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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
NASA NTRS1982

Flow through the tile gaps in the Space Shuttle Thermal Protection System

The problem of predicting aerodynamic loads on the insulating tiles of the Space Shuttle Thermal Protection System (TPS) is discussed and seen to require a method for predicting pressure and mass flux in the gaps between tiles. A mathematical model of the tile-gap flow is developed based upon a slow viscous (Stokes) flow analysis and is verified against available experimental data. This model derives the tile-gap pressure field from a solution of the two-dimensional Laplace equation; the mass flux vector is then calculated from the pressure gradient. The means for incorporating this model into a lumped-parameter network analogy for porous-media flow is also given. The flow model shows tile-gap mass flux to be very sensitive to the gap width indicating a need for coupling the TPS flow and tile displacement calculations. Finally recommendations are made concerning additional analytical and experimental work to improve TPS flow predictions.

Dwoyer, D. L.↗
DOE OSTI2025

Gap-filled methane and carbon dioxide fluxes across two ecosystem states at the US-OWC AmeriFlux site (2015−2016, 2020−2022)

This dataset contains gap-filled measurements of methane flux (FCH4), net ecosystem CO2 exchange (NEE) partitioned into gross primary productivity (GPP) and ecosystem respiration (RE), as well as latent heat flux (LE) from a Great Lakes coastal freshwater wetland at the US-OWC AmeriFlux site. The dataset covers the peak growing seasons (June−September) of 2015−2016, dominated by Typha spp., and 2020−2022, characterized by floating-leaved species (lotus and water lily). These data were generated to investigate how rising water levels and vegetation shifts influence CH4 and CO2 fluxes across two distinct ecosystem states in this wetland. The dataset, provided in CSV format, includes half-hourly gap-filled flux data from June to September for 2015, 2016, 2020, 2021, and 2022. The gap-filled data refers to measurements where missing values due to instrument issues or quality control were filled using artificial neural networks (ANNs).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2012

Constraining Gamma-Ray Pulsar Gap Models with a Simulated Pulsar Population

With the large sample of young gamma-ray pulsars discovered by the Fermi Large Area Telescope (LAT), population synthesis has become a powerful tool for comparing their collective properties with model predictions. We synthesised a pulsar population based on a radio emission model and four gamma-ray gap models (Polar Cap, Slot Gap, Outer Gap, and One Pole Caustic). Applying gamma-ray and radio visibility criteria, we normalise the simulation to the number of detected radio pulsars by a select group of ten radio surveys. The luminosity and the wide beams from the outer gaps can easily account for the number of Fermi detections in 2 years of observations. The wide slot-gap beam requires an increase by a factor of 10 of the predicted luminosity to produce a reasonable number of gamma-ray pulsars. Such large increases in the luminosity may be accommodated by implementing offset polar caps. The narrow polar-cap beams contribute at most only a handful of LAT pulsars. Using standard distributions in birth location and pulsar spin-down power (E), we skew the initial magnetic field and period distributions in a an attempt to account for the high E Fermi pulsars. While we compromise the agreement between simulated and detected distributions of radio pulsars, the simulations fail to reproduce the LAT findings: all models under-predict the number of LAT pulsars with high E , and they cannot explain the high probability of detecting both the radio and gamma-ray beams at high E. The beaming factor remains close to 1.0 over 4 decades in E evolution for the slot gap whereas it significantly decreases with increasing age for the outer gaps. The evolution of the enhanced slot-gap luminosity with E is compatible with the large dispersion of gamma-ray luminosity seen in the LAT data. The stronger evolution predicted for the outer gap, which is linked to the polar cap heating by the return current, is apparently not supported by the LAT data. The LAT sample of gamma-ray pulsars therefore provides a fresh perspective on the early evolution of the luminosity and beam width of the gamma-ray emission from young pulsars, calling for thin and more luminous gaps.

Pierbattista, Marco↗
NASA NTRS1973

Thermal protection system gap heating rates of the Rockwell International flat plate heat transfer model (OH2A/OH2B)

Heat transfer data for the Rockwell International Flat Plate Thermocouple Model are presented. The model simulated the Space Shuttle Vehicle Thermal Protection System. Data were recorded for locations in and around various size gaps for various gap orientation configurations. The test was conducted at Mach 5.1 for free-stream Reynolds number per foot values from 500,000 to 1,500,000.

