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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Ligand-Based Compound Activity Prediction via Few-Shot Learning

Predicting the activities of new compounds against biophysical or phenotypic assays based on the known activities of one or a few existing compounds is a common goal in early stage drug discovery. This problem can be cast as a “few-shot learning” challenge, and prior studies have developed few-shot learning methods to classify compounds as active versus inactive. However, the ability to go beyond classification and rank compounds by expected affinity is more valuable. We describe Few-Shot Compound Activity Prediction (FS-CAP), a novel neural architecture trained on a large bioactivity data set to predict compound activities against an assay outside the training set, based on only the activities of a few known compounds against the same assay. Our model aggregates encodings generated from the known compounds and their activities to capture assay information and uses a separate encoder for the new compound whose activity is to be predicted. The new method provides encouraging results relative to traditional chemical-similarity-based techniques as well as other state-of-the-art few-shot learning methods in tests on a variety of ligand-based drug discovery settings and data sets.

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

Cardinal: Seismic and Geoacoustic Array Processing

Data collected via seismic and infrasound array deployments are leveraged in the geosciences to detect and characterize a myriad of natural and anthropogenic sources. These deployments consist of numerous sensors placed in a predetermined configuration to amplify signal strength and improve the efficacy of array processing techniques used to measure signal directionality and waveform coherence. High‐fidelity feature extraction is often predicated on interstation distance as well as the frequency content and wavelength of an incident signal. Numerous array processing softwares analyze data in sequential frequency bands to obtain a more detailed characterization of a signal. However, current algorithms are limited in their ability to determine optimal array configuration for each band. We introduce an open‐source Python code, called Cardinal, to process seismic and infrasound array data in discretized time–frequency space with the option of applying an adaptive array design to determine optimal subarray configuration for each frequency band. To reduce computational time, the array processing step can be run in parallel using multithreading. Furthermore, the software has the capability to aggregate array processing results from different time–frequency pixels to produce separate sets of detections, or families, with added utility via the application of an adaptive semblance threshold, which aids in isolating signals‐of‐interest from coherent background noise. Upon appropriate configuration, Cardinal exhibits the potential to combine distinct seismic and infrasound phases into separate families.

Adaptive Array↗
DOE OSTI2025

Framework to select robust energy retrofit measures for residential communities

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.

Shu, Lei↗
DOE OSTI2025

Adoption of AI in the Utility T&D Sector: Use Cases, Consequence, Assessment and Benefits

Digital transformation and utilization of artificial intelligence (AI) in the electric grid are fundamentally changing the industry’s approach to common problems and enabling a broader paradigm shift in grid planning and operations. The change in approach is circularly both enabling and driving modernization, with load growth and reliable management of data center and AI infrastructure shifting away from planning approaches with relatively predictable behaviors and toward a mix of consumer and industrial choices that surpass human cognitive abilities to process. This movement has potential to condition humans to not understand the system on which the AI depends, while requiring it for development of the necessary infrastructure. Approaches which would address most likely grid conditions and events, such as faults, aging of equipment, and weather, now must also account for large loads which shift not based upon weather or time of day, but the computational load. Quantifying computational load is independent of the traditional grid forecasting variables, where a data center’s aggregate load is determined by user and AI system behavior and decoupled from normal grid planning and operations. AI is both the cause and solution for these challenges, with new grid planning tools integrating massive amounts of decisions into frameworks.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

The Influence of Mesoscale Particle Structure on the Electrode Degradation and Resultant Electrochemistry of Lithium Ion Cells With Nickel Rich Lithium Nickel Manganese Cobalt Oxide Positive Electrodes

