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At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to ab initio molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of ab initio convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the ab initio reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sub-pilot-scale Production of High-Value Products from U.S. Coals

Investigators from the University of Utah, University of Wyoming and Marshall University pursued a program to study the conversion of raw coal to high-value products of carbon fiber and silicon carbide. Team members also developed an initial framework for a data portal that can incorporate laboratory data on coal processing and product quality, and also work with tools for machine learning for data analysis, data visualization and economic assessment. Experimental R&D efforts focused on the conversion of raw coal to coal tar and other byproducts, and the resulting tar intermediates were upgraded to form anisotropic and isotropic pitch materials. These pitch materials were produced from coal using both thermal (pyrolysis) and chemical (mild solvolysis liquefaction) decomposition of raw coal. Four different coals were studied: Utah bituminous coal (Sufco), Wyoming PRB coal (Black Thunder), Illinois bituminous coal (Illinois #6), and West Virginia bituminous coal (Flying Eagle). Both metallurgical-grade coking coals and lower-grade steam coals were investigated, and controlled secondary gas-phase reactions were used during a two-stage pyrolysis process to induce cracking and condensation reactions among the pyrolytic tar species. This approach successfully improved the performance of the lower grade coals for yielding pitch materials, with properties more consistent with a commercial-grade pitch that had previously demonstrated success for quality carbon fiber production. The use of waste plastic materials was also studied, to help improve physical and chemical characteristics of the intermediate tars and final pitch product; in particular, for lowering the pitch softening point to an acceptable level for melt spinning carbon fiber. Mild solvolysis liquefaction was also used as a method for producing pitch for carbon fiber production. As expected, significantly higher pitch yields were obtained using this approach, and waste plastic materials were also successfully used to reduce pitch softening point to an acceptable level. The plastic materials were also utilized to create a solvent for the mild solvolysis process, and this plastic-derived solvent was shown to provide results consistent with more expensive commercial chemical solvents, and could thus avoid the need for costly recovery and recycle of a liquefaction solvent. Additional experimental R&D focused on the production of silicon carbide (β-SiC) from the residual char byproduct from pitch production, and also on the production of carbon fiber from the anisotropic pitch. SiC was successfully synthesized using a mixture of residual char and sandstone at a ratio of 1:1. Reaction temperature and residence time were optimized and yielded a product purity of 81%. For carbon fiber production, the most successful pitch samples were obtained from the mild solvolysis liquefaction approach, combined with the use of a plastic (HDPE)-derived solvent. Fiber properties improved over time as laboratory fiber production methodologies improved, and final yields of carbon fiber were obtained with a diameter of 12.14 ± 1.10 um, Modulus of 173.73 ± 15.25 GPa, and Tensile Strength of 1.04 ± 0.10 GPa. A proof-of-concept Modern Community Research Data Portal (MCRDP) was developed and deployed for coal and coal-derived pitch characterization, with the full support of (i) remote web-based access, (ii) distributed analysis, (iii) interactive visualization and exploration, (iv) shared and long-term data access, (v) advanced query capabilities and (vi) real-time collaboration. The Coal to Products Data Portal “coaltoproducts.org” provides researchers with space to store and share data within a project, tools for analyzing and understanding data for scientific investigation, and the ability to publish data to the broader community for reproducibility. The portal leverages the Material Commons 2.0 (MC) platform developed by the Center for PRedictive Integrated Structural Materials Science (PRISMS) of the University of Michigan, to achieve long-term longevity of data collections and, more importantly, collaborative science. A number of data visualization tools were also assessed and implemented for interrogating the experimental and modeling data. The machine learning portion of this project analyzed datasets from two different coal conversion processes performed on a diverse set of coal samples from both the coal pyrolysis experiments and the solvent liquefaction experiments. The work was initiated by exploring standard regression models on the pyrolysis data, aiming to understand the impact of sample characteristics and processing conditions on key product metrics. Over the course of the project, the focus expanded to include a variety of machine learning tools, delving into both supervised and unsupervised learning methods. Models tested on the pyrolysis data included linear, ridge, lasso, elastic-net, Gaussian process, random forest regression, and AutoSklearn, and the approach was continually refined to enhance predictive accuracy and model interpretability. Similar techniques were applied to the liquefaction data with an additional focus on feature engineering. Along with mesophase content, additional outputs of interest were the pitch yield, softening point, and QI content. Insights derived from these analyses are crucial in determining the factors influencing the quality and yield of coal-derived products. As the work progressed, the research evolved from foundational model comparisons to analyses of random forests, decision paths, and feature importance scores. A thorough market analysis was performed to examine the prospects of coal-based carbon fibers. The best opportunities for coal come from its lower and more stable price relative to petroleum, particularly for subbituminous coals, which is the primary advantage that a coal refinery may have over a petroleum refinery. Before a commercial CTP production facility can be modeled, however, several things need to be understood regarding the nature of the would-be coal refinery. These include the technology to be deployed, the size of facility, the volume(s) of co-product(s), and the waste and emissions profile of the plant. The volume of co-products and waste may be substantial and will require separate market analysis to ensure viability. In the near-term, the importance of coal tar pitch, in the form of carbon pitch, to the aluminum and steel industries is likely to overshadow the alternative use of this material as an input for carbon fiber. The importance of steel and aluminum in building materials, and the need for carbon materials in their manufacturing, will ensure that demand for these products remains for the long run. In addition, carbon fiber may also be the best substitute for steel and aluminum well into the future. While society will eventually be able to shift production of much of its electricity needs to renewables, it will not be able to shift away from fossil fuels for production of high-strength construction and vehicular materials. Demand for carbon fiber is expected to increase quickly, but the volume of carbon fiber and the amount of coal that would be needed to produce even a sizeable share of this market may still be relatively small compared to current coal production. Thus, other coal-based products like graphene, graphite, carbon foams, resins, and carbon-based building products will play important roles in sustaining coal production as coal-fired power generation continues to decline.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

