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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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DOE OSTI2023

Interface Coating Design for Dynamic Voltage Stability of Solid‐State Batteries

Abstract Intrinsic or interface thermodynamic voltage windows of solid electrolytes are often narrower than the operational voltage range needed by a full battery, thus various interface decomposition reactions can happen in a practical solid‐state battery. Experimentally, it is found that a proper battery design utilizing the reactions can lead to a dynamic evolution from interface instability to stability, giving the so‐called dynamic voltage stability for advanced battery performance. Here, first the state‐of‐the‐art understanding is articulated about how the dynamic voltage stability should be interpreted in physical picture and treated in computation, emphasizing the potential importance of nonequilibrium reaction pathways. The constrained ensemble computational approach is further applied across most types of solid‐state electrolytes to systematically evaluate and compare their dynamic stability voltage windows in response to the mechanical constriction effect. High‐throughput calculations are used to search for coating materials for different interfaces between sulfide, halide, and oxide electrolytes and typical cathode materials with enhanced dynamic voltage stability. A comparison with experiment is given to highlight the value of these computational predictions.

Wang, Yichao↗

DOE OSTI2023

Plasma‐catalytic synthesis of acrylonitrile from methane and nitrogen

Here, in this work, we demonstrate plasma-catalytic synthesis of hydrogen and acrylonitrile (AN) from CH 4 and N 2 . The process involves two steps: (1) plasma synthesis of C 2 H 2 and HCN in a nominally 1:1 stoichiometric ratio with high yield up to 90% and (2) downstream thermocatalytic reaction of these intermediates to make AN. The effect of process parameters on product distributions and specific energy requirements are reported. If the catalytic conversion of C 2 H 2 and HCN in the downstream thermocatalytic step to AN were perfect, which will require further improvements in the thermocatalytic reactor, then at the maximum output of our 1 kW radiofrequency 13.56 MHz transformer, a specific energy requirement of 73 kWh kg AN −1 was determined. The expectation is that scaling up the process to higher throughputs would result in decreases in specific energy requirement into the predicted economically viable range less than 10 kWh kg AN −1 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Spanning surface interaction length scales for the design of industrial antifouling materials

Fouling of surfaces introduces significant operational and economic challenges in marine and membrane systems. While empirical fouling assays and surface science methodologies have independently introduced fouling resistant methodologies and functionalities within each of these respective application spaces, they remain limited by their inability to extrapolate findings across length scales and inform materials design strategies. This perspective emphasizes the need to bridge the macroscale fouling assays conducted in the marine community and molecular-scale insights obtained from the membranes community. Here, by uniting these approaches, generalized design strategies can be developed across applications, leading to the realization of effective antifouling materials over a wide spectrum of operating conditions and foulant types. Standardized and high-throughput methodologies are proposed as tools to unify research efforts to drive the development of next-generation antifouling coatings and treatments.

fouling assays↗

DOE OSTI2026

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Accurate and efficient interatomic potentials are essential for molecular dynamics (MD) simulations of radiation damage, gas diffusion, and phase stability in complex ceramics such as LiAlO 2 , especially under extreme conditions relevant to tritium production. Here, we evaluate the performance of six machine-learned interatomic potentials (MLIPs), moment tensor potential (MTP), Gaussian approximation potential, deep potential (DeePMD), atomic cluster expansion (ACE), message-passing ACE (multilayer atomic cluster expansion (MACE) pretrained) and MACE (trained from-scratch), all trained on the same density functional theory dataset with inclusion of tritium. The MLIPs are benchmarked against traditional Buckingham and ReaxFF potentials in terms of energy accuracy, density predictions, thermal equilibration behavior, threshold displacement energy (E d ), tritium diffusivity, and computational cost. Among the models, MTP shows the best overall balance between efficiency and accuracy, with low force and energy errors and realistic E d values for Li and Al. The ACE and MACE (pretrained and trained from scratch) models exhibit high E d (>200 eV) and unphysical pair interactions. DeePMD underestimates Ed due to overly repulsive behavior even at equilibrium distances. All models over-estimate tritium diffusion but the pretrained MACE model behaves well during tritium-diffusion simulations up to 500 K, maintaining diffusivities in the physically consistent 10 −11 m 2 /s range. Finally, we quantify the computational cost of each potential in large-scale atomic/molecular massively parallel simulator, finding that only MTP is more efficient than traditional empirical potentials, while others are significantly more expensive. These findings explain the trade-offs between accuracy and computational cost in MLIP development and provide essential guidance for use in high-throughput radiation damage and gas diffusion simulations in nuclear ceramics.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2023

