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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Corrected Score Function Framework for Modelling Circadian Gene Expression

Many biological processes display oscillatory behaviour based on an approximately 24 h internal timing system specific to each individual. One process of particular interest is gene expression, for which several circadian transcriptomic studies have identified associations between gene expression during a 24 h period and an individual's health. A challenge with analysing data from these studies is that each individual's internal timing system is offset relative to the 24 h day-night cycle, where day–night cycle time is recorded for each collected sample. Laboratory procedures can accurately determine each individual's offset and determine the internal time of sample collection. However, these laboratory procedures are labour-intensive and expensive. Here, in this paper, we propose a corrected score function framework to obtain a regression model of gene expression given internal time when the offset of each individual is too burdensome to determine. A feature of this framework is that it does not require the probability distribution generating offsets to be symmetric with a mean of zero. Simulation studies validate the use of this corrected score function framework for cosinor regression, which is prevalent in circadian transcriptomic studies. Illustrations with data from three circadian transcriptomic studies further demonstrate that the proposed framework consistently mitigates bias relative to using a score function that does not account for this offset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Experimental validation of a co-simulation architecture for modeling whole-building and detailed electrical distribution performance

This article presents an experimental validation of a co-simulation architecture for simultaneously modeling whole-building energy performance and detailed building electrical distribution system performance. The co-simulation architecture consists of a whole-building energy model (EnergyPlus®) embedded within a Modelica-based building electrical distribution system library called the Building Electrical Efficiency Analysis Model (BEEAM) using the Functional Mock-up Interface standard. We validate the model using experimental data collected at a full-scale test cell within Lawrence Berkeley National Laboratory’s FLEXLAB® facility. In conclusion, we show that the co-simulation model accurately predicts the electrical, mechanical, and thermal performance of the test cell for typical loads with both an AC and a DC electrical distribution topology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Route to Design Novel Functional Peptides by Applying a Denoising Diffusional Model to mRNA Display Libraries

In vitro directed evolution techniques, such as mRNA display, enable peptide ligand discovery and optimization. However, physical libraries that rely on a genetic code can only search a small fraction of sequence space due to inherent biases in the genetic code and experimental limitations. To address this challenge, denoising diffusion implicit models (DDIMs) are applied to generate novel peptide ligands against B‐cell lymphoma extra‐large (Bcl‐x L ), a key cancer target. Starting with high‐throughput sequencing data from previous selections, a DDIM is trained to produce novel sequences with high affinity binding. Experimental validation confirms that most generated sequences are functionally equivalent to the original library members for Bcl‐x L binding and demonstrated comparable binding kinetics and affinity relative to the wildtype and nearest original neighbors. Importantly, this approach generated rare sequences not easily accessible via mutation and directed evolution. These results indicate that DDIMs can complement and expand directed evolution data, efficiently exploring underrepresented regions of sequence space. This approach provides a broadly applicable framework for accelerating ligand discovery and optimizing molecular properties across diverse targets.

Qi, Pearl [Mork Family Department of Chemical Engi↗
DOE OSTI2026

Validating corrosion models: Influence of physical properties

In chloride containing environments, two metals in physical contact can undergo galvanic corrosion limiting the lifetime of components. Being able to accurately predict galvanic corrosion damage distributions over time has not been widely presented in literature. Therefore, Finite Element Method (FEM) corrosion models were experimentally validated for two galvanic couples as a function of governing equations, environment, anode material, anode:cathode ratio, and time. Carbon steel/stainless steel (CS/SS) and zinc/stainless steel (Zn/SS) galvanic couples were exposed to NaCl solutions at room temperatures for up to 14 days. For the galvanic couples and environments, the Laplace equation with variable conductivity and reactions was sufficient to model the experimental corrosion damage. Increasing the chloride concentration for the CS/SS galvanic couple, regardless of the anode:cathode ratio, decreased the observed and modeled corrosion damage. Decreasing the anode:cathode ratio (i.e., increasing the cathode length), increased the experimentally observed and modeled corrosion damage. For small anode:cathode ratios, the governing equations deviate over long time periods with the Nernst-Plank equation being conservative. For the Zn/SS galvanic couple, the cathode length controls the dominant cathodic reduction reaction. For small cathode lengths, the hydrogen evolution reaction is dominant. For larger cathode lengths, the oxygen reduction reaction is dominant. The results are discussed with regard to the influence of solution chemistry, ohmic drop, and governing reactions. Overall, validated FEM models were presented, and the resultant models, physics, and mechanisms can be applied to other corrosion scenarios with confidence.