The mechanism driving a solid–solid phase transition in a biomacromolecular crystal

A solid-solid phase transition (SSPT) occurs between distinguishable crystalline forms. Because of its importance in application and theory in material science and condensed matter physics, SSPT has been studied most extensively in metallic alloys, inorganic salt or small organic molecular crystals, but much less so in biomacromolecular crystals. In general, the mechanism of SSPT at atomic and molecular levels is not well understood. Here, we describe the ordered molecular rearrangements in biomacromolecular crystals of the adenine riboswitch (riboA) aptamer using real-time serial crystallography and solution atomic force microscopy (AFM). The large, ligand-induced conformational changes drive the initial phase transition from the apo unit cell (AUC) to the trans unit cell 1 (TUC1). During this transition, coaxial stacking of P1 duplexes becomes the dominant packing interface, whereas P2-P2 interactions are almost completely disrupted, resulting in “floating” layers of molecules. The coupling points in TUC1 and their local conformational flexibility allow the molecules to reorganize to achieve the more densely packed and energetically favorable bound unit cell (BUC). Our study thus reveals the interplay between the conformational changes and the crystal phases—the underlying mechanism that drives the phase transition. Using polarized video microscopy (PVM) to monitor the SSPT in small crystals at high ligand concentration, we have identified the time window during which the major conformational changes take place, and simulated the in crystallo kinetics. Together, these results provide the spatiotemporal information necessary for informing time-resolved crystallography (TRX) experiments. Moreover, this study illustrates a practical approach to characterize SSPT in transparent crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Cu–Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and in Situ TEM Study

