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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
DOE OSTI2024

Velocity Dependence of Rate‐And‐State Friction in Granular Fault Gouge and Implications for Slow‐Slip Events

Abstract The standard rate‐and‐state friction (RSF) has extensively captured frictional behaviors, but it fails to explain the velocity dependence of frictional stability transition and widespread slow‐slip events (SSEs) in experiments and nature adequately. An alternative microphysical Chen‐Niemeijer‐Spiers (CNS) model can well describe the velocity dependence of frictional behaviors of granular gouges. Using the original CNS model, standard RSF parameters can be quantified microphysically. However, some micro‐parameters are not easy to estimate quantitatively, making it difficult to extrapolate to natural and experimental conditions. Here, we simplify the microphysically‐derived RSF parameters including direct effect a , evolution effect b , and critical slip distance D c , as well as equivalent values ( a eq , b eq , and D eq ). The simplified friction parameters directly illustrate their velocity dependence, namely the essentially constant a , a eq , and D c , negatively velocity‐dependent b and b eq , as well as varying D eq for different laws. They are roughly consistent with experimental results in various fault gouges. A modified CNS model is further derived from the original CNS model, establishing a direct link between the standard RSF and CNS models. The modified CNS model exhibits virtually identical frictional behaviors to the original CNS, but differs from the standard RSF at large velocity perturbations. Moreover, the linearized stability analysis indicates that the critical stiffness for the modified CNS model is velocity‐dependent. Compared with the standard RSF, the modified CNS model not only explains the velocity dependence of frictional stability transition, but also exhibits a more gradual transition for SSEs with a broader range of stiffness ratios.

Geochemistry & Geophysics↗
DOE OSTI2024

High Pressure Melting Curve of Fe‐Si: Implication for the Thermal Properties in Mercury's Core

The motion of liquid iron (Fe) alloy materials in the outer core drives the dynamo, which generates Mercury's magnetic field. The assessment of core models requires laboratory measurements of the melting temperature of Fe alloys at high pressure. Here, we experimentally determined the melting curve of Fe9wt%Si and Fe17wt%Si up to 17 GPa using in situ and ex situ measurements of intermetallic fast diffusion that serves as the melting criterion in a large-volume press. Our determined melting slopes are comparable with previous studies up to about 17 GPa. However, when extrapolated, our melting slopes significantly deviate from previous studies at higher pressures. For Mercury's core with a model composition of Fe9wt%Si, the melting temperature-depth profile determined in our study is lower by ∼150–250 K when compared with theoretical calculations. Using the new melting curve of Fe9wt%Si and the electrical resistivity values from a previous study of Fe8.5wt%Si, we estimate that the electronic thermal conductivity of liquid Fe9wt%Si is 30 Wm −1 K −1 at the Mercury's CMB pressure of 5 GPa and 37 Wm −1 K −1 at an assumed ICB of 21 GPa, corresponding to heat flux values of 23 mWm −2 and 32 mWm −2 , respectively. These values provide new constraints on the core models.

58 GEOSCIENCES↗
DOE OSTI2024

Phase Transitions and Thermal Equation of State of Fe‐9wt.%Si Applied to the Moon and Mercury

Abstract Accurate knowledge of the phase transitions and thermoelastic properties of candidate iron alloys, such as Fe‐Si alloys, is essential for understanding the nature and dynamics of planetary cores. The phase diagrams of some Fe‐Si alloys between 1 atm and 16 GPa have been back‐extrapolated from higher pressures, but the resulting phase diagram of Fe 83.6 Si 16.4 (9 wt.% Si) is inconsistent with temperature‐induced changes in its electrical resistivity between 6 and 8 GPa. This study reports in situ synchrotron X‐ray diffraction (XRD) measurements on pre‐melted and powder Fe 83.6 Si 16.4 samples from ambient conditions to 60 GPa and 900 K using an externally heated diamond‐anvil cell. Upon compression at 300 K, the bcc phase persisted up to ∼38 GPa. The hcp phase appeared near 8 GPa in the pre‐melted sample, and near 17 GPa in the powder sample. The appearance of the hcp phase in the pre‐melted sample reconciles the reported changes in electrical resistivity of a similar sample, thus resolving the low‐pressure region of the phase diagram. The resulting high‐temperature Birch‐Murnaghan equation of state (EoS) and thermal EoS based on the Mie‐Gruneisen‐Debye model of the bcc and hcp structures are consistent with, and complement the literature data at higher pressures. The calculated densities based on the thermal EoS of Fe‐9wt.%Si indicate that both bcc and hcp phases agree with the reported core density estimates for the Moon and Mercury.