Carbon steel↗
DOE OSTI2025

Computational discovery of a novel double transition metal nitride MXene and its applications as an anchoring and catalytic material in Li–Se batteries

Double transition metal (DTM) nitride MXenes offer enhanced electrical conductivity and tunable catalytic properties compared to conventional carbide-based MXenes. In this study, we employed first-principles density functional theory (DFT) calculations to discover and computationally validate a novel DTM nitride MXene, Nb 2 TiN 2 , derived from its MAX phase precursor and investigated its potential as an anchoring material (AM) for Li–Se batteries. This newly proposed MXene expands the compositional landscape of DTM nitrides and opens new avenues for functional material design. We performed a comprehensive analysis of the thermodynamic and electronic properties of Nb 2 TiAlN 2 , and the MAX phase precursor to Nb 2 TiN 2 to assess its structural stability and exfoliation potential. Exfoliation energy calculations confirmed the feasibility of synthesizing Nb 2 TiN 2 from Nb 2 TiAlN 2 . We then explored the functionalized form, Nb 2 TiN 2 S 2 , evaluating its capability to serve as an effective anchoring material (AM) in Li–Se batteries by analyzing the reaction mechanisms and kinetics of the selenium reduction reaction (SeRR). Our results indicate that Nb 2 TiN 2 S 2 exhibits a strong binding affinity for lithium polyselenides (Li 2 Se n ), effectively suppressing the shuttle effect. Gibbs free energy calculations for the rate-limiting step of the SeRR reveal favorable kinetics and reduced reaction barriers. Overall, this study provides a detailed evaluation of the structural and electronic properties of a newly proposed DTM nitride MXene and its S-functionalized derivative and the catalyzing effect of Nb 2 TiN 2 S 2 in accelerating the reaction kinetics in Li–Se batteries. These findings underscore the potential importance of the further exploration of MXenes to address current challenges in high-performance Li–Se batteries.

25 ENERGY STORAGE↗
DOE OSTI2024

Calibration and validation of the foundation for a multiphase strength model for tin

In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory. The shear modulus model for the ambient phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress–strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston–Tonks–Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to ⁠10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid–liquid but never a fully liquid state, never fully lost strength, stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An energetic link between order and strength in metals: A nanocrystalline strength limit in high-entropy alloys and intermetallic compounds

The metallurgy and materials communities have long understood and exploited fundamental links between chemical and structural ordering in metallic solids to tailor their mechanical properties. We extend these ideas to include prediction of the nanocrystalline strength limit in high-entropy alloys and intermetallic compounds, where a breakdown occurs in the classical Hall-Petch strengthening behavior. The highest reported strength achievable through alloying has rapidly climbed and given rise to new classifications of materials with extraordinary properties, with a notable case being nanocrystalline metals. High-entropy alloys (chemically disordered, concentrated solid solutions) and intermetallic compounds are two boundary cases of how tailored order can be used to manipulate mechanical behavior. Here, we show that the complex electronic-structure mechanisms governing the peak strength of alloys and pure metals can be reduced to a few physically meaningful parameters based on their atomic arrangements and used – with no fitting parameters – to predict the maximum strength of these materials. This includes a generalized energy-based accounting for the degree of structural and chemical ordering that allows for rapid and reasonably accurate prediction of peak strength (validated in the nanocrystalline limit) as a function of temperature. Predictions of maximum strength based on the activation energy (with all materials properties derived from DFT calculations or experiments) for a stress-driven transition to an amorphous state is shown to accurately describe the breakdown in Hall-Petch behavior at the smallest crystallite sizes for pure metals, intermetallic compounds, high-entropy alloys, and metallic glasses. Further, this activation energy is also shown to be directly proportional to interstitial electronic charge density, which is a good predictor of ductility, stiffness (moduli), and phase stability in high-entropy alloys and solid metals generally. The proposed framework suggests the possibility of coupling ordering and intrinsic strength to mechanisms like dislocation nucleation, hydrogen embrittlement, and transport properties, such as through correlations between the activation energies for amorphization with stacking-fault and grain boundary energies. It additionally opens the prospect for greatly accelerated structural materials design and development to address materials challenges limiting more sustainable and efficient use of energy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modulation of thermal conductivity of iron-doped ß-Ga2O3 by helium-ion irradiation

