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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Inverse Design of Photonic Surfaces via High throughput Femtosecond Laser Processing and Tandem Neural Networks

Abstract This work demonstrates a method to design photonic surfaces by combining femtosecond laser processing with the inverse design capabilities of tandem neural networks that directly link laser fabrication parameters to their resulting textured substrate optical properties. High throughput fabrication and characterization platforms are developed that generate a dataset comprising 35280 unique microtextured surfaces on stainless steel with corresponding measured spectral emissivities. The trained model utilizes the nonlinear one‐to‐many mapping between spectral emissivity and laser parameters. Consequently, it generates predominantly novel designs, which reproduce the full range of spectral emissivities (average root‐mean‐squared‐error < 2.5%) using only a compact region of laser parameter space 25 times smaller than what is represented in the training data. Finally, the inverse design model is experimentally validated on a thermophotovoltaic emitter design application. By synergizing laser‐matter interactions with neural network capabilities, the approach offers insights into accelerating the discovery of photonic surfaces, advancing energy harvesting technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Computational studies reveal Fluorine based quinolines to be potent inhibitors for proteins involved in SARS-CoV-2 assembly

World is witnessing one of the worst pandemics of this century caused by SARS-CoV-2 virus which has affected millions of individuals. Despite rapid efforts to develop vaccines and drugs for COVID-19, the disease is still not under control. Chloroquine (CQ) and Hydroxychloroquine (HCQ) are two very promising inhibitors which have shown positive effect in combating the disease in preliminary experimental studies, but their use was reduced due to severe side-effects. Here, we performed a theoretical investigation of the same by studying the binding of the molecules with SARS-COV-2 Spike protein, the complex formed by Spike and ACE2 human receptor and a human serine protease TMPRSS2 which aids in cleavage of the Spike protein to initiate the viral activation in the body. Both the molecules had shown very good docking energies in the range of -6kcal/mol. Subsequently, we did a high throughput screening for other potential quinoline candidates which could be used as inhibitors. From the large pool of ligand candidates, we shortlisted the top three ligands (binding energy -8kcal/mol). We tested the stability of the docked complexes by running Molecular Dynamics (MD) simulations where we observed the stability of the quinoline analogues with the Spike-ACE2 and TMPRSS2 nevertheless the quinolines were not stable with the Spike protein alone. Thus, although the inhibitors bond very well with the protein molecules their intrinsic binding affinity depends on the protein dynamics. Moreover, the quinolines were stable when bound to electronegative pockets of Spike-ACE2 or TMPRSS2 but not with Viral Spike protein. We also observed that a Fluoride based compound: 3-[3-(Trifluoromethyl)phenyl]quinoline helps the inhibitor to bind with both Spike-ACE2 and TMPRSS2 with equal probability. The molecular details presented in this study would be very useful for developing quinoline based drugs for COVID-19 treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Simulating excited-state complex ensembles: Fluorescence and solvatochromism in amine-arene exciplexes

Exciplexes are excited-state complexes formed as a result of partial charge transfer from the donor to the acceptor species when one moiety of the donor–acceptor pair is electronically excited. The arene–amine exciplex formed between oligo-(p-phenylene) (OPP) and triethylamine (TEA) is of interest in the catalytic photoreduction of CO2 because it can compete with complete electron transfer to the OPP catalyst. Therefore, formation of the exciplex can hinder the generation of a radical anion OPP·− necessary for subsequent CO2 reduction. We report an implementation of a workflow automating quantum-chemistry calculations that generate and characterize an ensemble of structures to represent this exciplex state. We use FireWorks, Pymatgen, and Custodian Python packages for high-throughput ensemble generation. The workflow includes time-dependent density functional theory optimization, verification of excited-state minima, and exciplex characterization with natural transition orbitals, exciton analysis, excited-state Mulliken charges, and energy decomposition analysis. Fluorescence spectra computed for these ensembles using Boltzmann-weighted contributions of each structure agree better with experiment than our previous calculations based on a single representative exciplex structure [Kron et al., J. Phys. Chem. A 126, 2319–2329 (2022)]. The ensemble description of the exciplex state also reproduces an experimentally observed red shift of the emission spectrum of [OPP-4–TEA]* relative to [OPP-3–TEA]*. The workflow developed here streamlines otherwise labor-intensive calculations that would require significant user involvement and intervention.