Berrada, Meryem↗
DOE OSTI2025

Control Mechanisms for Self‐Sealing in Activated Clay‐Rich Faults Through Controlled Hydraulic Injection Experiment

Abstract In a high‐pressure injection fault activation experiment conducted at the Mont Terri underground research laboratory in Switzerland, the transmissivity of the Opalinus Clay fault significantly increased due to opening and shearing. The fluid injection, spanning a few hours, generated a 10 m radius fault activation patch. Subsequent pressure pulse tests conducted bi‐weekly for a year revealed the gradual return of fault transmissivity to its initial state. The study utilized fluid pressure decay analysis, optical fiber monitoring, continuous active source seismic measurements and borehole displacement sensors for measuring fault displacements. The fault zone exhibited a dilation of approximately 1.4 mm, associated with both normal and tangential movements during activation, resulting in a sudden transmissivity increase from 1 × 10 −12 to 3.2 × 10 −7 m 2 /s. Early post‐activation, transient compaction and the subsequent slow compaction were observed, transitioning to an extension regime. The pressure pulse tests demonstrated a rapid transmissivity drop by more than two orders of magnitude within the first 10 days, followed by a gradual and less pronounced decrease. Plastic shear and compaction dominated the transmissivity evolution until 70 days after injection ended, followed by a period where additional factors, such as clay mineral swelling, influenced the behavior. Extrapolation suggested a sealing process taking at least 50 years after the initial activation. Plain Language Summary A field‐scale fault activation experiment offers valuable insights into the elasto‐plastic processes governing the sealing of shale faults. The experiment reveals a rapid increase in the fault's transmissivity by approximately five orders of magnitude during activation. Subsequent observations show a gradual transmissivity decrease by about three orders of magnitude post‐activation, with slow long‐term plastic shear and compaction of the fault competing against secondary processes, notably clay mineral swelling. All conceptual models employed to interpret these field data converge on the estimation that the fault's return to its initial low transmissivity state would require a minimum of 50 years. Key Points High‐pressure injection fault activation experiment at the Mont Terri underground research laboratory Continuous transmissivity measurements record self‐sealing inside a clay‐rich fault zone Transmissivity undergoes a phase of domination by slow plastic compaction and shearing during the initial post‐activation period, with mineral swelling exerting its influence over the long term

Guglielmi, Yves↗
DOE OSTI2025

A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate

A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.

Atmosphere model↗
DOE OSTI2026

Integrating Maximum Entropy Production Theory and Machine Learning to Improve Global Evapotranspiration Modeling

Accurate estimation of terrestrial evapotranspiration (ET) is vital for understanding global water and energy cycles. However, current global ET estimations are not well constrained. This study introduces an integrated framework combining the Maximum Entropy Production (MEP) theory with Random Forest (RF) model to improve global ET estimation. Specifically, in contrast to direct ET estimation by the RF model, the integrated framework (MEP‐RF) trains to predict error of MEP‐simulated ET. MEP‐RF outperforms RF in spatiotemporal extrapolation. Attribution analysis with in situ observations reveals that the inputs of MEP are the most critical variables for the ET process, including net radiation, vegetated area, soil moisture, and surface temperature. We further drive MEP‐RF with global reanalysis and satellite data sets of these four inputs, yielding a global mean terrestrial ET of 548 mm/year, with 77% attributed to transpiration. The global ET increased at a rate of 0.85 mm/year per year during 2003–2021, primarily due to vegetation greening rather than rising temperature, while decreasing soil moisture led to decreasing regional ET. The integrated framework provides a novel approach for the estimation of global ET without the need for hard‐to‐obtain and thus uncertain inputs, such as wind speed, surface roughness, aerodynamic and canopy stomatal resistance. Therefore, MEP‐RF offers an independent method on existing global ET products. It represents a promising physically based approach that can be incorporated into Earth System Models to enhance water and energy cycle simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Satellite remote sensing data can be used to model marine microbial metabolite turnover

