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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Large Polaron Screening Delays Mobility Roll-Off in Halide Perovskites under High Injection

The interplay between carrier density regimes and the various scattering processes is fundamental for understanding charge transport efficiency in high-performance semiconductors. Combining ultrafast time-resolved terahertz spectroscopy (TRTS) and transient microwave photoconductivity (TRMC), we measure carrier mobilities over 7 orders of magnitude (10 13 –10 20 carriers cm –3 ), reaching a regime where carrier–carrier scattering dominates and mobility begins to roll off. We find the Caughey–Thomas model can be effectively employed for transport modeling in 3D metal halide perovskites (MHPs), and the mobility roll-off near ∼10 19 cm –3 can be tuned by MHP composition. Carrier lifetime in MHPs starts decreasing at much lower carrier densities due to interband multi-carrier recombination, while intraband carrier mobility remains unchanged as carrier–carrier scattering is screened by the presence of polarons. The validated Caughey–Thomas model provides a practical input for device simulations, prevents overestimation of diffusion length under high injection, and suggests that MHPs are excellent candidates for unipolar device applications under high carrier densities.

14 SOLAR ENERGY↗
DOE OSTI2024

A Structurally Diverse Compound Screening Library to Identify Substrates for Diamine, Polyamine, and Related Acetyltransferases

Spermidine/spermine N-acetyltransferases (SSATs) and other types of polyamine acetyltransferases (PAATs) acetylate diamines and/or polyamines. These enzymes are evolutionarily related and belong to the Gcn5-related N-acetyltransferase (GNAT) superfamily, yet we lack a fundamental understanding of their substrate specificity and/or promiscuity toward different compounds. Many of these enzymes are known or are predicted to acetylate polyamines, but in the cell there are other types of compounds that contain moieties derived from polyamines that may be the native substrates for these enzymes. To learn more about the identity of substrates that are acetylated, we selected and screened 17 different GNAT enzymes for activity toward a set of structurally diverse compounds that contained different types of amine moieties (e.g., aminopropyl, aminobutyl, etc.). These compounds included diamines, triamines, and polyamines containing primary amino groups, and they had structural diversity with variation of the chain length and presence or absence of internal amino groups and other functional groups. We found 12 of the 17 enzymes acetylated at least one of the compounds. Some enzymes were selective toward acetylating only one compound while others exhibited substrate promiscuity toward numerous compounds. Our experimental results ultimately allowed us to pinpoint specific substrates that could be further investigated to more fully understand substrate specificity versus promiscuity of GNAT enzymes and the role of acetylated small molecules in cells.

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

Dielectric screening in perovskite photovoltaics

Abstract The performance of perovskite photovoltaics is fundamentally impeded by the presence of undesirable defects that contribute to non-radiative losses within the devices. Although mitigating these losses has been extensively reported by numerous passivation strategies, a detailed understanding of loss origins within the devices remains elusive. Here, we demonstrate that the defect capturing probability estimated by the capture cross-section is decreased by varying the dielectric response, producing the dielectric screening effect in the perovskite. The resulting perovskites also show reduced surface recombination and a weaker electron-phonon coupling. All of these boost the power conversion efficiency to 22.3% for an inverted perovskite photovoltaic device with a high open-circuit voltage of 1.25 V and a low voltage deficit of 0.37 V (a bandgap ~1.62 eV). Our results provide not only an in-depth understanding of the carrier capture processes in perovskites, but also a promising pathway for realizing highly efficient devices via dielectric regulation.

14 SOLAR ENERGY↗
DOE OSTI2024

Natural diversity screening, assay development, and characterization of nylon-6 enzymatic depolymerization

