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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Review of alternative fuels data bases

Based on an analysis of the interaction of fuel physical and chemical properties with combustion characteristics and indicators, a ranking of the importance of various fuel properties with respect to the combustion process was established. This ranking was used to define a suite of specific experiments whose objective is the development of an alternative fuels design data base. Combustion characteristics and indicators examined include droplet and spray formation, droplet vaporization and burning, ignition and flame stabilization, flame temperature, laminar flame speed, combustion completion, soot emissions, NOx and SOx emissions, and the fuels' thermal and oxidative stability and fouling and corrosion characteristics. Key fuel property data is found to include composition, thermochemical data, chemical kinetic rate information, and certain physical properties.

Harsha, P. T.↗
DOE OSTI2025

Metagenome-assembled genomes from topsoils collected during NEON campaign in East River, CO (06/14/2018-06/28/2018)

The Watershed Function Science Focus Area (WF SFA) at Lawrence Berkeley National Lab is working to build a mechanistic understanding of the distribution and dynamics of biogeochemical processes in mountainous watersheds and their response to perturbation. In June 2018, the NEON (National Ecological Observatory Network) Airborne Observatory Platform (AOP) performed a taskable airborne imaging campaign to collect visible to shortwave infrared (VSWIR) imaging spectroscopy and LiDAR data across 330 km2 in the Upper East River at Crested Butte, CO. We conducted a parallel ground sampling campaign to sample vegetation traits, as well as soil physical, chemical, and microbiological characteristics. We collected these samples from 438 sites across 12 locations spanning much of the elevation, topographic, and geologic variability across the study area. A subset of 250 samples were used for soil metagenomics which is presented here. In addition, at each site, vegetation samples were collected to measure species-specific leaf water content and leaf mass area, foliar elemental composition and foliar CN stable isotope ratios. Soil samples were collected to measure soil physical properties which include bulk density and soil texture analysis. A suite of soil chemical properties was measured from the samples collected at each site, including pH, organic matter, concentrations exchangeable cations, total elemental composition, and the concentrations of extractable N pools (e.g. total free amino acids, ammonium, nitrate, dissolved organic N, and total dissolved N). Additionally, we have measured soil microbial biomass CN stoichiometry. Here, we present 1982 metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from topsoil collected from during NEON 2018 campaign. All metagenomes were sequenced at JGI (Joint Genome Institute) (GOLD Study ID: Gs0149986). Metagenomes were assembled using JGI Metagenome Workflow (10.1128/mSystems.00804-20). The dataset includes (1) zip files for 1982 MAG fasta files (neon_genomes1-5.tar.gz, split into 5 tarballs to keep tarballs under 0.5 GB), (2) neon_Gs0149986_samples_soilproperties_metagenomes.csv: the sample information together with the accession numbers for the underlying metagenomes and the associated soil physical and chemical measurements in NMDC (National Microbiome Data Collaborative) compliant format, (3) neon_Gs0149986.kml: location bounding box file for the sampled locations, (4) samples.csv: sample metadata file used to register Internationall Generic Sample Numbers (IGSNs), (5) flmd.csv: file level metadata file, and (6) dd.csv: data dictionary file. This work was 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↗
NASA NTRS2005

Diagnostics of Carbon Nanotube Formation in a Laser Produced Plume: Spectroscopic in situ nanotube detection using spectral absorption and surface temperature measurements by black body emission