Sampling ecosystems, even at a local scale, at the temporal and spatial resolution necessary to capture natural variability in microbial communities are prohibitively expensive. We extrapolated marine surface microbial community structure and metabolic potential from 72 16S rRNA amplicon and 8 metagenomic observations using remotely sensed environmental parameters to create a system-scale model of marine microbial metabolism for 5904 grid cells (49 km2) in the Western English Chanel, across 3 years of weekly averages. Thirteen environmental variables predicted the relative abundance of 24 bacterial Orders and 1715 unique enzyme-encoding genes that encode turnover of 2893 metabolites. The genes’ predicted relative abundance was highly correlated (Pearson Correlation 0.72, P-value <10-6) with their observed relative abundance in sequenced metagenomes. Predictions of the relative turnover (synthesis or consumption) of CO2 were significantly correlated with observed surface CO2 fugacity. The spatial and temporal variation in the predicted relative abundances of genes coding for cyanase, carbon monoxide and malate dehydrogenase were investigated along with the predicted inter-annual variation in relative consumption or production of ~3000 metabolites forming six significant temporal clusters. These spatiotemporal distributions could possibly be explained by the co-occurrence of anaerobic and aerobic metabolisms associated with localized plankton blooms or sediment resuspension, which facilitate the presence of anaerobic micro-niches. This predictive model provides a general framework for focusing future sampling and experimental design to relate biogeochemical turnover to microbial ecology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Microbial dormancy improves development and experimental validation of ecosystem model

Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life history traits and functions may be necessary to predict climate feedbacks due to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here, we enhanced the Microbial-Enzyme-mediated Decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, i.e., MEND with dormancy and MEND without dormancy, against long-term (270 d) lab incubations of four soils with isotopically-labeled substrates. MEND without dormancy adequately fitted multiple observations (total and 14C respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. The MEND with dormancy improved estimates of microbial biomass by 20 71% over the MEND without dormancy. We observed large differences for two fitted model parameters, the specific maintenance and growth rates for active microbes, depending on whether dormancy was considered. Together our model extrapolations of the incubation study show that long-term soil incubations with observations in multiple carbon pools are necessary to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field- and global-scale simulations and enable more confident predictions of feedbacks between environmental change and carbon cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Predicting taxonomic and functional structure of microbial communities in acid mine drainage

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Alpine soil carbon is vulnerable to rapid microbial decomposition under climate cooling

As climate cooling is increasingly regarded as important natural variability of long-term global warming trends, there is a resurging interest in understanding its impact on biodiversity and ecosystem functioning. Here in this work, we report a soil transplant experiment from lower to higher elevations in a Tibetan alpine grassland to simulate the impact of cooling on ecosystem community structure and function. Three years of cooling resulted in reduced plant productivity and microbial functional potential (for example, carbon respiration and nutrient cycling). Microbial genetic markers associated with chemically recalcitrant carbon decomposition remained unchanged despite a decrease in genes associated with chemically labile carbon decomposition. As a consequence, cooling-associated changes correlated with a decrease in soil organic carbon (SOC). Extrapolation of these results suggests that for every 1°C decrease in annual average air temperature, 0.1 Pg (0.3%) of SOC would be lost from the Tibetan plateau. These results demonstrate that microbial feedbacks to cooling have the potential to differentially impact chemically labile and recalcitrant carbon turnover, which could lead to strong, adverse consequences on soil C storage. Lastly, our findings are alarming, considering the frequency of short-term cooling and its scale to disrupt ecosystems and biogeochemical cycling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

The terrestrial biosphere as a net source of greenhouse gases to the atmosphere

The terrestrial biosphere can release or absorb the greenhouse gases, carbon dioxide (CO2), methane (CH 4 ) and nitrous oxide (N 2 O), and therefore has an important role in regulating atmospheric composition and climate 1 . Anthropogenic activities such as land-use change, agriculture and waste management have altered terrestrial biogenic greenhouse gas fluxes, and the resulting increases in methane and nitrous oxide emissions in particular can contribute to climate change 2, 3 . The terrestrial biogenic fluxes of individual greenhouse gases have been studied extensively 4, 5, 6 , but the net biogenic greenhouse gas balance resulting from anthropogenic activities and its effect on the climate system remains uncertain. Here we use bottom-up (inventory, statistical extrapolation of local flux measurements, and process-based modelling) and top-down (atmospheric inversions) approaches to quantify the global net biogenic greenhouse gas balance between 1981 and 2010 resulting from anthropogenic activities and its effect on the climate system. We find that the cumulative warming capacity of concurrent biogenic methane and nitrous oxide emissions is a factor of about two larger than the cooling effect resulting from the global land carbon dioxide uptake from 2001 to 2010. This results in a net positive cumulative impact of the three greenhouse gases on the planetary energy budget, with a best estimate (in petagrams of CO 2 equivalent per year) of 3.9 ± 3.8 (top down) and 5.4 ± 4.8 (bottom up) based on the GWP100 metric (global warming potential on a 100-year time horizon). Lastly, our findings suggest that a reduction in agricultural methane and nitrous oxide emissions, particularly in Southern Asia, may help mitigate climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Quantifying global soil carbon losses in response to warming

