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DOE OSTI2025

Modulation of the Electronic and Vibrational Landscape in Lead Organic Chalcogenides

Metal organic chalcogenides (MOCs) are an emerging class of two-dimensional (2D) materials featuring tunable band gaps and strong light-matter interactions with great potential for optical and polaritonic applications. Lead organic chalcogenides (LOCs) stand out among MOCs for sustaining long-lived coherent optical phonons despite their distorted lattices. However, the strong electron–phonon coupling also leads to rapid charge carrier self-trapping, hindering carrier transport. Herein, we demonstrate that charge carrier self-trapping is suppressed in Se- and Te-based LOCs. Specifically, the expansive Se and Te orbitals give rise to more dispersive conduction bands and reduced electron effective masses, thereby mitigating carrier self-trapping. As a result of reduced carrier self-trapping, the Se- and Te-based LOCs exhibit enhanced band-to-band photoluminescence and improved charge transport performance. Furthermore, our results provide a synthetic route to Se- and Te-based LOC single crystals and demonstrate the potential of orbital engineering to tune their electronic and phononic properties.

Chalcogenides↗

DOE OSTI2022

Engineering metal–metal oxide surfaces for high-performance oxygen reduction on Ag–Mn electrocatalysts

Understanding fundamental material–property relationships in mixed-element catalyst systems is crucial to advancing the viability of renewable electrochemical energy technologies, an important part of creating a more sustainable future. Herein, we report our insight on the nature and dynamics of highly active silver–manganese oxide (Ag–MnOx) catalyst surfaces for the oxygen reduction reaction (ORR) via a combined experimental–theoretical approach. Experimentally, we synthesize well-mixed Ag–Mn co-deposited thin films that are measurably flat and smooth, despite Mn surface migration and oxidation upon air exposure and electrochemical measurements. Cyclic voltammetry in 0.1 M KOH demonstrates up to 10-fold specific activity enhancements over pure Ag at 0.8 V vs. RHE for Ag-rich films (70–95% Ag in bulk). To further understand the Ag–Mn system, separate samples were synthesized with small amounts of Mn sequentially deposited onto the surface of a pure Ag thin film (Mn@Ag), ranging from partial to full surface coverage (down to 0.3 nm Mn $cm^{-2}_{geo}$ ~ 0.2 μg Mn $cm^{-2}_{geo}$). These sequentially deposited Mn@Ag films show analogous performance to their co-deposited counterparts indicating similar enhanced active sites. With density functional theory (DFT), we calculate that this enhancement arises from the tuned d-band of these material surfaces owing to the optimal hybridization of the electronic structures in specific Ag and MnO x geometries. Together, electrochemical measurements, DFT calculations, X-ray absorption spectroscopy, and valence-band X-ray photoelectron spectroscopy suggest synergistic electronic interactions between Ag and MnOx yield enhanced oxygen adsorption, and thus ORR activity, with DFT highlighting the Ag–MnO x interface sites as the most enhanced. This work demonstrates how combined experimental–theoretical methods can help design electrocatalysts with enhanced electrocatalytic properties and understand the nature of complex mixed metal–metal oxide surfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Data-Driven Invertible Neural Surrogates of Atmospheric Transmission

We present Data-Driven Invertible Neural Surrogates of Atmospheric transmission, or DINSAT. DINSAT is a novel framework for inferring an atmospheric transmission profile from a spectral scene. This framework leverages a lightweight, physics-based simulator that is automatically tuned -- by virtue of autodifferentiation and differentiable programming -- to construct a surrogate atmospheric profile to model the observed data. The framework has utility in (i) performing atmospheric correction, (ii) recasting spectral data between various modalities (e.g. radiance and reflectance at the surface and at the sensor), and (iii) inferring atmospheric transmission profiles, such as absorbing bands and their relative magnitudes. We demonstrate the utility of these methods by performing a canonical atmospheric correction task for the purposes of further analysis - in this case, target detection within a scene.

