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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

An Interface–Type Memristive Device for Artificial Synapse and Neuromorphic Computing

Interface-type (IT) metal/oxide Schottky memristive devices have attracted considerable attention over filament-type (FT) devices for neuromorphic computing because of their uniform, filament-free, and analog resistive switching (RS) characteristics. The most recent IT devices are based on oxygen ions and vacancies movement to alter interfacial Schottky barrier parameters and thereby control RS properties. However, the reliability and stability of these devices have been significantly affected by the undesired diffusion of ionic species. Herein, a reliable interface-dominated memristive device is demonstrated using a simple Au/Nb-doped SrTiO 3 (Nb:STO) Schottky structure. The Au/Nb:STO Schottky barrier modulation by charge trapping and detrapping is responsible for the analog resistive switching characteristics. Because of its interface-controlled RS, the proposed device shows low device-to-device, cell-to-cell, and cycle-to-cycle variability while maintaining high repeatability and stability during endurance and retention tests. Furthermore, the Au/Nb:STO IT memristive device exhibits versatile synaptic functions with an excellent uniformity, programmability, and reliability. A simulated artificial neural network with Au/Nb:STO synapses achieves a high recognition accuracy of 94.72% for large digit recognition from MNIST database. These results suggest that IT resistive switching can be potentially used for artificial synapses to build next-generation neuromorphic computing.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Field Demonstration of the Brick Ontology to Scale up the Deployment of ASHRAE Guideline 36 Control Sequences

Many commercial buildings have a vast network of sensors as part of their building automation systems (BAS) that allows opportunities for energy consumption and cost savings by deploying advanced control sequences. However, this resource is often underutilized since BAS are typically programmed with simple control sequences with limited potential to deliver on these opportunities. The recent availability of ASHRAE Guideline 36 (G36) with advanced HVAC control sequences supports control retrofits in existing buildings to unlock much of the savings potential. However, barriers such as the lack of standard naming convention of building assets and data points, proprietary equipment and BAS, and the inherent uniqueness of buildings and their systems prevent building stakeholders from adopting any “plug-and-play” implementation of G36. Instead, control vendors must often undertake the manual and labor-intensive point mapping process to identify a data stream’s functional and spatial relationship within the HVAC system along with other relevant contexts and map it to the new control sequences. The vendor must carry out the point mapping process in each individual building since the mapping is unlikely to port over to another building. Even for the same building, the point mapping process can occur multiple times if various control vendors implement different control retrofits and/or multiple control retrofits happen over the lifecycle of the building. Then, there is the likelihood that G36 control sequences are programmed uniquely to the building, preventing the same implementation from being reused in another. Therefore, this paper presents a field demonstration of how we leveraged the Brick ontology with BACnet, OpenBuildingControl’s Control Description Language (CDL), and open-source support tools to implement scalable and portable advanced building controls. These tools provide standardized semantic descriptions and relationships of the building’s assets and data points (Brick), standardized communication protocol to read from and write to the building’s BAS (BACnet), and standardized code implementations (CDL) of standardized advanced control strategies (G36). We implemented G36’s hot water supply temperature setpoint reset in a Berkeley, CA building for this field demonstration. This field demonstration aims to show how integrating these tools may streamline the deployment of advanced control sequences such as G36 in a consistent manner regardless of differences found across buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Temperature-dependent dielectric function of tantalum nitride formed by atomic layer deposition for tunnel barriers in Josephson junctions

Here, we report the dielectric functions of insulating tantalum nitride (TaN) films deposited by atomic layer deposition (ALD) on 300 mm Si/SiO 2 substrates to evaluate their suitability as tunnel barriers in tantalum-based Josephson junctions (JJs) for superconducting quantum circuits. Temperature-dependent ellipsometric angles were measured for ALD TaN films with nominal thicknesses of 13 and 25 nm at an incidence angle of 70°, over photon energy ranges of 0.03–0.7 eV (80–300 K) and 0.5–6.5 eV (80–600 K). These data were used to develop a dispersion model for insulating ALD TaN that incorporates a Tauc–Lorentz oscillator with an optical bandgap of 1.5–1.8 eV to describe interband transitions. The extracted dielectric function shows insulating behavior (mid-infrared transparency) at all temperatures and for both film thicknesses tested. In particular, ALD TaN does not exhibit infrared absorption due to free carriers, even at elevated temperatures, consistent with the insulating behavior required for a JJ tunnel barrier. Results from transmission electron microscopy (including selected area electron diffraction) and x-ray diffraction are also discussed. Sputter depth-profile x-ray photoelectron spectroscopy shows an N/Ta ratio of ∼1.2 throughout the film. The lower bandgap, low roughness, and thermal stability of ALD TaN compared to AlO x suggest the possibility of fabricating JJs with thicker barriers while achieving critical current densities required for qubits, enabling improved thickness and composition control, reduced topography, and resistance to aging.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Influence of Solidification Velocity on Partitioning and Precipitate Evolution in a Multicomponent, Soft Magnetic Alloy Processed by Laser Melting

