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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Emergent Above-Gap Photoluminescence in Molecularly Engineered Hybrid Bilayer Crystals

Bilayer crystals, built by stacking two-dimensional (2D) covalent monolayers, give rise to coupled excitonic states whose properties are constrained by fixed lattice symmetry and orientation. Replacing one covalent monolayer with a 2D molecular crystal––held together by non-covalent forces––overcomes this limitation, as molecular functional groups afford tunable in-plane lattice geometry, intermolecular spacing, and interlayer coupling, providing a powerful knob for exciton engineering. Here, in this study, we report four-atom-thick hybrid bilayer crystals (HBCs) synthesized by directly growing single-crystalline PDI molecular crystal atop WS 2 monolayers, which exhibit a robust photoluminescence (PL) peak 120 meV above the WS 2 optical band gap alongside a belowgap emission. Both peaks display strong polarization anisotropy—nearing unity for the above-gap emission—and maintain a perfectly linear power-law dependence up to an excitation density of ~10 7 mW/cm 2 , indicative of coexisting localized and delocalized excitonic states. Substituting PDI with a PTCDA monolayer on WS 2 fully quenches PL, demonstrating molecular control over excitonic emission. Lattice scale ab initio GW and GW-BSE calculations reveal a significantly hybridized bilayer band structure in PDI/WS 2 that supports interlayer excitonic species both above and below the WS 2 gap with strong polarization anisotropy, in excellent agreement with experiment. Our work introduces a molecule-based bilayer platform for the bottom-up design and control of excitonic phenomena in atomically thin optoelectronic and quantum materials.

2D materials↗
DOE OSTI2022

Supervised learning and the finite-temperature string method for computing committor functions and reaction rates

A central object in the computational studies of rare events is the committor function. Though costly to compute, the committor function encodes complete mechanistic information of the processes involving rare events, including reaction rates and transition-state ensembles. Under the framework of transition path theory, Rotskoff et al. [Proceedings of the 2nd Mathematical and Scientific Machine Learning Conference, Proceedings of Machine Learning Research (PLMR, 2022), Vol. 145, pp. 757–780] proposes an algorithm where a feedback loop couples a neural network that models the committor function with importance sampling, mainly umbrella sampling, which collects data needed for adaptive training. Here, in this work, we show additional modifications are needed to improve the accuracy of the algorithm. The first modification adds elements of supervised learning, which allows the neural network to improve its prediction by fitting to sample-mean estimates of committor values obtained from short molecular dynamics trajectories. The second modification replaces the committor-based umbrella sampling with the finite-temperature string (FTS) method, which enables homogeneous sampling in regions where transition pathways are located. We test our modifications on low-dimensional systems with non-convex potential energy where reference solutions can be found via analytical or finite element methods, and show how combining supervised learning and the FTS method yields accurate computation of committor functions and reaction rates. We also provide an error analysis for algorithms that use the FTS method, using which reaction rates can be accurately estimated during training with a small number of samples. The methods are then applied to a molecular system in which no reference solution is known, where accurate computations of committor functions and reaction rates can still be obtained.

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

Enhanced Catalytic Activity of a Vanadium-Doped Mesoporous Octahedral Molecular Sieve-2 (K-OMS-2) toward Hydrogen Evolution Reaction

Hydrogen evolution reaction (HER) is the cathodic reaction in electrochemical water splitting that produces H 2 in a renewable pathway, primarily to be used in fuel cells. K-OMS-2, a manganese octahedral molecular sieve, has the potential to replace Pt as a catalyst for HER due to its redox and electrochemical properties, which can be further improved by doping metal cations into the K-OMS-2 framework. In this work, we synthesized 1, 5, and 10% vanadium- (V) doped mesoporous K-OMS-2 under mild acidic reaction conditions. Here, the mesoporous nature of the samples was confirmed by BET analysis. X-ray photoelectron spectroscopy confirmed that Mn has 2+, 3 +, and 4+ oxidation states, and V is present as V 4+ in V-K-OMS-2 samples. HER performance of 1% V-K-OMS-2 in an acidic medium shows the best activity with -0.32 V overpotential. The overpotential was lowered by 0.56 V upon doping 1% V into the K-OMS-2 framework. The 1% V-K-OMS-2 material has a Tafel slope of 129 mV/decay and an electrochemically active surface area (ECSA) of 127.5 cm 2 ECSA .