Koch, James V.↗

DOE OSTI2024

Online and Offline Identification of False Data Injection Attacks in Battery Sensors Using a Single Particle Model

The cells in battery energy storage systems are monitored, protected, and controlled by battery management systems whose sensors are susceptible to cyberattacks. False data injection attacks (FDIAs) targeting batteries’ voltage sensors affect cell protection functions and the estimation of critical battery states like the state of charge (SoC). Inaccurate SoC estimation could result in battery overcharging and over discharging, which can have disastrous consequences on grid operations. This paper proposes a three-pronged online and offline method to detect, identify, and classify FDIAs corrupting the voltage sensors of a battery stack. To accurately model the dynamics of the series-connected cells a single particle model is used and to estimate the SoC, the unscented Kalman filter is employed. FDIA detection, identification, and classification was accomplished using a tuned cumulative sum (CUSUM) algorithm, which was compared with a baseline method, the chi-squared error detector. Online simulations and offline batch simulations were performed to determine the effectiveness of the proposed approach. Throughout the batch simulations, the CUSUM algorithm detected attacks, with no false positives, in 99.83% of cases, identified the corrupted sensor in 97% of cases, and determined if the attack was positively or negatively biased in 97% of cases.

25 ENERGY STORAGE↗

DOE OSTI2022

Estimation of High-Altitude Electromagnetic Pulse Signal Leakage into Power Generation Facilities: Simulations and Measurements

The coupling of early-time high-altitude electromagnetic pulse signals into a power generation facility was investigated. This study encompasses both electromagnetic simulation and on-site experimental efforts that are relevant to shielding effectiveness estimation with a passive technique. As an initial step in understanding the attenuation models involved, simulations were carried out as a function of frequency, building properties, and signal incidence angle. Testing was then performed at the US Department of Energy’s Oak Ridge National Laboratory campus to identify the equipment needed for signal collection and to fine-tune the experimental procedure. Finally, as an application of the models and methods presented, measurements were conducted at a power generation plant to derive the field transfer functions and coupled high-altitude electromagnetic pulse waveforms for various locations within the facility.

42 ENGINEERING↗

DOE OSTI2026

Deep Learning and Photogrammetric Reconstruction for Automated Crack Detection and Dimensional Measurement in Mining Operations

Surface crack detection and dimensional measurement at active mining sites present significant safety and operational challenges. Manual inspection methods are labor-intensive, spatially incomplete, and expose personnel to hazardous environments, while existing automated approaches have been developed primarily for concrete civil infrastructure and have not been validated on the complex, variable surfaces characteristic of mining environments. This dissertation presents an automated pipeline that integrates deep learning semantic segmentation with Structure-from-Motion photogrammetry to detect surface cracks and measure their aperture, length, and vertical displacement from standard RGB imagery acquired during routine Uncrewed Aerial Vehicle (UAV) survey operations, without requiring additional sensor hardware or manual measurement. The pipeline combines a U-Net architecture with an EfficientNet-B0 encoder, pretrained on the SDNET2018 concrete crack dataset and fine-tuned on a mining-specific dataset spanning laboratory concrete specimens, coal refuse impoundment embankments, and post-blast limestone quarry benches. Photogrammetric reconstruction is performed using COLMAP Structure-from-Motion and Multi-View Stereo, with crack segmentation masks projected into the reconstructed point cloud to enable three-dimensional vertical displacement measurement through local plane fitting and bimodal surface detection. The pipeline was validated across 36 controlled laboratory specimens at three imaging distances and four vertical displacement levels, achieving aperture measurement RMSE of 0.047 cm and R² of 0.954, and vertical displacement RMSE of 0.140 cm and R² of 0.966, against independent caliper measurements. Field application at a coal refuse impoundment in southwestern Pennsylvania detected 71 crack components across the embankment crest, with a dominant longitudinal crack exhibiting aperture values reaching 28 cm and a 95th percentile vertical displacement of 35.53 cm, consistent in magnitude and spatial distribution with simultaneously acquired LiDAR-derived estimates. Application across four post-blast limestone quarry bench datasets in California successfully characterized blast-induced fracture networks at ground sampling distances ranging from 0.59 to 1.23 cm/pixel, with detected crack geometries physically consistent with observable surface conditions at each site. The results demonstrate that deep learning-based crack detection and photogrammetric measurement can be integrated into routine UAV inspection workflows at mining sites, providing repeatable, scalable, and quantitative crack characterization across surface types, crack scales, and displacement magnitudes not previously addressed in the literature. The pipeline requires no dedicated surveying equipment beyond the UAV platforms already deployed at mine sites for survey and monitoring purposes, supporting practical adoption within existing operational workflows.