Nickel-rich lithium nickel manganese cobalt oxide, NMC (LiNi x Mn y Co z O 2 ), materials are desirable positive electrodes in lithium ion batteries, providing high capacity and energy density. The mesoscale structure of NMC materials is commonly a polycrystalline aggregate, providing opportunity for short lithium ion transport distance of the small primary particles, yet facile material handling due to the larger secondary particles. On (de)lithiation the NMC unit cell changes volume, where the anisotropic strain can result in secondary particle fracture. This secondary particle fracture process during cycling has been associated with several degradation modes of NMC materials in LIBs. In this work, a milling process was determined whereby the secondary particles could be pre-fractured with retention of the parent primary particle crystallographic structure, crystallite size, and morphology, providing the ability to unambiguously determine the influence of NMC secondary particle size and mesostructure on resultant functional behavior. The bulk and interfacial properties and electrochemistry of as-received commercially obtained polycrystal NMC811 (LiNi 0.8 Mn 0.1 Co 0.1 O 2 , PC NMC) and its milled MPC NMC counterpart were compared. The smaller secondary particle size, higher surface area MPC NMC was found to result in 1) greater cathode tortuosity, 2) reduced surface Ni on the cycled MPC cathodes consistent with surface reconstruction, and 3) increased Ni deposition on the anodes from cathode-anode crosstalk. These factors manifested in unfavorable electrochemical behavior of MPC with lower functional capacity at both 1C and C/10 rates and higher impedance over extended moderate voltage (dis)charge cycling.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Estimating irrigation water use from remotely sensed evapotranspiration data: Accuracy and uncertainties at field, water right, and regional scales

Irrigated agriculture is the dominant user of water globally, but most water withdrawals are not monitored or reported. As a result, it is largely unknown when, where, and how much water is used for irrigation. Here, we evaluated the ability of remotely sensed evapotranspiration (ET) data, integrated with other datasets, to calculate irrigation water withdrawals and applications in an intensively irrigated portion of the United States. We compared irrigation calculations based on an ensemble of satellite-driven ET models from OpenET with reported groundwater withdrawals from hundreds of farmer irrigation application records and a statewide flowmeter database at three spatial scales (field, water right group, and management area). At the field scale, we found that ET-based calculations of irrigation agreed best with reported irrigation when the OpenET ensemble mean was aggregated to the growing season timescale (bias = 1.6–4.9%, R 2 = 0.53–0.74), and agreement between calculated and reported irrigation was better for multi-year averages than for individual years. At the water right group scale, linking pumping wells to specific irrigated fields was the primary source of uncertainty. At the management area scale, calculated irrigation exhibited similar temporal patterns as flowmeter data but tended to be positively biased with more interannual variability. Disagreement between calculated and reported irrigation was strongly correlated with annual precipitation, and calculated and reported irrigation agreed more closely after statistically adjusting for annual precipitation. The selection of an ET model was also an important consideration, as variability across ET models was larger than the potential impacts of conservation measures employed in the region. From these results, we suggest key practices for working with ET-based irrigation data that include accurately accounting for changes in soil moisture, deep percolation, and runoff; careful verification of irrigated area and well-field linkages; and conducting application-specific evaluations of uncertainty.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unraveling the Heterogeneous but Ordered Microstructure of the Nonionic Deep Eutectic Solvent Formed by Lauric Acid and N -Methylacetamide

The nonionic deep eutectic solvent, formed by lauric acid (LA) and N-methylacetamide (NMA), has been shown to have a heterogeneous molecular structure in which the LA and NMA form nonpolar and polar domains, respectively. Previous vibrational spectroscopy experiments demonstrated that the ability of the LA domains to solvate compounds was limited to long carbon chains, whereas other nonpolar molecules, such as W(CO) 6 , were found to be solvated by both LA and NMA. These experiments were not fully compatible with the previously proposed micelle-like structure of the nonpolar domains of the LA-NMA DES. In this work, the modeling of the DES molecular structure is pursued using classical molecular dynamics simulations. The new classical model reproduces both the SAXS structural factors and the previously experimentally derived interaction map for these LA-NMA DESs. In addition, the simulation also shows that LA-NMA DESs form highly organized LA aggregates that are difficult to disorganize. Further evidence of the correct description provided by the newly derived model is obtained using a moderately polar probe: chloroform-d. Computations using the classical model have a good agreement with the solvation behavior of the probe derived from experiments, in which the location of the probe is found to be mostly within the polar domain of the DES. The computational model also demonstrates that the probe solvation is a consequence of the tightly packed LA structure, which causes nonpolar molecules to be located at the interphase of the DES nonpolar domains.