Here, the development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with in situ environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)R45° missing row reconstruction (MRR). In situ ETEM experiments validate these predictions and show Ni segregation followed by NiO nucleation and growth in regions without MRR, with secondary nucleation and growth of Cu 2 O in MRR regions. Our approach based on combining disparate computational components and in situ ETEM provides a holistic description of the oxidation mechanism in CuNi, which applies to other alloy systems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Sharp-tip enhanced catalytic CO oxidation by atomically dispersed Pt 1 /Pt 2 on a raised graphene oxide platform

Revealing structure–activity relationships of graphene-supported atomically dispersed transition metal catalysts is of key importance in catalysis chemistry and materials science. In this work, we report a first-principles theoretical study on O 2 activation and CO oxidation catalyzed by atomically dispersed Pt 1 /Pt 2 anchored on a raised graphene (Gr) platform. The unique sharp-tip structure of the protuberant graphene platform (carbon-tip) can collect extra polarization electrons on the Pt-tip, promoting electron transfer between the catalyst and adsorbates, and greatly enhancing the chemical activity. Higher carbon-tips on defective graphene oxide with a carbon vacancy (Gr-V) produce more polarization electrons, a more localized electric field, and a larger up-shift of the d-orbital center of the Pt-tip, resulting in better catalytic performance. The carbon-tip enhancement reduces the energy barrier for the CO oxidation on Pt 1 O 2 /Gr-V from 1.19 eV to 0.56 eV compared with Pt 1 on planar Gr. In addition, CO poisoning is significantly alleviated, with the CO poisoning rate Γ θ reduced from 2.00 eV to 0.52 eV. Moreover, a linear relationship between the activation barrier and the binding energy of adsorbates is found for various atomically dispersed Pt-tip catalysts on the raised graphene oxide platform. Importantly, the order of catalytic activity is consistent with the carbon-tip enhancement, i.e., Pt 1 O 2 /Gr-V > Pt 2 O 4 /Gr-V > Pt 1 O 2 /Gr > Pt 2 O 4 /Gr. These findings provide important guidance for rational design of atomically dispersed catalysts.

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

An in situ shearing x-ray measurement system for exploring structures and dynamics at the solid–liquid interface

Revealing interfacial structure and dynamics has been one of the essential thematic topics in material science and condensed matter physics. Synchrotron-based x-ray scattering techniques can deliver unique and insightful probing of interfacial structures and dynamics, in particular, in reflection geometries with higher surface and interfacial sensitivity than transmission geometries. We demonstrate the design and implementation of an in situ shearing x-ray measurement system, equipped with both inline parallel-plate and cone-and-plate shearing setups and operated at the advanced photon source at Argonne National Laboratory, to investigate the structures and dynamics of end-tethered polymers at the solid-liquid interface. With a precise lifting motor, a micrometer-scale gap can be produced by aligning two surfaces of a rotating upper shaft and a lower sample substrate. A torsional shear flow forms in the gap and applies tangential shear forces on the sample surface. The technical combination with nanoscale rheology and the utilization of in situ x-ray scattering allow us to gain fundamental insights into the complex dynamics in soft interfaces under shearing. In this work, we demonstrate the technical scope and experimental capability of the in situ shearing x-ray system through the measurements of charged polymers at both flat and curved interfaces upon shearing. Furthermore, through the in situ shearing x-ray scattering experiments integrated with theoretical simulations, we aim to develop a detailed understanding of the short-range molecular structure and mesoscale ionic aggregate morphology, as well as ion transport and dynamics in soft interfaces, thereby providing fundamental insight into a long-standing challenge in ionic polymer brushes with a significant technological impact.

