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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Coordination-Dependent Chemical Reactivity of TFSI Anions at a Mg Metal Interface

Charge transfer across the electrode–electrolyte interface is a highly complex and convoluted process involving diverse solvated species with varying structures and compositions. Despite recent advances in in situ and operando interfacial analysis, molecular specific reactivity of solvated species is inaccessible due to a lack of precise control over the interfacial constituents and/or an unclear understanding of their spectroscopic fingerprints. However, such molecular-specific understanding is critical to the rational design of energy-efficient solid–electrolyte interphase layers. We have employed ion soft landing, a versatile and highly controlled method, to prepare well-defined interfaces assembled with selected ions, either as solvated species or as bare ions, with distinguishing molecular precision. Equipped with precise control over interfacial composition, we employed in situ multimodal spectroscopic characterization to unravel the molecular specific reactivity of Mg solvated species comprising (i.e., bis(trifluoromethanesulfonyl)imide, TFSI – ) anions and solvent molecules (i.e., dimethoxyethane, DME/G1) on a Mg metal surface relevant to multivalent Mg batteries. In situ multimodal spectroscopic characterization revealed higher reactivity of the undercoordinated solvated species [Mg-TFSI-G1] + compared to the fully coordinated [Mg-TFSI-(G1) 2 ] + species or even the bare TFSI – . These results were corroborated by the computed reaction pathways and energy barriers for decomposition of the TFSI – within Mg solvated species relative to bare TFSI – . Finally, we evaluated the TFSI reactivity under electrochemical conditions using Mg(TFSI) 2 –DME-based phase-separated electrolytes representing different solvated constituents. Based on our multimodal study, we report a detailed understanding of TFSI – decomposition processes as part of coordinated solvated species at a Mg-metal anode that will aid the rational design of improved sustainable electrochemical energy technologies.

25 ENERGY STORAGE↗
DOE OSTI2024

Active and Transfer Learning of High-Dimensional Neural Network Potentials for Transition Metals

Classical molecular dynamics (MD) simulations represent a very popular and powerful tool for materials modeling and design. The predictive power of MD hinges on the ability of the interatomic potential to capture the underlying physics and chemistry. There have been decades of seminal work on developing interatomic potentials, albeit with a focus predominantly on capturing the properties of bulk materials. Such physics-based models, while extensively deployed for predicting the dynamics and properties of nanoscale systems over the past two decades, tend to perform poorly in predicting nanoscale potential energy surfaces (PESs) when compared to high-fidelity first-principles calculations. These limitations stem from the lack of flexibility in such models, which rely on a predefined functional form. Machine learning (ML) models and approaches have emerged as a viable alternative to capture the diverse size-dependent cluster geometries, nanoscale dynamics, and the complex nanoscale PESs, without sacrificing the bulk properties. Here, in this study, we introduce an ML workflow that combines transfer and active learning strategies to develop high-dimensional neural networks (NNs) for capturing the cluster and bulk properties for several different transition metals with applications in catalysis, microelectronics, and energy storage, to name a few. Our NN first learns the bulk PES from the high-quality physics-based models in literature and subsequently augments this learning via retraining with a higher-fidelity first-principles training data set to concurrently capture both the nanoscale and bulk PES. Our workflow departs from status-quo in its ability to learn from a sparsely sampled data set that nonetheless covers a diverse range of cluster configurations from near-equilibrium to highly nonequilibrium as well as learning strategies that iteratively improve the fingerprinting depending on model fidelity. All the developed models are rigorously tested against an extensive first-principles data set of energies and forces of cluster configurations as well as several properties of bulk configurations for 10 different transition metals. Our approach is material agnostic and provides a methodology to transfer and build upon the learnings from decades of seminal work in molecular simulations on to a new generation of ML-trained potentials to accelerate materials discovery and design.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Monitoring Redox Processes in Lithium-Ion Batteries by Laboratory-Scale Operando X-ray Emission Spectroscopy