Foster, T. F.↗
NASA NTRS2008

Intelligent Devices - Sensors and Actuators - A KSC Perspective

The primary objective of this workshop is to identify areas of advancement in sensor measurements and technologies that will help to define standard practices and procedures that will better enable the infusion into flight programs of sensors with improved capabilities but limited or no flight heritage. These standards would be crucial to demonstrating a methodology for validating current models while also creating the possibility of being able to have sufficient data to either update these models (e. g., spatial or temporal resolution, etc.) or develop new models based on the ability to simulate the new measured physical parameters. The workshop is also intended to narrow the gap between sensor measurements (and techniques), data processing techniques and the ability to make use of that data by gathering together experts in the field for a short workshop. This collaboration will unite NASA and other government agencies with contractor capabilities industry-wide to prevent duplication, spawn synergistic growth in sensor technology, help analysts make good engineering decisions and help focus new sensor maturation efforts to better meet future flight program customers' needs. This is the first such workshop designed to specifically address establishing a standardized protocol/methodology for demonstrating the technology readiness of non-flight heritage sensor systems. While other similar workshops are held covering many areas of interest to the sensor development community, no other meeting is specific enough to address this vital but often overlooked topic. By encouraging cross-fertilization of ideas from instrument experts from many different backgrounds, it is hoped that this workshop will initiate innovative new ideas and concepts in sensor development, calibration and validation. It is anticipated this workshop will repeat periodically as needed.

Mata, Carlos T.↗
DOE OSTI2025

HPDR: High-Performance Portable Scientific Data Reduction Framework

The rapid growth in scientific data generation is outpacing advancements in computing systems necessary for efficient storage, transfer, and analysis, particularly in the context of exascale computing. With the deployment of first-generation exascale computing systems and next-generation experimental facilities, this gap is widening and necessitates effective data reduction techniques to manage enormous data volumes. Over the past decade, various data reduction methods, including lossless compression, error-controlled lossy compression, and data refactoring, have been developed to accelerate I/O in scientific workflows. Despite significant reductions in data volume, these methods introduce considerable computational overhead, which can become the new bottleneck in data processing. To mitigate this, GPU-accelerated data reduction algorithms have been introduced. However, challenges remain in their integration into exascale workflows, including limited portability across different GPU architectures, substantial memory transfer overhead, and reduced scalability on dense multi-GPU systems. To address these challenges, we propose HPDR, a high-performance and portable data reduction framework. HPDR is designed to enable the execution of state-of-the-art reduction algorithms across diverse processor architectures while reducing memory transfer overhead to 2.3 % of the original, resulting in up to 3.5× faster throughput compared to existing solutions. It also achieves up to 96% of the theoretical speedup in multi-GPU settings. In addition, evaluations on accelerating I/O operations at scale up to 1,024 nodes of the Frontier supercomputer demonstrate that HPDR can achieve up to 103 TB/s reduction throughput, providing up to 4× acceleration in parallel I/O performance compared to existing data reduction routines. This work highlights the potential of HPDR to significantly enhance data reduction efficiency in exascale computing environments.

Chen, Jieyang [University of Oregon]↗
NASA NTRS2016

Results of the Lunar Exploration Analysis Group (LEAG) Gap Review: Specific Action Team (SAT), Examination of Strategic Knowledge Gaps (SKGs) for Human Exploration of the Moon

The Lunar Exploration Analysis Group (LEAG) was tasked by the Human Exploration Operations Mission Directorate (HEOMD) to establish a Specific Action Team (SAT) to review lunar Strategic Knowledge Gaps (SKGs) within the context of new lunar data and some specific human mission scenarios. Within this review, the SAT was to identify the SKGs that have been fully or partially retired, identify new SKGs resulting from new data and observations, and review quantitative descriptions of measurements that are required to fill knowledge gaps, the fidelity of the measurements needed, and if relevant, provide examples of existing instruments or potential missions capable of filling the SKGs.

Shearer, C. K.↗
DOE OSTI2025

Detailed simulations of the first deuterium-tritium-filled double shell implosions on the National Ignition Facility