36 MATERIALS SCIENCE↗
DOE OSTI2021

The Role of Water and Hydroxyl Groups in the Structures of Stetindite and Coffinite, MSiO 4 (M = Ce, U)

Orthosilicates adopt the zircon structure types ( I 4 1 /amd ), consisting of isolated SiO 4 tetrahedra joined by A-site metal cations, such as Ce and U. They are of significant interest in the fields of geochemistry, mineralogy, nuclear waste form development, and material science. Stetindite (CeSiO 4 ) and coffinite (USiO 4 ) can be formed under hydrothermal conditions despite both being thermodynamically metastable. Water has been hypothesized to play a significant role in stabilizing and forming these orthosilicate phases, though little experimental evidence exists. To understand the effects of hydration or hydroxylation on these orthosilicates, in situ high-temperature synchrotron and laboratory-based X-ray diffraction was conducted from 25 to ~850 °C. Stetindite maintains its I 4 1 /amd symmetry with increasing temperature but exhibits a discontinuous expansion along the a- axis during heating, presumably due to the removal of water confined in the [001] channels, which shrink against thermal expansion along the a -axis. Furthermore, additional in situ high-temperature Raman and Fourier transform infrared spectroscopy also confirmed the presence of the confined water. Coffinite was also found to expand nonlinearly up to 600 °C and then thermally decompose into a mixture of UO 2 and SiO 2 . A combination of dehydration and dehydroxylation is proposed for explaining the thermal behavior of coffinite synthesized hydrothermally. Additionally, we investigated high-temperature structures of two coffinite-thorite solid solutions, uranothorite (U x Th 1– x SiO 4 ), which displayed complex variations in composition during heating that was attributed to the negative enthalpy of mixing. Lastly, for the first time, the coefficients of thermal expansion of CeSiO 4 , USiO 4 , U 0.46 Th 0.54 SiO 4 , and U 0.9 Th 0.1 SiO 4 were determined to be α V = 14.49 × 10 –6 , 14.29 × 10 –6 , 17.21 × 10 –6 , and 17.23 × 10 –6 °C –1 , respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Structure and Dynamics of Domains in Ferroelectric Nanostructures – Phase-Field Modeling (Final Technical Report)

Domain pattern formation is one of the most common phenomena in nature and is a topic of immense interest in many fields ranging from materials science and physics to chemistry and biology. This DOE sponsored research project explored the basic science concerning the thermodynamic stability of mesoscale polarization domain patterns and their temporal evolution mechanisms during formation and subsequent switching in ferroelectric nanostructures and heterostructures. The project employed the computational phase-field method in combination of microelasticity and electrostatic theories. The research was carried out in close collaboration with a number of experimental groups who used High Resolution Transmission Electron Microscopy (HRTEM), in situ TEM with Scanning Probe Microscopy (SPM), or Piezoresponse Force Microscopy (PFM) to characterize the domain structures and dynamics of ferroelectric thin films and heterostructures and who grow high-quality ferroelectric and multiferroic thin films using advanced growth techniques such as Molecular Beam Epitaxy (MBE), Pulsed Laser Deposition (PLLD), and sputtering. The research efforts of the project help establish the phase-field method as the most powerful method for understanding and predicting domain structures in ferroelectric thin films and nanostructures. The findings of the project led to the basic understanding of stability and switching mechanisms of ferroelectric domains under different mechanical boundary conditions and under either homogeneous capacitor configurations or local fields using metallic probes as electrodes and the emergence of charged domain walls during domain switching. The project predicted the spatial length scales, temperature ranges, and electromechanical conditions for different polar states in heterostructures and guided the discovery of both transient and stable novel polarization states containing vortex lattices in oxide superlattices. The project resulted in 134 journal publications and 6 PhD theses with all the PhD graduates currently working in either academia or industry within the United States. The basic understanding on the stability of mesoscale polar states and pattern evolution achieved by the project improved our ability to control and engineer properties of ferroelectric thin films and heterostructures for potential applications in nanoscale electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Development of neural network force fields for corrosion studies