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

Properties and performance of lignin-based polyurethane foams from lignin and castor oil as synergistic bio-polyols

Lignin and castor oil with intrinsic hydroxyl groups are attractive green resources for polyurethane (PU) applications. However, lignin's heterogeneous and highly crosslinked structure, poor processability, as well as the feedstock variability, lead to inconsistent and poor performance of the final foam products. Castor oil-based polyurethane foam (PUF) has a relatively high price and density, which are big hurdles to its practical applications. Here, in this study, we hypothesized that synergistic bio-polyol mixtures composed of lignin and castor oil could balance the drawbacks from individual component. Castor oil could improve the dispersity of lignin, while lignin simultaneously addressed the issues caused by PUF prepared with castor oil, such as its high density and low thermal stability. For a comprehensive understanding of the effects of structural properties of lignin on its PUF processing and applications, various lignin fractions were isolated by co-solvent enhanced lignocellulosic fractionation (CELF) from different species, including hardwood, softwood, and herbaceous plants, and the processed lignins with different molecular weights were applied with castor oil. The lignin fractions with lower molecular weight showed good dispersity in castor oil with a high lignin content (up to 50 wt%) and completely replaced petroleum-based polyols. The produced foam with 50 wt% low-molecular-weight lignin fractions from woody biomass showed comparable/higher compressive strength (up to 20 psi) and thermal insulation performance (up to 5.69 R-value in −1 for 50 % L-Pine foam). In addition, this study revealed the relationship between lignin's structural properties and foam performance, providing insights for practical applications of lignin-based PUF.

Jeong, Soyeon [State Univ. of New York (SUNY), Syr↗
DOE OSTI2023

Biodegradable High-Molecular-Weight Poly(pentylene adipate- co -terephthalate): Synthesis, Thermo-Mechanical Properties, Microstructures, and Biodegradation

Poly(pentylene adipate-co-terephthalate) (PPAT) is a promising biobased and biodegradable polymer that can replace polyethylene in flexible packaging films where biodegradability is desired. High-molecular-weight (100K–145 KDa) aliphatic–aromatic polyester PPAT was successfully synthesized, and the effects of reaction conditions on molecular weight were reported. PPAT polyesters were characterized for polymer compositions, number-average unit length, thermal transitions, and rheological properties. PPAT compression-molded films were characterized for crystallinity and tensile properties to correlate micro- and macroproperties. PPAT compression-molded films exhibited up to a 76% higher tensile modulus than compression-molded films from poly(butylene adipate-co-terephthalate) (PBAT), making PPAT films potentially comparable with compression-molded films from linear low-density polyethylene (LLDPE). Finally, PPAT is biodegradable in soil and freshwater environments with estimated 90% biodegradation times of 504–580 and 604–845 days, respectively, while PBAT takes 971 days in soil and 395 days in freshwater.

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

Optimal Binding Affinity for Sieving Separation of Propylene from Propane in an Oxyfluoride Anion-Based Metal–Organic Framework

Highly efficient adsorptive separation of propylene from propane offers an ideal alternative method to replace the energy-intensive cryogenic distillation technology. Molecular sieving-type separation via high-performance adsorbents is targeted for superior selectivity, but the limit in adsorption capacity remains a great challenge. Here, we report an oxyfluoride-based ultramicroporous metal–organic framework UTSA-400, [Ni(WO 2 F 4 )(pyz) 2 ] (pyz = pyrazine), featuring one-dimensional pore channels that can accommodate the propylene molecules with optimal binding affinity while specifically excluding the propane molecules. The exposed oxide/fluoride pairs in UTSA-400 serve as strong functional sites for strengthened propylene–host interactions, accounting for a significantly enhanced propylene uptake, while the propane molecules are excluded due to the regulated host framework dynamics. The strong propylene binding enables near-saturation of propylene in the pore confinement at ambient conditions, leading to full utilization of pore space and superior packing density. Combined in situ infrared spectroscopy measurements and dispersion-corrected density functional theory calculations clearly unveil the nature of boosted host–guest binding. Direct production of polymer-grade (>99.5%) propylene with remarkable dynamic productivity is demonstrated by column breakthrough experiments. Furthermore, this work presents an example of pore engineering with atomic precision to break the trade-off in adsorptive separation through guest binding optimization.

