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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characterizing patterns and variability of building electric load profiles in time and frequency domains

The rapid development of advanced metering infrastructure provides a new data source—building electrical load profiles with high temporal resolution. Electric load profile characterization can generate useful information to enhance building energy modeling and provide metrics to represent patterns and variability of load profiles. Such characterizations can be used to identify changes to building electricity demand due to operations or faulty equipment and controls. In this study, we proposed a two-path approach to analyze high temporal resolution building electrical load profiles: (1) time-domain analysis and (2) frequency-domain analysis. Furthermore, the commonly adopted time-domain analysis can extract and quantify the distribution of key parameters characterizing load shape such as peak-base load ratio and morning rise time, while a frequency-domain analysis can identify major periodic fluctuations and quantify load variability. We implemented and evaluated both paths using whole-year 15-minute interval smart meter data of 188 commercial office building in Northern California. The results from these two paths are consistent with each other and complementary to represent full dynamics of load profiles. The time- and frequency-domain analyses can be used to enhance building energy modeling by: (1) providing more realistic assumptions about building operation schedules, and (2) validating the simulated electric load profiles using the developed variability metrics against the real building load data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Abstract Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ET o ), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ET o , was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Biopolymer-Templated Titania Film Formation for Nanostructured Coatings Revealed by Machine Learning-Supported Time-Resolved Analysis

This study presents a machine learning approach to derive the film formation of biopolymer-templated titania nanostructures during spray deposition, in combination with in situ grazing-incidence small-angle X-ray scattering (GISAXS). A neural network trained on synthetic GISAXS data directly predicts domain-size distributions from experimental two-dimensional scattering patterns, capturing the full kinetics of nanostructure evolution with high temporal resolution. The predictions reveal hierarchical size distributions and periodic growth features, consistent with layer-by-layer spray deposition and validated by complementary scanning electron microscopy (SEM) imaging. Quantitative comparison with conventional parametric GISAXS fits shows good qualitative agreement, with systematic differences explained by domain-shape assumptions and resolved by applying a geometric scaling factor. Simulated SEM-like surfaces derived from neural network outputs reproduce the porous, foam-like nanoscale morphology observed experimentally, reinforcing the method’s credibility. This integrated approach enables real-time, nondestructive, statistically averaged monitoring of bulk nanostructure development in functional coatings, offering a scalable methodology to accelerate the characterization and process control of sustainably manufactured nanostructured titania films for energy-related applications such as photocatalysis and photovoltaics.

Heger, JulianEliah↗
DOE OSTI2020

Machine learning analysis of perovskite oxides grown by molecular beam epitaxy

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and $\textit{k}$-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO 3 growths, heteroepitaxial SrTiO 3 grown on scandate substrates, BaSnO 3 films grown on SrTiO 3 substrates, and LaNiO 3 films grown on SrTiO 3 substrates. We report this analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control overgrowth processes and film quality.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Central alarm station interface for situation awareness

A method for analyzing security alarms is provided. The method comprising receiving alarm data from a number of security sensors generating alarms, wherein the alarm data includes spatio-temporal data regarding where and when an alarm is triggered. The alarm data is displayed on a user interface. The alarm data is analyzed for spatio-temporal relationships among the alarms according to a number of pattern recognition algorithms. The alarms are identified as genuine alarms if a discernable pattern is recognized among the alarms.

Speed, Ann E.↗
DOE OSTI2025

Longitudinal Patterns in Nitrate Revealed Through Drone‐Based Measurements in an Agriculturally Influenced Midwestern U.S. River