To fully understand the chemistry and physics of corrosion, novel methods of simulation must be developed. One approach is designing machine learning (ML) algorithms integrated with density functional theory to develop adaptive force fields to gain insight into corrosion behavior namely at the surface of metal oxides. Current methods of modeling corrosion are slow due to the computational cost of resolving both reaction mechanics and mass transport processes. Machine learning methods can be implemented to obtain structure-activity relationships at both the molecular and bulk scale while still retaining the accuracy of density functional theory (DFT) and significantly decreasing the time needed for simulations of complex chemical processes in the various environments of corrosion. Multiscale models are needed for corrosion studies to fully understand its processes not only at the atomic length scale (chemical bonding, energies, and forces), but also at the nano and meso length scales (solid-state physics and material science processes). Current methods of study include DFT, molecular dynamics, and Monte Carlo. The limitation of DFT is that only a small number of atoms or molecules can be simulated at that level of theory. Density functional theory is used to study the electronic structure of atoms and molecules, and calculate the force component of each atom. However, these calculations are limited to about 1000 atoms. Custom periodic boundary conditions (PBC) can be used to describe the various environments and defects that affect the atomic forces to produce a large data set from which a training set can be derived. Machine learning can be utilized to overcome the barrier of modeling macroscopic and multi-scale processes from ab initio calculations through the development of adaptive force fields. Local environments determine the atomic forces of a given system, therefore adaptive force fields must be created to produce reliable quantum mechanical calculations. This can be achieved by developing a learning algorithm that uses the mapped atomic forces or fingerprint as an input to produce energies and magnetic moments as output. A systematic approach was used to begin to build a data set in order to accurately describe the atomic forces in various environments. In Figure 4 below, a simple PBC cell of Fe{sub 2}O{sub 3} was first optimized. A surface optimization was performed next, followed by a hydroxylated surface optimization. Once this calculation has converged, the adsorption of halide species to the hydroxylated surface will be investigated. TensorFlow is an open source platform for machine learning developed by Google. Using a high level application program interface (API) such as Keras allows for building and training ML models easily in a number of different environments and languages. For this project, a neural network was developed within Anaconda in Python. Future Work: Further development of reference data set; Refining neural network and learning algorithm; Fingerprinting atomic environment to enable mapping of atomic force components; Choosing appropriate training set from reference data; Learning from training set and enabling non-linear mapping of training set fingerprints and the atomic forces; Estimation of uncertainty to identify ranges of outside applicability; Testing and analysis of molecular dynamic simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Acoustic Properties of Aerogels: Current Status and Prospects

Noise reduction remains an important priority in the modern society, in particular, for urban areas and highly populated cities. Insulation of buildings and transport systems such as cars, trains, and airplanes has accelerated the need to develop advanced materials. Various porous materials, such as commercially available foams and granular and fibrous materials, are commonly used for sound mitigating applications. In this review, a special class of advanced porous materials, aerogels, is examined, and an overview of the current experimental and theoretical status of their acoustic properties is provided. Aerogels can be composed of inorganic matter, synthetic or natural polymers, as well as organic/inorganic composites and hybrids. Aerogels are highly porous nanostructured materials with a large number of meso‐ and small macropores; the mechanisms of sound absorption partly differ from those of traditional porous absorbers possessing large macropores. The understanding of the acoustic properties of aerogels is far from being complete, and experimental results remain scattered. It is demonstrated that the structure of the aerogel provides a complex three‐dimensional architecture ideally suited for promising high‐performance materials for acoustic mitigation systems. This is in addition to the numerous other desirable properties that include low density, low thermal conductivity, and low refractive index.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High‐Throughput Evaluation of Hardening Coefficients of Eight Alloying Elements in Magnesium

