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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Robotics for HVAC applications: A critical review and future perspectives

Recent advances in artificial intelligence (AI), enhanced computational capabilities, and innovations in sensors and hardware have driven the increasing development and application of robots in heating, ventilation, and air conditioning (HVAC) systems. We selected and reviewed 101 studies published between 2005 and 2025, sourced from IEEE Xplore, Scopus, Web of Science, and the ACM Digital Library. To analyze these works, we developed a five-dimensional analytical framework (morphology, sensing, navigation, task execution, and system integration), inspired by the Springer Handbook of Robotics and tailored specifically for robotic applications in HVAC. Based on the reviewed studies, six distinct tasks spanning the entire HVAC lifecycle have been identified. Among the six tasks, inspection and maintenance dominate (59 %), followed by indoor monitoring and auditing (21 %), whereas leakage detection, comfort support, and installation/retrofit remain less explored. To address the identified gaps, this review proposes future research directions including investigating robot-aware HVAC design principles, developing multimodal HVAC sensing and data fusion techniques, enhancing robot training and hardware capabilities, and expanding robotic applications beyond Maintenance and Operations (M&O). The findings from this review inform future robotics research for HVAC applications and ultimately enhance system affordability, energy efficiency, resilience or reliability, and occupant environmental comfort. Moreover, it seeks to inspire researchers to explore the intersections of robotics, computer science, building science, and HVAC engineering fostering advancements in this multidisciplinary field.

AI↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Ten questions concerning low-cost indoor air quality sensors: Perspectives from research and practice

Low-cost indoor air quality (IAQ) sensors are increasingly being used in homes and commercial and public buildings, driven by growing concerns about the impact of air on health, cognitive performance, and occupant wellbeing. These sensors offer a potentially transformative opportunity to increase spatial and temporal coverage of IAQ monitoring at a fraction of the cost of conventional reference instruments. However, their widespread use raises questions around accuracy, calibration, placement, data handling and interpretation, and integration into existing standards and workflows. This paper presents ten critical questions concerning the use of low-cost IAQ sensors in buildings, drawing on the latest empirical research, field deployments, and emerging practice. It discusses potential frameworks for deployment and evaluation, examines current sensor capabilities for measuring common pollutants, identifies methodological gaps in validation and uncertainty quantification, and outlines the extent to which existing IAQ standards can accommodate sensor-based evidence. The paper also explores how monitoring needs and deployment models vary by building type, the potential of real-time IAQ data to support building operations, and the ethical and legal implications of widespread sensor use. While significant challenges remain in ensuring data quality and building stakeholder trust, new applications are emerging through open data initiatives and advances in analytics and visualization. As the technology, science, and standards co-evolve, low-cost IAQ sensors are poised to become integral to routine building operation, building science, and environmental health research.

Parkinson, Thomas↗
DOE OSTI2023

Impact of phenanthrene co-administration on the toxicokinetics of benzo[a]pyrene in humans. UPLC-accelerator mass spectrometry following oral microdosing

Current risk assessments for environmental carcinogens rely on animal studies utilizing doses orders of magnitude higher than actual human exposures. Epidemiological studies of people with high exposures (e.g., occupational) are of value, but rely on uncertain exposure data. In addition, exposures are typically not to a single chemical but to mixtures, such as polycyclic aromatic hydrocarbons (PAHs). The extremely high sensitivity of accelerator mass spectrometry (AMS) allows for dosing humans with known carcinogens with de minimus risk. In this study UPLC-AMS was used to assess the toxicokinetics of [ 14 C]-benzo[a]pyrene ([ 14 C]-BaP) when dosed alone or in a binary mixture with phenanthrene (Phe). Plasma was collected for 48 h following a dose of [ 14 C]-BaP (50 ng, 5.4 nCi) or the same dose of [ 14 C]-BaP plus Phe (1250 ng). Following the binary mixture, C max of [ 14 C]-BaP significantly decreased (4.4-fold) whereas the volume of distribution (V d ) increased (2-fold). Further, the toxicokinetics of twelve [ 14 C]-BaP metabolites provided evidence of little change in the metabolite profile of [ 14 C]-BaP and the pattern was overall reduction consistent with reduced absorption (decrease in C max ). Although Phe was shown to be a competitive inhibitor of the major hepatic cytochrome P-450 (CYP) responsible for metabolism of [ 14 C]-BaP, CYP1A2, the high inhibition constant (K i ) and lack of any increase in unmetabolized [ 14 C]-BaP in plasma makes this mechanism unlikely to be responsible. Rather, co-administration of Phe reduces the absorption of [ 14 C]-BaP through a mechanism yet to be determined. Furthermore, this is the first study to provide evidence that, at actual environmental levels of exposure, the toxicokinetics of [ 14 C]-BaP in humans is markedly altered by the presence of a second PAH, Phe, a common component of environmental PAH mixtures.

