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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Highly microporous Parinari Curatellifolia carbon nanomaterials for supercapacitors

High surface area nanostructured electrodes are essential for understanding charge storage mechanisms in high capacitance supercapacitors. Additionally, a growing number of studies provide credible evidence that the highest capacitance in carbon nanomaterials is attainable only when the pore size is in micropore range. In this study therefore, micropore-dominatedcarbon materials derived from Parinari curatellifolia (PC) waste seeds are prepared via pyrolysis and subsequent chemical activation with KOH. SEM, EDAX, TEM, Raman, and XRD studies show that the microstructure and composition of the as-prepared microporous carbon materials are influenced by adjusting the KOH to carbon mass ratio. Sorption studies demonstrate a very high BET surface area of 1898 m 2 g –1 , type I isotherm, and 99% micropore content in all the samples. The BET surface area, S micro , V micro , and total volume increased with increasing KOH content during activation. The fabricated electrodes exhibit high specific capacitances of 423 F g –1 at 5 mV s –1 and cyclic stability of 98% after 50000 cycles both of which are indicative of our carbon materials huge potential in making high performance supercapacitors. The specific capacitance increased with the micropore content in the samples signifying that the pores enhanced diffusion and hence the charge storage. The PC carbon materials deliver high specific capacitance due to the enhancement of diffusion and charge storage stemming from the synergetic interplay of the aforementioned textural parameters. As a result, the PC carbon materials therefore provide good prospects for supercapacitor applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

Continual learning in the presence of repetition

Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often considered in standard benchmarks for CL. Unlike with the rehearsal mechanism in buffer-based strategies, where sample repetition is controlled by the strategy, repetition in the data stream naturally stems from the environment. This report provides a summary of the CLVision challenge at CVPR 2023, which focused on the topic of repetition in class-incremental learning. The report initially outlines the challenge objective and then describes three solutions proposed by finalist teams that aim to effectively exploit the repetition in the stream to learn continually. The experimental results from the challenge highlight the effectiveness of ensemble-based solutions that employ multiple versions of similar modules, each trained on different but overlapping subsets of classes. This report underscores the transformative potential of taking a different perspective in CL by employing repetition in the data stream to foster innovative strategy design.

Class-incremental learning↗
DOE OSTI2023

Cumulative fission yields of short-lived fission products from 235 U and 239 Pu measured by HPGe gamma-ray spectroscopy

In this study, we present a preliminary investigation focused on determining cumulative fission yields for short-lived fission products. Our analysis involves examining gamma spectra from the irradiated samples of 235 U and 239 Pu using the High Flux Isotope Reactor. The motivation stems from the observed discrepancy in the antineutrino energy spectrum within the range of 5 to 7 MeV. While several hypotheses have been proposed, a thorough analysis of fission yields provides an additional way of gaining insight into this unexplained phenomenon. Our study suggests that the measured gamma rays from 100 Nb, 140 Cs and 95 Sr are consistent with the expected values. However, 93 Rb, 96 Y, 97 Y and 142 Cs cannot be quantified due to insufficient statistics, interference from other gamma rays and the Compton scattering background. Additionally, the calculated cumulative fission yields based on the measured 140 Cs and 95 Sr are found to be consistent with the JEFF3.3 fission yield library. In conclusion, the present work shows that the potential of improving gamma-ray spectroscopy in the fission yields as a means to improve our understanding of the antineutrino spectrum.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Unsupervised domain adaptation for radioisotope identification in gamma spectroscopy

Training machine learning models for radioisotope identification using gamma spectroscopy remains an elusive challenge for many practical applications, largely stemming from the difficulty of acquiring and labeling large, diverse experimental datasets. Simulations can mitigate this challenge, but the accuracy of models trained on simulated data can deteriorate substantially when deployed to an out-of-distribution operational environment. In this study, we demonstrate that unsupervised domain adaptation (UDA) can improve the ability of a model trained on synthetic data to generalize to a new testing domain, provided unlabeled data from the target domain are available. Conventional supervised techniques are unable to utilize this data because the absence of isotope labels precludes defining a supervised classification loss. Instead, we first pretrain a spectral classifier using labeled synthetic data and subsequently leverage unlabeled target data to align the learned feature representations between the source and target domains. We compare a range of different UDA techniques, finding that minimizing the maximum mean discrepancy (MMD) between source and target feature vectors yields the most consistent improvement to testing scores. For instance, using a custom transformer-based neural network, we achieved a testing accuracy of $0.904 \pm 0.022$ on an experimental LaBr test set after performing unsupervised feature alignment via MMD minimization, compared to $0.754 \pm 0.014$ before alignment. Overall, our results highlight the potential of using UDA to adapt a radioisotope classifier trained on synthetic data for real-world deployment.