This study examines the impact of helium-ion irradiation on the thermal conductivity of ß-Ga2O3. A laser-based spatial domain thermoreflectance technique is used to investigate thermal conductivity map for both un-irradiated and irradiated ß-Ga2O3, which are then validated against simulation results derived from density functional theory-based phonon transport simulations. Since helium bubble evolution was ob- served at the nanoscale using transmission electron microscopy, the simulation study was carried out on eight distinct helium-induced sites in ß-Ga2O3. Our findings indicate a reduction in thermal conductivity for the irradiated samples. Experimental results show a significant reduction in thermal conductivity in irradiated samples, with de- creases of approximately 25% along the [100] direction and 40% along [001] directions. Phonon transport simulations closely replicate these findings, particularly when helium occupying interstitial sites, predicting reductions of ˜53% along [100] and ˜50% along [001] directions. This work underscores the role of irradiation-induced microstructural changes in the heat transport properties of ß-Ga2O3 which is crucial for its application in sensor devices in extreme environments.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Impact of SO 2 on NiFe Nanoparticle Exsolution and Dissolution from LaFe 0.9 Ni 0.1 O 3 Perovskite Oxides

Ni-doped LaFeO 3 perovskite oxide is a promising cathode material for solid oxide electrolysis cells (SOECs) designed for CO 2 /H 2 O coelectrolysis. Here, the performance of LaFe 0.9 Ni 0.1 O 3 is being investigated under real-world conditions that include exposure to acid gases, such as SO 2 , relevant to SOEC operation. Experiments show that LaFe 0.9 Ni 0.1 O 3 exsolves NiFe nanoparticles, along with the formation of surface SO 4 2– and SO 3 2– after being exposed to 200 ppm of SO 2 . This suggests that the ionic diffusion of Ni 3+ and Fe 3+ between the bulk and the surface remains unaffected throughout the exsolution–dissolution–exsolution cycle. Thermochemical water splitting has been employed as a probe reaction to evaluate the catalytic properties of the exsolved NiFe nanoparticles. These nanoparticles demonstrated improved hydrogen production compared to bare perovskite oxide substrates. However, after exposure to SO 2 , the formation of Fe-rich NiFe nanoparticles led to poor thermocatalytic performance and rapid deactivation of the perovskite at elevated temperatures. Density functional theory (DFT) analysis was utilized to validate the experimental findings, indicating a significantly negative reaction energy for water splitting over exsolved Fe, as well as stronger binding of SO 2 to Fe than to Ni. Computational analysis further suggests that the presence of surface sulfate promotes the formation of Fe-rich NiFe nanoparticles, aligning with the experimental results. Overall, this study clarifies how SO 2 affects the structure of SOEC perovskite oxide candidate materials. Future engineering efforts should focus on enhancing nanoparticle exsolution and sulfur resistance, which is crucial for improving the hydrogen production capacity of La-based perovskite oxides for electro- and thermocatalytic water splitting in real environments containing acid gases.

Najimu, Musa [Univ. of Southern California, Los An↗
DOE OSTI2024

PHYSLITE - A new reduced common data format for ATLAS

The High Luminosity LHC (HL-LHC) era brings unprecedented computing challenges that call for novel approaches to reduce the amount of real and Monte Carlo-simulated data that is stored, while continuing to support the rich physics program of the ATLAS experiment. With the beginning of LHC Run 3, ATLAS introduced a new common data format, PHYS, that replaces most of the analysis-specific formats that were used in Run 2, and therefore reduces the disk storage significantly. ATLAS also launched the prototype of another common format, PHYSLITE, that is about a third of the size of PHYS. PHYSLITE will be the main format for ATLAS at the HL-LHC and aims to serve 80% of all physics analyses. To simplify analysis workloads and further reduce disk usage it is designed to largely replace user-defined analysis n-tuples and consequently contains pre-calibrated objects. Various forms of validations are in place to ensure correct functionality for users. Developments continue towards HL-LHC to improve the PHYSLITE format further.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Enhancing high-fidelity neural network potentials through low-fidelity sampling