Crack detection, Dimensional Measurement↗

DOE OSTI2021

Single-hole couplings in GaAs/AlGaAs double dots probed with transport and EDSR spectroscopy

Here, we report a detailed study of the tunnel barriers within a single-hole GaAs/AlGaAs double quantum dot device (DQD). For quantum information applications as well as fundamental studies, careful tuning and reliable measurements of the barriers are important requirements. In order to tune a DQD device adequately into the single-hole electric dipole spin resonance regime, one has to employ a variety of techniques to cover the extended range of tunnel couplings. In this work, we demonstrate four separate techniques, based upon charge sensing, quantum transport, time-resolved pulsing, and electron dipole spin resonance spectroscopy to determine the couplings as a function of relevant gate voltages and magnetic field. Measurements were performed under conditions of both symmetric and asymmetric tunnel couplings to the leads. Good agreement was observed between different techniques when measured under the same conditions. The results indicate that even in this relatively simple circuit, the requirement to tune multiple gates and the consequences of real potential profiles result in non-intuitive dependencies of the couplings as a function of the plunger gate voltage and the magnetic field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Conjugated Polymer Controlled Morphology and Charge Transport of Small-Molecule Organic Semiconductors

In this study, we report an efective approach to tune the crystallization, microstructure and charge transport of solution-processed organic semiconductors by blending with a conjugated polymer additive poly(3-hexylthiophene) (P3HT). When 6,13-bis(triisopropylsilylethynyl) pentacene (TIPS pentacene) was used as a model semiconductor material to mix with diferent amount of P3HT, their intermolecular interactions led to distinctive TIPS pentacene flm morphologies, including randomly-oriented crystal ribbons, elongated needles with enhanced long-range order, and grasslike curved microwires with interlinkages. Each type of morphology was found to further correlate to considerably diferent charge transport and device performance. As compared to pristine TIPS pentacene devices, bottom-gate, top-contact OTFTs with 2% in weight P3HT additive showed a 2-fold and 5-fold improvement of average feld-efect mobility and performance consistency (defned as the ratio of average mobility to the standard deviation), respectively. The improvement in transistor electrical performance can be attributed to the combined efect of enhanced crystal orientation and uniformity, as well as increased areal coverage. This work can be applied beyond the particular example demonstrated in this study and to tune the charge transport of other small-molecule organic semiconductors in general.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Colossal Cryogenic Electro‐Optic Response Through Metastability in Strained BaTiO 3 Thin Films

The search for thin film electro-optic materials that can retain superior performance under cryogenic conditions has become critical for quantum computing. Barium titanate thin films show large linear electro-optic coefficients in the tetragonal phase at room temperature, which is severely degraded down to ≈200 pm V −1 in the rhombohedral phase at cryogenic temperatures. There is immense interest in manipulating these phase transformations and retaining superior electro-optic properties down to liquid helium temperature. Utilizing the thermodynamic theory of optical properties, a large low-temperature electro-optic response is designed by engineering the energetic competition between different ferroelectric phases, leading to a low-symmetry monoclinic phase with a massive electro-optic response. The existence of this phase is demonstrated in a strain-tuned BaTiO 3 thin film that exhibits a linear electro-optic coefficient of 2516 ± 100 pm V −1 at 5 K, which is an order of magnitude higher than the best reported performance thus far. Importantly, the electro-optic coefficient increases by 100 × during cooling, unlike the conventional films, where it degrades. Further, at the lowest temperature, significant higher order electro-optic responses also emerge. These results represent a new framework for designing materials with property enhancements by stabilizing highly tunable metastable phases with strain.

barium titanate↗

DOE OSTI2025

Atomically Fine-Tuning Organic–Inorganic Carbon Molecular Sieve Membranes for Hydrogen Production

Polymeric membranes with great processability are attractive for the H 2 /CO 2 separation required for hydrogen production from renewable biomass with carbon capture for utilization and sequestration. However, it remains elusive to engineer polymer architectures to obtain desired sub-3.3 Å ultramicropores to efficiently sieve H 2 from CO 2 . Herein, we demonstrate a scalable way of carbonizing polybenzimidazole (PBI) at low temperatures, followed by vapor phase infiltration (VPI) to atomically narrow ultramicropores throughout the films, forming hybrid organic–inorganic carbon molecular sieves (CMSs). One VPI cycle (100 s) for the PBI carbonized at 500 °C remarkably increases H 2 /CO 2 selectivity from 9.6 to 83 at 100 °C, surpassing Robeson’s upper bound. The CMS demonstrates a stable H 2 /CO 2 separation performance when challenged with simulated syngas streams and can be fabricated into thin-film composite membranes, outperforming state-of-the-art membranes. Finally, the scalable approach can be ubiquitous to molecularly fine-tune ultramicropores of leading polymeric membranes to further improve their size-sieving ability and thus separation efficiency.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Local Structure Analysis and Modelling of Lignin‐Based Carbon Composites through the Hierarchical Decomposition of the Radial Distribution Function