Chemistry↗
DOE OSTI2022

The CALPHAD approach for HEAs: Challenges and opportunities

Phases are key microstructural features that determine material properties. The most intuitive way of representing phase stability in a material is by its phase diagram. The vastness of the composition space is the most compelling reason for investigating high-entropy alloys (HEAs), but it also creates major challenges in controlling phases and microstructures of HEAs due to lack of experimental phase diagrams in highly concentrated multicomponent space. The CALPHAD (Calculation of Phase Diagram) approach, although it needs improvements, is the only viable way to calculate multicomponent phase diagrams. The two major property databases in CALPHAD, Gibbs energy and mobility, when coupled, can provide not only the equilibrium phase information, but also information about diffusion and kinetics. In this article, we will review the use of the CALPHAD approach in understanding phase stability and diffusion in high-entropy alloys. Finally, we will discuss the notable trend of coupling the high-throughput CALPHAD approach with machine learning in HEA design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High-throughput exploration of the WMoVTaNbAl refractory multi-principal-element alloys under multiple-property constraints

Development of next-generation gas turbines requires the design and fabrication of novel high-temperature structural materials capable of operating beyond 1300°C. Here, we propose a high-throughput alloy design framework under multiple-property constraints to discover new refractory multi-principal element alloys (MPEAs) for high-temperature applications. The framework treats the development of MPEAs as a composition-agnostic constraint satisfaction problem, i.e., no prescriptions are made concerning the design space before performing investigatory calculations. We target alloys in the WMoVTaNbAl chemistry space that are predicted to meet constraints on the following properties simultaneously: single-phase stability, density, solidus temperature, yield strength at 1300°C, and ductile-to-brittle-transition temperature. These properties are relevant to both applications in gas turbines and manufacturability. A set of 214 MoNbV-rich alloys meet these relevant constraints. These feasible alloys are investigated with density functional theory (DFT) to provide a fundamental electronic basis for their superior properties. Three compositionally representative alloys from the feasible design space (Mo 45 Nb 35 Ta 5 V 15 , Mo 25 Nb 50 V 20 W 5 , and Mo 30 Nb 35 Ta 5 V 25 W 5 ) are selected with a k-medoids-based design scheme for detailed DFT analysis and experimental characterization. The DFT analysis predicted a single-phase BCC at high temperatures with a high yield strength for all three MPEAs, in agreement with CALPHAD (CALculation of PHAse Diagrams) and experiments, respectively. These three alloys are benchmarked against a public database of 1546 MPEAs. Concerning the aforementioned constraints, the Mo 30 Nb 35 Ta 5 V 25 W 5 alloy outperforms these 1546 MPEAs. The present work demonstrates the ability of the proposed design methodology to identify candidate alloys for a given application under multiple property constraints in a combinatorically vast design space.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The LDRD ER “Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats” was conceived to address a need for rapid, scalable, evaluation of computationally designed therapeutic or prophylactic antibodies and vaccine antigens, two important classes of protein medical countermeasure (MCM). This was done in complement to a computationally driven LDRD 20ERD032 “Active Learning for Rapid Design of Vaccines and Antibodies.” Natural antibodies and antigens are often insufficiently broad or robust across different pathogens and their variants. Leveraging a collaboration of simulation driven machine learning, structural expertise, and high-throughput characterization of candidate antibodies, we successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro. Our antibody design work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, we computationally designed derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Our designs were successful, including a pair of designs which provide potent neutralization of not only Omicron BA.1 and BA.1.1, but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗
DOE OSTI2025

An NV − center in magnesium oxide as a spin qubit for hybrid quantum technologies

Recent predictions suggest that oxides, such as MgO and CaO, could serve as hosts of spin defects with long coherence times and thus be promising materials for quantum applications. However, in most cases, specific defects have not yet been identified. Here, by using a high-throughput first-principles framework and advanced electronic structure methods, we identify a negatively charged complex between a nitrogen interstitial and a magnesium vacancy in MgO with favorable electronic and optical properties for hybrid quantum technologies. We show that this NV − center has stable triplet ground and excited states, with singlet shelving states enabling optical initialization and spin-dependent readout. We predict several properties, including absorption, emission, and zero-phonon line energies, as well as zero-field splitting tensor, and hyperfine interaction parameters, which can aid in the experimental identification of this defect. Our calculations show that due to a strong pseudo-Jahn Teller effect and low-frequency phonon modes, the NV − center in MgO is subject to a substantial vibronic coupling. We discuss design strategies to reduce such coupling and increase the Debye-Waller factor, including the effect of strain and the localization of the defect states. We propose that the favorable properties of the NV − defect, along with the technological maturity of MgO, could enable hybrid classical-quantum applications, such as spintronic quantum sensors and single qubit gates.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific ap- plications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Net- works (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate band- widths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer perfor- mance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application- level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher pre- diction accuracy in up to 90% of the cases with very noisy real-life performance measurements.