Multicomponent alloys have recently been shown to overcome the structural-functional barrier associated with soft magnetic materials. Here, single laser tracks have been used to investigate the effects of solidification velocity on the Al 7 Fe 32.6 Co 27.7 Ni 27.7 Ta 5 (at pct) alloy. Ta-rich cell wall partitioning was shown to affect the final distribution and size of the precipitating L1 2 phase, after aging. These findings demonstrate that solidification rate control offers the potential to tailor localized performance in alloys with size-dependent precipitation properties.

Brookins, Jeffrey M. [Oak Ridge National Laborator↗
DOE OSTI2025

Topology-Aware Reinforcement Learning for Voltage Control: Centralized and Decentralized Strategies

Volt-VAR control (VVC) methods based on deep reinforcement learning (DRL) can effectively control distribution grid voltage and minimize power loss by implementing corrective and preventive control measures on the reactive power output of inverter-based distributed energy resources (DERs). However, model-free DRL-based VVC approaches usually cannot capture the important topological feature of the power system since they use a fully-connected network (FCN) to deliver the action. Therefore, this paper proposes a graph convolutional network (GCN)-based DRL approach that can employ the topological information of the network to take better control action for regulating the voltage. Our implementation allows for both centralized and decentralized configurations, utilizing a single agent and multiple agents respectively. Although the centralized GCN-based DRL approach has its advantages of minimizing voltage fluctuation and power loss, it is not suitable for large scale power systems due to its challenges in terms of scalability, computation speed and potential single points of failure. Therefore, these problems can be resolved using the decentralized GCN-based DRL approach. Moreover, to ensure the safe operation of the model, our proposed approach incorporates an exponential barrier function while formulating the reward function for each agent. To validate performance of the proposed approaches, the proposed model is tested on modified IEEE test systems and the performances are measured in terms on voltage fluctuation reduction, minimization of power loss and computational speed. Finally, the results show that the proposed topology-aware approach outperforms the FCN-based DRL approach in terms of reducing voltage fluctuation and minimizing power loss of the network. Moreover, it is shown that the decentralized GCN-based DRL has faster computational speed than other approaches.

42 ENGINEERING↗
DOE OSTI2023

Fecal microbiota transplantation ameliorates type 2 diabetes via metabolic remodeling of the gut microbiota in db/db mice

Gut microbiome (GM) deregulation has been implicated in major conditions such as obesity and type 2 diabetes (T2DM). Our previous prospective study indicated that fecal microbiota transplantation (FMT) successfully improved patients with T2DM. We hypothesized that FMT may be a potential therapeutic method for T2DM, but its precise mechanisms in T2DM remains to be elucidated. Eight db/m mice were FMT donors and control mice, and 16 genetically diabetic db/db mice were equally divided into two groups (db/db+phosphate-buffered saline (PBS) group, db/db+FMT group). The db/db+FMT group was administered fresh fecal suspension (0.2 mL/mice) daily for 4 weeks. Analysis of the GM and serum metabolome was carried out by 16S ribosomal RNA sequencing and liquid chromatogram-mass spectrometry, respectively. Effects of FMT on the gut barrier and pancreas were assessed using protein assays, messenger RNA, immunohistology and clinical indicators testing. Our results showed that FMT treatment of db/db mice relieves a series of clinical indicators, including fasting plasma glucose, serum insulin and oral glucose tolerance test among others. Compared with non-diabetic control mice, db/db+PBS mice exhibited decreased abundance of Ruminococaceae, Porphyromonadaceae and increased abundance of Rikenellaceae and Lactobacillaceae. FMT treatment reversed this effect on the microbiome. Eleven metabolites were changed between the db/db+PBS and db/db+FMT groups. Correlation analysis showed that the structural changes of the GM were correlated with host metabolite levels. We further showed that FMT treatment of db/db mice improved intestinal barrier function, reduced inflammation and caused an alteration in the number of circulating immune cells. FMT-mediated changes in the GM, serum metabolites, intestinal epithelial barrier, inflammation and circulating immune cells play an important role in the efficacy of FMT on T2DM disease progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Overview of Quantum Sensing Materials and Techniques for Energy Sector Applications

