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DOE OSTI2025

QUPITER -- Space Quantum Sensors for Jovian-Bound Dark Matter

We propose utilizing space quantum sensors to detect ultralight dark matter (ULDM) bound to planetary bodies, focusing on Jupiter as the heaviest planet in the solar system. Leveraging Jupiter's deep gravitational potential and the wealth of experience from numerous successful missions, we present strong sensitivity projections on the mass and couplings of scalar ULDM. Future space missions offer unique opportunities to probe the ULDM interactions using quantum sensors, including superconducting quantum interference device (SQUID) magnetometers. By measuring dark matter-induced magnetic field oscillations, we expect to achieve sensitivity orders of magnitude beyond the terrestrial probes and significantly improve detection prospects of theoretically motivated ULDM candidates.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R 2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Quasi‐Steady State Supersaturation: Do High Values Derived From ESCAPE Represent Real High Supersaturations and the Potential for Condensational Invigoration?

Deep convective clouds were intensively sampled during the Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) with coordinated flights of the NRC Convair-580 and SPEC Learjet. A total of 219 updraft core segments were sampled over coastal Texas and Louisiana under diverse meteorological conditions. Median updraft properties included widths of ∼1 km, velocities of 4.8 m s −1 , droplet number concentrations of ∼400 cm −3 , and liquid water contents of 0.9 g m −3 . The limitations of using the quasi-steady state approximation to derive supersaturations were explored. Supersaturation (S QSS ) estimated from in situ observations under a quasi-steady state assumption averaged 0.4% but occasionally exceeded 2%, with values >1% (high supersaturations) identified as statistical outliers. Two case studies illustrated the conditions linked to high supersaturations. In a storm over the Gulf, median core S QSS reached 2.46% in the developing stage compared to 2.17% in the mature stage under similar thermodynamic conditions. In a storm over coastal Louisiana, S QSS peaked near 11% within a 13.7-m s −1 updraft, accompanied by predominantly supercooled liquid droplets at −13°C and exceptionally low diameter concentrations of 0.29 mm cm −3 . Bootstrap analysis of all sampled cores showed that high supersaturations are most probable in cold and mixed-phase regimes with moderate to strong updrafts and are strongly influenced by vertical velocity and droplet number concentrations. In conclusion, while extreme supersaturations (∼10%) were rare, their occurrence underscores the need for targeted multiplatform observations to resolve their spatiotemporal variability and assess their potential role in deep convective invigoration.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Differential Seismic Phase Detection Probability as a Potential Discriminant of Explosions and Earthquakes

Deep learning models trained to estimate the probability of seismic P and S phases are rapidly expanding the scale of local event detections. Here, we evaluate the potential for deep learning model output phase detection probabilities to contribute to event‐type classification, particularly discrimination of single‐fired borehole explosions and earthquakes at local distances (<300 km). Motivated by the empirical success of P/S amplitude ratios, we consider the difference between P and S pick probability output from previously developed phase detection models, P prob −S prob ⁠, as a discriminant. Test data include M L ∼1–4 earthquakes and explosions observed by common seismographs in ten geologically diverse localities. Depending on the picking model and training data, binary classification using P prob −S prob with at least three stations can achieve approximately equivalent classification accuracy as P/S amplitude ratios without requiring any customization. Joint classification with P/S and P prob −S prob improves accuracy for most quality control scenarios. Pick probabilities are an efficient attribute to consider in explosion discrimination because they can be automated byproducts of event detection. They avoid the binary choice of picking or not picking weakly visible S waves common to explosions.

Duan, Chenglong [Rice Univ., Houston, TX (United S↗

DOE OSTI2024

Deep Eutectic Solvents as Candidates for Lithium Isotope Enrichment

Nuclear fusion is a phenomenon that is well known within the nuclear physics community as a viable option for alternative energy as many natural gases and fossil fuels are phased out of commercial use. Deuterium and tritium fusion reactions are currently the leading candidates for nuclear fusion, with a major limiting factor being a means to produce tritium on an industrial scale. Lithium-6 is a well-known isotope that can produce tritium and helium following a fission reaction with a neutron. Unfortunately, the lithium-6 enrichment methods are limited to the COLEX process, which leaves behind an alarming amount of mercury waste as a potential environmental contaminant. Deep eutectic solvents are believed to be a potential alternative to lithium isotope separations due to the ease of generation, in addition to the minimum environmental waste generated when these solvents are employed. Previous studies have suggested that deep eutectic solvents are capable of separating lithium isotopes by utilizing a 2-thenoyltrifluoroacetone and trioctylphosphine oxide system that can biphasically react with a buffered solution containing lithium chloride. This system displays a separation factor of 1.068, which when compared to the 1.054 separation within the COLEX process, makes it a potential candidate for lithium-6/7 separation. Within this study, we investigate this system in comparison to two newly synthesized deep eutectic solvents and find that within these acetylacetone-based systems, little isotopic separation is observed. We investigate these systems both experimentally and computationally, showing the different lithium cation affinities, in addition to proposing how the electron-donating or -withdrawing nature can influence these systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗

DOE OSTI2024

Untapped Potential of Fluoride Ions in Maximizing the Electrochemical Stability of Deep Eutectic Solvents

Advancing the batteries is pivotal to propelling our society toward a sustainable, electrified future. The stability of electrolytes forms the backbone of energy storage systems. This is particularly the case for redox flow batteries (RFBs). Their deployability depends on their longevity and dependability. The presence of unstable electrolytes can trigger undesirable reactions, degrade performance, and lead to battery failure. Aqueous electrolytes, with limited electrochemical stability window (ESW), are prone to hydrogen and oxygen evolution. Conversely, non-aqueous electrolytes offer enhanced stability. In this study, we unveil the ESW of a non-aqueous eutectic solvent, comprising choline fluoride and ethylene glycol—a composition that has eluded experimental investigation until now. Our findings show that the stability window, reduction and oxidation potential limits of deep eutectic solvents, are sensitive to variations in the halide component of the ammonium salt. Furthermore, this work not only highlights the benefits of novel deep eutectic solvents but also sets the stage for their strategic use in future battery electrolytes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Deep learning forecasts the spatiotemporal evolution of fluid-induced microearthquakes

Microearthquakes generated by subsurface fluid injection record the evolving stress state and permeability of reservoirs. Forecasting their spatiotemporal evolution is therefore critical for applications such as enhanced geothermal systems, carbon dioxide sequestration and other geoengineering applications. Here we propose a transformer neural network model that ingests hydraulic stimulation history and prior microearthquake observations to forecast four key quantities: cumulative microearthquake count, cumulative logarithmic seismic moment, and the 50th- and 95th-percentile extents of the microearthquake cloud. Applied to the EGS Collab Experiment 1 dataset, the model achieves R2 > 0.98 for the 1-s forecast horizon and R2 > 0.88 for the 15-s forecast horizon across all targets, and supplies uncertainty estimates through a learned standard deviation term. These accurate, uncertainty-quantified forecasts enable real-time inference of fracture propagation and permeability evolution, demonstrating the strong potential of deep-learning approaches to improve seismic-risk assessment and guide mitigation strategies in future fluid-injection operations.

Chung, Jaehong↗

DOE OSTI2024

Improved deep learning prediction of antigen–antibody interactions

Identifying antibodies that neutralize specific antigens is crucial for developing effective immunotherapies, but this task remains challenging for many target antigens. The rise of deep learning–based computational approaches presents a promising avenue to address this challenge. Here, we assess the performance of a deep learning approach through two benchmark tests aimed at predicting antibodies for the receptor-binding domain of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Three different strategies for constructing input sequence alignments are employed for predicting structural models of antigen–antibody complexes. In our initial testing set, which comprises known experimental structures, these strategies collectively yield a significant top-ranked prediction for 61% of cases and a success rate of 47%. Notably, one strategy that utilizes the sequences of known antigen binders outperforms the other two, achieving a precision of 90% in a subsequent test set of ~1,000 antibodies, balanced between true and control antibodies for the antigen, albeit with a lower recall of 25%. Our results underscore the potential of integrating deep learning methods with single B cell sequencing techniques to enhance the prediction accuracy of antigen–antibody interactions.

Science & Technology - Other Topics↗

DOE OSTI2025

Improved guided-wave acoustic defect detection and localization in pipes under varying temperature conditions using deep learning