Yun, Daqing↗
DOE OSTI2023

Physics constrained unsupervised deep learning for rapid, high resolution scanning coherent diffraction reconstruction

By circumventing the resolution limitations of optics, coherent diffractive imaging (CDI) and ptychography are making their way into scientific fields ranging from X-ray imaging to astronomy. Yet, the need for time consuming iterative phase recovery hampers real-time imaging. While supervised deep learning strategies have increased reconstruction speed, they sacrifice image quality. Furthermore, these methods’ demand for extensive labeled training data is experimentally burdensome. Here, we propose an unsupervised physics-informed neural network reconstruction method, PtychoPINN, that retains the factor of 100-to-1000 speedup of deep learning-based reconstruction while improving reconstruction quality by combining the diffraction forward map with real-space constraints from overlapping measurements. In particular, PtychoPINN gains a factor of 4 in linear resolution and an 8 dB improvement in PSNR while also accruing improvements in generalizability and robustness. This blend of performance and computational efficiency offers exciting prospects for high-resolution real-time imaging in high-throughput environments such as X-ray free electron lasers (XFELs) and diffraction-limited light sources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Broadband X-ray ptychography using multi-wavelength algorithm

Ptychography is a rapidly developing scanning microscopy which is able to view the internal structures of samples at a high resolution beyond the illumination size. The achieved spatial resolution is theoretically dose-limited. A broadband source can provide much higher flux compared with a monochromatic source; however, it conflicts with the necessary coherence requirements of this coherent diffraction imaging technique. In this paper, a multi-wavelength reconstruction algorithm has been developed to deal with the broad bandwidth in ptychography. Compared with the latest development of mixed-state reconstruction approach, this multi-wavelength approach is more accurate in the physical model, and also considers the spot size variation as a function of energy due to the chromatic focusing optics. Therefore, this method has been proved in both simulation and experiment to significantly improve the reconstruction when the source bandwidth, illumination size and scan step size increase. It is worth mentioning that the accurate and detailed information of the energy spectrum for the incident beam is not required in advance for the proposed method. Further, we combine multi-wavelength and mixed-state approaches to jointly solve temporal and spatial partial coherence in ptychography so that it can handle various disadvantageous experimental effects. The significant relaxation in coherence requirements by our approaches allows the use of high-flux broadband X-ray sources for high-efficient and high-resolution ptychographic imaging.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Spray-Coated Silver as Backside Metal for III–V Photovoltaic Devices on GaAs and Ge Substrates

The accelerated increase in demand for III-V space photovoltaics on GaAs and Ge substrates, as well as growing interests in terrestrial applications, motivate the development of cost-effective, high-throughput processing routes of these materials. Here, in this study, we assess spray-coated silver (Ag) back contact metallization as a substitute for electron-beam-evaporated metals currently used in industry. We find that the spray-coated Ag films are dense and continuous. By means of quantum efficiency, dark current-voltage, and illuminated current-voltage characterizations, we show that spray-coated GaAs and Ge solar cells perform similarly to baseline devices with electroplated Au, including under high current densities. We estimate that the thresholds for specific contact resistance below which back contacts do not significantly contribute to resistive loss are 2.1 x 10 -1 Ω•cm 2 for GaAs and 4.7 x 10 -2 Ω•cm 2 for Ge. We experimentally confirm that our spray-coated samples meet these requirements. Peel tests show that the adhesion of plain spray-coated Ag films to the back of p-type Ge substrates used in III-V solar cells is currently insufficient, whereas adhesion to p-type GaAs substrates is outstanding and requires no further optimization.

14 SOLAR ENERGY↗
DOE OSTI2022

Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina

Abstract Structure‐based virtual high‐throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the ‘docking power’. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein‐ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.