Liquid–solid diffusion couples (LSDCs) are employed to generate a composition gradient in the single‐phase hexagonal closed‐packed (hcp) solid solution with compositions up to the solubility limit of various solutes in Mg. Nanoindentation scanning across the composition gradient in LSDCs allows effective evaluation of composition‐dependent hardness of eight alloying elements (Al, Ca, Ce, Gd, Li, Sn, Y, and Zn) in the hcp Mg phase. The hardening coefficients, an indicator of the potency of solid‐solution hardening, are evaluated from the measured composition‐hardness data and correlated with various materials properties such as atomic radius, shear modulus, and elastic modulus of the solutes. The rank of hardening potency of Al, Gd, Sn, Y, and Zn measured by nanoindentation is in good agreement with that measured by microindentation reported in the literature. The hardening coefficient (potency) from the strongest to the weakest is Ce > Ca > Y ≈ Gd > Zn > Al ≈ Sn > Li in Mg‐based hcp binary solid solutions. The hardening coefficient is found to be closely correlated with the strengthening potency.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Biosourced Antioxidants for Chemical Durability Enhancement of Perfluorosulfonic Acid Membrane

Abstract The chemical durability of perfluorosulfonic acid (PFSA) membranes is a topic of growing interest to meet Department of Energy (DOE) durability targets for heavy‐duty vehicle (HDV) applications. State‐of‐the‐art membranes like Nafion, rely on the use of cerium, heteropolyacids, and other inorganic additives to increase PFSA chemical durability. A less explored avenue for the oxidative stabilization of PFSA and hydrocarbon membranes is the use of organic antioxidants. No reversible organic antioxidant has been demonstrated to date which can enhance membrane lifetime by factors comparable to cerium. Here, ellagic acid (EA) is demonstrated as a promising radical scavenger for PFSA's. It is found that the incorporation of EA enhances the chemical durability of Nafion by 160%. EA, when incorporated with cerium as an electron donorenhances Nafion durability by at least 80% compared to a membrane incorporated with just cerium in DOE‐defined durability tests. EA is found to be reversible in acidic conditions like those of fuel cells and its reversibility could be further enhanced by the use of suitable co‐antioxidants.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Fast Photoactuation Driven by Supramolecular Polymers Integrated into Covalent Networks

Abstract The design of robotic soft matter capable of emulating the complex movements of living organisms such as mechanical actuation, shape transformation, and autonomous translation remains a grand challenge in soft materials science. Functionalized hydrogels are excellent candidates for such materials since they can operate in water and are highly responsive to their environment, but their response times can be slow. This work investigates fast photoactuation of hybrid bonding hydrogels composed of peptide amphiphile (PA) supramolecular nanofibers bonded covalently to merocyanine‐based (MCH + ) photoresponsive networks. By incorporating ionizable acrylic acid (AA) co‐monomers in these networks, photoactuation at nearly neutral pH is observed, which in turn enables a new mechanism to accelerate the response by triggering the bundling of supramolecular nanofibers by rapid proton exchange reactions. Furthermore, this rapid response and its consequent large shape transformations lead to hydrogels capable of spontaneously tracking external light sources inspired by pedicellariae, defensive organs present in echinoderms like the starfish and the sea urchin. This work suggests that hybrid bonding polymers (HBPs), which leverage the interplay between supramolecular assemblies and covalent networks, offer novel strategies to design rapidly actuating soft robotic materials.

Cezan, S. Doruk↗
DOE OSTI2025

Tetrahedral Tilting and Lithium‐Ion Transport in Halide Argyrodites Prepared by Rapid, Microwave‐Assisted Synthesis

Abstract This study demonstrates a rapid, dry, microwave‐assisted (MW) synthesis method that enables preparation of halide argyrodites ( , , ) in less than 20 min. The structures and ion transport properties of the resulting materials are compared with those synthesized by conventional solid‐state synthesis methods. The microwave‐assisted method leads to increased site disorder and elevated Arrhenius prefactors (), which lead to an order of magnitude improvement in the 30 ionic conductivity of MW‐. X‐ray pair distribution function analysis (XPDF) reveals significant rotational disorder of the units, which is impacted by the synthesis method, choice of halide, and presence of / site disorder. These rotational displacements are strongly correlated with ion transport, specifically and entropy of migration (). Overall, this study demonstrates a rapid synthesis route for preparing high‐quality halide argyrodite solid‐state electrolytes in less than 20 min, and further unravels atomistic insights into the interplay of structural disorder, rotational dynamics, and ion transport mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Tailored Graphene Micropatterns by Wafer-Scale Direct Transfer for Flexible Chemical Sensor Platform