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

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Understanding and Tailoring Diffusion and Co-Adsorption Inside the Confined Pores of Metal-Organic Frameworks (Final Scientific/Technical Report for Award DE-SC0019902)

The aim of this program was to gain a fundamental understanding of the behavior of various guest molecules in nano-confined environments, such as metal organic frameworks (MOFs), using a combination of novel synthesis, ab initio modeling, and in situ characterization. Through this project, we developed a concise understanding of the mechanisms that control adsorption/desorption of gaseous molecules and their mixtures, leading to design/synthesis guidelines for MOFs with desired functionality. We further developed methods to disentangle kinetic from thermodynamic effects during adsorption, as well as to characterize the interactions at play. In the first funding cycle, the focus was on the unambiguously characterization of co-adsorption and diffusion of gasses/vapors and their mixtures. In the second funding cycle, the focus was on characterizing the effects of the nano-confinement on the kinetics and thermodynamics of adsorption processes inside MOFs, again with an emphasis on mixtures of gasses and vapors. The nano-confinement can tip the thermodynamic vs. kinetic balance, and current understanding and theory based on single-component analysis can lead to incorrect predictions for mixtures. This is of particular interest in real-world applications, where gasses/vapors are typically mixed, contain impurities, or are often exposed to humid conditions. Our main findings were: (i) within confined environments the adsorption behavior of mixed gasses/vapors can be drastically different from the “sum” of the corresponding single phases; (ii) co-adsorption is often competitive and detrimental to performance, but it can also be cooperative and beneficial; (iii) in some co-adsorbed gasses/vapors, molecules that are strongly bound in the single-component phase can be replaced by molecules that are nominally weaker bound (molecular exchange) due to guest-guest interactions that lower the kinetic barriers and favor the final adsorption state; (iv) kinetic and thermodynamic effects can be precisely controlled through pore-size engineering and synthesis; and, (v) kinetic effects can be identified and disentangled from thermodynamic effects during adsorption through a series of sequential and simultaneous gas loading measurements. The short-term goal of this program was the controlling and understanding of common MOF systems in real-world situations where gasses/vapors are mixed, which will have an important impact on industrial processes and applications from gas storage and sequestration to catalysis and sensors. The long-term goals include the development of theoretical and experimental methods for gaining a fundamental understanding of adsorption/reaction processes within MOFs, as well as new guidelines for synthesizing MOFs with tailored physical and chemical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

N-type molecular doping of a semicrystalline conjugated polymer through cation exchange

Control of electrical doping is indispensable in any semiconductor device, and both efficient hole and electron doping are required for many devices. In organic semiconductors, however, electron doping has been essentially more problematic compared to hole doping because in general organic semiconductors have low electron affinities and require dopants with low ionization potentials that are often air-sensitive. Here, we adapt an efficient molecular doping method, so-called ion-exchange doping, to dope electrons in a polymeric semiconductor. We initially reduce the polymeric semiconductor using one electron transfer from molecular dopants, and then the ionized dopants in the resulting air-unstable films are replaced with secondary ions via cation exchange. Improved ambient stability and crystallinity of the doped polymeric semiconductors are achieved when a specific bulky molecular cation was chosen as the secondary ion, compared to conventional methods. The presented strategy can overcome the trade-off relationship between reducing capability and ambient stability in molecular dopants, and a wider selection of dopant ions will help to realize ambient-stable electron conductors.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Predicting the Mechanical Response of Polyhydroxyalkanoate Biopolymers Using Molecular Dynamics Simulations

