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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Equilipy

There is high demand for high-throughput calculations of phase equilibria based on the CALPHAD method (CALculation of PHAse Diagram). High-throughput calculations are possible through HPC, currently by a commercial program called Thermo-Calc. However, the number of nodes/processors are limited to the number of purchased license (16 processors per license). This program provides a toolkit for high-throughput calculations of phase equilibria by the CALPHAD method. The program is prepared for Python environment, so that it can be easily installed and used together with other open-source programs. The program can be run in supercomputer.

Kwon, Sunyong [Oak Ridge National Laboratory (ORNL↗
DOE OSTI2023

Application of Cell Painting for chemical hazard evaluation in support of screening-level chemical assessments

‘Cell Painting’ is an imaging-based high-throughput phenotypic profiling (HTPP) method in which cultured cells are fluorescently labeled to visualize subcellular structures (i.e., nucleus, nucleoli, endoplasmic reticulum, cytoskeleton, Golgi apparatus / plasma membrane and mitochondria) and to quantify morphological changes in response to chemicals or other perturbagens. HTPP is a high-throughput and cost-effective bioactivity screening method that detects effects associated with many different molecular mechanisms in an untargeted manner, enabling rapid in vitro hazard assessment for thousands of chemicals. Here, 1201 chemicals from the ToxCast library were screened in concentration-response up to ~100 μM in human U-2 OS cells using HTPP. A phenotype altering concentration (PAC) was estimated for chemicals active in the tested range. PACs tended to be higher than lower bound potency values estimated from a broad collection of targeted high-throughput assays, but lower than the threshold for cytotoxicity. In vitro to in vivo extrapolation (IVIVE) was used to estimate administered equivalent doses (AEDs) based on PACs for comparison to human exposure predictions. AEDs for 18/412 chemicals overlapped with predicted human exposures. Phenotypic profile information was also leveraged to identify putative mechanisms of action and group chemicals. Of 58 known nuclear receptor modulators, only glucocorticoids and retinoids produced characteristic profiles; and both receptor types are expressed in U-2 OS cells. Thirteen chemicals with profile similarity to glucocorticoids were tested in a secondary screen and one chemical, pyrene, was confirmed by an orthogonal gene expression assay as a novel putative GR modulating chemical. Most active chemicals demonstrated profiles not associated with a known mechanism-of-action. However, many structurally related chemicals produced similar profiles, with exceptions such as diniconazole, whose profile differed from other active conazoles. Overall, the present study demonstrates how HTPP can be applied in screening-level chemical assessments through a series of examples and brief case studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Simple Scattering: Lipid nanoparticle structural data repository

Lipid nanoparticles (LNPs) are being intensively researched and developed to leverage their ability to safely and effectively deliver therapeutics. To achieve optimal therapeutic delivery, a comprehensive understanding of the relationship between formulation, structure, and efficacy is critical. However, the vast chemical space involved in the production of LNPs and the resulting structural complexity make the structure to function relationship challenging to assess and predict. New components and formulation procedures, which provide new opportunities for the use of LNPs, would be best identified and optimized using high-throughput characterization methods. Recently, a high-throughput workflow, consisting of automated mixing, small-angle X-ray scattering (SAXS), and cellular assays, demonstrated a link between formulation, internal structure, and efficacy for a library of LNPs. As SAXS data can be rapidly collected, the stage is set for the collection of thousands of SAXS profiles from a myriad of LNP formulations. In addition, correlated LNP small-angle neutron scattering (SANS) datasets, where components are systematically deuterated for additional contrast inside, provide complementary structural information. The centralization of SAXS and SANS datasets from LNPs, with appropriate, standardized metadata describing formulation parameters, into a data repository will provide valuable guidance for the formulation of LNPs with desired properties. To this end, we introduce Simple Scattering, an easy-to-use, open data repository for storing and sharing groups of correlated scattering profiles obtained from LNP screening experiments. Here, we discuss the current state of the repository, including limitations and upcoming changes, and our vision towards future usage in developing our collective knowledge base of LNPs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Accelerating polyketide synthase engineering for high TRY production of biofuels and bioproducts (Final Technical Report)