Multispectral Quantitative Phase Imaging Using a Diffractive Optical Network

As a label‐free imaging technique, quantitative phase imaging (QPI) provides optical path length information of transparent specimens for various applications in biology, materials science, and engineering. Multispectral QPI measures quantitative phase information across multiple spectral bands, permitting the examination of wavelength‐specific phase and dispersion characteristics of samples. Herein, the design of a diffractive processor is presented that can all‐optically perform multispectral quantitative phase imaging of transparent phase‐only objects within a snapshot. The design utilizes spatially engineered diffractive layers, optimized through deep learning, to encode the phase profile of the input object at a predetermined set of wavelengths into spatial intensity variations at the output plane, allowing multispectral QPI using a monochrome focal plane array. Through numerical simulations, diffractive multispectral processors are demonstrated to simultaneously perform quantitative phase imaging at 9 and 16 target spectral bands in the visible spectrum. The generalization of these diffractive processor designs is validated through numerical tests on unseen objects, including thin Pap smear images. Due to its all‐optical processing capability using passive dielectric diffractive materials, this diffractive multispectral QPI processor offers a compact and power‐efficient solution for high‐throughput quantitative phase microscopy and spectroscopy.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Extraction of Pure Plastic Resins From PCR Plastic Waste by Solvent-Targeted Recovery and Precipitation (STRAP)

For this work, we have been developing a solvent‐based plastic recycling technology called STRAP. The technology is based on dissolving a targeted plastic resin in a specific solvent that does not dissolve other resins. We have demonstrated STRAP in thousands of bench scale experiments for a large variety of wastes. Recently we have demonstrated the technology for PCR, using mixed plastic wastes (MPWs), from a wet Material Recovery Facility (MRF). The process includes (1) infrared (IR) characterization to determine the plastic composition for accurate selection of the solvent to be used for the extraction of the pure resins. (2) Shredding to the right size and aspect ratio required for flowable and fast dissolvable process. (3) Mixing the MPW in the first solvent to dissolve the first resin. (4) Filtration of the solution plastic blend, to separate the nondissolved plastic from the solution. (5) Further filtration of the solution to remove micron‐sized particle of pigments and fibers. (6) Cooling for precipitation. (7) Filtration of pure resins. (8) Drying of a pure resin. (9) Extrusion of the resin to pellets. (10) Generating films or other products from the pure resin. Steps 1–10 can be considered as one‐cycle that extracted the first resin. (11) A second resin can be extracted with a respective solvent from the plastic that did not dissolve in the first cycle and following steps 1–10 described above. The process also includes characterization of interim and final products. The effort includes building a pilot system at 25 kg/h throughput. We will present specific results for various PCR.

IR characterization↗

DOE OSTI2025

Automation for Electric Vehicle Battery Pack Disassembly

Battery‐electric vehicles (EVs) are growing exponentially. The demand for these batteries is expected to increase sevenfold by 2035. The EV batteries reach their end of life when the capacity fades to 70%–80% of new, with some being removed from the primary applications with even lower levels of degradation. These batteries can be used in less demanding applications. The disassembly process is currently manual, slow, unsafe, and expensive. Automation is needed to increase the throughput. EV battery packs feature various continually changing designs and form factors, which limit the usefulness of deterministically programmed robotic solutions. The conceptual robotic disassembly of EV batteries has attracted the attention of researchers. However, while many approaches have been proposed, practical implementations are lacking. Here, we review proposed concepts for EV battery disassembly and describe the selected approach, with elements of partial solutions validated in a laboratory setting, including the selection of commercial solutions, the development of custom end effectors, and methodologies for detection, localization, and classification of fasteners. The computer vision tasks employed an overhead 2D camera to detect the type of battery pack and approximate localization of fasteners, and a 3D camera mounted on the robotic arm for precise localization (position and tilt) and classification.