Polyhydroxyalkanoates (PHAs) have emerged as a promising class of biosynthesizable, biocompatible, and biodegradable polymers to replace petroleum-based plastics for addressing the global plastic pollution problem. Although PHAs offer a wide range of chemical diversity, the structure–property relationships in this class of polymers remain poorly established. In particular, the available experimental data on the mechanical properties is scarce. In this contribution, we have used molecular dynamics simulations employing a recently developed forcefield to predict chemical trends in mechanical properties of PHAs. Specifically, we make predictions for Young’s modulus, and yield stress for a wide range of PHAs that exhibit varying lengths of backbone and side chains as well as different side chain functional groups. Deformation simulations were performed at six different strain rates and six different temperatures to elucidate their influence on the mechanical properties. Our results indicate that Young’s modulus and yield stress decrease systematically with increase in the number of carbon atoms in the side chain as well as in the polymer backbone. In addition, we find that the mechanical properties were strongly correlated with the chemical nature of the functional group. The functional groups that enhance the interchain interactions lead to an enhancement in both the Young’s modulus and yield stress. Finally, we applied the developed methodology to study composition-dependence of the mechanical properties for a selected set of binary and ternary copolymers. Overall, our work not only provides insights into rational design rules for tailoring mechanical properties in PHAs, but also opens up avenues for future high throughput atomistic simulation studies geared towards identifying functional PHA polymer candidates for targeted applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Structured for success: conjugated polymer binders with tailored composition and architecture for lithium-ion batteries

Conjugated polymer binders are replacing conventional binders in lithium-ion batteries. Herein, we examine how molecular engineering and hierarchical nanostructuring govern binder functionality and electrochemical performance. Lithium-ion batteries (LIBs) are the leading energy storage technology, yet enhancing their energy density and cycle life remains critical. Significant progress has been made in high-capacity anodes and high-voltage cathodes, but their performance is hindered by electrode degradation, where it is related to the behaviors of binders at the surface and interface. Conventional non-conductive binders like poly(vinylidene difluoride) (PVDF), combined with conductive additives, often fail to maintain electrical pathways under repeated volume changes. Alternatively, conjugated polymer binders have emerged as a superior alternative, simultaneously offering intrinsic conductivity, mechanical flexibility, and strong adhesion through π-conjugated backbones and functional groups. Their tunable molecular structure enables efficient electron/ion transport while mitigating electrode cracking. Additionally, the development of hierarchically ordered nanostructures in conjugated polymer binder can further enhance their electrochemical performance. This review examines the design principles of conjugated polymer binders, focusing on molecular engineering and nanostructural control to optimize their performance in high-loading electrodes, such as silicon-based anodes. By addressing key challenges in binder functionality, these advanced materials pave the way for next-generation high-energy-density LIBs.

Jin, Xiuyu↗
DOE OSTI2026

Ultra-High Vacuum Outgassing Characterization of Thermally Processed Low-Carbon Steel for Advanced Particle Accelerator and Gravitational Wave Detector Applications

This dissertation investigated AISI 1020 low-carbon steel as an alternative vacuum chamber material to conventional stainless steel for ultra-high vacuum (UHV) and extreme-high vacuum (XHV) applications. After a 400 °C/48 h bake, AISI 1020 tube chambers achieved a hydrogen outgassing rate of 2.4 × 10¿¹6 Torr·L·s¿¹·cm¿², approximately 2,300 times lower than the prebaked 316L stainless-steel comparator, among the lowest hydrogen outgassing rates ever reported for an uncoated metallic vacuum chamber. Bare and magnetite-coated AISI 1020 chambers were then compared using throughput and rate-of-rise methods. The magnetite coating yielded 5× lower water outgassing at room temperature, but this advantage disappeared after 80 °C baking. After full thermal conditioning (400 °C/48 h prebake followed by 150 °C/96 h and 200 °C/110 h), bare steel achieved 25× lower hydrogen outgassing than the magnetite-coated chamber (9.6 × 10¿¹6 Torr·L·s¿¹·cm¿²) and >99% H2 purity with carbon species below RGA detection. Monte Carlo molecular flow simulations of a CEBAF photogun beamline (96 scenarios) showed that replacing 304L stainless steel with AISI 1020 reduces equilibrium H2 pressure by a factor of 833; a single 304L electrode contributes 98.8% of the gas load despite occupying only 9.1% of the internal surface area. A 500-m Einstein Telescope beampipe screening showed that corrugated bellows contribute 18% of the gas load from only 0.7% of the surface area. A five-model adsorption isotherm framework applied to 22 pumpdown datasets (164 fits with AR(1)-GLS correction) established that the experimental protocol, not the material, controls isotherm identifiability: Dubinin–Radushkevich wins isothermal pipe pumpdowns; Langmuir wins thermally dominated chamber bakes. Cross-dataset joint fitting of the AISI 1020 pipe pumpdowns yielded an H2 diffusion activation energy Ed = 7.24 ± 1.28 kcal·mol¿¹, consistent with trap dominated diffusion in commercial low-carbon steels. Two companion innovations were developed: a Variable Conductance Device (VCD, patent pending IDF-00723) for XHV outgassing measurement, and VacuumDesignerPro (VDP), a MATLAB-based design tool validated against LIGO benchmarks.