Carbon nanotubes hold great promise for material advancements in the areas of composites and electronics. The advancement of research in these areas is dependent upon the availability of carbon nanotubes to a broad spectrum of academic and industrial researchers. Although there has been much progress made in reducing the costs of carbon nanotubes and increasing the quality and purity of the products, an increase in demand for still less expensive and specific nanotubes types has also grown. This summer's work has involved two experiments that have been designed to further the understanding of the dynamics and chemical mechanisms of carbon nanotube formation. It is expected that a better understanding of the process of formation of nanotubes will aid current production designs and stimulate ideas for future production designs increasing the quantity, quality, and production control of carbon nanotubes. The first experiment involved the measurement of surface temperature of the target as a function of time with respect to the ablation lasers. A peak surface temperature of 5000 K was determined from spectral analysis of black body emission from the target surface. The surface temperature as a function of various changes in operating parameters was also obtained. This data is expected to aid the modeling of ablation and plume dynamics. The second experiment involved a time and spatial measurement of the spectrally resolved absorbance of the laser produced plume. This experiment explored the possibility of developing absorbance and fluorescence to detect carbon nanotubes during production. To attain control over the production of nanotubes with specific properties and reduce costs, a real time in situ diagnostics method would be very beneficial. Results from this summer's work indicate that detection of nanotubes during production may possibly be used for production feed back control.

DeBoer, Gary D.↗
DOE OSTI2022

Atom-Specific Probing of Electron Dynamics in an Atomic Adsorbate by Time-Resolved X-Ray Spectroscopy

The electronic excitation occurring on adsorbates at ultrafast timescales from optical lasers that initiate surface chemical reactions is still an open question. Here, in this work, we report the ultrafast temporal evolution of x-ray absorption spectroscopy (XAS) and x-ray emission spectroscopy (XES) of a simple well-known adsorbate prototype system, namely carbon (C) atoms adsorbed on a nickel [Ni(100)] surface, following intense laser optical pumping at 400 nm. We observe ultrafast (~100 fs) changes in both XAS and XES showing clear signatures of the formation of a hot electron-hole pair distribution on the adsorbate. This is followed by slower changes on a few picoseconds timescale, shown to be consistent with thermalization of the complete C/Ni system. Density functional theory spectrum simulations support this interpretation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2001

Metalorganic Chemical Vapor Deposition of Ruthenium-Doped Diamond like Carbon Films

We investigated metalorganic precursor deposition using a Microwave Electron Cyclotron Resonance (ECR) plasma for depositing metal-doped diamondlike carbon films. Specifically, the deposition of ruthenium doped diamondlike carbon films was investigated using the decomposition of a novel ruthenium precursor, Bis(ethylcyclopentadienyl)-ruthenium (Ru(C5H4C2H5)2). The ruthenium precursor was introduced close to the substrate stage. The substrate was independently biased using an applied RF power. Films were characterized using Fourier Transform Infrared Spectroscopy (FTIR), Transmission Electron Microscopy (TEM) and Four Point Probe. The conductivity of the films deposited using ruthenium precursor showed strong dependency on the deposition parameters such as pressure. Ruthenium doped sample showed the presence of diamond crystallites with an average size of approx. 3 nm while un-doped diamondlike carbon sample showed the presence of diamond crystallites with an average size of 11 nm. TEM results showed that ruthenium was atomically dispersed within the amorphous carbon network in the films.

Sunkara, M. K.↗
NASA NTRSDate not supplied

Exploring rock-regolith interfaces in Jezero crater with Mars 2020 SHERLOC

The Perseverance rover successfully landed in Jezero crater, Mars in February 2021 at the Octavia E. Butler landing site and began its mission to explore and sample an ancient crater lake basin. Principal goals of the Mars 2020 mission include characterizing the geology of Mars and seeking signs of ancient microbial life via the spacecraft cameras and spectroscopic instruments onboard. The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) instrument is a deep UV Raman spectrometer that utilizes a 248.6nm pulsed laser. Part of SHERLOC is a color camera known as the Wide Angle Topographic Sensor for Operations and eNgineering (WATSON). The SHERLOC suite provides coordinated, spectroscopic and imaging capabilities at high spatial resolution, to detect minerals and organic molecules in microtextural context. By pairing high spatial resolution (~100 μm) resonance Raman and native fluorescence spectroscopy with microscopic imaging in a novel spacecraft capability, SHERLOC enables texture-specific molecular composition measurements of rock and regolith targets on Mars. Coordinated rock-regolith observations illuminate unique insights into weathering processes and thereby to primary properties of rocks in Jezero crater. This work describes the potential of rock-regolith interfaces to preserve unique records of geological processes in Jezero crater and can powerfully supplement observations of the more general rock record on Mars. Linking observations of local rock texture with associated regolith reveals important lithologic information based on the interrelationship between differential weathering behavior and mineralogy, grain size, and cement chemistry. Preliminary observations indicate that the polygonally fractured lithotype common near the Octavia E. Butler landing site may weather by granular disintegration and/or surface creep, a relation that can be uniquely observed at the rock-regolith interface. SHERLOC -specific observations of microtextural and elemental composition transitions presented here trace rock-regolith boundaries at multiple indurated surfaces adjacent to regolith. At these locales, grain-scale based examinations suggest chemical weathering could be related to a variably distributed coating or rind on dark rock targets that may be mafic in composition. Granule deposits overlying widely distributed fine-grained material are also observable. Mineral identifications of each phase are presented, with cross-scale comparisons to the remote insights gained by the SuperCam instrument.

