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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 OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2024

Idaho National Laboratory Integrated Multisite SSHAC Level 3: Probabilistic Volcanic Hazards Assessment

The Idaho National Laboratory (INL) resides on the eastern Snake River Plain (ESRP), part of the Snake River Plain (SRP) with a complex origin and geologic history of volcanism. Much of the Quaternary (last 2.58 million years) volcanism within 400 km of INL is genetically associated with a major thermal anomaly referred to as the Yellowstone hotspot, which is currently located more than 180 km northeast of INL. Near INL, local volcanic sources include silicic domes near its southern border, and numerous dike-fed basaltic vents of the ESRP, some of which are near or within the INL boundaries. Quantitative probabilistic assessments of screened-in volcanic hazardous phenomena for nine different facility complexes at the INL are presented for a Senior Seismic Hazard Analysis Committee (SSHAC) Level 3 (SL3) study. The comprehensive, integrated multisite SSHAC study consists of a single regional Probabilistic Volcanic Hazards Assessment (PVHA) that pertains to all nine INL facility complexes, with site-specific information developed at each respective area of interest (AOI), referred to as the "INL facility AOI" (Figure ES-1). Hazard products are generated for specified INL facility AOIs for use by multiple stakeholders from different agencies in risk-informed decision-making regarding site selection, operations, and design of nuclear facilities at INL, consistent with U.S. Department of Energy (DOE) and U.S. Nuclear Regulatory Commission (NRC) regulatory guidance. The study also serves as the basis for future periodic safety assessments required for existing DOE facilities, such as 10-year evaluations of natural-phenomena hazards. Elements of the INL PVHA including initial characterization, screening, quantitative assessments of eruption and hazard potential, and consideration of facility needs are conducted using the three phases of the SSHAC process: evaluation, integration, and documentation. As per regulatory guidance, the SSHAC framework provided the necessary processes and procedures for the PVHA Technical Integration (TI) team, at three workshops, five formal working meetings and many TI team remote meetings, to conduct initial characterization, screen the volcanic hazards, create PVHA model inputs, exercise those models in the PVHA, consider facility-specific volcanic hazard needs, and perform final hazard calculations. The Participatory Peer Review Panel (PPRP) provided independent oversight and performed process and technical reviews of the PVHA throughout its duration. The study included an extensive New Data Collection and Analyses (NDCA) program developed by the PVHA TI team to reduce uncertainties in hazard-significant elements in the PVHA model. NDCA activities generated 20 reports providing important contributory datasets and results to the project database for characterizing the ESRP. For example, a report compiling the dimensions of ESRP shield volcanoes and lava fields was used to construct volcanic footprints (areas of impact), was compared with data from INL subsurface cores, and was used to validate the results of lava-flow inundation modeling on the contemporary terrain. Another example is the acquisition of aeromagnetic data over INL and its surrounding area, with maps of buried magmatic features (e.g., subsurface volcanoes and swarms of feeder dikes) that informed the PVHA conceptual model of volcanism. The SSHAC evaluation process was used to conduct all elements of the PVHA including initial characterization and screening. Existing data and NDCA activities provided the foundation to develop the tectonomagmatic conceptual model of volcanism for the region of geographical interest in the SRP and Yellowstone hotspot volcanic system, and for considering volcanoes in the western US. Considering Quaternary volcanic sources active during this period, the screening approach identified and evaluated magma compositions, types of eruptive and intrusive phenomena, types of hazardous phenomena, and proximity of sources to INL. The PVHA TI team evaluated 18 types of magmatic sources in terms of 20 potentially hazardous phenomena, resulting in 360 screening decisions. The screening process resulted in 140 screened-in hazardous volcanic phenomena for Quaternary volcanic sources 1) proximal to INL facility complexes in the ESRP (64 basaltic and 54 silicic), 2) regional sources associated with the Yellowstone caldera system and Blackfoot Reservoir volcanic field (7), and 3) more distal sources from thirteen Cascade volcanoes and two volcanoes at Long Valley caldera (CA).

58 GEOSCIENCES↗
DOE OSTI2025

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Lithium–Sulfur Batteries: 3D Printed Tools and Assembly Techniques for Repeatable Lab-Scale Coin Cell Manufacturing

The coin cell form factor has become a standard in energy storage research owing to its relatively low cost, low quantity of requisite electroactive material, fast experimental turnover, and capability to generate publishable quality data. Coin cell assembly at the lab-scale requires precise manual alignment of the many internal components of a cell to yield a properly functioning device. Truly repeatable device assembly presents a significant challenge to the acquisition of reliable data. In this report, we present a three-dimensional (3D) printable design for a coin cell alignment device and describe techniques for a generalized workflow to achieve reproducible coin cell assembly of lithium-based batteries. Standardized assembly techniques using readily accessible, purpose-built tools were demonstrated for labscale production of lithium−sulfur (Li−S) coin cells. Li−S batteries fabricated using the alignment device as well as conductive adhesive incorporated workflows afforded coin cells with up to 35% reduction of assembly times, comparable or better functional cell yields, and improved electrochemical device performance with more consistent and up to 150 mAh/g higher average discharge capacities coupled with Coulombic efficiency increases of up to 2.0% over manually aligned cell assembly techniques.