Polyketide synthase (PKS) enzymes have a modular, deterministic logic that holds the potential to act as a flexible chemical factory for the biological production of a huge diversity of valuable small molecule compounds. However, engineering a custom PKS to produce a specific desired product currently requires years of trial and error, for reasons that remain poorly understood. In this project, we have developed a rapid, high throughput, Design-Build-Test-Learn (DBTL) cycle for polyketide synthases (PKSs) and demonstrate its utility for production of materials precursors. The objectives are 1) to develop a rapid, high-throughput (HT) DBTL cycle for PKSs that will enable production of a large number of unnatural, organic molecules on demand at high titer, rate, and yield (TRY); 2) to demonstrate the utility of the PKS DBTL cycle to produce three molecules: one commodity chemical (caprolactam or valerolactam) and two novel materials precursors (caprolactam or valerolactam derivatives); and 3) to demonstrate the utility of the PKS DBTL cycle to increase the TRY of one molecule (caprolactam or valerolactam). In this project, we have successfully demonstrated our high throughput PKS DBTL pipeline, and have biologically produced valerolactam and several other novel nylon monomers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Accelerating polyketide synthase engineering for high TRY production of biofuels and bioproducts (CRADA Final Report)

Polyketide synthase (PKS) enzymes have a modular, deterministic logic that holds the potential to act as a flexible chemical factory for the biological production of a huge diversity of valuable small molecule compounds. However, engineering a custom PKS to produce a specific desired product currently requires years of trial and error, for reasons that remain poorly understood. In this project, we have developed a rapid, high throughput, Design-Build-Test-Learn (DBTL) cycle for polyketide synthases (PKSs) and demonstrate its utility for production of materials precursors. The objectives are 1) to develop a rapid, high-throughput (HT) DBTL cycle for PKSs that will enable production of a large number of unnatural, organic molecules on demand at high titer, rate, and yield (TRY); 2) to demonstrate the utility of the PKS DBTL cycle to produce three molecules: one commodity chemical (caprolactam or valerolactam) and two novel materials precursors (caprolactam or valerolactam derivatives); and 3) to demonstrate the utility of the PKS DBTL cycle to increase the TRY of one molecule (caprolactam or valerolactam). In this project, we have successfully demonstrated our high throughput PKS DBTL pipeline, and have biologically produced valerolactam and several other novel nylon monomers.

09 BIOMASS FUELS↗
DOE OSTI2025

Instrumentation and methods for efficient time-resolved X-ray crystallography of biomolecular systems with sub-10 ms time resolution

Time-resolved X-ray crystallography has great promise to illuminate structure–function relations and key steps of enzymatic reactions with atomic resolution. The dominant methods for chemically-initiated reactions require complex instrumentation at the X-ray beamline, significant effort to operate and maintain this instrumentation, and enormous numbers (∼10 5 –10 9 ) of crystals per time point. We describe instrumentation and methods that enable high-throughput time-resolved study of biomolecular systems using standard crystallography sample supports and mail-in X-ray data collection at standard high-throughput cryocrystallography synchrotron beamlines. The instrumentation allows rapid reaction initiation by mixing of crystals and substrate/ligand solution, rapid capture of structural states via thermal quenching with no pre-cooling perturbations, and yields time resolutions in the single-millisecond range, comparable to the best achieved by any non-photo-initiated method in both crystallography and cryo-electron microscopy. Our approach to reaction initiation has the advantages of simplicity, robustness, low cost, adaptability to diverse ligand solutions and small minimum volume requirements, making it well suited to routine laboratory use and to high-throughput screening. We report the detailed characterization of instrument performance, present structures of binding of N -acetylglucosamine to lysozyme at time points from 8 ms to 2 s determined using only one crystal per time point, and discuss additional improvements that will push time resolution toward 1 ms.