Early defect detection in pipelines is critical across industries, particularly in the oil and gas sector, where failures result in significant maintenance costs and operational disruptions. Acoustic guided-wave techniques are widely used for nondestructive evaluation of pipeline defects due to their long-distance propagation capability. However, environmental variations, sensitivity limitations, and complex signal interpretation challenges limit the effectiveness of traditional signal processing approaches with guided-wave signals. Recent advances in deep learning methods have demonstrated remarkable success in solving complex real-world problems in many fields. In particular, deep-learning-based signal processing holds substantial promise to overcome limitations and challenges of conventional signal processing. This study presents a deep learning framework for pipeline inspection using acoustic guided-wave signals under temperature varying environments. The proposed framework employs a dual-path one-dimensional convolutional autoencoder that combines defect detection, localization, and temperature prediction functions. The proposed system utilizes multi-mode and broadband acoustic waves with an optimized number of sensors that provide high accuracy while retaining practical simplicity. Experimental validation is performed on a carbon steel pipe. The results indicate exceptional defect detection accuracy and precise defect localization with a mean absolute error of 66 mm. The proposed technique also predicts the effective average temperature of the pipe with a mean absolute error of 0.2°C. Comparative analysis shows superior performance of the proposed method over a traditional method previously developed by the authors' team. These results highlight the potential of integrating deep learning methods into guided-wave pipeline inspection systems to improve reliability under varying environmental conditions.

42 ENGINEERING↗

DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗

DOE OSTI2024

Bottom-up design of actinide materials from molecular clusters: Demonstration of a general-purpose simulation capability leveraging machine-learned atomic potentials

Actinide thin-film coatings such as uranium dioxide (UO 2 ) play an important role in nuclear reactors and other mission-relevant applications, but realization of their potential requires a deep fundamental understanding of the chemical vapor deposition (CVD) processes used for their growth. The slow experimental progress can be attributed, in part, to the standard safety guidelines associated with handling uranium byproducts, which are often corrosive, toxic, and radioactive. Accurate simulation techniques, when used in concert with experiment, can improve laboratory safety, material durability, and deliverable timeframes. However, state-of-the-art computational methods are either insufficiently accurate or intractably expensive. To remedy this situation, in this project we suggested a machine-learning (ML) accelerated workflow for simulating molecular clustering toward deposition. As a benchmark test case, we considered molecular clustering in steam and assessed independent components of our workflow by comparing with measured thermodynamic properties of water. After analyzing each component individually and finding no fundamental barrier to realization of the workflow, we attempted to integrate the ML component, a Sandia-developed tool called FitSNAP. As this was the first application of FitSNAP to atoms and molecules in the gas phase at Sandia, the method required more fitting data than was originally anticipated. Systematic improvements were made by including in the fit data diatomic potentials, molecular single-bond-breaking curves, and symmetry-constrained intermolecular potentials. We concluded that our strategy provides a feasible pathway toward modeling CVD and related processes, but that extensive training data must be generated before it can be of practical use.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Effect of Ambient Organic Acids on the Water Structure at ${\rm TiO}_{2}$ Interfaces

A molecular-level understanding of the effects of ambient organic compounds on the wettability of titanium dioxide ( ${\rm TiO}_{2}$ ) surfaces is relevant to many of its energy-related and environmental applications. Herein, we focus on two common atmospheric carboxylic acids, formic and acetic acid, and characterize their adsorption/ desorption at the aqueous interfaces of anatase and rutile ${\rm TiO}_{2}$ using molecular dynamics with an ab initio deep neural network potential. Our simulations show that these acids prefer to be localized in the interfacial water layers close to the ${\rm TiO}_{2}$ surface where they are stabilized by the interaction/exchange of their acid proton with a surface oxygen, rather than chemisorb at the surface Ti sites by displacing the adsorbed water. Notably, these acids make the surface of anatase hydrophobic, whereas the larger fraction of adsorbed water dissociation can offset their effect on rutile. Furthermore, these results provide a picture where carboxylic acids control the wettability of ${\rm TiO}_{2}$ largely through acid-base chemistry at the interface rather than chemisorption on the oxide surface, a finding that can help improve the design of self-cleaning surfaces and photocatalytic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Speciation and diffusive dynamics in hydrated grain boundaries of complex oxide Gd2Ti2O7

Abstract Grain boundaries in polycrystalline materials significantly affect their properties, such as ionic transport, corrosion, and chemical durability. The pyrochlore compound (Gd 2 Ti 2 O 7 ) is employed as a model for complex oxides and is known for its diverse applications, including nuclear waste immobilization. Density functional theory-based first-principles molecular dynamics simulations were performed at different temperatures on the hydrated grain boundary system. The results show extensive transformations within the grain boundaries among hydrous water species (OH − , H 2 O, and H 3 O + ). The temperature dependence of self-diffusion coefficients follows Arrhenius behavior, with an activation energy of 35.9 kJ/mol for hydrogen and 46.3 kJ/mol for oxygen. The lifetime of OH − is about three to four times longer than that of H 2 O at temperatures from 800 to 2100 K, suggesting the greater stability of OH − over H 2 O, a unique characteristic of the grain boundaries. The estimated lifetime of the hydrous species decreases as the temperature increases, with an activation energy of 9.9 kJ/mol for OH − and 13.4 kJ/mol for H 2 O. While Gd 3 + is more mobile than Ti 4+ , both the Gd 3 + and Ti 4+ cations are orders of magnitude less mobile than the water species. The results suggest that water species are much more mobile within grain boundaries than in the bulk crystal and have the potential to penetrate deep into polycrystalline materials through grain boundaries, leading to grain boundary degradation and dissolution. The different mobilities of cations in complex oxides can lead to leaching of certain cations and incongruent dissolution during the chemical weathering of Earth and industrial materials.