Batteries↗
DOE OSTI2026

Heat load measurements for the PIP-II pHB650 cryomodule

Phase-3 testing of the pHB650 cryomodule at the PIP-II Injector Test Facility was conducted to evaluate the effectiveness of heat load mitigations performed after earlier phases of testing and to continue pinpointing any sources of unexpectedly high heat loads.. The programme measured HTTS, LTTS, and 2 K isothermal/non-isothermal loads under "standard", "linac", and "simulated dynamic" operating modes, recording data both inside the cryomodule and across the bayonet can circuits. Thermal-acoustic oscillations were eliminated by replacing the original G10 cooldown-valve stem with a stainless-steel stem fitted with wipers. A newly developed Python script automated acquisition of ACNET data, performed real-time heat-load calculations, and generated plots and tables that were posted to the electronic logbook within minutes, vastly reducing manual effort and accelerating feedback between SRF and cryogenics teams. Analysis showed that JT heat-exchanger effectiveness and temperature stratification in the two-phase and relief piping strongly influence the observed loads and helped isolate sources of excess heat. The campaign demonstrates that rigorous pre-test planning, real-time diagnostics, and automated reporting can improve both accuracy and efficiency, providing a template for future PIP-II cryomodule tests and for implementing targeted heat-load mitigations.

Porwisiak, D. [Fermilab; Wroclaw Tech. U.] (ORCID:↗
DOE OSTI2026

Near-field passive sensor for the monitoring of high-temperature oxidative corrosion of metals

This study reports on the development and testing of a passive wireless device designed to track temperature and corrosion behavior in SS304H stainless steel under elevated temperatures. The ANSYS HFSS software was utilized to model and optimize the design of an inductor-capacitor (LC) resonator passive wireless sensors fabricated using platinum designs printed onto an aluminum oxide support operating at frequencies between (50–190 MHz). The optimal LC wireless sensor designs were then fabricated using screen-printing and sintering methods. Here, the sensors were tested by placing the sensor onto polished SS304H flat substrates and heated to 900–1050 °C in air. Wireless acquisition of sensor data during heating, cooling, and isothermal stages was achieved through a Pt loop antenna connected to an RF signal generator and a network analyzer.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Adsorption Hysteresis Under Control: Tuning Host–Guest Interactions via a Genetic Algorithm

Mesoporous adsorbent materials offer a large volumetric capacity; however, cyclic adsorption/desorption processes in these systems often suffer from hysteresis and may require a significant pressure swing to access this capacity. To mitigate hysteresis, a proposed strategy is to include nucleation sites on the walls of the mesoporous material to facilitate droplet and bubble formation, lowering the free energy barriers to the respective phase transitions. It is unclear, however, what combination of adsorbate− adsorbent interactions and spatial patterning would be beneficial for a given application, considering that improvements to some sorption properties may come at the expense of other attributes. To understand these interconnected observables, we examine two model systems, planar-slit and cylindrical pores with tunable interaction sites, using GPU-accelerated transition matrix Monte Carlo simulations. The simulations provide a free energy map of the pressure−adsorption space in a matter of minutes, which we use to track adsorption isotherm characteristics as a function of adsorbent properties. We then leverage the rapid acquisition of simulation data to construct a genetic algorithm to iteratively modify interaction sites of the slit-pore wall to minimize the hysteresis of this system without sacrificing uptake. We find that the adsorption branch of the isotherm is easily modulated via the average host−guest interaction strength, but desorption is only adjustable if there is a suitable bubble nucleation site. Within the context of a slit-pore system, we identify relative interaction strengths and patch sizes required to gain control over both branches of the hysteresis loop.

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

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. Here, in this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

artificial intelligence↗
DOE OSTI2024

HERMES

HERMES - High-speed Event Retrieval and Management for Enhanced Spectral imaging code. This code is meant to unpack and process neutron imaging data from the TPX3Cam made by Amsterdam Scientific Instruments. The TPX3Cam utilizes the Timepix3 chip in a single photon counting mode of image acquisition. The photon counting data streaming off the TPX3Cam needs to be process and analyzed in order to create final images. Therefore we are developing both python and cpp codes to allow for users of the TPX3Cam to efficiently analyze data and create images.