The first indirectly driven, liquid DT-filled double shell inertial confinement fusion (ICF) implosions have recently been successfully performed on the National Ignition Facility (NIF). Double shells are a class of alternative designs that use a low-Z outer shell to compress a foam cushion that accelerates a high-Z inner shell to efficiently compress a liquid DT core. Double shells are challenging to fabricate, field, and model. Important engineering features enabling double shell fabrication include a fill-tube penetrating all shells and a carefully designed and very narrow (few μm) step-joint in the ablator. Due to the higher density materials involved, high Atwood number instabilities are also important at many material interfaces. In this paper, numerical simulations of double shell implosions using the Los Alamos National Laboratory multi-physics radiation-hydrodynamics code xRAGE will be discussed. An extensive effort has been under way for several years to develop the code capabilities for ICF simulations in a common modeling framework to allow ease of simulation setup and standardization of the computational methodology. This paper will present a wide range of simulation results capturing, quantifying, and comparing the impact of all these degradation mechanisms on implosion performance. Brief comparisons with recent experimental results and suggestions for future improvements will also be discussed. Our results suggest that capsule surface roughness and the step-joint gap have the largest impact on implosion performance. Initial experimental data may suggest that the sensitivity to the step-joint gap could provide the dominant explanation for DT-filled double shell experiments that have been fielded on NIF thus far.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Advancements and opportunities to improve bottom–up estimates of global wetland methane emissions

Wetlands are the single largest natural source of atmospheric methane (CH 4 ), contributing approximately 30% of total surface CH 4 emissions, and they have been identified as the largest source of uncertainty in the global CH 4 budget based on the most recent Global Carbon Project CH 4 report. High uncertainties in the bottom–up estimates of wetland CH 4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH 4 emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH 4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH 4 emissions, as well as how they are used in top–down inversions. These bottom–up estimates, using (1) empirical biogeochemical modeling (e.g. WetCHARTs: 125–208 TgCH 4 yr -1 ); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH 4 yr -1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH 4 yr -1 ). Bottom–up estimates are subject to significant uncertainties (~80 Tg CH 4 yr -1 ), and the ranges of different estimates do not overlap, further amplifying the overall uncertainty when combining multiple data products. These substantial uncertainties highlight gaps in our understanding of wetland CH 4 biogeochemistry and wetland inundation dynamics. Major tropical and arctic wetland complexes are regional hotspots of CH 4 emissions. However, the scarcity of satellite data over the tropics and northern high latitudes offer limited information for top–down inversions to improve bottom–up estimates. Recent advances in surface measurements of CH 4 fluxes (e.g. FLUXNET-CH 4 ) across a wide range of ecosystems including bogs, fens, marshes, and forest swamps provide an unprecedented opportunity to improve existing bottom–up estimates of wetland CH 4 estimates. We suggest that continuous long-term surface measurements at representative wetlands, high fidelity wetland mapping, combined with an appropriate modeling framework, will be needed to significantly improve global estimates of wetland CH 4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH 4 observations directed at wetlands.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2010

Use of NASA Satellite Data to Improve Coastal Cypress Forest Management

Problem: Information gaps exist regarding health status and location of cypress forests in coastal Louisiana (LA). Such information is needed to aid coastal forest conservation and restoration programs. Approach to Issue Mitigation: Use NASA data to revise cypress forest cover type maps. Landsat and ASTER data. Use NASA data to identify and track cypress forest change. Landsat, ASTER, and MODIS data. Work with partners and end-users to transfer useful products and technology.

Spurce, Joseph↗
NASA NTRS2009

Human Research Program Human Health Countermeasures Element Bone and Muscle Risk Standing Review Panel (SRP)

The Bone and Muscle Risk Standing Review Panel (SRP) met at the NASA Johnson Space Center (JSC) on October 4-6, 2009 to discuss the areas of current and future research targeted by the Human Health Countermeasures (HHC) Element of the Human Research Program (HRP). Using evidence-based knowledge as a background for identified risks to astronaut health and performance, NASA had identified gaps in knowledge to address those risks. Ongoing and proposed tasks were presented to address the gaps. The charge to the Bone and Muscle Risk SRP was to review the gaps, evaluate whether the tasks addressed these gaps and to make recommendations to NASA s HRP Science Management Office regarding the Panel's review. The Bone and Muscle Risk SRP consisted of scientists who are experts in muscle, bone, or both and could evaluate the existing evidence with sufficient knowledge of the potential effects of long duration exposure to microgravity. More important, although expertise in basic science is important, the SRP was requested to evaluate the practicality of the proposed efforts in light of the realistic demands placed on the HRP. In short, all tasks presented in the Integrated Research Plan (IRP) should address specific questions related to the challenges faced by the astronauts as a result of prolonged exposure to microgravity. All tasks proposed to fill the gaps in knowledge should provide applied, translational data necessary to answer the specific questions. Several presentations were made to the SRP during the site visit and the SRP spent sufficient time to address the panel charge, either as a group or in separate sessions for the Bone and Muscle Risk subgroups. The SRP made a final debriefing to the HRP Program Scientist, Dr. John B. Charles, on October 6, 2009. Taking the evidence and identified risks as givens, the SRP concluded that 1) integration of information should lead to a more comprehensive approach to identifying the gaps, 2) not all tasks addressed the gaps as stated in the IRP, 3) better access should be given to the SRP to existing data to include in its review, and 4) there were some missing gaps and tasks. As a result the SRP recommended 1) combining certain gaps, which in some cases were addressed with identical tasks, 2) additional gaps and tasks to address some of the gaps, 3) deleting some gaps and tasks to better focus the efforts of NASA s HRP in prioritizing their efforts, and 4) prioritizing gaps to address significant issues needing resolution in the short term while maintaining an awareness of long-term goals. The SRP commended the efforts by the HHC Element to integrate knowledge gaps and design tasks to address the higher order questions. This integration did not apply to the Bone and Muscle Risk alone, but the musculoskeletal system as its functions integrate with issues in