Lalor, Peter W.↗
DOE OSTI2023

TRISO fuel performance analysis: Uncertainty quantification toward optimization

Tri-structural isotropic (TRISO) fuel particles are a fuel form being considered for potential use in next-generation nuclear reactors (i.e., high-temperature gas-cooled reactors). Though the TRISO fuel manufacturing process has continually advanced in recent years, particle comparisons still reveal statistical variations and uncertainties in terms of geometric configurations and material properties. Given that the physical processes ongoing in TRISO fuel particles during reactor operation are highly correlated with each other, a small degree of uncertainty in one model may lead to significant uncertainty in another. This makes appropriate uncertainty quantification of TRISO fuel particles essential. However, one may wonder about the extent to which the current version of TRISO particles has been optimized, and whether any room remains for further improvements. This paper quantifies TRISO fuel performance model uncertainties that stem from geometric and material data. For this analysis, the BISON code was used, and the Advanced Gas Reactor (AGR)-2 experiment served as a reference case. A total of 10 5 calculations was performed for the uncertainty and optimization analysis, altering the geometric and material data within their uncertainty range. Lastly, the optimization potential of TRISO particles is evaluated from a fuel performance perspective.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
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 OSTI2025

Plasmonic Ag nanocomposite phosphate glasses produced via γ-ray irradiation as reduction route

This paper reports on the impact of γ-ray irradiation (10, 100 kGy) on melt-quenched Ag + -doped phosphate glass and the effects of subsequent thermal processing leading to the production of plasmonic Ag nanocomposites. The γ-irradiated glasses were characterized alongside the pristine by differential scanning calorimetry (DSC), Raman spectroscopy, electron paramagnetic resonance (EPR) spectroscopy, optical absorption, and photoluminescence (PL) spectroscopy. DSC characterization showed consistent glass transition temperatures (T g ) before and after γ-irradiation whereas the crystallization temperatures tended to decrease with increasing γ-ray dose. However, a lack of alteration of the glass network structure was supported by Raman spectroscopy. Room temperature EPR spectra clearly showed the formation of phosphorus oxygen hole center (POHC) defects in the undoped host, in addition to another doublet likely associated with a P 3 defect. The presence of paramagnetic silver species encompassing Ag 2+ and 107/109 Ag 0 atoms was also indicated in the silver-activated glass together with POHC defects. Optical absorption spectra were also consistent with the presence of various radiation-induced centers. Further analyzing the glass absorption edge via Tauc plots suggested the formation of electron center (EC) defects in γ-irradiated samples wherein the silver-doped glass exhibited decreasing band gap energies with increasing γ-ray dose. The PL characterization showed the silver-related radio-PL effect was induced exhibiting broad band emission with two maxima around 500 and 625 nm stemming from various molecular Ag$^{x+}_{n}$ clusters. Emission decay analyses revealed that the longer wavelength emission exhibited slower decay. The highest radiation dose of 100 kGy however resulted in weaker emission and faster decay kinetics attributed to energy transfer between the luminescent silver species and POHC defects. Finally subjecting the γ-irradiated Ag-doped glasses to heat treatment near the T g at 490 °C led to the development of the surface plasmon resonance of Ag nanoparticles (NPs) and the vanishing of the Ag$^{x+}_{n}$ clusters luminescence. In conclusion, the presence of the matrix-related EC defects was deemed accountable for the thermally induced reduction and consequent precipitation of Ag NPs making the plasmonic glasses attractive for photonic applications such as nonlinear optics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evaluating MPI resource usage summary statistics

The Message Passing Interface (MPI) remains the dominant programming model for scientific applications running on today’s high-performance computing (HPC) systems. This dominance stems from MPI’s powerful semantics for inter-process communication that has enabled scientists to write applications for simulating important physical phenomena. MPI does not, however, specify how messages and synchronization should be carried out. Those details are typically dependent on low-level architecture details and the message characteristics of the application. Therefore, analyzing an application’s MPI resource usage is critical to tuning MPI’s performance on a particular platform. The result of this analysis is typically a discussion of the mean message sizes, queue search lengths and message arrival times for a workload or set of workloads. While a discussion of the arithmetic mean in MPI resource usage might be the most intuitive summary statistic, it is not always the most accurate in terms of representing the underlying data. In this paper, we analyze MPI resource usage for a number of key MPI workloads using an existing MPI trace collector and discrete-event simulator. Our analysis demonstrates that the average, while easy and efficient to calculate, is a useful metric for characterizing latency and bandwidth measurements, but may not be a good representation of application message sizes, match list search depths, or MPI inter-operation times. Additionally, we show that the median and mode are superior choices in many cases. We also observe that the arithmetic mean is not the best representation of central tendency for data that are drawn from distributions that are multi-modal or have heavy tails. Furthermore, the results and analysis of our work provide valuable guidance on how we, as a community, should discuss and analyze MPI resource usage data for scientific applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Human perception of ionizing radiation