Successes in biocatalytic polyester recycling have raised the possibility of deconstructing alternative polymers enzymatically, with polyamide (PA) being a logical target due to the array of amide-cleaving enzymes present in nature. Here, we screen 40 potential natural and engineered nylon-hydrolyzing enzymes (nylonases), using mass spectrometry to quantify eight compounds resulting from enzymatic nylon-6 (PA6) hydrolysis. Comparative time-course reactions incubated at 40-70 °C showcase enzyme-dependent variations in product distributions and extent of PA6 film depolymerization, with significant nylon deconstruction activity appearing rare. The most active nylonase, a NylC K variant we rationally thermostabilized (an N-terminal nucleophile (Ntn) hydrolase, NylC K -TS, T m = 87.4 °C, 16.4 °C higher than the wild-type), hydrolyzes 0.67 wt% of a PA6 film. Reactions fail to restart after fresh enzyme addition, indicating that substrate-based limitations, such as restricted enzyme access to hydrolysable bonds, prohibit more extensive deconstruction. Overall, this study expands our understanding of nylonase activity distribution, indicates that Ntn hydrolases may have the greatest potential for further development, and identifies key targets for progressing PA6 enzymatic depolymerization, including improving enzyme activity, product selectivity, and enhancing polymer accessibility.

09 BIOMASS FUELS↗
DOE OSTI2023

High-throughput screening of hypothetical metal-organic frameworks for thermal conductivity

Thermal energy management in metal-organic frameworks (MOFs) is an important, yet often neglected, challenge for many adsorption-based applications such as gas storage and separations. Despite its importance, there is insufficient understanding of the structure-property relationships governing thermal transport in MOFs. To provide a data-driven perspective into these relationships, here we perform large-scale computational screening of thermal conductivity k in MOFs, leveraging classical molecular dynamics simulations and 10,194 hypothetical MOFs created using the ToBaCCo 3.0 code. We found that high thermal conductivity in MOFs is favored by high densities (> 1.0 g cm -3 ), small pores (< 10 Å), and four-connected metal nodes. We also found that 36 MOFs exhibit ultra-low thermal conductivity (< 0.02 W m -1 K -1 ), which is primarily due to having extremely large pores (~65 Å). Furthermore, we discovered six hypothetical MOFs with very high thermal conductivity (>10 Wm -1 K -1 ), the structures of which we describe in additional detail.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-throughput screening of 2D materials identifies p-type monolayer WS2 as potential ultra-high mobility semiconductor

Abstract 2D semiconductors offer a promising pathway to replace silicon in next-generation electronics. Among their many advantages, 2D materials possess atomically-sharp surfaces and enable scaling the channel thickness down to the monolayer limit. However, these materials exhibit comparatively lower charge carrier mobility and higher contact resistance than 3D semiconductors, making it challenging to realize high-performance devices at scale. In this work, we search for high-mobility 2D materials by combining a high-throughput screening strategy with state-of-the-art calculations based on the ab initio Boltzmann transport equation. Our analysis singles out a known transition metal dichalcogenide, monolayer WS 2 , as the most promising 2D semiconductor, with the potential to reach ultra-high room-temperature hole mobilities in excess of 1300 cm 2 /Vs should Ohmic contacts and low defect densities be achieved. Our work also highlights the importance of performing full-blown ab initio transport calculations to achieve predictive accuracy, including spin–orbital couplings, quasiparticle corrections, dipole and quadrupole long-range electron–phonon interactions, as well as scattering by point defects and extended defects.

Chemistry↗
DOE OSTI2026

Data mining and computational screening of Rashba-Dresselhaus splitting and optoelectronic properties in two-dimensional perovskite materials

Recent developments highlighting the promise of two-dimensional perovskites have vastly increased the compositional search space in the perovskite family. This presents a great opportunity for the realization of highly performant devices and practical challenges associated with the identification of candidate materials. High-fidelity computational screening offers great value in this regard. In this study, we carry out a multiscale computational workflow, generating a dataset of two-dimensional perovskites in the Dion-Jacobson and Ruddlesden-Popper phases. Our dataset comprises ten B-site cations, four halogens, and over 20 organic cations across over 2000 materials. We compute electronic properties, thermoelectric performance, and numerous geometric characteristics. Furthermore, we introduce a framework for the high-throughput computation of Rashba-Dresselhaus splitting. Finally, we use this dataset to train machine learning models for the accurate prediction of band gaps, candidate Rashba-Dresselhaus materials, and partial charges. The work presented herein can aid future investigations of two-dimensional perovskites with targeted applications in mind.