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

Multi-scale signaling and tumor evolution in high-grade gliomas

Although genomic anomalies in glioblastoma (GBM) have been well studied for over a decade, its 5-year survival rate remains lower than 5%. We seek to expand the molecular landscape of high-grade glioma, composed of IDH-wildtype GBM and IDH-mutant grade 4 astrocytoma, by integrating proteomic, metabolomic, lipidomic, and post-translational modifications (PTMs) with genomic and transcriptomic measurements to uncover multi-scale regulatory interactions governing tumor development and evolution. Applying 14 proteogenomic and metabolomic platforms to 228 tumors (212 GBM and 16 grade 4 IDH-mutant astrocytoma), including 28 at recurrence, plus 18 normal brain samples and 14 brain metastases as comparators, reveals heterogeneous upstream alterations converging on common downstream events at the proteomic and metabolomic levels and changes in protein-protein interactions and glycosylation site occupancy at recurrence. Recurrent genetic alterations and phosphorylation events on PTPN11 map to important regulatory domains in three dimensions, suggesting a central role for PTPN11 signaling across high-grade gliomas.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types

Characterizing the tissue-specific binding sites of transcription factors (TFs) is essential to reconstruct gene regulatory networks and predict functions for non-coding genetic variation. DNase-seq footprinting enables the prediction of genome-wide binding sites for hundreds of TFs simultaneously. Despite the public availability of high-quality DNase-seq data from hundreds of samples, a comprehensive, up-to-date resource for the locations of genomic footprints is lacking. Here, we develop a scalable footprinting workflow using two state-of-the-art algorithms: Wellington and HINT. We apply our workflow to detect footprints in 192 ENCODE DNase-seq experiments and predict the genomic occupancy of 1,515 human TFs in 27 human tissues. We validate that these footprints overlap true-positive TF binding sites from ChIP-seq. We demonstrate that the locations, depth, and tissue specificity of footprints predict effects of genetic variants on gene expression and capture a substantial proportion of genetic risk for complex traits.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Heterotropic roles of divalent cations in the establishment of allostery and affinity maturation of integrin αXβ2

Allosteric activation and silencing of leukocyte β2-integrins transpire through cation-dependent structural changes, which mediate integrin biosynthesis and recycling, and are essential to designing leukocyte-specific drugs. Stepwise addition of Mg 2+ reveals two mutually coupled events for the αXβ2 ligand-binding domain—the αX I-domain—corresponding to allostery establishment and affinity maturation. Electrostatic alterations in the Mg 2+ -binding site establish long-range couplings, leading to both pH- and Mg 2+ -occupancy-dependent biphasic stability change in the αX I-domain fold. The ligand-binding sensorgrams show composite affinity events for the αX I-domain accounting for the multiplicity of the αX I-domain conformational states existing in the solution. On cell surfaces, increasing Mg 2+ concentration enhanced adhesiveness of αXβ2. This work highlights how intrinsically flexible pH- and cation-sensitive architecture endows a unique dynamic continuum to the αI-domain structure on the intact integrin, thereby revealing the importance of allostery establishment and affinity maturation in both extracellular and intracellular integrin events.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhanced pozzolanic reactivity in hydrogen-form zeolites as supplementary cementitious materials