Islam, Abu [Rochester Inst. of Technology, Rochest↗

DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗

DOE OSTI2025

Refining T c Prediction in Hydrides via Symbolic‐Regression‐Enhanced Electron‐Localization‐Function‐Based Descriptors

Hydrogen‐based materials are able to possess extremely high superconducting critical temperatures, T c s , due to hydrogen's low atomic mass and strong electron–phonon interaction. Recently, a descriptor based on the Electron Localization Function (ELF) has enabled the rapid estimation of the T c of hydrogen‐containing compounds from electronic networking properties, but its applicability has been limited by the small size and homogeneity of the training dataset used. Herein, the model is re‐examined, compiling a publicly available combined dataset of 244 binary and ternary hydride superconductors. The analysis shows that though ELF‐based networking remains a valuable descriptor, its predictive power declines with increasing compositional complexity. However, by introducing the molecularity index, defined as the highest value of the ELF at which two hydrogen atoms connect, and applying symbolic regression, the accuracy of the predictions can be substantially enhanced. These results establish a more robust framework for assessing superconductivity in hydride materials, facilitating accelerated screening of novel candidates through integration with crystal structure prediction methods or high‐throughput searches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Solvent‐Free Self‐Assembly for Scalable Preparation of Highly Crystalline Mesoporous Metal Oxides

Abstract Mesoporous metal oxides (MMOs) have been demonstrated great potential in various applications. Up to now, the direct synthesis of MMOs is still limited to the solvent induced inorganic‐organic self‐assembly process. Here, we develop a facile, general, and high throughput solvent‐free self‐assembly strategy to synthesize a series of MMOs including single‐component MMOs and multi‐component MMOs (e.g., doped MMOs, composite MMOs, and polymetallic oxide) with high crystallinity and remarkable porous properties by grinding and heating raw materials. Compared with the traditional solution self‐assembly process, the avoidance of solvents in this method not only greatly increases the yield of target products and synthesis efficiency, but also reduces the environmental pollution and the consumption of cost and energy. We believe the presented approach will pave a new avenue for scalable production of advanced mesoporous materials for various applications.

Xiong, Hailong↗

DOE OSTI2021

Engineering a Non‐Natural Photoenzyme for Improved Photon Efficiency**

Abstract Photoenzymes are biological catalysts that use light to convert starting materials into products. These catalysts require photon absorption for each turnover, making quantum efficiency an important optimization parameter. Flavin‐dependent “ene”‐reductases (EREDs) display latent photoenzymatic activity for synthetically valuable hydroalkylations; however, protein engineering has not been used to optimize this non‐natural function. We describe a protein engineering platform for the high throughput optimization of photoenzymes. A single round of engineering results in improved catalytic function toward the synthesis of γ, δ, ϵ‐lactams, and acyclic amides. Mechanistic studies show that key mutations can alter the enzyme's excited state dynamics, enhance its photon efficiency, and ultimately increase catalyst performance. Transient absorption spectroscopy reveals that engineered variants display dramatically decreased radical lifetimes, indicating an evolution toward a concerted mechanism.

Nicholls, Bryce T.↗

DOE OSTI2022

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure‐Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

Abstract A novel ab initio methodology based on high‐throughput simulations has permitted designing unique biselective organic structure‐directing agents (OSDAs) that allow the efficient synthesis of CHA/AEI zeolite intergrowth materials with controlled phase compositions. Distinctive local crystallographic ordering of the CHA/AEI intergrowths was revealed at the nanoscale level using integrated differential phase contrast scanning transmission electron microscopy (iDPC STEM). These novel CHA/AEI materials have been tested for the selective catalytic reduction (SCR) of NOx, presenting an outstanding catalytic performance and hydrothermal stability, even surpassing the performance of the well‐established commercial CHA‐type catalyst. This methodology opens the possibility for synthetizing new zeolite intergrowths with more complex structures and unique catalytic properties.

Bello‐Jurado, Estefanía↗

DOE OSTI2023

A General Autofluorescence Method to Characterize Polymerization Progress

Abstract An autofluorescence technique to characterize polymerization progress in real time/in line was developed, which functioned in the absence of typical fluorogenic groups on the monomer or polymer. The monomer dicyclopentadiene and polymer polydicyclopentadiene are hydrocarbons that lack traditional functional groups for fluorescence spectroscopy. Here, the autofluorescence of formulations containing this monomer and polymer during ruthenium‐catalyzed ring‐opening metathesis polymerization (ROMP) was harnessed for reaction monitoring. The methods fluorescence recovery after photobleaching (FRAP) and here‐developed fluorescence lifetime recovery after photobleaching (FLRAP) characterized polymerization progress in these native systems—without requiring exogenous fluorophore. (Auto)fluorescence lifetime recovery changes during polymerization correlated linearly to degree of cure, providing a quantitative link with reaction progress. These changing signals also provided relative rates of background polymerization, enabling comparison of 10 different catalyst‐inhibitor‐stabilized formulations. Multiple‐well analysis demonstrated suitability for future high‐throughput evaluation of formulations for thermosets. The central concept of the combined autofluorescence and FLRAP/FRAP method may be extendable to monitoring other polymerization reactions previously overlooked for lack of an obvious fluorescence handle.