Emily L. Cardarelli↗
NASA NTRS1979

Demand and supply of hydrogen as chemical feedstock in USA

Projections are made for the demand and supply of hydrogen as chemical feedstock in USA. Industrial sectors considered are petroleum refining, ammonia synthesis, methanol production, isocyanate manufacture, edible oil processing, coal liquefaction, fuel cell electricity generation, and direct iron reduction. Presently, almost all the hydrogen required is produced by reforming of natural gas or petroleum fractions. Specific needs and emphases are recommended for future research and development to produce hydrogen from other sources to meet the requirements of these industrial sectors. The data and the recommendations summarized in this paper are based on the Workshop 'Supply and Demand of Hydrogen as Chemical Feedstock' held at the University of Houston on December 12-14, 1977.

Huang, C. J.↗
DOE OSTI2023

Exceptional enhancement of mechanical properties in high-entropy alloys via thermodynamically guided local chemical ordering

Understanding the local chemical ordering propensity in random solid solutions, and tailoring its strength, can guide the design and discovery of complex, paradigm-shifting multicomponent alloys. First, we present a simple thermodynamic framework, based solely on binary enthalpies of mixing, to select optimal alloying elements to control the nature and extent of chemical ordering in high-entropy alloys (HEAs). Next, we couple high-resolution electron microscopy, atom probe tomography, hybrid Monte-Carlo, special quasirandom structures, and density functional theory calculations to demonstrate how controlled additions of Al and Ti and subsequent annealing drive chemical ordering in nearly random equiatomic face-centered cubic CoFeNi solid solution. We establish that short-range ordered domains, the precursors of long-range ordered precipitates, inform mechanical properties. Specifically, a progressively increasing local order boosts the tensile yield strengths of the parent CoFeNi alloy by a factor of four while also substantially improving ductility, which breaks the so-called strength–ductility paradox. Finally, we validate the generality of our approach by predicting and demonstrating that controlled additions of Al, which has large negative enthalpies of mixing with the constituent elements of another nearly random body-centered cubic refractory NbTaTi HEA, also introduces chemical ordering and enhances mechanical properties.

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

Metastable Clusters and Competitive Solvation Tune Ion Pairing at Liquid Interfaces

The balance of hydrophobic and hydrophilic interactions underlies emergent phenomena in complex multicomponent chemical systems. Here, we show that a supposedly ‘non–interacting’ nonpolar phase can be used to competitively solvate amphiphilic molecules at an oil/aqueous interface. This solvation, as probed by surface specific nonlinear spectroscopy and simulations, results in a molecularly thin corrugated phase boundary featuring metastable assemblies that alter the hydrogen bonding networks of water and the apparent ‘hard/soft’ descriptors used to describe ionic interactions. We show that competitive solvation enhances amphiphile mobility, opening up otherwise energetically inaccessible complexes that transiently interact with aqueous phase ions. These transient species impact ensemble binding affinities and may represent the molecular agents responsible for aspects of ionic transport and function. In conclusion, the result of this work highlights how seemingly unrelated nonpolar interactions feedback onto aqueous phase chemical phenomena, providing a pathway to tune phase separation and self-assembly to access new reaction pathways using interfaces for a range of chemical and biological systems.