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

Material Extrusion Printing of Poly(Acrylonitrile‐Styrene‐Acrylate) and Poly(Acrylonitrile‐Butadiene‐Styrene) Structures Reinforced with Poly(Phenylene Oxide) Additives for Improved Thermomechanical Properties and their Surface Analysis by Time‐of‐Flight Secondary Ion Mass Spectrometry

Fused filament fabrication offers the ability to 3D print complex geometries made from plastic filament materials; however, these parts are mechanically outperformed by parts created by traditional fabrication methods. To overcome this challenge, a high-performance polymer poly(2,6-dimethyl-1,4-phenylene oxide) (PPO) is incorporated as an additive into two common engineering thermoplastics, poly(acrylonitrile-styrene-acrylate) (ASA) and poly(acrylonitrile-butadiene-styrene) (ABS). Structures printed from these polymer blends are more mechanically robust compared to those prepared from the parent polymers, with low loading levels (1–5 wt%) of PPO improving the elastic strength by up to ≈30% relative to the parent terpolymers. Even at higher loading levels (10 and 20 wt% PPO), there is no evidence of additive aggregation in the model thin films, which is supported by compositional analysis of the copolymers and chemical analysis via time-of-flight secondary ion mass spectrometry. The enhancements in mechanical properties of ASA and ABS blends appear to be a consequence of homogeneous incorporation of the PPO additive. In conclusion, this work explores expanding materials-property space using miscible blends of engineering thermoplastics to improve mechanical performance as a general approach to overcoming challenges with parts created by melt-based material extrusion printing.

additive manufacturing↗
DOE OSTI2023

Smart Droplets Stabilized by Designer Surfactants: From Biomimicry to Active Motion to Materials Healing

The science and technologies of emulsion droplets have been a long‐term focus of extensive research endeavors for their practical utility across a breadth of industries, including pharmaceutical products, oil recovery processes, and the food sciences. However, with advances in materials chemistry and characterization tools, new emerging areas are arising with a focus on “smart droplets”. The versatility of emulsion droplets across is based on their ability to partition and create isolated systems with properties defined by the liquid–liquid interface, while preparative routes allow manipulation of droplet size, stability, and encapsulated contents. As described in this article, significant efforts are being devoted to creating new types of droplets by “activating” this interface through the incorporation of reactive structures that trigger droplet response to applied or environmental stimuli (e.g., pH, temperature, salt, or external fields). Moreover, parallels between droplets and live cells inspire efforts to conceive systems that resemble biological motifs or that can produce cellular behaviors that imitate biology (e.g., swarming, communication, or motion). Here, the authors highlight recent advances in smart droplets, with emphasis on organic, polymer, and/or particle surfactants that give rise to inter‐droplet communication (via aggregation, fusion, division, or mass transfer), droplet vehicles for controlled delivery, autonomous droplet motion, and tunable emulsion inversion. Especially emphasized is the macromolecular design to produce reactive and functional surfactants, which are crucial to responsive droplet behavior and their underlying mechanisms. More generally, the exquisite interplay between materials science and biology inspires the review of this research area that provides unique opportunities for insight and inspiration into the capabilities of new droplet designs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Nitrogen availability and summer drought, but not N:P imbalance, drive carbon use efficiency of a Mediterranean tree-grass ecosystem