Indergaard, John A. (ORCID:000000022367699X)↗
DOE OSTI2022

Integrated Computational Materials and Mechanical Modeling for Additive Manufacturing of Alloys with Graded Structure Used in Fossil Fuel Power Plants

Wire-arc additive manufacturing (WAAM) has demonstrated its unique capability of producing large-size alloy components with a significantly reduced fabrication time and enhanced geometry design freedom. In this project, the team has developed an ICME (Integrated Computational Materials Engineering) modeling framework, which supports the WAAM of the AUSC (Advanced Ultra-Supercritical) power plant components. The manufacturing design has been applied to Inconel 740H, steel P91, as well as the dissimilar alloy components between steel P91 and Inconel 740H. The ICME model framework is developed by considering two types of modeling. First, mechanistic modeling has been applied to control the printing quality and understand the sequence of the dissimilar printing of the wall structure. The following models have been included in the developed ICME framework: finite element thermal model, grain structure model, residual stress simulation, crystal plasticity model, CALPHAD-based precipitation kinetic model, phase stability prediction, thermal expansion predictive model, and heuristic creep model. Secondary, a physics-based machine learning model has also been developed based on the ICME model structure. The machine learning model development is based on the ICME model prediction with calibration of the experiments. In addition, the WAAM has been utilized as a high-throughput experimental tool rapidly generating a gradient of alloy composition to facilitate experimental database generation for process-structure-property relationships. Such a database directly supported the ICME-enhanced machine learning, which further assisted in intermediate composition block design between P91 and 740H. A high-throughput screening study of the oxidation resistance has been performed based on such high-throughput experimentation. Based on the computational design, several dissimilar alloy manufacturing with post-heat treatment have been performed with a comprehensive evaluation of mechanical performance, including hardness mapping, yield strength, creep resistance. In this project, the single component of P91 and 740H processed by WAAM after heat treatment designed by ICME has demonstrated higher performance in yield strength and creep resistance than the wrought materials. The P91 sample prepared by WAAM with ICME-designed heat treatment performs better than P92 in creep resistance. The designed graded alloy printing with intermediate block shows a promising performance that exceeds the traditional welding. Moreover, the current research indicates the high need for location-specific design analysis with uncertainty quantification, an important topic that deserves more dedicated research. The achievement of this project demonstrated the promising future of WAAM in structural alloy manufacturing for energy power plant development. Successful printing requires synergetic efforts made by manufacturing, mechanical, and materials sciences.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Finding the perfect imperfection: Accelerated, computationally driven discovery and design of quantum defects

Optically addressable spin defects have emerged as the leading platforms for quantum sensing and communication in solid-state systems. While traditional efforts have concentrated on a focused set of well-studied defects, recent advances in high-throughput computational methods have shown promise for large-scale exploration of defects across diverse semiconductor hosts. By cataloging key properties of quantum defects in computational databases, high-throughput screening techniques can systematically suggest and design novel candidates. In this article, we highlight recent advances in data-driven quantum defect design aimed at addressing critical materials science challenges such as host materials selection, defect stability, and desirable electronic and optical properties. Here, we emphasize the importance of electronic-structure-guided searches across various materials and illustrate how high-throughput computations contribute to our understanding of design principles for quantum defects. Additionally, we outline ongoing challenges and emerging opportunities in this rapidly developing field.

Xiong, Yihuang [Dartmouth College, Hanover, NH (Un↗
DOE OSTI2026

Functional characterization of glycosyltransferases in duckweed to enable predictive biology

Glycosyltransferases (GTs) catalyze the formation of glycosidic linkages to produce almost all complex carbohydrates. This project used a multi-disciplinary, high-throughput (HTP) biochemical and computational biology approach focused on duckweed as a model energy crop, to study carbohydrate metabolic processes. To achieve this, developed and carried out out high-throughput (HTP) functional characterization of plant glycosyltransferases (GTs) role of enzymatic microenvironments be assessed through a combined proteomic and computational biology approach, and the combined data was used to populate deep-learning frameworks to predict plant GT function. Functional validation achieved through this research is being used to assign gene function and study plant processes at the systems level to efficiently link the genome sequence with gene function. Together, the combined approaches used within this study provide a foundation for how computational prediction, in combination with high-throughput functional validation, can be used to study plant processes at the systems level and translate knowledge gained to efficiently link genome sequence with gene function in a species agnostic manner.