Pozzolans rich in silica and alumina react with lime to form cementing compounds and are incorporated into portland cement as supplementary cementitious materials (SCMs). However, pozzolanic reactions progress slower than portland cement hydration, limiting their use in modern construction due to insufficient early-age strength. Hence, alternative SCMs that enable faster pozzolanic reactions are necessary including synthetic zeolites, which have high surface areas and compositional purity that indicate the possibility of rapid pozzolanic reactivity. Synthetic zeolites with varying cation composition (Na-zeolite, H-zeolite), SiO 2 /Al 2 O 3 ratio, and framework type were evaluated for pozzolanic reactivity via Ca(OH) 2 consumption using ion exchange and in-situ X-ray diffraction experiments. Na-zeolites exhibited limited exchange reactions with KOH and Ca(OH) 2 due to the occupancy of acid sites by Na + and hydroxyl groups. Meanwhile, H-zeolites readily adsorbed K + and Ca 2+ from a hydroxide solution by exchanging cations with H + at Brønsted acid sites or cation adsorption at vacant acid sites. By adsorbing cations, the H-zeolite reduced the pH and increased Ca 2+ solubility to promote pozzolanic reactions in a system where Ca(OH) 2 dissolution/diffusion was a rate limiting factor. High H-zeolite reactivity resulted in 0.8 g of Ca(OH) 2 consumed per 1 g of zeolites after 16 h of reaction versus 0.4 g of Ca(OH) 2 consumed per 1 g of Na-zeolite. The H-zeolite modulated the pore fluid alkalinity and created a low-density amorphous silicate phase via mechanisms analogous to two-step C-S-H nucleation experiments. Finally, controlling these reaction mechanisms is key to developing next generation pozzolanic cementitious systems with comparable hydration rates to portland cement.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hydrogen Localization and Cluster Formation in α-Zr from First-Principles Investigations

We report alpha-zirconium (α-Zr) is a hydrogen atom host material used in nuclear energy and semiconducting applications due to Zr's excellent properties such as corrosion resistance, low thermal neutrons cross-section, and thermodynamically favorable interstitial sequestering of H atoms. Herein we use density functional theory simulations to systematically analyze the initial stages of H insertion in the Zr matrix, and determine optimal hydrogen localization, distribution, and clustering tendencies from a thermodynamics and kinetics perspective. Our results suggest that H occupation of tetrahedral sites is strongly favored, consistent with previous experimental and theoretical studies. Further, a cooperative effect in H localization is shown where H atoms favor clustering. The H pair is found to be the most stable by occupying tetrahedral sites with pairs at second nearest-neighbor positions (TTn2). Among the four H atom clusters, the most favorable configuration has two TTn2 pairs forming "box " or "zig-zag" shaped-cluster with the pairs separated by 3.03 or 3.25 Å, respectively. Nudged elastic band simulations suggest that the formation of TTn2 is facile and is limited by the small diffusion barrier of the monomer of less than 0.1 eV. Further, the formation routes for the 4H atom clusters are also presented. The current study can serve as a foundation to systematically model H atom clustering in metals and alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A comparison between residential relocation timing of Sydney and Chicago residents: A Bayesian survival analysis

We report that understanding households' behaviour in residential relocation timing is of great importance in the field of transport engineering and economics. This research aims to develop a residential relocation model by considering the potential dynamic impacts of other households' decisions and variables, including economic and demographic attributes, housing features, intra-household decision-making structures, travel mode choice, and other life-course attributes. A multivariate parametric survival model with both fixed and time-varying covariates is developed. To the best of the authors' knowledge, this study is the first paper in the literature of residential relocation timing to propose the use of a Bayesian model in contrast to the widely used classic frequentist approach and have conducted a discussion on its advantages. An emerging residential relocation dataset collected for two cities in Australia and the USA (Sydney and Chicago cities) has been used, which covers residence, vehicle ownership, occupation, education, economic and demographic attributes of respondents. A comprehensive comparison between the results of two cities and a comparison between two Bayesian and frequentist approaches are made. This study confirms the impact of life-course variables, intra-household decision-making behaviours, and sociodemographic attributes on home mobility. According to the model outputs, the accelerating or decelerating impact of explanatory variables on the relocation timing has been almost the same in the two cities. The Bayesian model was confirmed to have some advantages over the frequentist model, including being straightforward to interpret, availability of making inferences on the results, and ease of handling complex models, and optimisation convergence complexities.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Assessing the hygrothermal performance of bio-based materials in building wall systems