The energy sector has become increasingly dependent upon highly sensitive sensing devices for a wide range of applications. Variables such as temperature, pH, electromagnetic fields, ions, and pressure must be measured with high precision, often in harsh conditions (e.g. highly corrosive environments due to high temperature, pressure and humidity). These sensors are deployed in infrastructure such as transformers, pipelines, mines, nuclear power plants, and other areas to ensure safe operating conditions and uninterrupted, optimized service. Moreover, new opportunities for sensors have emerged due to the expansion of smart grids/meters, driverless vehicles, and the discovery of new oil, gas, and critical mineral deposits. The continued maturation of quantum sensing technologies offers exciting opportunities for quantum-enhanced measurements within the energy sector that may provide significant improvements in sensitivity beyond the classical limit. Here, an overview of established and emerging quantum materials for sensing applications will be provided, from trapped ions to color centers in diamond and silicon carbide. Associated quantum sensing methods for these materials will be discussed, with an emphasis on platforms that are nearing commercial viability. Specific application opportunities within the energy sector for quantum sensors will then be analyzed, including oil/gas discovery, greenhouse gas emission monitoring, pH and ion sensing, current measurement in electric vehicle batteries, and quantum-enhanced spectroscopy. Remaining barriers, such as quantum sensor platform miniaturization and ruggedization, will also be analyzed. A specific project at the National Energy Technology Laboratory involving the functionalization of qubits using metal-organic frameworks for enhanced quantum sensing will also be highlighted. Here, nitrogen vacancy centers (NV) in nanodiamonds, a commercially available qubit with long coherence times and utilizable quantum properties at room temperature, are encapsulated in a controlled way using the metal-organic framework ZIF-8. The ZIF-8 provides a well-defined, porous scaffold that can be controlled to promote the selective uptake of specific target analytes, such as gas molecules or ions. The quantum sensing performance of this composite material is either preserved or enhanced following ZIF-8 encapsulation; the optically detected magnetic resonance response of the NV nanodiamonds with and without the ZIF-8 coating are identical, while the ZIF-8 coating increases the longitudinal spin relaxation lifetime (T1) of the NV centers, an important parameter for spin relaxometry-based quantum sensing experiments. Taken together, these results demonstrate the importance of qubit functionalization as a crucial step for rationally designing high performance quantum sensors.

Crawford, Scott↗
NASA NTRSDate not supplied

A Model Predictive Control Approach for In-Flight Acoustic Constraint Compliance

Vehicle noise remains one of the major barriers to public acceptance of Urban Air Mobility-class aircraft. This work focuses on motion planning for aircraft in noise-sensitive areas. A nonlinear Model Predictive Path Integral~(MPPI) control law is used to generate a finite-horizon trajectory that satisfies acoustic level constraints at a set of observer locations. The MPPI framework places no restrictions on the class of state-dependent cost functionals that can be employed, making it well-suited for use with sophisticated acoustic models and metrics, in addition to dynamic and mission-relevant constraints. The proposed control law is also suitable for implementation in a real-time application. A simulation example demonstrates the ability of the controller to modify the flight trajectory in order to satisfy acoustic constraints at multiple measurement locations.

Aircraft motion planning↗
DOE OSTI2022

Machine learning reveals factors that control ion mobility in anti-perovskite solid electrolytes