Tracking changes in the chemical state of transition metals in alkali-ion batteries is crucial to understanding the redox chemistry during operation. X-ray absorption spectroscopy (XAS) is often used to follow the chemistry through observed changes in the chemical state and local atomic structure as a function of the state-of-charge (SoC) in batteries. In this study, we utilize an operando X-ray emission spectroscopy (XES) method to observe changes in the chemical state of active elements in batteries during operation. Operando XES and XAS were compared by using a laboratory-scale setup for four different battery systems: LiCoO 2 (LCO), Li[Ni 1/3 Co 1/3 Mn 1/3 ]O 2 (NMC111), Li[Ni 0.8 Co 0.1 Mn 0.1 ]O 2 (NMC811), and LiFePO 4 (LFP) under a constant current charging the battery in 10 h (C/10 charge rate). We show that XES, despite narrower chemical shifts in comparison to XAS, allows us to fingerprint the battery SOC in real time. We further demonstrate that XES can be used to track the change in net spin of the probed atoms by analyzing changes in the emission peak shape. As a test case, the connection between net spin and the local chemical and structural environment was investigated by using XES and XAS in the case of electrochemically delithiated LCO in the range of 2–10% lithium removal.

25 ENERGY STORAGE↗
DOE OSTI2025

Raman Digital Twin of Monolayer Janus Transition Metal Dichalcogenides

Monolayer transition metal dichalcogenides (TMDs) are a key class of two-dimensional (2D) materials with broad technological potential. Their Janus counterparts exhibit unique properties due to broken out-of-plane symmetry and further enrich the functionalities of TMDs. However, experimental synthesis and identification of Janus TMDs remain challenging. It is thus highly desirable to have a rapid, simple, and in situ characterization technique to monitor, in real time, the conversion process from the parent to Janus structure. Raman spectroscopy stands out for such a task as it is a powerful, nondestructive, and very commonly used tool to characterize 2D materials both in situ and ex situ. To realize the full potential of Raman spectroscopy on rapid characterization of Janus TMDs, we present a computational “Raman digital twin” library for various monolayer Janus TMDs in both 2H and Td phases. We focus on group-6 TMDs: MoS 2 , WS 2 , MoSe 2 , WSe 2 , MoTe 2 , WTe 2 and their Janus variants: MoSSe, MoSTe, MoSeTe, WSSe, WSTe, and WSeTe. Using first-principles density functional theory (DFT), we calculate their vibrational properties and predict distinct Raman fingerprints. These phonon and Raman signatures reflect each material’s structural symmetry and atomic composition, enabling clear identification via Raman spectroscopy. Our theoretical work supports experimental efforts by providing benchmarks for material identification, structural analysis, and quality control. In conclusion, the computational library expedites the discovery and development of Janus 2D materials, facilitating tighter integration between theoretical predictions and experimental validation.

Chalcogenides↗
DOE OSTI2021

Deciphering the source of primary biological aerosol particles: a pollen case study

Primary biological aerosol particles (PBAPs) are microscopic solids suspended in the atmosphere emitted by biological systems and play critical roles in the atmosphere and at the atmosphere-biosphere interface, impacting human health, climate, and the ecosystem function. Understanding the sources of PBAPs is necessary to decipher the mechanistic interactions between aerosols, climate, and distinct ecosystem components. However, the detection of specific PBAPs in complex ambient aerosol samples is challenging. We performed metabolomics analyses of pollen from three pollinating tree species and ambient samples collected during the peak pollination period of each species. Random Forest and sPLS-DA machine learning methods were employed to evaluate whether metabolic signatures of ambient samples can reveal the source of the main pollen particles present in the atmosphere. Our results suggest that atmospheric eco-metabolomics techniques combined with sophisticated statistical methods can decipher the origin of abundant PBAPs from complex ambient samples. Developing complete libraries containing high-resolution metabolomic fingerprints of the major PBAPs present in the atmosphere would significantly advance future research to accurately understand the role of PBAPs in the atmosphere, ecosystems and human health.