Building envelope systems are crucial in regulating thermal and moisture exchange between interior and exterior environments, accounting for approximately 28 % of building energy consumption in the United States with walls being the primary contributors. Improper selection of building envelope materials can lead to moisture-related issues, reduced resilience, and compromised durability. Hygrothermal performance assessment is a key factor in efficient building design. As such, improving the energy and hygrothermal performance of opaque wall materials, through careful assessment of material choices, is essential to enhancing building resilience, lowering energy costs, and improving occupant comfort. As the building industry seeks new strategies to reduce material energy intensity, bio-based materials emerge as a promising solution. However, their long-term hygrothermal performance in building envelope systems remains underexplored. To fill this gap, this study evaluates the hygrothermal behavior of 13 bio-based materials in residential wall systems across three U.S. climate zones. Laboratory experiments were performed to measure material properties such as density, thermal conductivity, moisture transmission, and sorption isotherms. These data were integrated into the WUFI® simulation tool to assess wall hygrothermal performance in Houston, Baltimore, and Chicago. A three-phase modeling approach was used: (1) baseline residential walls with oriented strand board (OSB) and gypsum board; (2) replacing OSB with bio-based materials; and (3) replacing drywall with bio-based materials. Results showed that the evaluated bio-based materials maintained acceptable moisture thresholds of ≤ 16 % across all climates, confirming their viability as an alternative for current sheathing materials. Furthermore, this study provides a foundation for future research and innovation in material science on the use of certain bio-based materials in high-performance, low energy use residential construction. Ultimately, providing critical data, offering a database of bio-based material properties, and supplying a simulation-based approach will help designers make informed decisions for future efficient building practices.

Bio-based materials↗
DOE OSTI2017

The need for non- or minimally-invasive biomonitoring strategies and the development of pharmacokinetic/pharmacodynamic models for quantification

Advancements in Exposure Science involving the development and deployment of biomarkers of exposure and biological response are anticipated to significantly (and positively) influence health outcomes associated with occupational, environmental and clinical exposure to chemicals/drugs. To achieve this vision, innovative strategies are needed to develop multiplex sensor platforms capable of quantifying individual and mixed exposures (i.e. systemic dose) by measuring biomarkers of dose and biological response in readily obtainable (non-invasive) biofluids. Secondly, the use of saliva (alternative to blood) for biomonitoring coupled with the ability to rapidly analyze multiple samples in real-time offers an innovative opportunity to revolutionize biomonitoring assessments. In this regard, the timing and number of samples taken for biomonitoring will not be limited as is currently the case. In addition, real-time analysis will facilitate identification of work practices or conditions that are contributing to increased exposures and will make possible a more rapid and successful intervention strategy. The initial development and application of computational models for evaluation of saliva/blood analyte concentration at anticipated exposure levels represents an important opportunity to establish the limits of quantification and robustness of multiplex sensor systems by exploiting a unique computational modeling framework. The use of these pharmacokinetic models will also enable prediction of an exposure dose based on the saliva/blood measurement. This novel strategy will result in a more accurate prediction of exposures and, once validated, can be employed to assess dosimetry to a broad range of chemicals in support of biomonitoring and epidemiology studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Paradeisos: A perfect hashing algorithm for many-body eigenvalue problems

Here, we describe an essentially perfect hashing algorithm for calculating the position of an element in an ordered list, appropriate for the construction and manipulation of many-body Hamiltonian, sparse matrices. Each element of the list corresponds to an integer value whose binary representation reflects the occupation of single-particle basis states for each element in the many-body Hilbert space. The algorithm replaces conventional methods, such as binary search, for locating the elements of the ordered list, eliminating the need to store the integer representation for each element, without increasing the computational complexity. Combined with the “checkerboard” decomposition of the Hamiltonian matrix for distribution over parallel computing environments, this leads to a substantial savings in aggregate memory. While the algorithm can be applied broadly to many-body, correlated problems, we demonstrate its utility in reducing total memory consumption for a series of fermionic single-band Hubbard model calculations on small clusters with progressively larger Hilbert space dimension.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Accelerated impurity solver for DMFT and its diagrammatic extensions