The efficacy of neural network potentials (NNPs) critically depends on the quality of the configurational datasets used for training. Prior research using empirical potentials has shown that well-selected liquid–solid transitional configurations of a metallic system can be translated to other metallic systems. This study demonstrates that such validated configurations can be relabeled using density functional theory (DFT) calculations, thereby enhancing the development of high-fidelity NNPs. Training strategies and sampling approaches are efficiently assessed using empirical potentials and subsequently relabeled via DFT in a highly parallelized fashion for high-fidelity NNP training. Our results reveal that relying solely on energy and force for NNP training is inadequate to prevent overfitting, highlighting the necessity of incorporating stress terms into the loss functions. To optimize training involving force and stress terms, we propose employing transfer learning to fine-tune the weights, ensuring that the potential surface is smooth for these quantities composed of energy derivatives. This approach markedly improves the accuracy of elastic constants derived from simulations in both empirical potential-based NNPs and relabeled DFT-based NNPs. Overall, this study offers significant insights into leveraging empirical potentials to expedite the development of reliable and robust NNPs at the DFT level.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Dataset for manuscript "Rotational Memory Function of SPC/E water"

Memory effect are essential for dynamics of condensed materials and are responsible for non-exponential relaxation of correlation functions of dynamic variables through the memory function entering the memory equation. Memory functions of dipole rotations for polar liquids have never been calculated. We present here calculations of memory functions and single-dipole rotations and of the overall system dipole moment for SPC/E water measured by dielectric spectroscopy. The memory functions for single-particle and collective dynamics turn out to be nearly identical. This result validates theories of dielectric spectroscopy in terms of single-particle time correlation function and the connection between the collective and single-particle relaxation times in terms of the Kirkwood factor. The dataset includes single particle and system dipole moments, including their time-dependence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Rotational memory function of SPC/E water

Memory effects are essential for the dynamics of condensed materials and are responsible for non-exponential relaxation of correlation functions of dynamic variables through the memory function. Memory functions of dipole rotations for water have never been calculated directly from molecular dynamics simulations. We present here calculations of memory functions for single-dipole rotations and for the overall dipole moment of the sample for SPC/E water. The normalized memory functions for single-particle and collective dipole dynamics turn out to be nearly identical. This result validates theories of dielectric spectroscopy in terms of single-particle time correlation functions and the connection between the collective and single-particle relaxation times through the Kirkwood factor. The dielectric function in this formalism contains no new dynamic information that does not exist in the single-dipole correlation function. A short memory time, ≲1 fs, justifies the use of the mathematics of rotational diffusion to describe the dynamics of a single molecular dipole moment in bulk water. Here, an analytical equation for the rotational memory time is derived.

Asthagiri, Dilipkumar N. [Oak Ridge National Labor↗
DOE OSTI2026

A Scalable Test-Bed for Direct Current Current Transformer Automated Functional Testing and Characterization

This article describes a scalable test-bed for automation of measurements of the direct current current transformers (DCCT). Automation of measurement involves functional measurements, fault event based measurements and data validation. The test-bed is designed to measure up to 20 DCCTs at a time with software based measurement repetition checks and validation of the measurements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Calibration and validation of the foundation for a multiphase strength model for tin

In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a 10 multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory (DFT). The shear modulus model for the ambient β phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the β phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress-strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston-Tonks-Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to 10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability 20 of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid-liquid but never fully liquid state, never fully lost strength, 25 stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Massively parallel and universal approximation of nonlinear functions using diffractive processors