2D materials, such as graphene, exhibit great potential as functional materials for numerous novel applications due to their excellent properties. The grafting of conventional micropatterning techniques on new types of electronic devices is required to fully utilize the unique nature of graphene. However, the conventional lithography and polymer-supported transfer methods often induce the contamination and damage of the graphene surface due to polymer residues and harsh wet-transfer conditions. In this work, a novel strategy to obtain micropatterned graphene on polymer substrates using a direct curing process is demonstrated. Employing this method, entirely flexible, transparent, well-defined self-activated graphene sensor arrays, capable of gas discrimination without external heating, are fabricated on 4 in. wafer-scale substrates. Finite element method simulations show the potential of this patterning technique to maximize the performance of the sensor devices when the active channels of the 2D material are suspended and nanoscaled. This study contributes considerably to the development of flexible functional electronic devices based on 2D materials.

2D materials, Graphene, Microscale patterning, Che↗
DOE OSTI2021

A Sodium–Antimony–Telluride Intermetallic Allows Sodium-Metal Cycling at 100% Depth of Discharge and as an Anode-Free Metal Battery

In this work, repeated cold rolling and folding is employed to fabricate a metallurgical composite of sodium–antimony–telluride Na 2 (Sb 2/6 Te 3/6 Vac 1/6 ) dispersed in electrochemically active sodium metal, termed “NST-Na.” This new intermetallic has a vacancy-rich thermodynamically stable face-centered-cubic structure and enables state-of-the-art electrochemical performance in widely employed carbonate and ether electrolytes. NST-Na achieves 100% depth-of-discharge (DOD) in 1 m NaPF 6 in G2, with 15 mAh cm –2 at 1 mA cm –2 and Coulombic efficiency (CE) of 99.4%, for 1000 h of plating/stripping. Sodium-metal batteries (SMBs) with NST-Na and Na 3 V 2 (PO 4 ) 3 (NVP) or sulfur cathodes give significantly improved energy, cycling, and CE (>99%). An anode-free battery with NST collector and NVP obtains 0.23% capacity decay per cycle. Imaging and tomography using conventional and cryogenic microscopy (Cryo-EM) indicate that the sodium metal fills the open space inside the self-supporting sodiophilic NST skeleton, resulting in dense (pore-free and solid electrolyte interphase (SEI)-free) metal deposits with flat surfaces. The baseline Na deposit consists of filament-like dendrites and “dead metal”, intermixed with pores and SEI. Density functional theory calculations show that the uniqueness of NST lies in the thermodynamic stability of the Na atoms (rather than clusters) on its surface that leads to planar wetting, and in its own stability that prevents decomposition during cycling.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optically Excited Lasing in a Cavity‐Based, High‐Current‐Density Quantum Dot Electroluminescent Device

Abstract Laser diodes based on solution‐processable materials can benefit numerous technologies including integrated electronics and photonics, telecommunications, and medical diagnostics. An attractive system for implementing these devices is colloidal semiconductor quantum dots (QDs). The progress towards a QD laser diode has been hampered by rapid nonradiative Auger decay of optical‐gain‐active multicarrier states, fast device degradation at high current densities required for laser action, and unfavorable competition between optical gain and optical losses in a multicomponent device stack. Here we resolve some of these challenges and demonstrate optically excited lasing from fully functional high‐current density electroluminescent (EL) devices with an integrated optical resonator. This advance has become possible due to excellent optical gain properties of continuously graded QDs and a refined device architecture, which allows for highly efficient light amplification in a thin, EL‐active QD layer.

36 MATERIALS SCIENCE↗