Abstract Carbonized lignin has been proposed as a sustainable and domestic source of activated, amorphous, graphitic, and nanostructured carbon for many industrial applications as the structure can be tuned through processing conditions. However, the inherent variability of lignin and its complex physicochemical structure resulting from feedstock and pulping selection make the Process‐Structure‐Property‐Performance (PSPP) relationships hard to define. In this work, radial distribution functions (RDFs) from synchrotron X‐ray and neutron scattering of lignin‐based carbon composites (LBCCs) are investigated using the Hierarchical Decomposition of the Radial Distribution Function (HDRDF) modelling method to characterize the local atomic environment and develop quantitative PSPP relationships. PSPP relationships for LBCCs defined by this work include crystallite size dependence on lignin feedstock as well as increasing crystalline volume fraction, nanoscale composite density, and crystallite size with increasing reduction temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Learning-based CO 2 concentration prediction: Application to indoor air quality control using demand-controlled ventilation

There have been increasing concerns over the air quality inside buildings as high levels of bio-effluents can cause nausea, dizziness, headaches, and fatigue to the people working in those spaces. First published in 2004 as Standard 62.1, ASHRAE Standard 62.2-2019 requires highly occupied spaces to implement heating, ventilation, and air conditioning (HVAC) that can dilute contaminants produced by occupants. In this regard, occupant-centric ventilation control has been regarded as an effective practice to maintain a satisfactory indoor air quality (IAQ) when dealing with highly variable occupancy environments. However, few established models in current literature and practice consider dynamic occupancy behavior and adaptive IAQ control. To address this gap, a dynamic indoor CO2 model is constructed using machine learning algorithms to forecast CO2concentrations across a range of forecasting horizons. Herein, we tuned and compared six state-of-the-algorithms—including Support Vector Machine, Ada Boost, Random Forest, Gradient Boosting, Logistic Regression, and Multilayer Perceptron. The algorithms’ performances are validated using CO 2 and historical meteorological data collected from a campus classroom with a variable occupancy rate. Simulation results showed that Multilayer Perceptron can strongly predict the volatile CO 2 behavior and also outperforms other algorithms in terms of accuracy. Furthermore, a control strategy capable of modeling and detecting dynamic patterns of CO 2 level is utilized to modulate the ventilation rate in real-time and also reduce the energy consumption. The proposed controller reduced the HVAC fan’s energy consumption by 51.4% and provide ventilation as needed per the ASHRAE standards.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

The impact of hot-press conditions on the durability of polymer electrolyte membrane fuel cells

The proton exchange membrane integrity can be compromised during hot-press fabrication of membrane electrode assemblies (MEAs) causing premature cell failures during operation. In this work, infrared (IR) thermography was used as a diagnostic tool to spatially visualize hydrogen (H 2 ) crossover and identify process-induced-membrane irregularities (PIMs). These irregularities were identified as seed locations for MEA failures. Fine tuning of hot-press conditions was used to mitigate premature cell failures informed by accelerated stress testing (AST). The impact of PIMs on the initial performance, high-frequency resistances, open-circuit voltage, and H 2 crossover are reported. Nafion XL and 212 membranes, hot-pressed with a force of 16 kg/cm 2 and temperature of 120°C, were found to be consistently irregularity-free. Irregularity-free MEAs using Nafion 211, 212, and XL membranes demonstrated AST lifetime improvements of 58, 64 and 400%, respectively, compared to those fabricated with non-optimized conditions. In conclusion, this work highlights the importance of fabrication parameters on premature cell failures.

08 HYDROGEN↗

DOE OSTI2021

Demonstration of a 20‐W membrane‐external‐cavity surface‐emitting laser for sodium guide star applications

Abstract We present a record 20‐W continuous‐wave output power membrane‐external‐cavity surface‐emitting laser (MECSEL) double‐bonded to silicon carbide (SiC) heat spreaders emitting around 1178 nm. We achieved a maximum slope efficiency of 27.7% and an optical‐to‐optical efficiency of 26.7%. By utilising an intracavity birefringent filtre, the wavelength was tuned over 75 nm and maximum output power of 12.7 W was recorded at 1178 nm. This study shows the potential of MECSELs to achieve high‐performance and cost‐effective optically pumped semiconductor disk lasers targeting applications in sodium guide stars.