09 BIOMASS FUELS↗
DOE OSTI2024

Millimeter-Scale Exfoliation of hBN with Tunable Flake Thickness for Scalable Encapsulation

As a two-dimensional (2D) dielectric material, hexagonal boron nitride (hBN) is in high demand for applications in photonics, nonlinear optics, and nanoelectronics. Unfortunately, the high-throughput preparation of macroscopic-scale, high-quality hBN flakes with controlled thickness is an ongoing challenge, limiting device fabrication and technological integration. In this report we present a metal thin-film exfoliation method to prepare hBN flakes with millimeter-scale dimension, near-unity yields, and tunable flake thickness distribution from 1-7 layers, a substantial improvement over scotch tape exfoliation. The single crystallinity and high quality of the exfoliated hBN are demonstrated with optical microscopy, atomic force microscopy, Raman spectroscopy, and second harmonic generation. We further explore a possible mechanism for the effectiveness and selectivity based on thin-film residual stress measurements, density functional theory calculations, and transmission electron microscopy imaging of the deposited metal films. We find that the magnitude of the residual tensile stress induced by thin film deposition plays a key role in determining exfoliated flake thickness in a manner which closely resembles 3D semiconductor spalling. Lastly, we demonstrate that our exfoliated, large-area hBN flakes can be readily incorporated as encapsulating layers for other 2D monolayers. Altogether, this method brings us one step closer to the high throughput, mass production of hBN-based 2D photonic, optoelectronic, and quantum devices.

2D materials↗
DOE OSTI2021

KS4A-Omics1.0_FspDS682

Soil fungi facilitate the translocation of inorganic nutrients from soil minerals to other microorganisms and plants. This ability is particularly advantageous in impoverished soils, because fungal mycelial networks can bridge otherwise spatially disconnected and inaccessible nutrient hotspots. However, the molecular mechanisms underlying fungal mineral weathering and transport through soil remains poorly understood. Here, we addressed this knowledge gap by directly visualizing nutrient acquisition and transport through fungal hyphae in a mineral doped soil micromodel using a multimodal imaging approach. Here, we observed how a representative of common saprotrophic soil fungi, Fusarium sp. DS 682, exhibited a mechanosensory response (thigmotropism) around obstacles and through pore spaces (~12 μm) in the presence of minerals.This study establishes the significance of fungal biology and nutrient translocation mechanisms in maintaining fungal growth under water and nutrient limitations in a soil-like microenvironment, using a high-throughput multi-omic analysis approach. Data package KS4A-Omics.1.0_FspDS682 (Publication: Fungal Mineral Weathering Mechanisms Revealed Through Direct Molecular Visualization) contents reported here are the first version (1.0) and contain pre- and post-processed data using high throughput data capture technologies for multi-omic analysis and integration, this data package contains raw and post-processed experimental data for X-Ray Absorption Near Edge Structure Spectroscopy (XANES/XRF), Optical Microscopy, Proteomics, Scanning Electron Microscope (SEM), Time-of-Flight Secondary Ion Mass Spectroscopy (ToF-SIMS), X-Ray Diffraction Spectroscopy (XRD) files, and X- Ray Photoelectron Spectroscopy (XPS) using EMSL capabilities. This data package DOI contains a comprehensive collection of high-throughput multi-omics data and process metadata catalog. Support files include additional data download contents “Read Me” with dataset descriptor information and data source method application ontologies (see data dictionary section). Reported data download content is structured for compliance with reported guidelines provided by community standard initiatives and publisher stakeholder policies supporting FAIR data principles.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Machine Learning Framework for Characterizing Processing–Structure Relationship in Block Copolymer Thin Films