Solid-state batteries are projected to exhibit improved energy densities and safety compared to liquid-electrolyte-based systems. Consequently, the development of solid electrolytes that can conduct cations at rates comparable to that of liquid electrolytes is an active area of research. To facilitate this search it is desirable to understand the chemical and structural features that control ion transport through a crystalline lattice. Here, machine learning (ML) is used to identify features that influence ion mobility in solids, quantify the relative importance of those features, and demonstrate how the variation of individual features alters ion mobility. Adopting the anti-perovskite lattice as a model system, ML algorithms were trained to predict migration barriers from a data set containing hundreds of barriers calculated using density functional theory. In this study, more than 10 6 feature sets of non-redundant descriptors were evaluated for their ability to predict migration barriers. The present analysis correctly reiterates the importance of several features that are known to influence mobility – for example, anion polarizability – but also identifies others whose importance is not widely recognized. Lattice properties such as hopping distance and channel width exhibit the greatest influence on cation mobility. Individual conditional expectation analyses performed on these features shows that barriers decrease as hopping distance decreases and as channel width increases. The defect formation energy – which is commonly associated with the concentration of defects, but not with their mobility – is identified as an important feature for interstitial migration. In sum, this analysis aids in the design of optimal solid electrolytes by simplifying the multi-dimensional design space to a sub-set of properties that are the most important.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

A Model Predictive Control Approach for In-Flight Acoustic Constraint Compliance

Vehicle noise remains one of the major barriers to public acceptance of Urban Air Mobility class aircraft. This work focuses on motion planning for aircraft in noise-sensitive areas. A nonlinear Model Predictive Path Integral (MPPI) control law is used to generate a finite horizon trajectory that satisfies acoustic level constraints at a set of (three-dimensional) observer locations. The MPPI framework places no restrictions on the class of state-dependent cost functionals that can be employed, making it well-suited for use with sophisticated acoustic models and metrics, in addition to dynamic and mission-relevant constraints. The model predictive control architecture is also suitable for implementation in a real-time application. A simulation example demonstrates the ability of the controller to modify the flight trajectory in order to satisfy acoustic constraints at multiple measurement locations.

acoustically-aware vehicle↗
NASA NTRS1994

Heat dissipation in controlled environment enclosures through the application of water screens

The use of plate glass-water thermal barriers in controlled environment facilities effectively reduces the thermal load within the plant growth chamber. This allows high PPFs to be provided for plant growth and development studies, adequate simulation of daily light integrals, and simulation of peak PPF's. Further, substantial amounts of incandescent lamp supplementation can be used to achieve simulation of daylight R:FR ratios which are needed to ensure adequate stem development in some species. While the focus in this paper is on the use of entire thermal barriers which separate the lighting enclosure from the plant growth chamber, the same principles apply to the use of water jackets for cooling individual lamps (such as can occur with xenon-arc lamps). In this instance, the barrier separating the lamps from the plant chamber can be much simpler (e.g., plexiglass) as the main function of the barrier is to separate the air ventilation of the lamp enclosure from the air system within the plant growth chamber. The main advantage of water as a thermal barrier is the negligible absorption of radiation in the photosynthetically-active and near infrared wavebands. Consequently, plate glass-water barriers typically allow transmission of approximately 90% of radiation in these regions. While ventilated double and triple glazing systems appear to be attractive alternative to water barriers from an operating standpoint, their significant absorption in the biologically-important wavebands (7 - 12%) with each glass layer and longer-wave cut-offs (typically 2500 - 4000 nm) makes them a much less attractive alternative. The data presented here demonstrate clearly that measurement of PPF alone is not an adequate representation of the radiation environment being used in a controlled environment study. The amounts and proportions of long-wave and short-wave radiation in a plant growth chamber are dependent on lamp type, lamp combination, presence of a thermal barrier, the type of thermal barrier between the lamps and the plant growing area and the overall construction and design of the chamber. It is important, therefore, in reporting results of controlled environment studies, to adequately describe both the details of the lighting system used and the characteristics of the radiation produced by that system, so results of different studies can be adequately evaluated and compared.

Warrington, I. J.↗
DOE OSTI2021

Strong Fermi-level pinning at metal contacts to halide perovskites

The performance of halide perovskite-based electronic and optoelectronic devices is often related to interfacial charge transport. To shed light on the underlying physical and chemical properties of CH 3 NH 3 PbI 3 (MAPbI 3 ) in direct contact with common electrodes Al, Ti, Cr, Ag, and Au, the evolution of interfacial properties and Fermi level pinning is systematically studied. Given a unique experimental facility, pristine interfaces without any exposure to ambient air were prepared. We observe aggregation of substantial amounts of metallic lead (Pb 0 ) at the metal/MAPbI 3 interface, resulting from the interfacial reaction between the deposited metal and iodine ions from MAPbI 3 . It is found that the Schottky barrier height at the metal/MAPbI 3 interface is independent of the metal work function due to strong Fermi level pinning, possibly due to the metallic Pb 0 aggregates, which act as interfacial trap sites. The charge neutrality level of MAPbI 3 is consistent with the energy level of Pb 0 -related defects, indicating that Pb 0 interfacial trap states can be nonradiative recombination sites. Here this work underlines that control of chemical bonding at interfaces is a key factor for designing future halide perovskite-based devices.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