Priante, D.↗

DOE OSTI2020

Spectral and photochemical diversity of tandem cysteine cyanobacterial phytochromes

The atypical trichromatic cyanobacterial phytochrome NpTP1 from Nostoc punctiforme ATCC 29133 is a linear tetrapyrrole (bilin)-binding photoreceptor protein that possesses tandem-cysteine residues responsible for shifting its light-sensing maximum to the violet spectral region. Using bioinformatics and phylogenetic analyses, in this study we established that tandem-cysteine cyanobacterial phytochromes (TCCPs) compose a well-supported monophyletic phytochrome lineage distinct from prototypical red/far-red cyanobacterial phytochromes. To investigate the light-sensing diversity of this family, we compared the spectroscopic properties of NpTP1 (here renamed NpTCCP) with those of three phylogenetically diverged TCCPs identified in the draft genomes of Tolypothrix sp. PCC7910, Scytonema sp. PCC10023, and Gloeocapsa sp. PCC7513. Recombinant photosensory core modules of ToTCCP, ScTCCP, and GlTCCP exhibited violet-blue–absorbing dark-states consistent with dual thioether-linked phycocyanobilin (PCB) chromophores. Photoexcitation generated singly-linked photoproduct mixtures with variable ratios of yellow-orange and red-absorbing species. The photoproduct ratio was strongly influenced by pH and by mutagenesis of TCCP- and phytochrome-specific signature residues. Our experiments support the conclusion that both photoproduct species possess protonated 15E bilin chromophores, but differ in the ionization state of the noncanonical “second” cysteine sulfhydryl group. We found that the ionization state of this and other residues influences subsequent conformational change and downstream signal transmission. We also show that tandem-cysteine phytochromes present in eukaryotes possess similar amino acid substitutions within their chromophore-binding pocket, which tune their spectral properties in an analogous fashion. Taken together, our findings provide a roadmap for tailoring the wavelength specificity of plant phytochromes to optimize plant performance in diverse natural and artificial light environments.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Application of deep learning methods for beam size control during user operation at the Advanced Light Source

Past research at the Advanced Light Source (ALS) provided a proof-of-principle demonstration that deep learning methods could be effectively employed to compensate for the significant perturbations to the transverse electron beam size induced by user-controlled adjustments of the insertion devices. However, incorporating these methods into the ALS’ daily operations has faced notable challenges. The complexity of the system’s operational requirements and the significant upkeep demands has restricted their sustained application during user operation. Here, we introduce the development of a more robust neural network (NN)-based algorithm that utilizes a novel online fine-tuning approach and its systematic integration into the day-to-day machine operations. Our analysis emphasizes the process of NN model selection, demonstrates the superior performance of the NN-based method over traditional feedback methods, and examines the effectiveness and resilience of the new algorithm during user-operation scenarios. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Autonomous quantum absorption refrigerators

Here, we propose a quantum absorption refrigerator using the quantum physics of resonant tunneling through quantum dots. The cold and hot reservoirs are fermionic leads, tunnel coupled via quantum dots to a central fermionic cavity, and we propose configurations in which the heat absorbed from the (very hot) central cavity is used as a resource to selectively transfer heat from the cold reservoir on the left to the hot reservoir on the right. Heat transport in the device is particle-hole symmetric; we find two regimes of cooling as a function of the dot energies—symmetric with respect to the Fermi energy of the reservoirs—and we associate them with heat transfer by electrons above the Fermi level and holes below the Fermi level. We also discuss optimizing the cooling effect by fine-tuning the energy of the dots as well as their linewidth and characterize regimes where the transport is thermodynamically reversible such that the Carnot coefficient of performance is achieved with zero cooling power delivered.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Maps of ice wedge thermokarst pool expansion from twenty-seven circumpolar survey areas

This repository includes data and code to accompany the manuscript 'Topography controls variability in circumpolar permafrost thaw pond expansion' by Abolt et al. The data include satellite imagery and derived maps of thermokarst pools from twenty-seven survey areas in North America and Siberia. The code, written in MATLAB (R2021a), contains demonstrations of the workflow for generating the maps. The demonstrations include training a generalized UNet for mapping thermokarst pools using data from three survey areas, 'fine tuning' the UNet for use at a specific survey area using transfer learning, applying a trained UNet to infer thermokarst pool extent within satellite imagery, and performing histogram matching as a pre-processing step to improve satellite imagery contrast. Contains MATLAB script files and M files, TIF files, shape files, XML, Excel, TXT, and CSV files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