Here, in this work, we address the question of whether humans can perceive ionizing radiation. We conducted a thorough review of the clinical and experimental literature related to ionizing radiation, with a focus on its acute effects. Specifically, we examined the three domains of X-ray perception found in animals (abdominal, olfactory, and retinal), which led us to instances of ionizing radiation-induced hearing and taste sensory phenomena in humans thus suggesting that humans can perceive X-rays across various sensory modalities via multiple mechanisms. We also analyzed literature to understand the mechanisms associated with reported symptoms, this led us to the concept of radiomodulation, an understudied modulatory effect of sub-ablative ionizing radiation doses on neurons. Based on this review of the literature we propose the hypothesis that a significant radiomodulation mechanism is the formation of reactive oxygen species from radiolysis which activates immune and sensory signal transduction mechanisms specifically related to the redox activity in TRP and K+ channels. Additionally, we find evidence to support the previous claims of perception stemming from Cherenkov radiation and ozone production which are perceived using canonical sensory modalities. Finally, for we provide a concise summary of the applications of ionizing radiation in clinical imaging and therapy, as well as prospects for future developments of radiation technologies for biomedical and fundamental research.

Ionizing radiation↗
DOE OSTI2024

Connecting radiation-driven changes in structural, thermal, and mechanical properties in several medical device polymers

Stemming from security threats and shortages in supply, a recent push has emerged to develop alternatives to radioisotope Cobalt-60 gamma radiation for sterilization of polymeric medical products, including electron beam (e-beam) and X-ray machine-based sources. However, before large-scale implementation, the effects of these non-isotope-based methods must be thoroughly investigated from several perspectives, involving their potential detrimental effects on the structural, thermal, and mechanical properties of polymers in medical devices. This paper investigates such effects in commonly used medical device polymers, including polypropylene homopolymer (PPH), polyolefin elastomer (POE), low-density polyethylene (LDPE), acrylonitrile butadiene styrene (ABS), and chlorobutyl rubber (CIIR). To connect radiation-driven changes in polymer structural and thermal properties and corresponding changes in mechanical properties, several characterization techniques were utilized, including gel permeation chromatography (GPC), Fourier transform infrared spectroscopy (FTIR), dynamic mechanical analysis (DMA) and differential scanning calorimetry (DSC). We found that the tensile strength of X-ray irradiated PPH, as well as X-ray, e-beam, and gamma irradiated CIIR decreases due to chain scission, which is corroborated by GPC and DMA. The GPC results of LDPE suggest chain scission, but interestingly without a corresponding decline in tensile strength. By comparison, POE and ABS appear to undergo further crosslinking with increasing irradiation dose, which generally strengthens them. Importantly, in the vast majority of the cases, there were small to no changes in properties upon changing radiation technologies (at a given dose), i.e., no radiation source dependence. However, in a few specific cases, radiation method dependencies did arise, for instance, in terms of the molecular weight of PPH being significantly increased upon X-ray irradiation, while the melting temperature, glass transition temperature, and elongation at break reduced compared to gamma. Overall, this study provides valuable insight into radiation-driven chemical and structural changes in polymers that produce changes in physical and functional properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Tuning gas separation performance of polyimide membranes with macrocyclic crown ether units

Membrane-based gas separation is an energy-efficient alternative to conventional thermally-driven separation processes. However, polymer membranes face the permeability-selectivity trade-off challenge, which stems from the broad size distribution of free volume voids. Here, this study reports a molecular design strategy to address this challenge through incorporating macrocyclic crown ether (CE) moieties into the backbone of Matrimid® polyimide, a commercial gas separation membrane. A series of CE-containing Matrimid®-like copolyimides were synthesized with systematically varied CE molar contents ranging from 3 to 20%. These copolyimides formed ductile, defect-free thin films suitable for membrane fabrication. Gas permeation tests revealed a non-monotonic relationship between permeability/selectivity and CE content. Notably, the copolyimide with only 5% CE demonstrated a 61% increase in CO 2 /CH 4 selectivity and a 13% increase in CO 2 permeability relative to pristine Matrimid®. Higher CE contents did not yield further performance improvements, which is likely due to the competing effects of chain packing disruption and π–π interactions among CE moieties at high content. This hypothesis was supported by wide-angle X-ray scattering (WAXS) analysis, density measurements, and fractional free volume calculations. These findings highlight the potential of macrocyclic crown ether incorporation strategies in fine tuning the microstructure of commercial polyimide gas separation membranes to surpass the traditional permeability-selectivity trade-off.