Here, we present ComCTQMC, a GPU accelerated quantum impurity solver. It uses the continuous-time quantum Monte Carlo (CTQMC) algorithm wherein the partition function is expanded in terms of the hybridisation function (CT-HYB). ComCTQMC supports both partition and worm-space measurements, and it uses improved estimators and the reduced density matrix to improve observable measurements whenever possible. ComCTQMC efficiently measures all one and two-particle Green's functions, all static observables which commute with the local Hamiltonian, and the occupation of each impurity orbital. ComCTQMC can solve complex-valued impurities with crystal fields that are hybridized to both fermionic and bosonic baths. Most importantly, ComCTQMC utilizes graphical processing units (GPUs), if available, to dramatically accelerate the CTQMC algorithm when the Hilbert space is sufficiently large. We demonstrate acceleration by a factor of over 600 (100) in a simulation of δ-Pu at 600 K with (without) crystal fields. In easier problems, the GPU offers less impressive acceleration or even decelerates the CTQMC. Here we describe the theory, algorithms, and structure used by ComCTQMC in order to achieve this set of features and level of acceleration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms

Computational singular perturbation (CSP) is a method to analyze dynamical systems. It targets the decoupling of fast and slow dynamics using an alternate linear expansion of the right-hand side of the governing equations based on eigenanalysis of the associated Jacobian matrix. This representation facilitates diagnostic analysis, detection and control of stiffness, and the development of simplified models. For this work, we have implemented CSP in a C++ open-source library CSPlib using the Kokkos parallel programming model to address portability across diverse heterogeneous computing platforms, i.e., multi/many-core CPUs and GPUs. We describe the CSPlib implementation and present its computational performance across different computing platforms using several test problems. Specifically, we test the CSPlib performance for a constant pressure ignition reactor model on different architectures, including IBM Power 9, Intel Xeon Skylake, and NVIDIA V100 GPU. The size of the chemical kinetic mechanism is varied in these tests. As expected, the Jacobian matrix evaluation, the eigensolution of the Jacobian matrix, and matrix inversion are the most expensive computational tasks. When considering the higher throughput characteristic of GPUs, GPUs performs better for small matrices with higher occupancy rate. CPUs gain more advantages from the higher performance of well-tuned and optimized linear algebra libraries such as OpenBLAS.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Spin-Orbit Coupling in Actinide Cations

The limiting case of Russell-Saunders coupling, which leads to a maximum spin alignment for the open shell electrons, usually explains the properties of high spin ionic crystals with transition metals. For actinide compounds, the spin-orbit splitting is large enough to cause a significantly reduced spin alignment. Novel concepts are used to explain the dependence of the spin alignment on the 5f shell occupation. We present evidence that the XPS of ionic actinide materials may provide direct information about the angular momentum coupling within the 5f shell.

Bagus, Paul S↗
DOE OSTI2024

A traffic accident dataset for Chattanooga, Tennessee

This publication presents an annotated accident dataset which fuses traffic data from radar detection sensors, weather condition data, and light condition data with traffic accident data (as illustrated in Fig. 1) in a format that is easy to process using machine learning tools, databases, or data workflows. The purpose of this data is to analyze, predict, and detect traffic patterns when accidents occur. Each file contains a timeseries of traffic speeds, flows, and occupancies at the sensor nearest to the accident, as well as 5 neighboring sensors upstream and downstream. It also contains information about the accident type, date, and time. In addition to the accident data, we provide baseline data for typical traffic patterns during a given time of day. Overall, the dataset contains 6 months of annotated traffic data from November 2020 to April 2021. During this timeframe, and 361 accidents occurred in the monitored area around Chattanooga, Tennessee. This dataset served as the basis for a study on topology-aware automated accident detection for a companion publication [1].

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Reinforcement learning building control approach harnessing imitation learning

Reinforcement learning (RL) has shown significant success in sequential decision making in fields like autonomous vehicles, robotics, marketing and gaming industries. This success has attracted the attention to the RL control approach for building energy systems which are becoming complicated due to the need to optimize for multiple, potentially conflicting, goals like occupant comfort, energy use and grid interactivity. However, for real world applications, RL has several drawbacks like requiring large training data and time, and unstable control behavior during the early exploration process making it infeasible for an application directly to building control tasks. To address these issues, an imitation learning approach is utilized herein where the RL agents starts with a policy transferred from accepted rule based policies and heuristic policies. This approach is successful in reducing the training time, preventing the unstable early exploration behavior and improving upon an accepted rule-based policy - all of these make RL a more practical control approach for real world applications in the domain of building controls.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