Rivas-Ubach, Albert↗
DOE OSTI2026

Opportunities and Limitations of Nuclear Magnetic Resonance Spectroscopy in Astrobiology

For decades, Nuclear Magnetic Resonance (NMR) spectroscopy has been utilized as a powerful tool in various scientific disciplines, most prominently in chemistry, to determine molecular structures or monitor reactions. While well established in various fields, NMR applications in astrobiology are still unclear. This work aims to explore the potential of NMR in astrobiology, highlighting strengths but also weaknesses. We illustrate capabilities of NMR with two applications: (1) recently developed methods for position-specific carbon isotope analysis of complex organics; and (2) well-established tools for performing quantitative compositional analysis of complex organic mixtures. By utilizing samples relevant to astrobiology, specifically the amino acid valine and analogue mixtures of organics, we showcase that molecules retain a source dependent and distinct intramolecular carbon isotope fingerprint. We demonstrate that compositional sample analysis provides an independent and complementary line of evidence pointing towards the origin of a molecule or mixture. Together, these NMR tools have the potential to support life detection efforts, and aid in distinguishing between biotic and abiotic samples. Finally, we discuss sensitivity, detection limits, the portability of NMR, and propose how integration with mass-spectrometry techniques will be imperative to enable more targeted and comprehensive analyses relevant to astrobiology, including in-situ analysis but also sample return missions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

CO 2 Laser Sintering of Garnet-Type Solid-State Electrolytes

We report the processing of garnet-type solid-state electrolytes remains challenging as densification conventionally requires high sintering temperatures and long processing times, which can result in severe Li loss, the formation of secondary phases, and thus high porosity and low ionic conductivity. Here, we report an ultrafast sintering method based on CO 2 laser scanning with the assistance of a heating stage. We demonstrate the rapid densification of low-packing-density Li 6.4 La 3 Zr 1.4 Ta 0.6 O 12 (LLZTO) films, which are difficult to densify by conventional furnace sintering methods. This unique approach has three fingerprint characteristics: (1) mitigation of Li loss through ultrafast sintering (dwelling time $\ll$1 s); (2) a unique anisotropic shrinkage behavior that greatly reduces film thickness; (3) wave-like surface topology from point scanning strategy that enables 3D interfacial contacts with electrode materials. Herein, highly dense (95.68%) and highly conductive (0.26 mS·cm –1 at 25 °C) LLZTO films are obtained through CO2 laser sintering. This work provides a unique, scalable, and widely applicable ultrarapid laser sintering technique to overcome the difficulties associated with classic methods for the integration of SSEs for practical all-solid-state Li-metal battery applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Local Structure of Sulfur Vacancies on the Basal Plane of Monolayer MoS 2

We report the nature of the S-vacancy is central to controlling the electronic properties of monolayer MoS 2 . Understanding the geometric and electronic structures of the S-vacancy on the basal plane of monolayer MoS 2 remains elusive. Here, operando S K-edge X-ray absorption spectroscopy shows the formation of clustered S-vacancies on the basal plane of monolayer MoS 2 under reaction conditions (H 2 atmosphere, 100–600 °C). First-principles calculations predict spectral fingerprints consistent with the experimental results. The Mo K-edge extended X-ray absorption fine structure shows the local structure as coordinatively unsaturated Mo with 4.1 ± 0.4 S atoms as nearest neighbors (above 400 °C in an H 2 atmosphere). Conversely, the 6-fold Mo–Mo coordination in the crystal remains unchanged. Electrochemistry confirms similar active sites for hydrogen evolution. The identity of the S-vacancy defect on the basal plane of monolayer MoS 2 is herein elucidated for applications in optoelectronics and catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Satellite-Dominated Sulfur L 2,3 X-ray Emission of Alkaline Earth Metal Sulfides

The sulfur L 2,3 X-ray emission spectra of the alkaline earth metal sulfides BeS, MgS, CaS, SrS, and BaS are investigated and compared with spectra calculations based on density functional theory. Very distinct spectral shapes are found for the different compounds. With decreasing electronegativity of the cation, that is, increasing ionic bonding character, the upper valence band width and its relative spectral intensity decrease. These general trends are qualitatively reproduced by the spectra calculations, which give quite an accurate description of the spectral shapes in the upper valence band region. On the low energy side of the sulfur 3s → 2p transition dominating the spectra, we find strong satellites caused by "semi-Auger" decays involving configuration interaction. These satellites, previously believed to be energetically forbidden for sulfur L 2,3 emission and only observed for the L 2,3 emission of Cl to Cr, increase in intensity as the bonding character becomes more ionic and dominate the spectra for SrS and BaS. The intensities, energies, and widths of the satellites vary strongly between the investigated compounds, giving a very specific spectral fingerprint that can be used for speciation analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor

Inhibition of the SARS-CoV-2 main protease (M pro ) is a major focus of drug discovery efforts against COVID-19. Here we report a hit expansion of non-covalent inhibitors of M pro . Starting from a recently discovered scaffold (The COVID Moonshot Consortium. Open Science Discovery of Oral Non-Covalent SARS-CoV-2 Main Protease Inhibitor Therapeutics. bioRxiv 2020.10.29.339317) represented by an isoquinoline series, we searched a database of over a billion compounds using a cheminformatics molecular fingerprinting approach. We identified and tested 48 compounds in enzyme inhibition assays, of which 21 exhibited inhibitory activity above 50% at 20 μM. Among these, four compounds with IC 50 values around 1 μM were found. Interestingly, despite the large search space, the isoquinolone motif was conserved in each of these four strongest binders. Room-temperature X-ray structures of co-crystallized protein–inhibitor complexes were determined up to 1.9 Å resolution for two of these compounds as well as one of the stronger inhibitors in the original isoquinoline series, revealing essential interactions with the binding site and water molecules. Molecular dynamics simulations and quantum chemical calculations further elucidate the binding interactions as well as electrostatic effects on ligand binding. The results help explain the strength of this new non-covalent scaffold for M pro inhibition and inform lead optimization efforts for this series, while demonstrating the effectiveness of a high-throughput computational approach to expanding a pharmacophore library.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2023

Integration of Yeast Episomal/Integrative Plasmid Causes Genotypic and Phenotypic Diversity and Improved Sesquiterpene Production in Metabolically Engineered Saccharomyces cerevisiae

The variability in phenotypic outcomes among biological replicates in engineered microbial factories presents a captivating mystery. Establishing the association between phenotypic variability and genetic drivers is important to solve this intricate puzzle. Here, we applied a previously developed auxin-inducible depletion of hexokinase 2 as a metabolic engineering strategy for improved nerolidol production in Saccharomyces cerevisiae, and biological replicates exhibit a dichotomy in nerolidol production of either 3.5 or 2.5 g L –1 nerolidol. Harnessing Oxford Nanopore’s long-read genomic sequencing, we reveal a potential genetic cause—the chromosome integration of a 2μ sequence-based yeast episomal plasmid, encoding the expression cassettes for nerolidol synthetic enzymes. This finding was reinforced through chromosome integration revalidation, engineering nerolidol and valencene production strains, and generating a diverse pool of yeast clones, each uniquely fingerprinted by gene copy numbers, plasmid integrations, other genomic rearrangements, protein expression levels, growth rate, and target product productivities. Τhe best clone in two strains produced 3.5 g L –1 nerolidol and ~0.96 g L –1 valencene. Comparable genotypic and phenotypic variations were also generated through the integration of a yeast integrative plasmid lacking 2μ sequences. Our work shows that multiple factors, including plasmid integration status, subchromosomal location, gene copy number, sesquiterpene synthase expression level, and genome rearrangement, together play a complicated determinant role on the productivities of sesquiterpene product. Integration of yeast episomal/integrative plasmids may be used as a versatile method for increasing the diversity and optimizing the efficiency of yeast cell factories, thereby uncovering metabolic control mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Role of Cu 3+ in the Oxygen Evolution Activity of Copper Oxides