All ecosystems contain both sources and sinks for atmospheric carbon (C). A change in their balance of net and gross ecosystem carbon uptake, ecosystem-scale carbon use efficiency (CUE ECO ), is a change in their ability to buffer climate change. However, anthropogenic nitrogen (N) deposition is increasing N availability, potentially shifting terrestrial ecosystem stoichiometry towards phosphorus (P) limitation. Depending on how gross primary production (GPP, plants alone) and ecosystem respiration (R ECO , plants and heterotrophs) are limited by N, P or associated changes in other biogeochemical cycles, CUE ECO may change. Seasonally, CUE ECO also varies as the multiple processes that control GPP and respiration and their limitations shift in time. We worked in a Mediterranean tree-grass ecosystem (locally called ‘dehesa’) characterized by mild, wet winters and summer droughts. We examined CUE ECO from eddy covariance fluxes over 6 years under control, +N and + NP fertilized treatments on three timescales: annual, seasonal (determined by vegetation phenological phases) and 14-day aggregations. Finer aggregation allowed consideration of responses to specific patterns in vegetation activity and meteorological conditions. We predicted that CUE ECO should be increased by wetter conditions, and successively by N and NP fertilization. Milder and wetter years with proportionally longer growing seasons increased CUE ECO , as did N fertilization, regardless of whether P was added. Using a generalized additive model, whole ecosystem phenological status and water deficit indicators, which both varied with treatment, were the main determinants of 14-day differences in CUE ECO . The direction of water effects depended on the timescale considered and occurred alongside treatment-dependent water depletion. Overall, future regional trends of longer dry summers may push these systems towards lower CUE ECO .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Evaluating an Earth system model from a water manager perspective

Abstract. The large spatial scale of global Earth system models (ESMs) is often cited as an obstacle to using the output by water resource managers in localized decisions. Recent advances in computing have improved the fidelity of hydrological responses in ESMs through increased connectivity between model components. However, the models are seldom evaluated for their ability to reproduce metrics that are important for and resonate with practitioners or that allow practitioners to situate higher-resolution model outputs within a cascade of uncertainty stemming from different models and scenarios. We draw on the combined experience of the author team and water manager workshop participants to identify salient water management metrics and evaluate whether they are credibly reproduced over the conterminous USA by the Community Earth System Model v2 (CESM2) Large Ensemble. We find that, while the exact values may not match the observations, aspects such as interannual variability can be reproduced by CESM2 for the mean wet day precipitation and length of dry spells. CESM2 also captures the proportion of total annual precipitation that derives from the heaviest rain days in watersheds that are not snow-dominated. Aggregating the 7 d mean daily runoff to two-digit Hydrological Unit Code (HUC2) watersheds also shows that rain-dominated regions capture the timing and interannual variability of annual maximum and minimum flows. We conclude that there is potential for far greater use of large-ensemble ESMs, such as CESM2, in long-range water management decisions to supplement high-resolution regional projections.

Tye, Mari R. (ORCID:0000000324911020)↗
DOE OSTI2025

On-Demand Selection of the Latent Domain Orientation in Spray-Deposited Block Copolymer Thin Films

With their ability to self-assemble spontaneously into well-defined nanoscale morphologies, block copolymer (BCP) thin films are a versatile platform to fabricate functional nanomaterials. An important challenge to wider deployment of BCPs in nanofabrication is combining precise control over the nanoscale domain orientation in BCP assemblies with scalable deposition techniques that are applicable to large-area, curved, and flexible substrates. Here, in this study, we show that spray-deposited smooth films of a nominally disordered BCP exhibit latent orientations, which can be prescriptively selected by controlling solvent evaporation during spray casting. Subsequent solvent vapor annealing triggers assembly toward highly ordered cylindrical morphologies along the pathway determined by solvent evaporation in the prior spray deposition stage. Faster evaporation promotes assembly of vertically oriented cylinders spanning the entire film thickness (100–300 nm). In comparison, slow solvent evaporation permits intermicellar aggregation and incipient cylinder formation in solution, which induces horizontal cylinder assembly upon annealing. The evaporatively controlled latent orientation mechanism presented herein elucidates how nonequilibrium phenomena during casting govern successive self-assembly pathways and facilitates a versatile method to dictate the domain orientation of BCP thin films on demand on flexible and highly curved substrates or in distinct pattern areas on the same substrate.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Probing Nanoscale Chemical Environments of Zinc in Diatoms