CO2 separation↗
DOE OSTI2022

Rheological effects of moisture content on the anatomical fractions of loblolly pine (Pinus taeda)

Here, this study focused on assessing the influence of moisture content on the rheological properties of different anatomical fractions of pine residue using the FT4 powder rheometer. Moisture content exhibited some influence on the 2 mm bark and needle fractions, increasing their cohesion, angle of internal friction, unconfined yield strength, and major principal stress. Stem and whole, on the other hand, presented minimal changes upon moisture introduction. All 4 mm fractions did not show many changes in their rheological properties with moisture introduction, which can be due to less particle-particle interactions and consequently reducing any liquid bridging the particles might undergo; however, they were shown to have a more unpredictable behavior compared to the 2 mm analogues (i.e., higher standard deviation) which can be due to a higher degree of particle-interlocking. Out of the three fractions, needle presented the poorest rheological properties for flowability in addition to being susceptible by moisture, unlike other fractions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Discrete element modeling of irregular-shaped soft pine particle flow in an FT4 powder rheometer

Pine residues are a commonly used biomass feedstock that consists of different anatomical fractions, each with distinct particle characteristics. Here, in this work, an experiment-informed discrete element model is developed to investigate the flowability of pine residues in an FT4 rheometer. Multi-sphere particles with distinct particle attributes are created to model each anatomical fraction type. A systematic analysis of specimens with varying particle characteristics (e.g., anatomical fraction type, particle shape, and size) is conducted to elucidate the relationship between particle attributes and flowability. The results show that stems recorded the highest axial force and torque and, correspondingly, the highest flow energy, which is attributed to their high stiffness and interlocking effect. Increasing their percentage in the mixture increases the flow energy while increasing needles tends to decrease flow energy. Knowledge gained in this study on the flow of anatomical fractions is important for the efficient and robust processing of pine residues.

09 BIOMASS FUELS↗
DOE OSTI2023

Recent progress in low energy neutrino scattering physics and its implications for the standard and beyond the standard model physics

Neutrinos continue to provide a testing ground for the structure of the standard model of particle physics as well as hints towards the physics beyond the standard model. Neutrinos of energies spanning over several orders of magnitude, originating in many terrestrial and astrophysical processes, have been detected via various decay and interaction mechanisms. At MeV scales, there has been one elusive process, until a few years ago, known as coherent elastic neutrino-nucleus scattering (CEvNS) that was theoretically predicted over five decades ago but was never observed experimentally. The recent experimental observation of the CEvNS process by the COHERENT collaboration at a stopped pion neutrino source has inspired physicists across many subfields. This has vital implications for nuclear physics, high-energy physics, astrophysics, and beyond. CEvNS, being a low-energy process, provides a natural window to study light, weakly-coupled, new physics in the neutrino sector. In this review, we intend to provide the current status of low energy neutrino scattering physics and its implications for the standard and beyond the standard model physics. We discuss the general formalism of calculating the tree-level CEvNS cross section and present estimated theoretical uncertainties on the CEvNS cross section stemming from different sources. We also discuss the inelastic scattering of tens of MeV neutrinos that have implications for supernova detection in future neutrino experiments. We discuss how the CEvNS experiments can be used as a testing ground for the Standard Model (SM) weak physics as well as in searching for the Beyond the Standard Model (BSM) physics signals. Any deviation from the SM predicted event rate either with a change in the total event rate or with a change in the shape of the recoil spectrum, could indicate new contributions to the interaction cross-section. The SM implications include the study of weak nuclear form factor and weak mixing angle. The BSM studies include non-standard interactions, neutrino electromagnetic properties, and sterile neutrino searches. In conclusion, stopped pion facilities are also a copious source of neutral and changed mesons that allow study of several dark sector physics scenarios such as vector portal models, leptophobic dark matter as well as axion-like particle searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Water heating energy use reductions from EPA WaterSense lavatory plumbing fittings