Nitrate concentrations in streams and rivers in the Midwestern United States are often elevated, reflecting the predominance of agriculture in the surrounding landscape. Recent advances in technology, including surface water drones and more precise sensors, provide opportunities to investigate nitrate dynamics with high spatial and temporal resolution. We deployed an aquatic drone, the AquaBOT, in a sixth-order, agriculturally influenced river to examine longitudinal patterns in water quality. Our goal was to measure the spatial and temporal heterogeneity in nitrate and nitrate removal processes and determine the influence of tributary inputs on main stem chemistry. We navigated the drone along a 12-km reach of the Des Moines River (Iowa, USA) nine times between June 2021 and August 2022. Across the deployments, mean nitrate concentration was positively related to discharge and was nearly two orders of magnitude higher in spring than summer. We observed contrasting patterns in main stem nitrate, which decreased downstream during some runs (e.g., 3.1–2.7 mg N L −1 in June 2021), demonstrating net nitrate uptake along the reach, and remained constant on other dates. Similarly, tributaries to the Des Moines had a varied influence on riverine nitrate. Tributaries either increased or decreased main stem nitrate concentrations depending on the tributary and the date. Nitrate removal rates were spatially and temporally variable but showed some consistency at the subreach (2 km) scale, with two subreaches often showing elevated rates of nitrate removal across dates. In conclusion, our study reveals nuanced heterogeneity in nitrate dynamics of the Des Moines River despite the homogeneity of agricultural land cover in the watershed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaporation of a Reactive Nanofluid Sessile Drop: Capturing Rapid Emergence of Surface Crystals with In Situ Synchrotron X-ray Diffraction

Mechanisms for surface pattern formation from evaporation of a reactive nanofluid sessile drop are not well understood. In contrast to the coffee-ring effect from inert particles, rapid chemical and morphological transformation of reactive nanoparticles upon rapid evaporative drying are challenging to probe experimentally. Here, using grazing-incidence X-ray surface scattering, the nanostructure of nascent surface patterns has been probed as a ZnO nanofluid sessile drop rapidly dries. The high temporal resolution enabled by the high flux of synchrotron X-rays allows the observation of the emergence of Zn(OH) 2 surface crystals from the onset of evaporation and their rapid evolution into the final residual surface pattern, via transient layered complexes evident from the temporary appearance of X-ray diffraction peaks preceding Zn(OH) 2 formation. The results offer mechanistic insights of morphogenesis of surface patterns from evaporation-induced self-assembly and self-organization of reactive nanofluids, previously untenable using other experimental methods.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sensitive Measures of Soil Health Reveal Carbon Stability Across a Management Intensity and Plant Biodiversity Gradient

Soil carbon (C) is a major driver of soil health, yet little is known regarding how sensitive measures of soil C shift temporally within a single growing season in response to short-term weather perturbations. Our study aimed to i) Examine how long-term management impacts soil C cycling and stability across a management intensity and plant biodiversity gradient and ii) Assess how sensitive soil health indicators change temporally over the course of a single growing season in response to recent weather patterns. Here we quantify a variety of sensitive soil C measures at four time points across the 2021 growing season at the W.K. Kellogg Biological Station’s Long Term Ecological Research Trial (LTER) located in southwest Michigan, USA. The eight systems sampled included four annual soybean ( Glycine max ) systems that ranged in management intensity (conventional, no-till, reduced input, and biologically-based), two perennial biofuel cropping systems (switchgrass ( Panicum virgatum) and hybrid poplars ( Populus nigra x P.maximowiczii )), and two unmanaged systems (early successional system and a mown but never tilled grassland). We found that unmanaged systems with increased perenniality enhanced mineralizable C (Min C) and permanganate oxidizable C (POXC) values. Additionally, all soil health indicators were found to be sensitive to changes in short-term weather perturbations over the course of the growing season. The implications of this study are threefold. First, this study assess indicators of labile and stable C pools over the course of the growing season and reflects the stability of soil C in different systems. Second, POXC, Min C, and ß-glucosidase (GLU) activity are sensitive soil health indicators that fluctuate temporally, which means that these soil health indicators could help elucidate the impact that weather patterns have on soil C dynamics. Lastly, for effective monitoring of soil C, sampling time and frequency should be considered for a comprehensive understanding of soil C cycling within a system.

Martin, Tvisha↗
DOE OSTI2020

Varying Contributions of Drivers to the Relationship Between Canopy Photosynthesis and Far‐Red Sun‐Induced Fluorescence for Two Maize Sites at Different Temporal Scales