Long, Alexander↗
DOE OSTI2026

The Art of Automation: Translating Electron Microscopy Workflows Into Automated Processes

Acquiring data using a scanning transmission electron microscope (STEM) is a complex, multi-step process. The intricacy of the process depends on the type of sample, composition of the material, desired results of the experiment, resolution requirement and other experimental factors. Each experiment presents unique complications, such as sample drift and contamination, that the microscopist must consider when acquiring data. All these challenges are handled fluidly and expertly by experienced microscopists, but to reach new levels of innovation in material development, including greater reproducibility, throughput, and precision, the automation of these workflows is essential. The initial phase of this work involved translating intuition-based workflows into discrete, programmable steps. Some common key stages in STEM workflows are the initial tuning, scanning the sample for areas of interest, and then acquiring the data. Each stage can be broken further into specific parameter adjustments, such as aberration correction and dwell time optimization, depending on the experiment. When deconstructing various experiments each step was assessed for automation feasibility based on the amount of real time operator decisions. There are steps that lend themselves to automation more readily than others, such as course focusing and sample screening, but there is potential for full automation of all stages with time. As an initial step, an automated montage routine was developed, allowing for the efficient acquisition of large portions of the sample without requiring continuous intervention from the operator. The automation of this small process of the procedure demonstrates the value of this capability. A major challenge in automation arises from discrepancies between commanded, reported and actual stage movements. Using systematic tests, stage movement was quantified. This error can be corrected algorithmically for more accurate workflows in the future. Expanding automation capabilities would result in larger, more efficient data acquisition which allows for more robust statistical analysis. Additionally, this work lays the groundwork for a closed loop system where machine learning algorithms would intake automatically acquired data and make real time decisions. By progressively automating this instrument, this work establishes the foundation for fully automated experimentation in transmission electron microscopy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Processed sap flow and fine-root trait data associated with summer drought responses in temperate trees in Lisle, Illinois, USA (2019–2021)

These data support the manuscript “Acquisitive root exploration strategies help maintain higher peak sap flux rates during summer drought, but more root biomass does not”. The dataset includes processed sap flow measurements and fine-root trait data collected between 2019 and 2021 from temperate monodominant tree plots established in the 1920s to 1930s ranging in size from 0.05 to 0.8 ha at The Morton Arboretum in Lisle, IL. Sap flow was measured with ICT sap flow sensors using the heat ratio method. Fine-root traits were measured from soil cores which includes specific root length (SRL), specific root area (SRA), diameter, biomass, and length for diameter classes ≤1 mm and ≤2 mm. The package contains comma separated value (CSV) data files and associated metadata that can be viewed and analyzed using common software such as spreadsheet programs, R, and Python. These data are used to investigate how variation in fine-root traits relate to tree water use and drought response during summer drought linking belowground root traits and aboveground physiological responses.

drought↗
DOE OSTI2025

Lowering the barrier to access information-rich transient kinetic data for machine learning methods

Transient kinetic data contain a wealth of information about intrinsic features of a catalyst as well as the reaction mechanism. Currently, high volume transient data is underutilized, and data science methods could both increase the value of information that can be extracted from this data, integrate experimental with theoretical data sources, and accelerate the pace of catalyst technology advancement. Transient kinetic characterizations with simple probe molecules exhibiting reversible adsorption, irreversible adsorption and bulk-surface diffusion are presented as training components for similar experiments with more complex surface reactions. In conclusion, by increasing the availability and accessibility of transient kinetic data through details of its structure and acquisition, we aim to decrease the barrier for data scientists to apply machine learning methods to this valuable data source.

Catalysis↗
DOE OSTI2025

Reward based optimization of resonance-enhanced piezoresponse spectroscopy

Dynamic spectroscopies in scanning probe microscopy (SPM) are critical for probing material properties, such as force interactions, mechanical properties, polarization switching, electrochemical reactions, and ionic dynamics. However, the practical implementation of these measurements is constrained by the need to balance imaging time and data quality. Signal to noise requirements favor long acquisition times and high frequencies to improve signal fidelity. However, these are limited on the low end by contact resonant frequency and photodiode sensitivity and on the high end by the time needed to acquire high-resolution spectra or the propensity for sample degradation under high field excitation over long times. The interdependence of key parameters such as instrument settings, acquisition times, and sampling rates makes manual tuning labor-intensive and highly dependent on user expertise, often yielding operator-dependent results. These limitations are prominent in techniques like dual amplitude resonance tracking in piezoresponse force microscopy that utilize multiple concurrent feedback loops for topography and resonance frequency tracking. Here, a reward-driven workflow is proposed that automates the tuning process, adapting experimental conditions in real time to optimize data quality. Furthermore, this approach significantly reduces the complexity and time required for manual adjustments and can be extended to other SPM spectroscopic methods, enhancing overall efficiency and reproducibility.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