Hot water savings from water-efficient lavatory fittings lead to reductions in water heating energy consumption, and ultimately to decreases in carbon emissions. This paper characterizes existing and proposed approaches used to estimate hot water savings and carbon emissions reductions stemming from the U.S. Environmental Protection Agency's WaterSense program. Also described are refinements that improve the accuracy of residential hot water use percentage estimates of lavatory fittings. The authors conclude that (1) hot water percentages for showers and faucets calculated using up-to-date, publicly available national data are consistent with those found by regional studies and household-level models of water use; (2) the accuracy of heating energy savings estimates attributable to WaterSense-labeled lavatory products, as well as associated emissions reductions, can be refined by modifying the existing energy factor/uniform energy factor (EF/UEF)-based estimation approach with available data. The refined approach accounts for more nuanced conditions than the EF/UED-based approach but depends on data not always available; and (3) the approaches described and intermediate outputs can be generalized for other water conservation programs or estimating purposes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Mild chemical recycling of carbon fiber-reinforced epoxy composites in aqueous buffers and development of hydrothermally recyclable vitrimer composites from recyclates

Carbon fiber-reinforced polymer (CFRP) composites have gained widespread adoption across diverse industries. However, their inherent stability, stemming from the crosslinked structure of the matrix resin, poses a significant challenge in managing the growing volume of CFRP waste. Consequently, there is a pressing need for an eco-friendly upcycling method that effectively recovers and reuses both the valuable carbon fibers and the polymer matrix from CFRP waste. This study presents a novel approach for upcycling CFRP waste under environmentally friendly conditions. Firstly, CFRP waste with an amine-cured epoxy matrix was completely decomposed in aqueous buffer solutions under mild conditions (≤ 220 °C, pH = 4.8). Further, decomposed matrix polymer (DMP) was employed to transform conventional epoxy resins into recyclable vitrimers. Lastly, new composites that can be hydrothermally recycled were fabricated using both DMP and recovered carbon fibers. As a result, this work proposes a novel strategy for achieving a circular economy within the CFRP industry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Stochastic multiscale modeling for quantifying statistical and model errors with application to composite materials

This paper provides a coherent and efficient computational framework for stochastic multiscale analysis of material systems in the presence of parametric uncertainties and modeling errors. Uncertainty in those model parameters that are not deduced as upscaled quantities is attributed to an uncertainty “germ”. While such parameters can appear at any scale, they are predominant at the finest analysis scale. Additional uncertainties stemming from statistical estimation, attributed to lack of data and model error, are associated with each submodel contributing to the multiscale system. Here, a robust and efficient framework based on a generalized extended polynomial chaos expansion (gEPCE) is proposed to simultaneously propagate all these uncertainties in order to provide a probabilistic representation of specific quantities of interest (QoI). We characterize the full probability distribution of the QoI and the uncertainty in the failure probability pertaining to its tails. By combining gEPCE with kernel density estimation (KDE) and directional derivatives, we construct sensitivity measures that connect these statistical metrics of QoI to the various sources of uncertainty to assess their individual and combined impacts. An illustrative problem featuring three-point bending of a composite beam is investigated to demonstrate the presented approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A global urban heat island intensity dataset: Generation, comparison, and analysis

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky surface UHII and canopy (air temperature) UHII; (2) the estimation methods often neglect anthropogenic disturbance, introducing uncertainties in the estimated UHII. To address these issues, this study proposes a new dynamic equal-area (DEA) method that can minimize the influence of various confounding factors on UHII estimates through a dynamic cyclic process. Utilizing the DEA method and leveraging various gridded temperature data, we develop a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Further, based on these estimates, we provide a comprehensive analysis of the UHII and its trends in global cities. The UHII is found to be greater than zero in >80% of cities, with global annual average magnitudes around 1.0 °C (day) and 0.8 °C (night) for surface UHII, and close to 0.5 °C for canopy UHII. Furthermore, an interannual upward trend in UHII is observed in >60% of cities, with global annual average trends exceeding 0.1 °C/decade (day) and over 0.06 °C/decade (night) for surface UHII, and slightly surpassing 0.03 °C/decade for canopy UHII. Notably, there exists a positive correlation between the magnitude and trend of UHII, suggesting that cities with stronger UHII tend to experience faster growth in UHII. Additionally, discrepancies in UHII are found between different temperature data, stemming not only from distinctions in data types (surface or air temperature) but also from differences in data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies (with or without gap filling). Overall, our proposed method, dataset, and analysis results have the potential to provide valuable insights for future urban climate studies. The UHII dataset is publicly available at https://doi.org/10.6084/m9.figshare.24821538.

54 ENVIRONMENTAL SCIENCES↗