Al-Allaq, Aiman H [Old Dominion University]↗
DOE OSTI2025

Probing Carbon Mineralization Mechanisms in Pore and Bulk Fluids by Harnessing Architected Calcium Silicates

The ability to synthesize materials with well-controlled pore structures gives us unprecedented control over probing fluid interactions with reactive interfaces and advancing calibrated insights into coupled chemo-morphological interactions. One of the primary challenges in developing crystalline silicate materials lies in achieving ordered pore structures. Existing approaches of producing amorphous mesoporous metal silicates via sol–gel methods and heat-treatment of these materials to produce crystalline phases cause the pore structures in the amorphous phases to collapse. To overcome this challenge, carbon coating of amorphous mesoporous calcium silicate particles is carried out to retain the pore structure, while the material is heated to produce crystalline calcium silicate with calcium sulfate inclusions. The pore diameter in these materials is about 3.9 nm, with a surface area and a pore volume of 28.75 m 2 /g and 0.092 cm 2 /g, respectively. The mechanisms of carbon mineralization are investigated by reacting architected calcium silicate with 1 M Na 2 CO 3 and monitoring the evolution in the structural phases using operando wide-angle X-ray scattering (WAXS) measurements. Formation of stable calcium carbonate polymorph or calcite and metastable calcium carbonate polymorph or vaterite in pore and bulk fluids, respectively, resulting from the reaction between Na 2 CO 3 and CaSiO 3 , are noted. The mechanisms associated with carbon mineralization are delineated using ReaxFF molecular dynamics (MD) simulations. The surface dissolution reaction is initiated by 2H + ions that replace a Ca 2+ ion in the Ca–silicate matrix. Ca 2+ ions in the solution initially react with water to form calcium hydroxide and eventually form calcium (bi)carbonate. A slow and gradual increase in the formation of sodium silicate in the solution resulting from the reactions of silicic acid or the silicon dioxide reaction with sodium hydroxide is noted. When carbon mineralization occurs in environments bearing interfacial fluids, calcite is the dominant calcium carbonate polymorph, as determined using experiments with pore fluids and molecular-scale simulations. In conclusion, these studies provide fundamental insights into the mechanisms underlying the carbon mineralization of calcium silicate informed by experiments and molecular-scale simulations.

Calcium↗
DOE OSTI2021

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast-irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with small unit cell (~200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of Al 90 Ce 10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al 90 Ce 10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.

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

Degradation of Anion Exchange Membranes by Cation Elimination: Impact on Water Uptake, Nanostructure, and Ionic Mobility

Anion exchange membranes (AEMs) are an attractive platform for fuel cell and electrolysis technologies since they enable the use of cheaper, nonplatinum group metal electrodes and components. However, the widespread adoption of AEM-based devices is limited by chemical degradation of the AEM in the highly alkaline medium of operation. Experimental studies report three major pathways of degradation, including substitution (S N 2), elimination (E2), and backbone cleavage. The decline in membrane performance is likely due to a cumulative effect of several pathways, which has proven to be difficult to disentangle through experiments. Here we use coarse-grained molecular simulations to isolate the impact of E2 degradation, where cationic sites are replaced by an alkene group, on AEM water uptake, nanoscale morphology, structure of the ion-conducting channels, water mobility, and ionic conductivity. Our studies focus on the well-studied model AEM polyphenylene oxide with tetraalkylammonium cationic sites (PPO-TMA). We find that about half of the hydrophobic groups resulting from the degradation retract into the hydrophobic domains of the membrane. The reduction in ion exchange capacity (IEC) and the hydrophobicity of the alkene groups decrease both the equilibrium water uptake (%WU) and the number of water molecules per cation (λ) of the membrane. Interestingly, the evolution of λ with the IEC seems to follow that of undegraded PPO-TMA membranes. Additionally, we ascribe this to the similarity between the structure of the degraded monomer and the neutral one in the polymer. We conclude that most of the performance losses originate in the lower hydration of the degraded membrane.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structural studies of the IFNλ4 receptor complex using cryoEM enabled by protein engineering