Agarwal, Rupesh↗
DOE OSTI2024

A high-throughput framework for lattice dynamics

We develop an automated high-throughput workflow for calculating lattice dynamical properties from first principles including those dictated by anharmonicity. The pipeline automatically computes interatomic force constants (IFCs) up to 4th order from perturbed training supercells, and uses the IFCs to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free energy and entropy. It performs phonon renormalization for dynamically unstable compounds to obtain real effective phonon spectra at finite temperatures and calculates the associated free energy corrections. The methods and parameters are chosen to balance computational efficiency and result accuracy, assessed through convergence testing and comparisons with experimental measurements. Deployment of this workflow at a large scale would facilitate materials discovery efforts toward functionalities including thermoelectrics, contact materials, ferroelectrics, aerospace components, as well as general phase diagram construction.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Photon counting with intensified charge coupled device (ICCD) – I. In-depth detector characterization

While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understanding of semiconductor catalysts has not yet been achieved. In this work, we present a high-throughput density functional theory investigation into the adsorption properties of 5 oxides of interest to the photocatalytic CO 2 reduction reaction: TiO 2 (rutile and anatase), SrTiO 3 , NaTaO 3 , and CeO 2 . Using a systematic approach, we exhaustively identify unique surfaces and construct adsorption structures to undergo geometry optimizations. We then perform a data-driven analysis, which reveals the presence of weak adsorption energy scaling relations, the propensity of adsorbates of interest to interact with oxygen surface sites, and the importance of slab deformation upon adsorption. Our findings are presented in the context of experimental observations and in comparison to previously studied classes of catalysts, such as pure metals and tellurium-containing semiconductors, and reinforce the need for a comprehensive approach to the study of site-specific surface phenomena on semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Systematic computational study of oxide adsorption properties for applications in photocatalytic CO 2 reduction

While the adsorption properties of transition metal catalysts have been widely studied, leading to the discovery of various scaling relations, descriptors of catalytic activity, and well-established computational models, a similar understanding of semiconductor catalysts has not yet been achieved. In this work, we present a high-throughput density functional theory investigation into the adsorption properties of 5 oxides of interest to the photocatalytic CO 2 reduction reaction: TiO 2 (rutile and anatase), SrTiO 3 , NaTaO 3 , and CeO 2 . Using a systematic approach, we exhaustively identify unique surfaces and construct adsorption structures to undergo geometry optimizations. We then perform a data-driven analysis, which reveals the presence of weak adsorption energy scaling relations, the propensity of adsorbates of interest to interact with oxygen surface sites, and the importance of slab deformation upon adsorption. Our findings are presented in the context of experimental observations and in comparison to previously studied classes of catalysts, such as pure metals and tellurium-containing semiconductors, and reinforce the need for a comprehensive approach to the study of site-specific surface phenomena on semiconductors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Explore the Potential to Formulate Feedstock Blends from Diverse Biomass inputs for Improved Processing Performance at Lower Costs

This work aims to understand the pelleting characteristics of 2-inch top-pine residue blended with switchgrass at high moisture content. The process variables tested are blend moisture content, length-to-diameter (L/D) ratio in the pellet die, and the blend ratio. A flat-die pellet mill was also used in this study. The pine and switchgrass blend ratios that were tested include (1) 25% 2-inch top pine residue with 75% switchgrass; (2) 50% 2-inch top pine residue with 50% switchgrass; and (3) 75% 2-inch top pine residue with 25% switchgrass. The pelleting process conditions tested included the L/D ratio in the pellet die (i.e., 1.5 to 2.6) and the blend moisture content (20 to 30%, w.b.). Analysis of experimental data indicated that blending 25% switchgrass with 75% 2-inch top pine residue and 50% switchgrass with 50% 2-inch top pine residue resulted in pellets with a bulk density of >550 kg/m3 and durability of >95. Optimization of the response surface models developed for process conditions in terms of product properties indicated that a higher L/D ratio of 2.6 and a lower blend-moisture content of 20% (w.b.) maximized bulk density and durability. Higher pine in the blends improved the pellet durability and reduced energy consumption. Tests were also conducted on blends of pine and switchgrass using a pilot scale pellet mill by varying the blend moisture content and compression ratio of the pellet die. Pelleting tests on pure pine and switchgrass and their blends in a pilot scale ring die pellet mill with a throughput of 1 ton/h has indicated pellets with durability >500 kg/m3 and durability in the range of >90 and >95 %. The demonstration of high moisture pelleting of blends of 2- and 6-inch top pine residues and switchgrass blends indicated that good quality pellets with durability >90 % and bulk density of >500 kg/m3 can be produced.

09 BIOMASS FUELS↗
DOE OSTI2024

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.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