Nonlinear computation is essential for a wide range of information processing tasks, yet implementing nonlinear functions using optical systems remains a challenge due to the weak and power-intensive nature of optical nonlinearities. Overcoming this limitation without relying on nonlinear optical materials could unlock unprecedented opportunities for ultrafast and parallel optical computing systems. Here, we demonstrate that large-scale nonlinear computation can be performed using linear optics through optimized diffractive processors composed of passive phase-only surfaces. In this framework, the input variables of nonlinear functions are encoded into the phase of an optical wavefront—e.g., via a spatial light modulator (SLM)—and transformed by an optimized diffractive structure with spatially varying point-spread functions to yield output intensities that approximate a large set of unique nonlinear functions–all in parallel. We provide proof establishing that this architecture serves as a universal function approximator for an arbitrary set of bandlimited nonlinear functions, also covering wavelength-multiplexed nonlinear functions as well as multi-variate and complex-valued functions that are all-optically cascadable. Our analysis also indicates the successful approximation of typical nonlinear activation functions commonly used in neural networks, including the sigmoid, tanh, ReLU (rectified linear unit), and softplus. We numerically demonstrate the parallel computation of one million distinct nonlinear functions, accurately executed at wavelength-scale spatial density at the output of a diffractive optical processor. Furthermore, we experimentally validated this framework using in situ optical learning and approximated 35 unique nonlinear functions in a single shot using a compact setup consisting of an SLM and an image sensor. These results establish diffractive optical processors as a scalable platform for massively parallel universal nonlinear function approximation, paving the way for new capabilities in analog optical computing based on linear materials.

Rahman, Md Sadman Sakib [University of California,↗
DOE OSTI2024

Machine Learning Discrimination and Ultrasensitive Detection of Fentanyl Using Gold Nanoparticle-Decorated Carbon Nanotube-Based Field-Effect Transistor Sensors

The opioid overdose crisis is a global health challenge. Fentanyl, an exceedingly potent synthetic opioid, has emerged as a leading contributor to the surge in opioid-related overdose deaths. The surge in overdose fatalities, particularly due to illicitly manufactured fentanyl and its contamination of street drugs, emphasizes the urgency for drug-testing technologies that can quickly and accurately identify fentanyl from other drugs and quantify trace amounts of fentanyl. In this paper, gold nanoparticle (AuNP)-decorated single-walled carbon nanotube (SWCNT)-based field-effect transistors (FETs) are utilized for machine learning-assisted identification of fentanyl from codeine, hydrocodone, and morphine. The unique sensing performance of fentanyl led to use machine learning approaches for accurate identification of fentanyl. Employing linear discriminant analysis (LDA) with a leave-one-out cross-validation approach, a validation accuracy of 91.2% is achieved. Meanwhile, density functional theory (DFT) calculations reveal the factors that contributed to the enhanced sensitivity of the Au-SWCNT FET sensor toward fentanyl as well as the underlying sensing mechanism. Finally, fentanyl antibodies are introduced to the Au-SWCNT FET sensor as specific receptors, expanding the linear range of the sensor in the lower concentration range, and enabling ultrasensitive detection of fentanyl with a limit of detection at 10.8 fg mL –1 .

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

Development of an interatomic potential for the Ta–Li system

A new interatomic potential for the Ta–Li system is introduced to facilitate the study of phase stability, mechanical properties and non-equilibrium dynamics after Li implantation in Ta. Here, this potential is based on a generalization of the embedded atom method (GEAM) and includes contributions from embedding energy, explicit two- and three-body interactions, and nonlocal many-body interaction terms. The parameters of the potential are optimized using energies and atomic forces for a wide range of configurations obtained from ab initio density functional theory (DFT) calculations. The potential is rigorously validated across a range of physical properties, including elastic constants, equations of state, phonon dispersion curves, point defect properties, and melting temperatures for different compositions. Although our potential is trained on a small dataset, its accuracy is comparable to that of available machine learning potentials for Li and Ta. Our simulations show that at temperatures below 500 K, Li atoms in Ta–Li alloys form clusters separated by Ta-rich domains, and we find no evidence of ordered phase formation. For Li concentrations below a few percent, Li atoms preferentially segregate to surfaces and grain boundaries. However, in alloys containing more than ~10% Li, the accumulation of Li in symmetric-tilt grain boundaries can lead to one of the following effects: formation of amorphous-like regions, changes in grain boundary structural units, or lateral movement of the grain boundary.

GEAM potential↗