A fast and sensitive size-exclusion chromatography method for plasma extracellular vesicle proteomic analysis

Extracellular vesicles (EVs) carry diverse biomolecules derived from their parental cells, making their components excellent biomarker candidates. However, purifying EVs is a major hurdle in biomarker discovery since current methods require large amounts of samples, are time-consuming and typically have poor reproducibility. Here we describe a simple, fast, and sensitive EV fractionation method using size exclusion chromatography (SEC) on a fast protein liquid chromatography (FPLC) system. Our method uses a Superose 6 Increase 5/150, which has a bed volume of 2.9 mL. The FPLC system and small column size enable reproducible separation of only 50 µL of human plasma in 15 min. To demonstrate the utility of our method, we used longitudinal samples from a group of individuals who underwent intense exercise. A total of 838 proteins were identified, of which, 261 were previously characterized as EV proteins, including classical markers, such as cluster of differentiation (CD)9 and CD81. Quantitative analysis showed low technical variability with correlation coefficients greater than 0.9 between replicates. The analysis captured differences in relevant EV proteins involved in response to physical activity. Our method enables fast and sensitive fractionation of plasma EVs with low variability, which will facilitate biomarker studies in large clinical cohorts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Unlocking hidden information in sparse small-angle neutron scattering measurements

Hypothesis Small-Angle Neutron Scattering (SANS) is a powerful technique for studying soft matter systems such as colloids, polymers, and lyotropic phases, providing nanoscale structural insights. However, its effectiveness is limited by low neutron flux, leading to long acquisition times and noisy data. Here, we hypothesize that Bayesian statistical inference using Gaussian Process Regression (GPR) can reconstruct high-fidelity scattering data from sparse measurements by leveraging intensity smoothness and continuity. Experiments and Simulations The method was benchmarked computationally and validated through SANS experiments on various soft matter systems, including wormlike micelles, colloidal suspensions, polymeric structures, and lyotropic phases. GPR-based inference was applied to both experimental and synthetic data to evaluate its effectiveness in noise reduction and intensity reconstruction. Findings GPR significantly enhances SANS data quality and therefore reducing measurement times by up to two orders of magnitude. This cost-effective approach maximizes experimental efficiency, enabling high-throughput studies and real-time monitoring of dynamic systems. It is particularly beneficial for weakly scattering and time-sensitive studies. Beyond SANS, this framework applies to other low-SNR techniques, including laboratory-based small-angle X-ray scattering and various dynamical scattering methods. Furthermore, it offers transformative potential for compact neutron sources, enhancing their viability for structural analysis in resource-limited settings.

Small angle neutron scattering↗
DOE OSTI2025

Predicting Trends in VOC Through Rapid, Multimodal Characterization of State-of-the-Art p-i-n Perovskite Devices

Perovskite photovoltaic technologies are approaching commercial deployment, yet single junction and tandem architectures both still have significant room to improve power conversion efficiency and stability. The ability to perform rapid screening of material quality after altering processing conditions is critical to accelerating the optimization and commercialization of perovskite-based technologies. Currently, researchers utilize a wide range of stand-alone metrology tools to isolate sources of power loss throughout a device stack, which can be slow and labor intensive. Here, we demonstrate the use of a multimodal metrology approach to rapidly determine the maximum achievable and predicted open circuit voltages of >100 perovskite devices during fabrication. Acquisition of these different data is facilitated by combining them into a single integrated measurement platform. We show that these data and automated analysis can be used to rapidly understand and ultimately predict quantitative trends in open circuit voltages of state-of-the-art device architectures. The data and automated analysis workflow presented provides a reliable approach to quickly identify absorber and charge transport layer combinations that can lead to improved open circuit voltages.

14 SOLAR ENERGY↗
DOE OSTI2025

HunStat2 – a simple and low-cost potentiostat with electrochemical impedance spectroscopy capability

We have developed a low-cost (30 USD), simple do-it-yourself (DIY) potentiostat with cyclic voltammetry (CV), open circuit potential (OCP) and electrochemical impedance spectroscopy (EIS) capability. The HunStat2 potentiostat is based on Analog Devices' AD5941 Analog Front End chip, which significantly simplifies the construction of potentiostats for both direct and alternating current (DC and AC, respectively) techniques. Interested readers are provided with circuit diagrams and a bill of materials to build the potentiostat on their own. In addition, control software is also provided free of charge. The software enables acquisition and visualization of data. In summary, HunStat2 introduces a simple and low-cost DIY potentiostat recommended for both analytical and educational purposes.

Vamos, Istvan [Lajos Petrik Vocational Chemistry S↗