Diatoms impact the biogeochemical cycling of zinc (Zn) due to elevated cellular Zn levels over other phytoplankton, and their strong biological uptake of Zn in the Southern Ocean sets the global distribution of Zn in ocean water columns. Past studies have revealed the abundance and spatial distribution of Zn in individual diatoms, while others have shown diverse Zn chemical species in whole-cell aggregates. However, intracellular and intercellular variations in Zn chemical forms remained unknown. For the first time, we applied a synchrotron X-ray nanoprobe to cultured diatoms (Phaeodactylum tricornutum and Chaetoceros muelleri) and resolved spatial heterogeneities of Zn chemistry. The result shows the partitioning of Zn between intracellular contents (soft parts) and frustules (mainly in the intracellular contents for P. tricornutum and similar Zn concentrations in the intracellular contents and frustules for C. muelleri). We found multiple Zn chemical species unevenly distributed within individual cells, where Zn–phosphoryl complexes were the most abundant followed by cysteine, histidine, biogenic silica, and carboxyl complexes. Cellular Zn species varied between individual organisms and were influenced by Zn availability. To our knowledge, this work presents the first X-ray measurements of trace metal speciation at sub-100 nm resolution in biological samples in their natural state. In conclusion, this research improves our ability to examine the biogeochemistry of Zn on the nanoscale and can help us understand the role of Zn in plankton growth and the Zn cycle in ocean waters.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Quantum teleportation coexisting with classical communications in optical fiber

The ability for quantum and conventional networks to operate in the same optical fibers would aid the deployment of quantum network technology on a large scale. Quantum teleportation is a fundamental operation in quantum networking, but has yet to be demonstrated in fibers populated with high-power conventional optical signals. Here we report, to the best of our knowledge, the first demonstration of quantum teleportation over fibers carrying conventional telecommunications traffic. Quantum state transfer is achieved over a 30.2-km fiber carrying 400-Gbps C-band classical traffic with a Bell state measurement performed at the fiber’s midpoint. To protect quantum fidelity from spontaneous Raman scattering noise, we use optimal O-band quantum channels, narrow spectro-temporal filtering, and multi-photon coincidence detection. Fidelity is shown to be well maintained with an elevated C-band launch power of 18.7 dBm for the single-channel 400-Gbps signal, which we project could support multiple classical channels totaling many terabits/s aggregate data rates. These results show the feasibility of advanced quantum and classical network applications operating within a unified fiber infrastructure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Importance of Spatially Continuous Urban Surface Properties in Urban‐Resolving Earth System Modeling

Accurate representation of urban properties and processes at higher resolutions in global modeling systems is essential for advancing our ability to capture the complexities of urban systems and informing effective resilience strategies. However, the prescription of coarse global-scale urban properties in most state-of-the-art Earth system models (ESMs) is limiting their potential for capturing urban signals as they advance toward kilometer-scale simulation capabilities. To bridge this gap in inadequate urban property representation and to advance urban-resolving Earth system modeling, this work integrates the newly-developed global 1 km-resolution facet-level urban surface property data set, U-Surf, into the land component of Community Earth System Model (CESM)—Community Terrestrial System Model (CTSM). The land-only CTSM simulations are validated against satellite measurements, ground-based urban weather stations, flux tower observations, and reanalysis data. Results demonstrate that the enhanced urban properties allow improved simulations of urban meteorology and surface energy fluxes compared to the default coarse-resolution categorical urban canopy parameters. Spatial scaling analysis reveals regime-dependent information loss during resolution aggregation, as well as substantial scale-dependent variations in urban surface energy flux representation. Furthermore, these findings have critical implications for coupled Earth system modeling when including the effect of land-atmosphere interaction. This work establishes a foundation for future urban-resolving kilometer-scale ESM development, which will enable systematic intra- and inter-city comparisons that inform urban adaptation strategies across diverse global urban environments.