Abstract Sun‐induced fluorescence (SIF) has been found to be strongly correlated with gross primary production (GPP) in a quasi‐linear pattern at the scales beyond leaves. However, the causes of the GPP:SIF relationship deviating from a linear pattern remain unclear. In the current study conducted at two maize sites in Nebraska in 2017 summer growing season, we investigated the relationship between GPP and SIF at 760 nm (F 760 ) at two temporal scales and quantified the contributions of incoming photosynthetically active radiation (PAR in ), fraction of absorbed PAR (fPAR), light use efficiency (LUE), and F 760 yield (F 760,y , defined as F 760 /(PAR in ×fPAR)) to GPP and F 760 variabilities to further understand the linearity and deviations in the GPP:F 760 relationship. We found the following: (1) For individual growth stages when canopy structure and chlorophyll content were stable, GPP and F 760 were strongly controlled by PAR in , while LUE and F 760,y had much lower contributions to the GPP:F 760 relationship; during this period, LUE and F 760,y had either a slightly negative or no clear relationship, which explained some deviations in the GPP:SIF relationship. (2) At the seasonal scale, the contribution of LUE to GPP variability as well as the contribution of F 760,y to F 760 variability increased and was comparable to the contribution of PAR in ; the LUE:F 760,y relationship showed a strong linear relationship, which strengthened the linear GPP:F 760 relationship. Both maize sites showed similar patterns. A framework was applied to estimate LUE at individual stages and as a result, significantly improved the GPP estimation, thus enhancing the SIF potential for inferring photosynthesis.

Miao, Guofang↗
DOE OSTI2024

Approximation of ice phenology of Maine lakes using Aqua MODIS surface temperature data

Studies of lake ice phenology have historically relied on limited in situ data. Relatively few observations exist for ice out and fewer still for ice in, both of which are necessary to determine the temporal extent of ice cover. Satellite data provide an opportunity to better document patterns of ice phenology across landscapes and relate them to the climatological drivers behind changing ice phenology. We developed a model, the Cumulative Sum Method (CSM), that uses daytime and nighttime surface temperature observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on board the Earth-observing Aqua satellite to approximate ice in (the onset of ice cover) and ice out from training datasets of 13 and 58 Maine lakes, respectively, during the 2002/2003 through 2017/2018 ice seasons. Ice in was signaled by reaching a threshold of cumulative negative degrees following the first day of the season below 0°C. Ice out was signaled by reaching a threshold of cumulative positive degrees following the first day of the year above 0°C. The comparison of observed and remotely sensed ice-in dates showed relative agreement with a correlation coefficient of 0.71 and a mean absolute error (MAE) of 9.8 days. Ice-out approximations had a correlation coefficient of 0.67 and an MAE of 8.8 days. Lakes smaller in surface area and nearer the Atlantic coast had the greatest error in approximation. Application of the CSM to 20 additional lakes in Maine produced a comparable ice-out MAE of 8.9 days. Ice-out model performance was weaker for the warmest years; there was a larger MAE of 12.0 days when the model was applied to the years 2019–2023 for the original 58 lakes. The development of this model, which utilizes daily satellite data, demonstrates the promise of remote sensing for quantifying ice phenology over short, temporal scales, and wider geographic regions than can be observed in situ, and allows exploration of the influence of surface temperature patterns on the process and timing of ice in and ice out.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Fuzzy cyber detection pattern matching

Mechanisms for identifying a pattern of computing resource activity of interest, in activity data characterizing activities of computer system elements, are provided. A temporal graph of the activity data is generated and a filter is applied to the temporal graph to generate one or more first vector representations, each characterizing nodes and edges within a moving window defined by the filter. The filter is applied to a pattern graph representing a pattern of entities and events indicative of the pattern of interest, to generate a second vector representation. The second vector representation is compared to the one or more first vector representations to identify one or more nearby vectors, and one or more corresponding subgraph instances are output to an intelligence console computing system as inexact matches of the temporal graph.