A Model Predictive Control Approach for In-Flight Acoustic Constraint Compliance

Vehicle noise remains one of the major barriers to public acceptance of Urban Air Mobility-class aircraft. This work focuses on motion planning for aircraft in noise-sensitive areas. A nonlinear Model Predictive Path Integral (MPPI) control law is used to generate a finite-horizon trajectory that satisfies acoustic level constraints at a set of (three-dimensional) observer locations. The MPPI framework places no restrictions on the class of state-dependent cost functionals that can be employed, making it well-suited for use with sophisticated acoustic models and metrics, in addition to dynamic and mission-relevant constraints. The model predictive control architecture is also suitable for implementation in a real-time application. A simulation example demonstrates the ability of the controller to modify the flight trajectory in order to satisfy acoustic constraints at multiple measurement locations.

acoustically-aware vehicle↗
DOE OSTI2025

Anodized Aluminum Oxide Membrane Ionic Memristors

Memory effect in ion transport (IT) at the solid–solution interface is uniquely attractive in that the conductance depends on or “memorizes” the previous states. Hysteretic and rectified transport properties offer exciting potential to developing advanced iontronics and neuromorphic functions, improving the efficiency of energy conversion and electrochemical processes, and overcoming the selectivity-throughput bottleneck in the enrichment of low abundant species for environment- and energy-friendly separations, among others. Herein, memory effects are discovered in the rectified electrokinetic IT through anodized aluminum oxide (AAO) membranes containing densely packed highly ordered nanochannels (10 10 per cm 2 ). Characteristic memristor responses of pinched current–potential loops are resolved in voltammetric experiments and successfully reproduced through finite element simulation. Excitatory and inhibitory conductance states are shown to arise from the enrichment and depletion of mobile charge carriers. Structurewise, the transport symmetry is broken by the barrier oxide layer (BOL) on the one end of the cylindrical nanochannels across the AAO membranes. Charge selectivity is attributed to the gradient(s) of the space charge density across the BOL characterized by depth profiling via X-ray photoelectron spectroscopy analysis. The space charge gradient(s) overcomes the fundamental limitation of widely exploited surface charge effects to enable intense rectification and hysteresis prevailing at very high ionic concentrations up to 1–2 M. A new strategy is developed for controlling the preferential IT direction and selectivity via counterion intercalation and extraction/exchange. Mechanistic understanding is further confirmed through parameter variations such as potential scan rate and ionic strength, which also demonstrates convenient controls of the related functions.

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

Field deployable trace radioisotope analysis through combined electrochemical and alpha spectroscopy methods

A method for depositing actinides directly onto a 4H-SiC Schottky barrier diode (SBD) detector as a field deployable actinides sensor was developed to enable off-site analysis, shortening the time to obtain critical information such as elements, concentration, and/or isotopic ratio related to the radiological situation. A thin film of Hg was first electrodeposited onto the Pt contact of the SiC diode, followed by chronoamperometric deposition of microgram levels of actinides under conventional control conditions. The 4H-SiC diode maintained consistent functionality through the electrodeposition process and showed resolved alpha-energy spectra that contained accurate isotopic information demonstrating the feasibility of a combined chemical and radiological sensor for field actinide detection and quantification.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Methanol Partial Oxidation on Cu(111) and PtCu(111) Single-Atom Alloy Surfaces: Effect of Surface Oxygen Coverage on Selectivity