Anions↗
DOE OSTI2024

PILBCP-IL Composite Ionomers for High Current Density Performance

Wide-spread commercialization of fuel cell electric vehicles using proton exchange membrane fuel cell (PEMFC) power sources requires that several existing limitations be addressed. These include: (1) a reduction in platinum (Pt) loading in the catalytic electrodes, (2) improvements in reactant and electronic mobility throughout the catalytic electrodes, (3) reduction in the reliance on materials derived from polluting “forever chemicals”, and (4) a significant improvement in the operational longevity of catalytic electrode components. In this project, a team of two universities, Drexel University and Texas A&M University, one national lab, National Renewable Energy Laboratory, and one company, General Motors, collaborated to develop a new cathode ionomer chemistry that would address these limitations and result in an improvement in performance over existing ionomer materials. The key technology developed through this collaborative project was a composite cathode ionomer encompassing an ionic liquid interlayer between Pt catalysts and a sulfonated polymerized ionic liquid block co-polymer (S-PILBCP) that possess the orthogonal properties of protonic conductivity and ionic liquid enhanced kinetics and durability (see schematic in Figure 1). The composite S-PILBCP ionomer eliminates many of the existing issues with perfluorosulfonic acid-based ionomers including active site blocking by sulfonate specific adsorption, restricted O 2 transport through ionomer films, limited humidity tolerance and active area loss for carbon pore confined catalyst particles, and use of polluting “forever chemicals”. Following successful integration of the developed composite ionic liquid into a PEMFC cathode catalyst layer, we demonstrate enhanced performance over Nafion containing cathodes with Pt/C and PtCo/C at both low and high current density. The performance with our composite S-PILBCP ionomer meets the Department of Energy (DOE) targets for light duty vehicle applications

08 HYDROGEN↗
DOE OSTI2021

Effects of non-thermal termolecular chemistry on detonation development in hydrogen (H2) / methane (CH4) - air mixtures

In combustion simulations, it is usually assumed that highly-energized collision complexes (either radical or stable species) formed in exothermic reactions are always under thermal equilibrium as they evolve through competing networks of reactions. However, in practical flames, non-trivial amounts of reactive radicals such as H, O and OH are present apart from O2. As a result, collisions of these reactive species with the energized collision complexes have been shown in recent studies to induce non-thermal reactivity. These studies have also demonstrated that such non-thermal reactions can be suitably represented in macroscopic kinetics models as chemically termolecular reactions. The present work was focused on identifying and quantifying the effects of including such reactions on the evolution of an initial deflagration front to a developing detonation in H2/CH4-air mixtures under boosted internal combustion (IC) engine conditions. Specifically, fully resolved simulations, with and without non-thermal reactivity, were performed for a constant volume reactor containing stoichiometric H2/CH4-air mixture. It was found that inclusion of termolecular chemistry resulted in a delayed onset of end-gas auto-ignition. Concurrently, the developing detonation intensity was observed to be significantly higher. Chemical explosive mode analysis (CEMA) was performed to identify the dominant species/reactions responsible for the observed variation in the results.

Detonation↗
DOE OSTI2020

Reveal of Uranium Bioremediation Mechanisms by Bacillus Species through Proteomics Studies