Glowacki, Julie↗
DOE OSTI2026

Sim-to-real supervised domain adaptation for radioisotope identification

Machine learning has the potential to improve the speed and reliability of radioisotope identification using gamma spectroscopy. However, meticulously labeling an experimental dataset for training is often prohibitively expensive, while training models purely on synthetic data is risky due to the domain gap between simulated and experimental measurements. In this research, we demonstrate that supervised domain adaptation can substantially improve the performance of radioisotope identification models by transferring knowledge between synthetic and experimental data domains. We consider two domain adaptation scenarios: (1) a simulation-to-simulation adaptation, where we perform multi-label proportion estimation using simulated high-purity germanium detectors, and (2) a simulation-to-experimental adaptation, where we perform multi-class, single-label classification using measured spectra from handheld lanthanum bromide (LaBr) and sodium iodide (NaI) detectors. We begin by pretraining a spectral classifier on synthetic data using a custom transformer-based neural network. After subsequent fine-tuning on just 64 labeled experimental spectra, we achieve a test accuracy of 96% in the sim-to-real scenario with a LaBr detector, far surpassing a synthetic-only baseline model (75%) and a model trained from scratch (80%) on the same 64 spectra. Furthermore, we demonstrate that domain-adapted models learn more human-interpretable features than experiment-only baseline models. Overall, our results highlight the potential for supervised domain adaptation techniques to bridge the sim-to-real gap in radioisotope identification, enabling the development of accurate and explainable classifiers even in real-world scenarios where access to experimental data is limited.

Lalor, Peter W.↗
NASA NTRS1994

Optoelectronic Tool Measures Steps And Gaps Between Tiles

Optoelectronic tool measures gaps between tiles and steps between faces of tiles. Eliminates need for difficult, tedious, time-consuming, and error-prone manual measurements. Automates measurements of steps and gaps to within 0.006 in. and records data automatically. Developed for inspecting thermal-insulation tiles on Space Shuttle. Adaptable to inspection of any other settings where steps and gaps critical.

Davis, Dick↗
NASA NTRS2005

An Improved Analysis of Forest Carbon Dynamics using Data Assimilation

There are two broad approaches to quantifying landscape C dynamics - by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques - which combine stock and flux observations with a dynamic model - improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3-year period, and include eddy flux and soil C02 efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)= -251 f 197g Cm-2 over the 3 years, compared with an estimate of -419 f 29gCm-2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5gCm-2 day-1, but the uncertainty on assimilated estimates averaged 0.47 g Cm-2 day-1, and only exceeded 0.5gC m-2 day-1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long-running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote-sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis - which might be provided indirectly by remotely sensed data - improves the analysis of NEE.

Williams, Mathew↗
NASA NTRSDate not supplied

Tracking 2020 Decreases in Carbon Dioxide Due to the COVID19 Pandemic in NASA’s GEOS Modeling system: Implications for Space-Based Carbon Monitoring