B. Ghosh, Dipta↗

DOE OSTI2025

Methanol at Water–TiO 2 Interfaces: Free Energies of Water and Methanol Dissociation

Methanol adsorption on TiO 2 surfaces has long been studied due to its role in enhancing photocatalytic hydrogen evolution, yet how it modulates surface chemistry under aqueous conditions remains little understood. Using molecular dynamics with an ab initio-based deep neural network potential, we find that methanol adsorption induces markedly different effects on the aqueous surfaces of anatase and rutile, the two common phases of TiO 2 . In anatase, methanol adsorption significantly enhances water dissociation, which is otherwise rare at the neat water interface. This enhancement arises from an alternative dissociation pathway mediated by surface-bound methoxyl groups. In contrast, methanol adsorption tends to suppress water dissociation on rutile, replacing it with thermodynamically favored methanol dissociation. Overall, methanol adsorption in an aqueous environment alters not only the availability of key reactive intermediates involved in hydrogen evolution but also the hydrogen source, which turns out to be primarily methanol on rutile, whereas both water and methanol are consumed on anatase. These results provide mechanistic insights into the coupled roles of organic adsorbates and water at photocatalytic interfaces, with implications on how methanol enhances the activity of H 2 evolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy

We present SCULPT (Supervised Clustering and Uncovering Latent Patterns with Training), a comprehensive software platform for analyzing tabulated high-dimensional multi-particle coincidence data from Cold Target Recoil Ion Momentum Spectroscopy (COLTRIMS) experiments. The software addresses critical challenges in modern momentum spectroscopy by integrating advanced machine learning techniques with physics-informed analysis in an interactive web-based environment. SCULPT implements uniform manifold approximation and projection for non-linear dimensionality reduction to reveal correlations in high-dimensional data. We also discuss potential extensions to deep autoencoders for feature learning and genetic programming for automated discovery of physically meaningful observables. A novel adaptive confidence scoring system provides quantitative reliability assessments by evaluating user-selected clustering quality metrics with predefined weights that reflect each metric’s robustness. The platform features configurable molecular profiles for different experimental systems, interactive visualization with selection tools, and comprehensive data filtering capabilities. Utilizing a subset of SCULPT’s capabilities, we analyze photo-double-ionization data measured using the COLTRIMS method for three-body dissociation of the D 2 O molecule, revealing distinct fragmentation channels and their correlations with physics parameters. The software’s modular architecture and web-based implementation make it accessible to the broader atomic and molecular physics community, significantly reducing the time required for complex multi-dimensional analyses. This opens the door to finding and isolating rare events exhibiting non-linear correlations on the fly during experimental measurements, which can help steer exploration and improve the efficiency of experiments.

Artificial neural networks↗

DOE OSTI2026

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗

DOE OSTI2024

Breakthrough Conductivity Enhancement in Deep Eutectic Solvents via Grotthuss–Type Proton Transport

There is an increasing demand for the development of ion-conducting electrolytes for energy storage systems. Much attention is directed toward deep eutectic solvents as potential candidates. In the search for highly conductive systems, the possibility of designing deep eutectic solvents with Grotthuss-type proton transport is widely overlooked. Herein, ethaline, a mixture of choline chloride and ethylene glycol is used in a 1:2 molar ratio, to induce a significant conductivity increase with the addition of water and sulfuric acid (H 2 SO 4 ). The achieved breakthrough conductivity is analyzed experimentally and simulated with ab initio molecular dynamics (AIMD). At sufficient water content, an H-bonding network is formed that leads to a significant breakthrough conductivity based on H 2 SO 4 -derived proton transfer following the long-established Grotthuss proton transport mechanism. This result is substantiated by the positive deviation from the ideal KCl line in the Walden plot. Specifically, the data series positioned above the reference line indicates a Grotthuss mechanism in action. The AIMD simulations demonstrate proton transfer between water and ethylene glycol, supported by simulation frames captured at various times.

36 MATERIALS SCIENCE↗