14 SOLAR ENERGY↗
DOE OSTI2021

Validating deep learning inference during chest X-ray classification for COVID-19 screening

Abstract The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Using machine learning with optical profilometry for GaN wafer screening

Abstract To improve the manufacturing process of GaN wafers, inexpensive wafer screening techniques are required to both provide feedback to the manufacturing process and prevent fabrication on low quality or defective wafers, thus reducing costs resulting from wasted processing effort. Many of the wafer scale characterization techniques—including optical profilometry—produce difficult to interpret results, while models using classical programming techniques require laborious translation of the human-generated data interpretation methodology. Alternatively, machine learning techniques are effective at producing such models if sufficient data is available. For this research project, we fabricated over 6000 vertical PiN GaN diodes across 10 wafers. Using low resolution wafer scale optical profilometry data taken before fabrication, we successfully trained four different machine learning models. All models predict device pass and fail with 70–75% accuracy, and the wafer yield can be predicted within 15% error on the majority of wafers.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Direct genome-scale screening of Gluconobacter oxydans B58 for rare earth element bioleaching

Abstract The transition to a sustainable energy economy will require an enormous increase in the supply of rare earth elements (REEs). Bioleaching offers a promising alternative to conventional hydrometallurgical methods for REE extraction from low-grade ores. However, exploiting this potential remains challenging due to large gaps in our understanding of the genetics involved, and inadequate biological tools to address them. We generated a highly non-redundant whole-genome knockout collection for the bioleaching microbe Gluconobacter oxydans B58, reducing redundancy by 85% compared to the previous best collection. This new collection was directly screened for bioleaching neodymium from a synthetic monazite powder, identifying 89 genes important for bioleaching, 68 of which have not previously been associated with this mechanism. We conducted bench-scale experiments to validate the extraction efficiency of promising strains: 8 demonstrated significant increases in extraction by up to 111% (δ GO_1598 , disruption of the gene encoding the orotate phosphoribosyltransferase enzyme PyrE), and one strain significantly reduced it by 97% (δ GO_1096 , disruption of the gene encoding the GTP-binding protein TypA). Notable changes in pH were only observed for 3 strains, suggesting an important role for non-acid mechanisms in bioleaching. These findings provide valuable insights into further enhancing REE-bioleaching by G. oxydans through genetic engineering.

Marecos, Sabrina (ORCID:0000000158529933)↗
DOE OSTI2020

High-throughput screening for discovery of benchtop separations systems for selected rare earth elements

Abstract Rare earth (RE) elements (scandium, yttrium, and the lanthanides) are critical for their role in sustainable energy technologies. Problems with their supply chain have motivated research to improve separations methods to recycle these elements from end of life technology. Toward this goal, we report the synthesis and characterization of the ligand tris[(1-hydroxy-2-oxo-1,2-dihydropyridine-3-carboxamido)ethyl]amine, H 3 1·TFA (TFA = trifluoroacetic acid), and complexes 1·RE (RE = La, Nd, Dy). A high-throughput experimentation (HTE) screen was developed to quantitatively determine the precipitation of 1·RE as a function of pH as well as equivalents of H 3 1·TFA. This method rapidly determines optimal conditions for the separation of RE mixtures, while minimizing materials consumption. The HTE-predicted conditions are used to achieve the lab-scale separation of Nd/Dy ( SF Nd/Dy = 213 ± 34) and La/Nd ( SF La/Nd = 16.2 ± 0.2) mixtures in acidic aqueous media.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Understanding the thermally activated charge transport in NaPb m SbQ m+2 (Q = S, Se, Te) thermoelectrics: weak dielectric screening leads to grain boundary dominated charge carrier scattering