The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by surface features, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were trained to predict domain orientation based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances (R 2 > 0.75), while AFM-based property predictions were less accurate (R 2 < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using SHapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Application of Machine Learning to Improve Biobased Glucaric Acid Production

Kalion, Inc. is an industrial biotechnology manufacturer of high-purity chemicals from biomass including glucaric acid to be used in the industrial, material, and pharmaceutical markets. The project goal is application of machine learning in conjunction with high-throughput cultivations and metabolomics to understand and overcome two different challenges: (i) improve glucaric acid productivity (the rate of production slows considerably after 48 hours, which limits the overall productivity that can be achieved in the process), and (ii) decrease its production costs (components from complex media are necessary to achieve robust production). Through a series of experiments, a set of metabolites contained in rich media that improved performance were identified using metabolomics, high throughput cultivations in bioreactors, and machine learning-friendly experimental designs. It was found that the substrate feeding strategy was the potential major driver to enhance bacterial performance of glucaric acid production. The major industrial impacts by this project are the improvement of the production process technology, as well as advancing infrastructures and workflows (where machine learning approaches in conjunction with high-throughput cultivation and metabolomics are used) to improve the analyses by making them more rapid and comprehensive.

BIOMASS FUELS↗
DOE OSTI2020

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A genetic link between leaf carbon isotope composition and whole-plant water use efficiency in the C 4 grass Setaria

Genetic selection for whole-plant water use efficiency (yield per transpiration; WUE plant ) in any crop-breeding programme requires high-throughput phenotyping of component traits of WUE plant such as intrinsic water use efficiency (WUE i ; CO 2 assimilation rate per stomatal conductance). Measuring WUE i by gas exchange measurements is laborious and time consuming and may not reflect an integrated WUE i over the life of the leaf. Alternatively, leaf carbon stable isotope composition (δ 13 C leaf ) has been suggested as a potential time-ntegrated proxy for WUE i that may provide a tool to screen for WUE plant . However, a genetic link between δ 13 C leaf and WUE plant in a C 4 species has not been well established. Thus, to confirm if there is a genetic relationship in a C 4 plant between δ 13 C leaf and WUE plant under well watered and water-limited growth conditions, a high-throughput phenotyping facility was used to measure WUE plant in a recombinant inbred line (RIL) population created between the C 4 grasses Setaria viridis and S. italica . Three quantitative trait loci (QTL) for δ 13 C leaf were found and co-localized with transpiration, biomass accumulation, and WUE plant . Additionally, WUE plant for each of the δ 13 C leaf QTL allele classes was negatively correlated with δ 13 C leaf , as would be predicted when WUE i influences WUE plant . These findings demonstrate that δ 13 C leaf is genetically linked to WUE plant , likely to be through their relationship with WUE i , and can be used as a high-throughput proxy to screen for WUE plant in these C 4 species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Disassembling 2D van der Waals crystals into macroscopic monolayers and reassembling into artificial lattices

Two-dimensional materials from layered van der Waals (vdW) crystals hold great promise for electronic, optoelectronic, and quantum devices, but technological implementation will be hampered by the lack of high-throughput techniques for exfoliating single-crystal monolayers with sufficient size and high quality. Here, we report a facile method to disassemble vdW single crystals layer by layer into monolayers with near-unity yield and with dimensions limited only by bulk crystal sizes. The macroscopic monolayers are comparable in quality to microscopic monolayers from conventional Scotch tape exfoliation. The monolayers can be assembled into macroscopic artificial structures, including transition metal dichalcogenide multilayers with broken inversion symmetry and substantially enhanced nonlinear optical response. This approach takes us one step closer to mass production of macroscopic monolayers and bulk-like artificial materials with controllable properties.