Abstract IFNλ4 has posed a conundrum in human immunology since its discovery in 2013, with its expression linked to complications with viral clearance. While genetic and cellular studies revealed the detrimental effects of IFNλ4 expression, extensive structural and functional characterization has been limited by the inability to express and purify the protein, complicating explanations of its paradoxical behavior. In this work, we report a method for robust production of IFNλ4. We then use yeast surface display to affinity-mature IL10Rβ and solve the 72 kilodalton structures of IFNλ4 (3.26 Å) and IFNλ3 (3.00 Å) in complex with their receptors IFNλR1 and IL10Rβ using cryogenic electron microscopy. Comparison of the structures highlights differences in receptor engagement and reveals a distinct 12-degree rotation in overall receptor geometry, providing a potential mechanistic explanation for differences in cell signaling, downstream gene induction, and antiviral activities. Further, we perform a structural analysis using molecular modeling and simulation to identify a unique region of IFNλ4 that, when replaced, enables secretion of the protein from cells. These findings provide a structural and functional understanding of the IFNλ4 protein and enable future comprehensive studies towards correcting IFNλ4 dysfunction in large populations of affected patients.

Science & Technology - Other Topics↗
DOE OSTI2025

Nuclear Magnetic Resonance Dynamics of LiTFSI–Pyrazole Eutectic Solvents

Deep Eutectic Solvents (DESs) have emerged as promising candidates to replace conventional organic solvents in various technological applications due to their low vapor pressure, non-flammability, and ease of preparation at low costs. In particular, Type IV DESs, which are composed of metal salts and hydrogen bond donors, are possible replacements for lithium-ion battery electrolytes. In this study, we investigate the molecular dynamics of solvents of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and pyrazole (PYR) at varying LiTFSI:PYR molar ratios (1:2, 1:3, 1:4, 1:5) using Nuclear Magnetic Resonance Dispersion (NMRD) and Pulsed Field Gradient (PFG) Nuclear Magnetic Resonance (NMR). PFG NMR reveals composition-dependent diffusion trends, while NMRD provides molecular-level insights into the longitudinal relaxation rate (R 1 = 1/T 1 ). Notably, the LiTFSI:PYR (1:2) sample shows distinct behavior across both techniques, exhibiting enhanced relaxation rates and lower self-diffusion for 1 H compared to the other nuclei ( 19 F and 7 Li), suggestive of stronger and more efficient Li + –pyrazole interactions, as confirmed by the modeling of the relaxation profiles. Our study advances understanding of ion dynamics in azole-based eutectic solvents, supporting their potential use in safer battery electrolytes.

FFC-NMR↗
DOE OSTI2025

Replacing non-biomedical concepts improves embedding of biomedical concepts

Embeddings are semantically meaningful representations of words in a vector space, commonly used to enhance downstream machine learning applications. Traditional biomedical embedding techniques often replace all synonymous words representing biological or medical concepts with a unique token, ensuring consistent representation and improving embedding quality. However, the potential impact of replacing non-biomedical concept synonyms has received less attention. Embedding approaches often employ concept replacement to replace concepts that span multiple words, such as non-small-cell lung carcinoma, with a single concept identifier (e.g., D002289). Also, all synonyms of each concept are merged into the same identifier. Here, we additionally leveraged WordNet to identify and replace sets of non-biomedical synonyms with their most common representatives. This combined approach aimed to reduce embedding noise from non-biomedical terms while preserving the integrity of biomedical concept representations. We applied this method to 1,055 biomedical concept sets representing molecular signatures or medical categories and assessed the mean pairwise distance of embeddings with and without non-biomedical synonym replacement. A smaller mean pairwise distance was interpreted as greater intra-cluster coherence and higher embedding quality. Embeddings were generated using the Word2Vec algorithm applied to a corpus of 10 million PubMed abstracts. Our results demonstrate that the addition of non-biomedical synonym replacement reduced the mean intra-cluster distance by an average of 8%, suggesting that this complementary approach enhances embedding quality. Future work will assess its applicability to other embedding techniques and downstream tasks. Python code implementing this method is provided under an open-source license.

algorithms↗