Cheng, Yifan [University of Illinois Urbana-Champa↗
DOE OSTI2025

Small-Molecule Models of Hydrogen-Evolving MX 2 (M = Mo, W; X = S, Se) Bulk Solids: Composition–Activity Relationships

Triangular metal chalcogenide clusters of the form [M 3 Q 7 L 3 ]An (M = Mo or W; Q = S or Se; L = i Bu 2 NCS 2 – , (CF 3 CH 2 ) 2 NCS 2 – , i Bu 2 NCSe 2 – , or i Bu 2 PS 2 – ; An = Cl – or I – ) have been investigated as molecular analogues of layered metal dichalcogenide (MX 2 ) H 2 -evolution catalysts. These clusters have been evaluated for their relative H 2 -evolving ability under a common photolysis protocol implementing [Ru(bpy) 3 ] 2+ as chromophore and Et 3 N as sacrificial electron donor. With M constant as Mo and with constant supporting ligand, clusters with an all-sulfide core enable greater H 2 -TON than clusters with an all-selenide core. A more active catalyst is produced by [Mo 3 S 7 (S 2 CN i Bu 2 ) 3 ] + I – than its W 3 analogue with the same core sulfide composition and supporting dithiocarbamate ligands. Dichalcogenocarbamate ligands provide more active catalysts than dialkyldithiophosphate ligated clusters, and within the dichalcogenocarbamate set, greater H 2 -turnovers correlate with more-electron-donating ligands (i.e., i Bu 2 NCS 2 – > (CF 3 CH 2 ) 2 NCS 2 – > i Bu 2 NCSe 2 – ). Cluster cations with Cl – as counteranion are very similar in activity H 2 -evolving levels to identical clusters with I – , ruling out any significant interfering effect by I – upon the electron transfer relay between Et 3 N and catalyst. In the aggregate, observations are consistent with a mechanism for H 2 evolution that involves reductive extrusion of H 2 from a metal hydride intermediate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Analyzing Risks of Virtual Private Network Connections

The use of Splunk for analyzing VPN logs is an effective approach for identifying vulnerabilities in network endpoints. Splunk, a powerful platform for searching, monitoring, and analyzing machine-generated data, enables organizations to aggregate VPN logs in real-time, providing insights into network activity, user behavior, and potential security risks. By indexing VPN traffic and authentication logs, security teams can track abnormal patterns such as multiple failed login attempts, unusual IP addresses, or unexpected changes in bandwidth usage, all of which could indicate potential vulnerabilities or breaches. With Splunk’s advanced search and reporting capabilities, users can create custom dashboards and alerts to detect suspicious activities. Automated searches can flag endpoints exhibiting unusual behavior, while correlation analysis can identify links between compromised devices and broader network vulnerabilities. In particular, Splunk's machine learning capabilities can be leveraged to predict and prevent threats by identifying trends that might otherwise be missed in traditional log analysis. This proactive approach to monitoring VPN logs allows for the early detection of security weaknesses, enabling rapid response and minimizing potential damage to network integrity. By enhancing endpoint visibility, Splunk plays a crucial role in securing remote connections and safeguarding sensitive information. Additionally, Splunk’s automation and alerting features allow teams to create custom workflows that notify them of vulnerable or misconfigured endpoints identified through Shodan. This synergy between Splunk’s log analysis and Shodan’s device intelligence enhances an organization’s ability to proactively identify and mitigate security risks, improving the overall resilience of their VPN infrastructure.

97 MATHEMATICS AND COMPUTING↗