The majority of the Earth’s terrestrial carbon is stored in the soil. If anthropogenic warming stimulates the loss of this carbon to the atmosphere, it could drive further planetary warming. Despite evidence that warming enhances carbon fluxes to and from the soil, the net global balance between these responses remains uncertain. Here we present a comprehensive analysis of warming- induced changes in soil carbon stocks by assembling data from 49 field experiments located across North America, Europe and Asia. We find that the effects of warming are contingent on the size of the initial soil carbon stock, with considerable losses occurring in high-latitude areas. By extrapolating this empirical relationship to the global scale, we provide estimates of soil carbon sensitivity to warming that may help to constrain Earth system model projections. Our empirical relationship suggests that global soil carbon stocks in the upper soil horizons will fall by 30 ± 30 petagrams of carbon to 203 ± 161 petagrams of carbon under one degree of warming, depending on the rate at which the effects of warming are realized. Under the conservative assumption that the response of soil carbon to warming occurs within a year, a business-as-usual climate scenario would drive the loss of 55 ± 50 petagrams of carbon from the upper soil horizons by 2050. This value is around 12-17 per cent of the expected anthropogenic emissions over this period. Despite the considerable uncertainty in our estimates, the direction of the global soil carbon response is consistent across all scenarios. This provides strong empirical support for the idea that rising temperatures will stimulate the net loss of soil carbon to the atmosphere, driving a positive land carbon–climate feedback that could accelerate climate change.

58 GEOSCIENCES↗
DOE OSTI2013

Colloidal ribbons and rings from Janus magnetic rods

Dipolar particles are fundamental building blocks in nature and technology, yet the effect of particle anisotropy is seldom explored. Here, we fabricate colloidal silica rods coated with a hemicylindrical magnetic layer to satisfy multiple criteria: nearly monodisperse, easily imaged and magnetic interaction that dominates over gravity. We confirm long-predicted features of dipolar assembly and stress the microstructural variety brought about by shape and constituent anisotropy, especially by extrapolating knowledge learned from literal molecules. In this colloidal system, we describe analogies to liquid crystalline deformations with bend, splay and twist; an analogy to cis/trans isomerism in organic molecules, which in our system can be controllably and reversibly switched; and a field-switching methodology to direct single ribbons into not only single but also multiple rings that can subsequently undergo hierarchical self-assembly. Furthermore, we highlight subtle material issues of control and design rules for reconfigurable dipolar materials with building blocks of complex shape.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Giant magnetic anisotropy and tunnelling of the magnetization in Li2(Li1-xFex)N

Large magnetic anisotropy and coercivity are key properties of functional magnetic materials and are generally associated with rare earth elements. Here we show an extreme, uniaxial magnetic anisotropy and the emergence of magnetic hysteresis in Li2(Li 1-x Fe x )N. An extrapolated, magnetic anisotropy field of 220 T and a coercivity field of over 11 T at 2 K outperform all known hard ferromagnets and single-molecular magnets. Steps in the hysteresis loops and relaxation phenomena in striking similarity to single-molecular magnets are particularly pronounced for x<<1 and indicate the presence of nanoscale magnetic centres. Quantum tunnelling, in the form of temperature-independent relaxation and coercivity, deviation from Arrhenius behaviour and blocking of the relaxation, dominates the magnetic properties up to 10 K. The simple crystal structure, the availability of large single crystals and the ability to vary the Fe concentration make Li2(Li 1-x Fe x )N an ideal model system to study macroscopic quantum effects at elevated temperatures and also a basis for novel functional magnetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Strength of iron at core pressures and evidence for a weak Earth’s inner core