Science & Technology - Other Topics↗
DOE OSTI2022

Silver Nanowire-Indium Zinc Oxide Composite Flexible Transparent Conducting Electrodes Made by Spin- coating and Photonic Curing

Realizing high-throughput, low-cost perovskite solar cell (PSC) manufacturing is highly sought-after in photovoltaic (PV) research in recent years. To fully achieve roll-to-roll (R2R) manufacturing of PSCs, it is important to consider the flexible transparent electrode (TE). PET/ITO is a commonly used substrate for making flexible PSCs. When optimizing transparent conducting materials, there is a tradeoff between sheet resistance (Rsh) and optical transparency. Because commercial PET/ITO substrates are made with slow (~1 m/min) vacuum deposition processes, they tend to be expensive. Therefore, it would be advantageous to develop a high-throughput, R2R compatible, solution-deposition approach for fabricating the TE on PET substrates. While various solution-deposition processes, such as blade coating or slot-die coating, can achieve the desired web speed of > 10 m/min, there is still a need to improve the post-deposition annealing step. One promising post-deposition processing technique is intense-pulsed-light processing, also known as photonic curing. Photonic curing delivers short (0.01 – 100 ms) pulses of broadband (200 – 1500 nm) light from a xenon flash lamp to the samples. Any materials in the sample stack that absorb light will convert the impinging light pulse into heat within the sample, which drives changes in the sample (calcination, phase change, crystallization, etc.). Photonic curing has three main advantages over thermal annealing: 1. Faster processing speed (milliseconds or seconds). 2. Compatibility with plastic substrates. 3. Smaller physical footprint and less wasted energy. Since the light pulses are on for a short time, the intensity can be high while the total energy delivered to the sample is low, minimizing damages to the plastic substrates. In this work, a hybrid TE material is fabricated on PET substrates using photonic curing. The hybrid TE material contains a layer of silver nanowires (AgNWs) and a layer of metal-oxide (InOx, ITO, IZO, etc.). The AgNWs increase the light absorbed by the film during the photonic curing process, which leads to higher processing temperatures, possibly improving the conversion of the metal-oxide layer. The AgNWs also enhance the electrical conductivity of the final TE layer after photonic curing. A AgNW and metal-oxide bilayer is formed by spin coating each solution onto the PET substrate sequentially followed by a single photonic curing process. We use average optical transmittance (Tavg) from 400 to 700 nm and average Rsh to evaluate the TE performance. The following photonic curing parameters are varied to optimize Tavg (maximize) and Rsh (minimize): Pulse voltage, pulse envelope, number of micro-pulses, duty cycle, number of pulses, and pulse repetition rate. Preliminarily, we also observe a significant impact on the TE properties by the volume of AgNW deposited during the spin coating deposition step. Using dispense volumes of 80 µL and 20 µL, we achieve samples with Tavg = 73%, Rsh = 19 Ω/sq, and roughness = 9 nm, and Tavg = 83%, Rsh = 58 Ω/sq, and roughness = 5.6 nm, respectively, after photonic curing.

14 SOLAR ENERGY↗
DOE OSTI2025

Engineering PHL7 for Improved Poly(Ethylene Terephthalate) Depolymerization via Rational Design and Directed Evolution

Enzymatic depolymerization of poly(ethylene terephthalate) (PET) has emerged as a promising approach for polyester recycling, and, to date, many natural and engineered PET hydrolase enzymes have been reported. For industrial use, PET hydrolases must achieve high depolymerization extent and exhibit excellent thermostability. Here, we engineered a natural PET hydrolase, Polyester Hydrolase Leipzig #7 (PHL7), through rational design and directed evolution using a high-throughput screening platform. Four new enzymes were engineered with enhanced properties compared with the parent enzyme, wild-type PHL7 (PHL7-WT), and other benchmark PET hydrolases, under the tested conditions. In bioreactors, the exemplary engineered enzyme, PHL7-Jemez, exhibited improved ability to depolymerize amorphous PET film compared with PHL7-WT at 2.9% and 20% substrate loadings, with 37% and 270% higher hydrolysis, respectively, after 48 h. This study develops several state-of-the-art PET hydrolases and demonstrates a directed evolution platform to engineer high-performance enzymes, which can accelerate enzyme discovery toward improved biocatalytic recycling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