López, Pía A.↗

DOE OSTI2023

Mechanisms of Biomolecular Self-Assembly Investigated Through In Situ Observations of Structures and Dynamics

Biomolecular self-assembly of hierarchical materials is a precise and adaptable bottom-up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This report will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning-based approaches, multiparametric characterization, and high-throughput methods can boost the development of biomolecular self-assembly. The objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self-assembly and to inspire the next generation of biomimetic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Multivariate Machine Learning Models of Nanoscale Porosity from Ultrafast NMR Relaxometry

Abstract Nanoporous materials are of great interest in many applications, such as catalysis, separation, and energy storage. The performance of these materials is closely related to their pore sizes, which are inefficient to determine through the conventional measurement of gas adsorption isotherms. Nuclear magnetic resonance (NMR) relaxometry has emerged as a technique highly sensitive to porosity in such materials. Nonetheless, streamlined methods to estimate pore size from NMR relaxometry remain elusive. Previous attempts have been hindered by inverting a time domain signal to relaxation rate distribution, and dealing with resulting parameters that vary in number, location, and magnitude. Here we invoke well‐established machine learning techniques to directly correlate time domain signals to BET surface areas for a set of metal‐organic frameworks (MOFs) imbibed with solvent at varied concentrations. We employ this series of MOFs to establish a correlation between NMR signal and surface area via partial least squares (PLS), following screening with principal component analysis, and apply the PLS model to predict surface area of various nanoporous materials. This approach offers a high‐throughput, non‐destructive way to assess porosity in c.a. one minute. We anticipate this work will contribute to the development of new materials with optimized pore sizes for various applications.

Fricke, Sophia N.↗

DOE OSTI2020

Solvent-Free Self-Assembly for Scalable Preparation of Highly Crystalline Mesoporous Metal Oxides

Mesoporous metal oxides (MMOs) have been demonstrated great potential in various applications. Up to now, the direct synthesis of MMOs is still limited to the solvent induced inorganic-organic self-assembly process. In this work, we develop a facile, general, and high throughput solvent-free self-assembly strategy to synthesize a series of MMOs including single-component MMOs and multi-component MMOs (e.g., doped MMOs, composite MMOs, and polymetallic oxide) with high crystallinity and remarkable porous properties by grinding and heating raw materials. Compared with the traditional solution self-assembly process, the avoidance of solvents in this method not only greatly increases the yield of target products and synthesis efficiency, but also reduces the environmental pollution and the consumption of cost and energy. We believe the presented approach will pave a new avenue for scalable production of advanced mesoporous materials for various applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

A Glycan Array-Based Assay for the Identification and Characterization of Plant Glycosyltransferases

Growing plants with modified cell wall compositions is a promising strategy to improve resistance to pathogens, increase biomass digestibility, and tune other important properties. In order to alter biomass architecture, a detailed knowledge of cell wall structure and biosynthesis is a prerequisite. We report here a glycan array-based assay for the high-throughput identification and characterization of plant cell wall biosynthetic glycosyltransferases (GTs). We demonstrate that different heterologously expressed galactosyl-, fucosyl-, and xylosyltransferases can transfer azido-functionalized sugar nucleotide donors to selected synthetic plant cell wall oligosaccharides on the array and that the transferred monosaccharides can be visualized “on chip” by a 1,3-dipolar cycloaddition reaction with an alkynyl-modified dye. The opportunity to simultaneously screen thousands of combinations of putative GTs, nucleotide sugar donors, and oligosaccharide acceptors will dramatically accelerate plant cell wall biosynthesis research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Tunable CHA/AEI Zeolite Intergrowths with A Priori Biselective Organic Structure‐Directing Agents: Controlling Enrichment and Implications for Selective Catalytic Reduction of NOx

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