The strength of iron at extreme conditions is crucial information for interpreting geophysical observations of the Earth’s core and understanding how the solid inner core deforms. However, the strength of iron, on which deformation depends, is challenging to measure and accurately predict at high pressure. Here we present shear strength measurements of iron up to pressures experienced in the Earth’s core. Hydrostatic X-ray spectroscopy and non-hydrostatic radial X-ray diffraction measurements of the deviatoric strain in hexagonally close-packed iron uniquely determine its shear strength to pressures above 200 GPa at room temperature. Applying numerical modelling of the rheologic behaviour of iron under pressure, we extrapolate our experimental results to inner-core pressures and temperatures, and find that the bulk shear strength of hexagonally close-packed iron is only ~ 1 GPa at the conditions of the Earth’s centre, 364 GPa and 5,500 K. This suggests that the inner core is rheologically weak, which supports dislocation creep as the dominant creep mechanism influencing deformation.

Gleason, A. E.↗
DOE OSTI2015

Targeted inhibition of metastatic melanoma through interference with Pin1-FOXM1 signaling

Melanoma is the most lethal form of skin cancer and successful treatment of metastatic melanoma remains challenging. BRAF/MEK inhibitors only show a temporary benefit due to rapid occurrence of resistance, whereas immunotherapy is mainly effective in selected subsets of patients. Thus, there is a need to identify new targets to improve treatment of metastatic melanoma. To this extent, we searched for markers that are elevated in melanoma and are under regulation of potentially druggable enzymes. Here, we show that the pro-proliferative transcription factor FOXM1 is elevated and activated in malignant melanoma. FOXM1 activity correlated with expression of the enzyme Pin1, which we found to be indicative of a poor prognosis. In functional experiments, Pin1 proved to be a main regulator of FOXM1 activity through MEK-dependent physical regulation during the cell cycle. The Pin1-FOXM1 interaction was enhanced by BRAF V600E , the driver oncogene in the majority of melanomas, and in extrapolation of the correlation data, interference with\ Pin1 in BRAF V600E -driven metastatic melanoma cells impaired both FOXM1 activity and cell survival. Importantly, cell-permeable Pin1-FOXM1-blocking peptides repressed the proliferation of melanoma cells in freshly isolated human metastatic melanoma ex vivo and in three-dimensional-cultured patient-derived melanoids. When combined with the BRAF V600E -inhibitor PLX4032 a robust repression in melanoid viability was obtained, establishing preclinical value of patient-derived melanoids for prognostic use of drug sensitivity and further underscoring the beneficial effect of Pin1-FOXM1 inhibitory peptides as anti-melanoma drugs. These proof-of-concept results provide a starting point for development of therapeutic Pin1-FOXM1 inhibitors to target metastatic melanoma.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Deep neural network improves the estimation of polygenic risk scores for breast cancer

Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. Here, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Cooking methods and kitchen ventilation availability, usage, perceived performance and potential in Canadian homes

Cooking is a substantial contributor to air pollutant exposures in many residences. Effective use of kitchen ventilation can mitigate exposure; however, information on its availability, usage, and potential to increase its use across the population has been limited. This study aimed to obtain nationally representative information on cooking methods, kitchen ventilation availability and usage, and the potential for education to increase effective usage. An online survey was sent to a representative sample of Canadian homes to collect data on cooking methods, the presence and use of mechanical kitchen ventilation devices, perceived device performance, and willingness to implement mitigation strategies. Responses were weighted to match key demographic factors and analyzed using non-parametric statistics. Among the 4500 respondents, 90% had mechanical ventilation devices over the cooktop (66% of which were vented to the outside), and 30% reported regularly using their devices. Devices were used most often for deep-frying, followed by stir-frying, sautéing or pan-frying, indoor grilling, boiling or steaming. Almost half reported rarely or never using their ventilation devices during baking or oven self-cleaning. Only 10% were fully satisfied with their devices. More frequent use was associated with the device being vented to the outdoors, having more than two speed settings, quiet operation if only one speed, covering over half of the cooktop, and higher perceived effectiveness. After being informed of the benefits of kitchen ventilation, 64% indicated they would consider using their devices more often, preferentially using back burners with ventilation, and/or using higher ventilation device settings when needed. This study provides population-representative data on the most used cooking methods, kitchen ventilation availability and usage, and influencing factors in Canadian homes. Such data are needed for exposure assessments and evaluating the potential to mitigate cooking-related pollutant exposures via more effective use of kitchen ventilation. The data can be reasonably extrapolated to the United States, given the similarities in residential construction practices and cultural norms between the two countries.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