Many thermoelectric materials feature irregular electrical conductivity with thermally activated transport below ~600 K and metallic behavior at high temperatures, despite possessing degenerate carrier concentrations. The suppression of the electrical conductivity ultimately degrades the thermoelectric performance on the cold side and limits the device energy conversion efficiency. As such, establishing the origin of the low temperature scattering and developing strategies to mitigate its effect are paramount issues. To date, the correct microscopic description of the low temperature carrier scattering remains an open issue, and there is little work addressing why some thermoelectric materials are more susceptible to the deleterious behavior. Here, we use the promising thermoelectric alloys of PbQ and NaSbQ 2 (Q = S, Se, Te) as model systems to address these concerns. We directly show the thermally activated transport stems from the scattering of charge carriers by the grain boundaries (GBs), and that the expected metallic electrical conductivity is recovered by preparing large grained samples with reduced densities of GBs. We furthermore study the electrical properties NaPb m SbSe m+2 as a function of NaSbSe 2 fraction, as well as those of the chalcogenide analogues, PbTe–NaSbTe 2 and PbS–NaSbS 2 , and demonstrate that the strength of GB scattering can be understood by utilizing simple chemical principles. By considering the polarizability of the host atoms, we directly relate the magnitude of GB scattering to the relative degree of charge carrier screening in each material, and demonstrate that GB scattering is strongest in the ionic NaSbQ 2 -rich compounds and weakest in more polarizable PbQ-rich phases. We finally show how these chemical arguments elegantly explain the strong GB scattering in numerous other thermoelectric materials. By uniting the deleterious charge transport properties exhibited by many different compounds into a common picture, we discuss how our work gives design principles for proper microstructure engineering in emerging thermoelectric materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A combinatorial droplet microfluidic device integrated with mass spectrometry for enzyme screening

Mass spectrometry (MS) enables detection of different chemical species with a very high specificity; however, it can be limited by its throughput. Integrating MS with microfluidics has a tremendous potential to improve throughput and accelerate biochemical research. In this work, we introduce Drop-NIMS, a combination of a passive droplet loading microfluidic device and a matrix-free MS laser desorption ionization technique called nanostructure-initiator mass spectrometry (NIMS). This platform combines different droplets at random to generate a combinatorial library of enzymatic reactions that are deposited directly on the NIMS surface without requiring additional sample handling. The enzyme reaction products are then detected with MS. Drop-NIMS was used to rapidly screen enzymatic reactions containing low (on the order of nL) volumes of glycoside reactants and glycoside hydrolase enzymes per reaction. MS “barcodes” (small compounds with unique masses) were added to the droplets to identify different combinations of substrates and enzymes created by the device. We assigned xylanase activities to several putative glycoside hydrolases, making them relevant to food and biofuel industrial applications. Overall, Drop-NIMS is simple to fabricate, assemble, and operate and it has potential to be used with many other small molecule metabolites.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (i.e., MR effect) that involve multiple potential energy surfaces (i.e., adiabatic spin splitting, ΔE H–L , and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic ΔE H–L and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (i.e., to within 1 kcal mol –1 MAE) for robust VHTS.

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

Methods to accelerate high-throughput screening of atomic qubit candidates in van der Waals materials

The discovery of atom-like spin emitters associated with defects in two-dimensional (2D) wide-bandgap (WBG) semiconductors presents new opportunities for highly tunable and versatile qubits. So far, the study of such spin emitters has focused on defects in hexagonal boron nitride (hBN). However, hBN necessarily contains a high density of nuclear spins, which are expected to create a strong incoherent spin-bath that leads to poor coherence properties of spins hosted in the material. Therefore, identification of new qubit candidates in other 2DWBG materials is necessary. Given the time demands of ab initio methods, new approaches for rapid screening and calculations of identifying properties of suitable atom-like qubits are required. In this work, we present two new methods for rapid estimation of the zero-phonon line (ZPL), a key property of atomic qubits in WBG materials. First, the ZPL is calculated by exploiting Janak’s theorem. For finite changes in occupation, we provide the leading-order estimate of the correction to the ZPL obtained using Janak’s theorem, which is more rapid than the standard method (ΔSCF). Next, we demonstrate an approach to converging excited states that is faster for systems with small strain than the standard approach used in the ΔSCF method. We illustrate these methods using the case of the singly negatively charged calcium vacancy in SiS2, which we are the first to propose as a qubit candidate. This work has the potential to assist in accelerating the high-throughput search for quantum defects in materials, with applications in quantum sensing and quantum computing.

Kuate Defo, Rodrick (ORCID:0000000242788196)↗