Shu, Xiaokui↗
DOE OSTI2021

Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop

Abstract Background Despite their widespread distribution and ecological importance, protists remain one of the least understood components of the soil and rhizosphere microbiome. Knowledge of the roles that protists play in stimulating organic matter decomposition and shaping microbiome dynamics continues to grow, but there remains a need to understand the extent to which biological and environmental factors mediate protist community assembly and dynamics. We hypothesize that protists communities are filtered by the influence of plants on their rhizosphere biological and physicochemical environment, resulting in patterns of protist diversity and composition that mirror previously observed diversity and successional dynamics in rhizosphere bacterial communities. Results We analyzed protist communities associated with the rhizosphere and bulk soil of switchgrass (SG) plants ( Panicum virgatum ) at different phenological stages, grown in two marginal soils as part of a large-scale field experiment. Our results reveal that the diversity of protists is lower in rhizosphere than bulk soils, and that temporal variations depend on soil properties but are less pronounced in rhizosphere soil. Patterns of significantly prevalent protists groups in the rhizosphere suggest that most protists play varied ecological roles across plant growth stages and that some plant pathogenic protists and protists with omnivorous diets reoccur over time in the rhizosphere. We found that protist co-occurrence network dynamics are more complex in the rhizosphere compared to bulk soil. A phylogenetic bin-based null model analysis showed that protists’ community assembly in our study sites is mainly controlled by homogenous selection and dispersal limitation, with stronger selection in rhizosphere than bulk soil as SG grew and senesced. Conclusions We demonstrate that environmental filtering is a dominant determinant of overall protist community properties and that at the rhizosphere level, plant control on the physical and biological environment is a critical driver of protist community composition and dynamics. Since protists are key contributors to plant nutrient availability and bacterial community composition and abundance, mapping and understanding their patterns in rhizosphere soil is foundational to understanding the ecology of the root-microbe-soil system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Environmental control on the distribution of metabolic strategies of benthic microbial mats in Lake Fryxell, Antarctica

Ecological theories posit that heterogeneity in environmental conditions greatly affects community structure and function. However, the degree to which ecological theory developed using plant- and animal-dominated systems applies to microbiomes is unclear. Investigating the metabolic strategies found in microbiomes are particularly informative for testing the universality of ecological theories because microorganisms have far wider metabolic capacity than plants and animals. We used metagenomic analyses to explore the relationships between the energy and physicochemical gradients in Lake Fryxell and the metabolic capacity of its benthic microbiome. Statistical analysis of the relative abundance of metabolic marker genes and gene family diversity shows that oxygenic photosynthesis, carbon fixation, and flavin-based electron bifurcation differentiate mats growing in different environmental conditions. The pattern of gene family diversity points to the likely importance of temporal environmental heterogeneity in addition to resource gradients. Overall, we found that the environmental heterogeneity of photosynthetically active radiation (PAR) and oxygen concentration ([O2]) in Lake Fryxell provide the framework by which metabolic diversity and composition of the community is structured, in accordance with its phylogenetic structure. The organization of the resulting microbial ecosystems are consistent with the maximum power principle and the species sorting model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado and New Mexico

Temporally continuous snow depth estimates are vital for understanding changing snow patterns in the Arctic and impacts on permafrost. We trained random forest machine learning models to predict snow depth from temperature data recorded at or just below the ground surface. Training data was collected at the Teller 27 Watershed and Kougarok 64 Hillslope during the 2021 - 2022 water year on the Seward Peninsula, Alaska using distributed temperature profiling (DTP) systems. We then applied this model to other sites where ground surface or shallow soil temperature data was available for at least one water year (see Related Datasets). Many of these temperature measurements were collocated with snow depth observations. Ground surface temperature (i.e. snow-ground interface temperature) is easy to measure using small, cheap and easy-to-deploy temperature sensors such as iButtons and TinyTags, and such measurements have previously been used to calculate a variety of snow metrics (e.g. snow onset date). However, this is the first study to estimate snow depth directly from ground surface temperature data. The present dataset contains one *.csv file which includes machine learning snow depth predictions at sites in Alaska, Norway, Siberia, Colorado, and New Mexico and one *.kml file including the locations of sites with snow depth predictions. No training data predictions are included in the *.csv file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Experimental and Computational Analysis of NEAMS Pebble Bed Reactors

The Advanced High-Temperature Pebble Bed Reactor concept leverages a particle-based fuel format consisting of discrete spherical graphite pebbles arrayed in a packed bed architecture. Thermal regulation achieved via flow of gas (e.g., helium) or liquid (e.g., molten salt) coolants through the void spaces between pebbles in the bed (characteristic pebble diameters: ~ 6 cm, gas cooled; ~ 3 cm, liquid cooled). This design is of interest owing to inherent advantages including passive safety features and highly efficient heat transfer characteristics that enable high power densities to be achieved. Fully leveraging these advantages requires a fundamental understanding of the complex coolant flow structure within the pebble-shaped fuel bed. Unfortunately, complexities associated with non-invasively probing the micro-scale flow phenomena within the complex network of void spaces between pebbles have hindered efforts to characterize the underlying transport phenomena. Without this fundamental foundation, it becomes challenging to rationally construct macroscopic transport models based upon volume averages of micro-scale parameters that accurately capture the observed flow behavior. Consequently, this lack of fundamental knowledge has limited previous work to coarse comparisons between theory and extremely limited experimental data. Moreover, these comparisons have been made only in terms of integral or macroscopic results. Therefore, multipoint measurements with a high level of spatial and temporal resolution are critically needed to fully map the complex flow patterns and to provide data at high spatial density to permit accurate volume averaging in the pebble bed. For this project, we performed a coordinated experimental and computational effort to quantitatively map the full-field velocity and temperature fields in the interstitial spaces within the pebble bed. The major accomplishments of this project included the important knowledge acquired for the flow mixing and heat transfer in the interstitial pore scales, the effects of flow mixing in the near-wall and far-wall regions, and the by-pass flows of pebble bed systems. The produced high-resolution experimental data (not broadly available in existing literature) were then used for the numerical studies enabling the refinement and validation of standard simulation tools (Nek5000).