Cu-based oxides and hydroxides represent an important class of materials from a catalytic and corrosion perspective. In this study, we investigate the formation of bulk and surface Cu 3+ species that are stable under water oxidation catalysis in alkaline media. So far, no direct evidence existed for the presence of hydroxides (CuOOH) or oxides, which were primarily proposed by theory. Here, this work directly places CuOOH in the oxygen evolution reaction (OER) Pourbaix stability region with a calculated free energy of −208.68 kJ/mol, necessitating a revision of known Cu–H 2 O phase diagrams. We also predict that the active sites of CuOOH for the OER are consistent with a bridge O* site between the two Cu 3+ atoms with onset at ≥1.6 V vs the reversible hydrogen electrode (RHE), aligning with experimentally observed Cu 2+/3+ oxidation waves in cyclic voltammetry of Fe-free and Fe-spiked copper in alkaline media. Trace amounts of Fe (2 μg/mL (ppm) to 5 μg/mL) in the solution measurably enhance the catalytic activity of the OER, likely due to the adsorption of Fe species that serve as the active sites . Importantly, modulation excitation X-ray absorption spectroscopy (ME-XAS) of a Cu thin-film electrode shows a distinct Cu 3+ fingerprint under OER conditions at 1.8 V vs RHE. Additionally, in situ Raman spectroscopy of polycrystalline Cu in 0.1 mol/L (M) KOH revealed features consistent with those calculated for CuOOH in addition to CuO. Overall, this work provides direct evidence of bulk electrochemical Cu 3+ species under OER conditions and expands our longstanding understanding of the oxidation mechanism and catalytic activity of copper.

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

Solvent Polarity Independent Symmetry-Breaking Charge Separation in a Slip-Stacked Covalent Terrylene Monoimide Dimer

The design of organic materials capable of efficient photoinduced charge separation in low-polarity environments is a critical challenge for advancing organic photovoltaics. Symmetry-breaking charge separation (SB-CS) offers a promising route to efficient charge separation with minimal energy loss; however, in conventional organic materials this process typically relies on polar solvents to stabilize the charge-separated state. Here, we investigate a slip-stacked terrylene monoimide dimer (TMI 2 ) engineered with intrinsic electronic asymmetry to circumvent this limitation. We demonstrate through a comprehensive suite of ultrafast spectroscopic techniques that TMI 2 undergoes remarkably efficient SB-CS regardless of solvent polarity enabled by the permanent dipole moment of TMI and the accessible intramolecular charge-transfer (ICT) character of its monomer units. Furthermore, analysis of vibronic coherences reveals that the initial state mixing is actively driven by a 193 cm –1 intermolecular mode, while the final SB-CS state is marked by a distinct vibrational fingerprint of the radical ion pair product. Our findings reveal how the intrinsic electronic asymmetry of the TMI monomers that constitute TMI 2 creates an efficient charge separation pathway that precludes the need for external stabilization from a polar solvent. Here, this work establishes a powerful molecular design principle for leveraging intrinsic monomeric asymmetry to achieve efficient charge separation in low-dielectric environments, with significant implications for the development of next-generation organic optoelectronic materials.

Aromatic compounds↗
DOE OSTI2026

Ultrafast Formation of Jahn–Teller Polarons Revealed by State-Selective Excitation in Correlated Spinel Co 3 O 4

Jahn–Teller polarons are quasiparticles that stem from symmetry breaking and strong local electron–phonon coupling. They originate from an excess charge carrier being dressed by a local lattice distortion, caused by the Jahn–Teller effect, and they critically impact electrical, structural, and magnetic properties in transition metal oxides. The observation of the microscopic steps involved in their formation is essential for enabling control over material properties through the targeted activation of local, site-specific modifications with light pulses. While Jahn–Teller distortion associated with polaron formation was predicted to contribute significantly to changes in electronic band gap and optical properties in Co 3 O 4 , its experimental observation remains elusive, requiring signatures of local symmetry reduction. In this work, we demonstrate Jahn–Teller polaron formation in spinel Co 3 O 4 . By exciting electronic transitions at 3.10 eV and 1.55 eV, we target either the Oh Cobalt(III) or the Td Cobalt(II) ions, and drive the subsequent coherent responses of the system through two different pathways. For the former, we demonstrate that ligand-to-metal charge transfer leads to Jahn–Teller polaron formation, which is linked to the deformation potential and magnetoelastic coupling. For the latter, we identify the coherent excitation of a T 2g phonon mode launched by on-site d-d electronic transitions. Key to our observations is the ability to target site-specific electronic excitations in spinel Co 3 O 4 using ultrafast optical pulses, while monitoring the ensuing low-energy collective modes through the coherent time-domain response of the material. Our approach, which combines the comparative analysis of experimental fingerprints with the support from density functional theory calculations, is broadly applicable to systems in which Jahn–Teller polaron physics has been theoretically predicted but remains experimentally unverified, and it underscores the potential of electronic-state targeting as a route to selectively excite and probe quasiparticle dynamics in solids.