The selective oxidation of methanol to formaldehyde on Cu surfaces is an important and well-studied reaction. However, a systematic analysis of product selectivity as a function of oxygen coverage on Cu(111) and Cu-based single-atom alloys (SAAs) has not been previously reported. In this work, we present a comprehensive investigation of deuterated methanol (CD 3 OH) partial oxidation on Cu(111) and 1% PtCu(111) SAA surfaces as a function of preadsorbed oxygen coverage. Temperature-programmed desorption (TPD) and X-ray photoelectron spectroscopy (XPS) reveal that isolated Pt atoms inhibit the initial surface oxidation of Cu(111) under low oxygen exposures. Despite this difference in oxidation kinetics, the product selectivity remains largely unaffected: on both Cu(111) and PtCu(111), formaldehyde (CD 2 O) is the predominant partial oxidation product over a broad range of oxygen coverages. The selectivity toward formaldehyde peaks at intermediate oxygen coverages (∼0.3 monolayers, ML), indicating the existence of an optimal oxygen loading for partial oxidation. Notably, the similar product selectivities on Cu(111) and PtCu(111) over a range of surface oxygen coverage indicate that Pt single atoms do not significantly alter the reaction pathway or shift the optimal oxygen coverage for formaldehyde formation. Control experiments confirm that Cu(111) is unreactive toward methanol in the absence of oxygen, while PtCu(111) surfaces produce a small amount of formaldehyde even when oxygen is not preadsorbed, indicating that isolated Pt atoms facilitate O−H activation at below 150 K, leading to H 2 desorption, followed by C−D activation at higher temperatures (∼350 K). Density functional theory (DFT)-based calculations show that Pt atoms increase the O 2 dissociation barrier relative to Cu(111), consistent with the observed inhibition of oxidation at low exposures. Overall, this work provides the first detailed selectivity map for methanol oxidation on oxidized Cu(111) and PtCu(111) SAA surfaces. By linking classical mechanistic insights such as methoxy- and formate-mediated pathways with single-atom alloy catalyst design, this work demonstrates that while Pt substitution modulates the oxidation kinetics and oxygen binding, the overall selectivity toward formaldehyde is governed primarily by oxygen coverage. These findings underscore the potential of isolated dopants to tune surface oxidation behavior without compromising the intrinsic partial oxidation selectivity of copper-based catalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stability and Performance of 3d Transition Metal Carbo‐Sulfides: A Density Functional Theory Exploration for Li‐Ion Battery Anodes

As the demand for high-performance and reliable energy storage devices continues to rise, identifying new anode materials is crucial for advancing Li-ion battery (LIB) technology. Inspired by recent experimental breakthroughs in synthesizing two-dimensional transition metal carbo-chalcogenides (2D-TMCCs), density functional theory calculations are performed to systematically explore their sulfide variants (TM 2 S 2 C) spanning all 3d transition metals in three possible phases. Through comprehensive evaluations of thermodynamic, dynamic, mechanical, and thermal stabilities, seven stable 2D-TMCC candidates are identified, four of which exhibit superior battery performance. Notably, V-based 2D-TMCCs across all three phases deliver moderate open-circuit voltages (OCV), efficient Li diffusion, and substantial capacities, making them promising candidates for industrial applications without requiring specific phase controls. A Cr-based 2D-TMCC (with sulfur atoms above carbon atoms) offers the highest capacity of 515.40 mAh g −1 , the lowest Li diffusion barrier, and an optimal OCV, highlighting its appealing potential as an anode material for LIBs. Furthermore, significant Li–Li spacing and pronounced electron delocalization in these four 2D-TMCCs suggest a reduced risk of dendrite formation. This work expands the 2D-TMCC family and identifies up-and-coming candidates for next-generation LIB anodes.

anode materials↗
DOE OSTI2025

Density functional theory-based surrogate kinetic models for heterogeneous reactions of hydrocarbon intermediates on silicon carbide

The increasing demand for high-performance materials in advanced technologies highlights the importance of achieving a fundamental understanding and potential control of silicon carbide (SiC) deposition processes. However, existing models often lack sufficient theoretical detail, relying heavily on empirical data and offering limited predictive capability. In particular, the complex surface chemistry governing SiC growth remains poorly understood. This study addresses these challenges by employing density functional theory (DFT) to investigate key heterogeneous reactions involving hydrocarbon intermediates on SiC surfaces, including dehydrogenation, hydrogenation, and carbon deposition. Transition state searches were conducted to identify reaction pathways and energy barriers. While first-principles calculations offer high accuracy, they are computationally intensive. To extend the utility of these first-principles results, vibrational analyses were performed using phonon-based statistical thermochemistry to compute temperature-dependent reaction rates which were used to develop Arrhenius-type surrogate kinetic models. Furthermore, the resulting framework provides a more rigorous, physically grounded basis for integrating atomistic insights into continuum-scale modeling, ultimately enabling improved prediction and optimization of SiC film growth in high-performance material systems.

Density Functional Theory↗