The COVID19 pandemic led to abrupt, worldwide changes in human activity and related emissions of air pollutants and greenhouse gases that are unprecedented in modern times. NASA satellites have demonstrated their ability to observe some of these impacts, particularly in relation to short-lived gases like nitrogen dioxide (NO2), which is emitted primarily from transportation. Bottom-up analyses of carbon dioxide (CO2) emissions suggest that the growth of atmospheric CO2 has also slowed, but differences are much more subtle than for NO2 because of the lifetime of atmospheric CO2 and sectoral differences in emission reductions. Estimates of CO2 from NASA’s Orbiting Carbon Observatory 2 (OCO-2) provide a unique view of COVID19 impacts, but observed changes in the column-average CO2 can be difficult to interpret because of gaps in spatial coverage. Assimilating these data into the Goddard Earth Observing System (GEOS), an integrated Earth system model with an advanced Constituent Data Assimilation System (CoDAS), helped to reveal changes in CO2 that are consistent with separate, bottom-up analyses of emissions reductions. Both indicate that from February-April of 2020, the growth in CO2 over Europe, North America, and Asia was roughly 0.3 ppm less than during the previous four years. Anomalies derived from gap-filled GEOS OCO-2 CoDAS products contribute to a joint effort by the world’s space agencies to track COVID19 impacts on the Earth. However, attribution of these changes is complicated by interannual variability in atmospheric circulation and the influence of climate on ocean and land carbon sinks. We discuss these results from the perspective of space-based carbon monitoring, which has received considerable support over the past decade from NASA. Our results demonstrate the accomplishments of current sensors and data assimilation systems, but also highlight challenges in providing high quality, low latency information to the public. In particular, understanding and attributing CO2 changes during 2020 requires year-specific information about land and ocean fluxes, which is often delayed for months or even years. We discuss current limitations and potential solutions to address these lags, which would support more reliable and timely space-based carbon monitoring.

COVID-19↗
NASA NTRS2021

Three-dimensional estimation of deciduous forest canopy structure and leaf area using multi-directional, leaf-on and leaf-off airborne lidar data

Airborne laser scanning (ALS) has been widely used to map gap probability and leaf area index (LAI) distribution at plot and landscape scales. As an indirect measurement, most ALS methods to estimate LAI combine waveform or point density information with supporting field measurements such as the leaf angle distribution, gap probability, or direct LAI measures. The development of a more independent estimation approach would facilitate more widespread use of existing ALS data to investigate patterns of forest structure and build realistic 3-D vegetation scenes to simulate remote sensing imagery and energy balance. Here, we develop a data processing workflow (named PVlad) using ALS point cloud apparent reflectance to estimate LAI and voxel-based leaf area density (LAD), aiming to reduce the need for associated field measurements such as the gap probability. The adaptation of the path volume (PV) concept derived from apparent reflectance integrates information from multi-directional ALS pulses, and quantifies the percentage exploration of each voxel for classification and occlusion correction, such that rigorous volumetric sampling approaches can be developed to derive LAI and LAD. The PVlad workflow was applied to discrete-return lidar data (Riegl VQ480i) acquired by NASA Goddard's LiDAR, Hyperspectral and Thermal Imager (G-LiHT) Airborne Imager during leaf-on (summer) and leaf-off (spring) conditions at the Smithsonian Environmental Research Center (SERC). The estimates of LAI and LAD captured structural differences between mature, logged, and intermediate-aged stands over eight deciduous forest plots. The derived LAI values were compared to field litter collection measurements, and the derived LAD vertical distribution was compared to the output of the VoxLAD model using terrestrial laser scan (TLS) field survey data. Using voxel sizes ranging from 0.5 m to 5 m, overall LAI estimation showed linear fitting coefficient bias and for 1 and 2 m voxel sizes, and vertical LAD distribution showed strong correlation with and for 0.5 and 1m voxel sizes. For every forest stand, upper-canopy LAD had a low variance for voxel sizes of ≤ . Application of PVlad to the G-LiHT and other similar ALS data archives enables the development of fine-resolution LAI map products, including voxelization of LAD for ecosystem science and radiative transfer simulations of remote sensing imagery or surface energy balance.

Tiangang Yin↗
NASA NTRS1986

Analysis of Jovian decametric data: Study of radio emission mechanisms

Catalogues of approx. 200 decametric arcs and approx. 200 gaps between arcs were studied, in an effort to reconcile the data with predictions for the model wherein reflections of Io-induced currents each emit in a conical pattern and yield a distinct radio arc. The most recent interpretations of this data suggest that these Io-produced Alfven waves persist for at least one or two passages of Io, and that the emission cone half angles are approx. 40 to 90 deg., varying from arc to arc. Below 1.2 MHz it was discovered that Jupiter emits radiation strongly modulated in frequency with periods of approx. 200 kHz; this quasi-sinusoidal emission (MSA) can shift more than 180 deg. in phase over periods of 6 seconds, although these shifts are usually much smaller. MSA is not strongly correlated with the longitudes of Io or Jupiter, and typically occurs in patches covering approx. 500 kHz or more for periods of a few minutes. Furthermore, this modulation sometimes resembles a train of impulses in frequency with exponential decays toward high frequencies. Comparison of these results with the previous studies of V-shaped S-bursts is suggestive of an emission mechanism.

Staelin, D. H.↗
DOE OSTI2024

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