Lattices↗
DOE OSTI2022

Middle to Late Holocene Sea Surface Temperature and Productivity Changes in the Northeast Pacific

Variations of the sea surface temperature (SST) and primary productivity in the northeast Pacific have far‐reaching implications. In addition to influencing the regional and global temperature and hydroclimate, these conditions also control marine ecosystems and their services, which subsequently impact regional economies. Yet, our understanding of the variability and controls of northeast Pacific SST and productivity on timescales exceeding observational records remains limited. Here, we use marine sediment records from seven locations, spanning 25.2°N–59.6°N, in the northeast Pacific to characterize the millennial‐scale variability of SST and productivity from 9,000 to 1,000 years BP. We explore the dynamics of their spatiotemporal evolution and compare these data with transient climate model outputs to identify potential drivers. Through a heat budget analysis and optimal fingerprinting analysis, we characterize the spatial pattern of forcings. We find that SST varied spatially in the northeast Pacific, with higher latitudes exhibiting greater magnitude changes than lower latitudes, which differs from previous work suggesting regional synchronicity and coherence during the Holocene. Our analysis did not find evidence for coherent variability of primary producer community nor carbon export, highlighting the difficulty of identifying the complex interactions between environmental conditions, producers, and carbon export. Model‐proxy disagreement demonstrates the need for higher resolution model frameworks, but shows nonetheless that observed variability in the proxy records can be explained by a combination of greenhouse gas and orbital forcing. Here we suggest that the complex SST variations and marine ecosystem responses to forced changes are important factors that can drive disagreements in model projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Rates of Sea‐Level Rise Are Highly Sensitive to Ice Viscosity Parameters in Model Benchmarks

Glacier flow plays a major role in current and future rates of globally averaged sea-level rise. The viscosity of glacial ice, controlling the rate of flow, decreases as stress increases and is highly sensitive to the value of the stress exponent, $n$, in the constitutive equation for viscous flow. Glaciologists and climate modelers almost exclusively assume $n=3$ when modeling ice flow and projecting sea-level rise through forward modeling. However, recent work suggests that $n\approx 4$ better fits observations, prompting the question: How sensitive are projections of sea-level rise to the value of $n$? We use an established community ice flow model and standard benchmark experiments designed as an idealized representation of Pine Island Glacier, West Antarctica. While initializing an $n=3$ model to match observations of an $n=4$ ice sheet is possible, we find that incorrectly assuming $n=3$ when in fact $n=4$ dramatically underestimates rates of sea-level rise. The scale of this error grows nonlinearly with the magnitude of the climate forcing, acting to increase projection uncertainties. Additionally, we find that models often account for this stress-dependent rheology mismatch during model initialization in a way that masks this rheological effect in the short term while leaving model outputs vulnerable to larger biases in longer-term projections. Initializations to observations of Pine Island Glacier display similar rheology-mismatch fingerprints to our idealized example.

climate sensitivity↗
DOE OSTI2025

Optoelectronic polymer memristors with dynamic control for power-efficient in-sensor edge computing

Abstract As the demand for edge platforms in artificial intelligence increases, including mobile devices and security applications, the surge in data influx into edge devices often triggers interference and suboptimal decision-making. There is a pressing need for solutions emphasizing low power consumption and cost-effectiveness. In-sensor computing systems employing memristors face challenges in optimizing energy efficiency and streamlining manufacturing due to the necessity for multiple physical processing components. Here, we introduce low-power organic optoelectronic memristors with synergistic optical and mV-level electrical tunable operation for a dynamic “control-on-demand” architecture. Integrating signal sensing, featuring, and processing within the same memristors enables the realization of each in-sensor analogue reservoir computing module, and minimizes circuit integration complexity. The system achieves 97.15% fingerprint recognition accuracy while maintaining a minimal reservoir size and ultra-low energy consumption. Furthermore, we leverage wafer-scale solution techniques and flexible substrates for optimal memristor fabrication. By centralizing core functionalities on the same in-sensor platform, we propose a resilient and adaptable framework for energy-efficient and economical edge computing.

Optics↗