Radionuclides, such as Uranium (U) and heavy metals continue to pose threats to the ecosystem health and processes at the Department of Energy (DOE) managed, Savannah River Site (SRS), located along the Savannah River near Aiken, SC. Such co-contaminated environments are difficult to remediate using conventional excavation and disposal or pump-and-treat approaches. Globally, more than 109 tons of uranium contaminated areas pose a long term threat to human and ecological health. Even with presented low concentrations in the brain, central nervous effects are still observed. Uranium and depleted uranium (DU) have long term effects on the kidneys. Some small general health effects include severe headaches and breathing problems. Uranus ions are toxic to living cells because they inhibit metabolism of carbohydrates by blocking ATP binding sites. Bioremediation by microorganisms represents an alternative solution, which is advantageous because of the possibility of biosorbent regeneration, cost-effectiveness, increased metal removal and easy recovery of some valuable metals. Bacillus sp. bacterium was previously used in the bioremediation of heavy metals in coal mine run off waters of SRS. However, its ability to bioremediate uranium was unknown so far. Hence, in the present study, uranium bioremediation by Bacillus sp. bacterium was investigated. The mechanism of bioremediation was also revealed through proteomics studies. Heavy metals contamination poses a serious threat to water, soil and human health. Soil and water are contaminated due to excessive exploitation of uranium mines for generation of nuclear energy and weaponry. It is not degradable easily and persist in soil and water for a long period of time due to its long half- life. Savannah river site (SRS) is one of the uranium contaminated sites. The physical or chemical remediation techniques are costly and complex. Microbial system approaches with competent bacteria has received increased attention due to its adaptability in various environmental matrices and cost effectiveness. However, even though there are multiple suggested pathways (F1), the specific mechanisms that drive this behavior are still unclear, especially with popular microorganisms such as Bacillus species. In a previous research (Ibeanusi et al, 2003) Bacillus sp. was shown to detoxify and precipitate a variety of heavy metals in coal pile runoff waters of SRS site. Additionally, genomic analysis demonstrated that this microorganism posses multiple attributes for chemical transport regulation and metabolic pathways. U remediation occurs during the first 20 hours of exposure. During this time period, Bacillus sp. may work primarily under two mechanisms - sorption and accumulation. These two mechanisms simultaneously work to protect the microorganism from high concentrations. Figure 10 demonstrates that certain proteins are up regulated and down regulated under extreme stressful conditions. Membrane fraction proteins were up regulated. Cytosolic fraction proteins were down regulated. This protein information coincides with the adsorption behavior. Bacillus sp. is a good candidate for U remediation at various concentrations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Unveiling X-ray absorption signatures of boron nitride via first-principles simulation and machine learning

Boron nitride (BN) allotropes hold great promise in many advanced applications ranging from optical and photonic devices to energy storage and battery systems to tribological components. The diverse functionalities of this material stem from BN’s highly tunable structural and electronic properties, which are governed by the versatile boron–nitrogen bonding configurations. Exploring the structural landscape of BN can unveil novel structures possessing unique properties suited for specific applications, therefore accelerating the design of next-generation advanced functional materials. In this work, we leverage boron K-edge X-ray absorption spectroscopy (XAS) as an effective probe for local structural features and chemical environments. A total of 210 BN crystal structures are generated via analogies to the extensive array of carbon allotropes, and XAS is simulated for each unique local motif within the resulting collection of structures. A mapping between structural features and spectral signatures was established by synergizing first-principle simulations with data-driven based post-analysis approaches. Specifically, we developed a neural network model that can satisfactorily predict spectra line shapes from local structural descriptors. Toward automatic spectroscopic interpretation of any new BN structures, supervised machine learning models, trained on this structure–spectrum dataset, can accurately infer local coordination environments from simulated XAS, highlighting the strength of this unique approach of combining high-fidelity first-principles simulation and machine-learning to accelerate target design of novel BN materials via rational understanding of local structure-spectrum correlations.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers

Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.

Discrete Wavelet Transform (DWT)↗
DOE OSTI2025

High entropy alloys as catalysts: A focused review

A brief literature review of recent experimental and computational efforts on the use of high entropy alloys (HEAs) as catalysts is presented, while also sharing some perspectives and future insights. To fully broach the vast compositional possibilities of HEA materials, integrating computational modeling with high-throughput experimental synthesis and validation is necessary to accelerate their design and development. Once identified, specific HEAs can be a class of materials for the next generation of catalysts when addressing global challenges related to energy independence, commodity chemical production, environmental remediation, abating emissions, and modernized domestic supply chain resilience.

