimized process enables efficient biomass utilization for sustainable fuels and chemicals production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