42 ENGINEERING↗
DOE OSTI2022

Ionization-Based Optical Structures for the Control of Short-Pulse Lasers (Full LDRD Report)

High-power lasers require enormous beam sizes to avoid optics damage; without plasma control of light, future higher-power systems will have prohibitive costs. The laser-induced formation of ionization patterns in gases provides an opportunity to manipulate the spatial and temporal properties of femtosecond to picosecond beams with precise control at high intensity, providing a path towards compact multi-petawatt lasers. In this project, we proposed new concepts of diffractive optics elements relying on ionization-based structures, including a holographic lens and a compression grating. The theoretical characterization of these optical elements was supported by particle-in-cell simulations. Experiments have been carried out to achieve reflective elements (i.e., plasma-based gratings) that demonstrated high robustness and efficiencies on the order of 50%, which validates the feasibility of our new concepts and paves the way for future plasma-based high-power laser systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Rapid Memory Footprint Access Diagnostics

Footprint and reuse distance measure temporal locality and therefore do not capture the significance of access patterns (spacial locality). A strided access pattern has the largest possible footprint but usually has the best performance. To highlight exposed memory latency, we separate footprint into strided (prefetchable) and irregular (non-prefetchable) access components and calculate the growth rate of each. To rapidly compute these footprint access diagnostics, we present two methods, whole-program and precise. Current footprint analyses can cause 200× or more slowdown with realistic inputs and are therefore impractical. Our whole-program method reduces the overhead to 10% by computing upper bounds, but still yields inter-procedural insight through a call path profile. Our precise method uses additional static analysis and profiling to refine the upper bounds for intra-procedural loop nests. We evaluate our approaches using benchmarks that vary access patterns (strided \vs unpredictable), sparsity (all words in a cache line \vs some), and reuse (varying and repeated accesses per element). Notably, for loop nests with unpredictable accesses, the precise method's accuracy is within 10% of ideal. The whole-program method has sufficient accuracy to diagnose bottlenecks.

data locality, memory footprint, footprint access ↗
DOE OSTI2026

Transient Catalytic Reaction Analysis Through Signal Defragmentation

The Temporal Analysis of Products (TAP) pulse response technique provides valuable insights into catalytic function and reaction kinetics. However, complex fragmentation patterns in the TAP mass spectrometry signals can complicate precise quantification, particularly when analyzing transient gas flux data typical of TAP experiments. This work demonstrates a standard defragmentation method that deconvolves transient TAP signals while maintaining the temporal resolution of the experiment. First, the integrals of calibration gas fluxes are used to determine the fingerprint fragmentation pattern and construct a fragmentation matrix. This matrix is then used to defragment experimental flux data at each recorded time point via a non-negative least squares regression. The effectiveness of this method is demonstrated using virtual data and control experiments with a TAP reactor system. The defragmentation is then applied to the more complex propane dehydrogenation reaction on a chromia/alumina catalyst, which can contain up to ten significant gas species in the reactor outlet. Initial propane pulsing reveals an induction period during which propane is fully oxidized to CO2, followed by partial reduction to CO. Afterwards, there is a transition in chemistries towards coking and propylene production. Our example illustrates a practical method for the accurate determination of the time-dependent reactant/product concentrations and rates for a thorough analysis of the propane dehydrogenation kinetics. This approach can be broadly applied to any transient mass spectrometry experiment for a better understanding of catalyst-reaction dynamics.

36 - MATERIALS SCIENCE↗