42 - ENGINEERING↗
DOE OSTI2023

Oxygen-Vacancy Abundant Nanoporous Ni/NiMnO 3 /MnO 2 @NiMn Electrodes with Ultrahigh Capacitance and Energy Density for Supercapacitors

High-performance energy storage devices (HPEDs) play a critical role in the realization of clean energy and thus enable the overarching pursuit of nonpolluting, green technologies. Supercapacitors are one class of such lucrative HPEDs; however, a serious limiting factor of supercapacitor technology is its sub-par energy density. Here, this report presents hitherto unchartered pathway of physical deformation, chemical dealloying, and microstructure engineering to produce ultrahigh-capacitance, energy-dense NiMn alloy electrodes. The activated electrode delivered an ultrahigh specific-capacitance of 2700 F/cm 3 at 0.5 A/cm 3 . The symmetric device showcased an excellent energy density of 96.94 Wh/L and a remarkable cycle life of 95% retention after 10,000 cycles. Transmission electron microscopy and atom probe tomography studies revealed the evolution of a unique hierarchical microstructure comprising fine Ni/NiMnO 3 nanoligaments within MnO 2 -rich nanoflakes. Theoretical analysis using density functional theory showed semimetallic nature of the nanoscaled oxygen-vacancy-rich NiMnO 3 structure, highlighting enhanced carrier concentration and electronic conductivity of the active region. Furthermore, the geometrical model of NiMnO 3 crystals revealed relatively large voids, likely providing channels for the ion intercalation/de-intercalation. The current processing approach is highly adaptable and can be applied to a wide range of material systems for designing highly efficient electrodes for energy-storage devices.

25 ENERGY STORAGE↗
DOE OSTI2023

Bioinspired mechanical mineralization of organogels

Mineralization is a long-lasting method commonly used by biological materials to selectively strengthen in response to site specific mechanical stress. Achieving a similar form of toughening in synthetic polymer composites remains challenging. In previous work, we developed methods to promote chemical reactions via the piezoelectrochemical effect with mechanical responses of inorganic, ZnO nanoparticles. Herein, we report a distinct example of a mechanically-mediated reaction in which the spherical ZnO nanoparticles react themselves leading to the formation of microrods composed of a Zn/S mineral inside an organogel. The microrods can be used to selectively create mineral deposits within the material resulting in the strengthening of the overall resulting composite.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Deep learning of electrochemical CO 2 conversion literature reveals research trends and directions

Large-scale and openly available material science databases are mainly composed of computer simulation results rather than experimental data. Some examples include the Materials Project, Open Quantum Materials Database, and Open Catalyst 2022. Unfortunately, building large-scale experimental databases remains challenging due to the difficulties in consolidating locally distributed datasets. In this work, focusing on the catalysis literature of CO 2 reduction reactions (CO 2 RRs), we present a machine learning (ML)-based protocol for selecting highly relevant papers and extracting important experimental data. First, we report a document embedding method (Doc2Vec) for collecting papers of greatest relevance to the specific target domain, which yielded 3154 CO 2 RR-related papers from six publishers. Next, we developed named entity recognition (NER) models to extract twelve entities related to material names (catalyst, electrolyte, etc.) and catalytic performance (Faradaic efficiency, current density, etc.). Further, among several tested models, the MatBERT-based approach achieved the highest accuracy, with an average F1-score of 90.4% and an F1-score of 95.2% in a boundary relaxation evaluation scheme. The accurate and accelerated NER-based data extraction from a large volume of catalysis literature enables temporal trend analyses of the CO 2 RR catalysts, products, and performances, revealing the potentially effective material space in CO 2 RRs. While this work demonstrates the effectiveness of our ML-based text mining methods for specifically CO 2 RR literature, the methods and approach are applicable to and may be used to accelerate the development of other catalytic chemical reactions.

36 MATERIALS SCIENCE↗