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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Error analysis of leaf area estimates made from allometric regression models

Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).

Feiveson, A. H.↗
DOE OSTI2016

AGR-1, AGR-2 and AGR-3/4 Dimensional Change Data Analysis

A series of Advanced Gas Reactor (AGR) experiments have been completed in the Advanced Test Reactor at Idaho National Laboratory in support of qualification and development of tristructural isotropic fuel. Each AGR test consists of multiple independently controlled and monitored capsules containing fuel compacts placed in a graphite cylinder. These capsules are instrumented with thermocouples embedded in the graphite, enabling temperature control. The fuel compacts are composed of fuel particles surrounded by a graphitic A3 matrix material. Dimensional change in AGR fuel compacts is vital because the swelling or shrinkage affects the size of the gas gaps that are used to control temperatures. Analysis of dimensional change in the AGR fuel compacts is needed to establish the variables directly relating to compact shrinkage. The variables initially identified for consideration were matrix density, compact density, fuel packing fraction, uranium loading, fuel particle diameter, cumulative fast neutron fluence, and volume average time average fuel temperature. In addition to the data available from the AGR experiments, the analysis included specimens formed from the same A3 matrix material used in Advanced Graphite Creep (AGC) experiments, which provide graphite creep data during irradiation for design and licensing purposes. The primary purpose of including the AGC specimens was to encompass dimensional behavior at zero packing fraction, zero uranium loading, and zero particle diameter. All possible combinations of first-order variable regressions were considered in the analysis. The study focused on identifying the best regression models for percent change in diameter, length, and volume. Bootstrap analysis was used to ensure the resulting regression models were robust and well-performing. The variables identified as very significant in predicting change in one or more dimensions (diameter, length, and volume) are volume average time average temperature, fast fluence, compact density, packing fraction and fuel particle diameter. Due to the presence of confounding effects between several variables, interpretation of these results is equivocal; the use of multiple statistical tests provides additional confidence in the conclusion.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2015

Lung Texture in Serial Thoracic Computed Tomography Scans: Correlation of Radiomics-based Features With Radiation Therapy Dose and Radiation Pneumonitis Development

Purpose: To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP). Methods and Materials: A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0-168 days) and after (5-120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identified in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature's ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction. Results: For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59-0.84). Conclusions: A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

High-Density and Very-Low-Density Lipoprotein Have Opposing Roles in Regulating Tumor-Initiating Cells and Sensitivity to Radiation in Inflammatory Breast Cancer

Purpose: We previously demonstrated that cholesterol-lowering agents regulate radiation sensitivity of inflammatory breast cancer (IBC) cell lines in vitro and are associated with less radiation resistance among IBC patients who undergo postmastectomy radiation. We hypothesized that decreasing IBC cellular cholesterol induced by treatment with lipoproteins would increase radiation sensitivity. Here, we examined the impact of specific transporters of cholesterol (ie lipoproteins) on the responses of IBC cells to self-renewal and to radiation in vitro and on clinical outcomes in IBC patients. Methods and Materials: Two patient-derived IBC cell lines, SUM 149 and KPL4, were incubated with low-density lipoproteins (LDL), very-low-density lipoproteins (VLDL), or high-density lipoproteins (HDL) for 24 hours prior to irradiation (0-6 Gy) and mammosphere formation assay. Cholesterol panels were examined in a cohort of patients with primary IBC diagnosed between 1995 and 2011 at MD Anderson Cancer Center. Lipoprotein levels were then correlated to patient outcome, using the log rank statistical model, and examined in multivariate analysis using Cox regression. Results: VLDL increased and HDL decreased mammosphere formation compared to untreated SUM 149 and KPL4 cells. Survival curves showed enhancement of survival in both of the IBC cell lines when pretreated with VLDL and, conversely, radiation sensitization in all cell lines when pretreated with HDL. In IBC patients, higher VLDL values (>30 mg/dL) predicted a lower 5-year overall survival rate than normal values (hazard ratio [HR] = 1.9 [95% confidence interval [CI]: 1.05-3.45], P=.035). Lower-than-normal patient HDL values (<60 mg/dL) predicted a lower 5-year overall survival rate than values higher than 60 mg/dL (HR = 3.21 [95% CI: 1.25-8.27], P=.015). Conclusions: This study discovered a relationship among the plasma levels of lipoproteins, overall patient response, and radiation resistance in IBC patients and IBC patient-derived cell lines. A more expansive study is needed to verify these observations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Direct Breakthrough Curve Prediction From Statistics of Heterogeneous Conductivity Fields

In this paper, we present a methodology to predict the shape of solute breakthrough curves in heterogeneous aquifers at early times and/or under high degrees of heterogeneity, both cases in which the classical macrodispersion theory may not be applicable. The methodology relies on the observation that breakthrough curves in heterogeneous media are generally well described by lognormal distributions, and mean breakthrough times can be predicted analytically. The log-variance of solute arrival is thus sufficient to completely specify the breakthrough curves, and this is calibrated as a function of aquifer heterogeneity and dimensionless distance from a source plane by means of Monte Carlo analysis and statistical regression. Using the ensemble of simulated groundwater flow and solute transport realizations employed to calibrate the predictive regression, reliability estimates for the prediction are also developed. Additional theoretical contributions include heuristics for the time until an effective macrodispersion coefficient becomes applicable, and also an expression for its magnitude that applies in highly heterogeneous systems. Finally, it is seen that the results here represent a way to derive continuous time random walk transition distributions from physical considerations rather than from empirical field calibration.

58 GEOSCIENCES↗
DOE OSTI2023

Measuring environmental and cost benefits of riparian buffers for drinking water production in a Midwest watershed

Here, this study focuses on the economic value and carbon benefits of riparian buffers in urban drinking water production. The impact of riparian buffers on the waterworks operation in the Raccoon River watershed was quantified using the following metrics: nitrate concentration, days of nitrate removal operation, material and energy cost (based on 17 years of historical records with a watershed model), regression, and cost analysis. The findings indicate that the presence of riparian buffers in agricultural land can substantially decrease nitrate concentration in the water intake of the waterworks during the crop-growing season: 19% in April, 9% in May, and 11% in June. These reductions mean less nitrate treatment of the plant intake flow: 23% in April, 12% in May, and 3% in June. These changes lead to significant resource savings: 425 metric tons of sodium chloride (NaCl), 147 810 kWh of electricity, 253 metric tons of powdered activated carbon, and 20.8 million liters of fresh water. The buffers would also reduce greenhouse gas emissions by 86.9 metric tons (CO 2 equivalent) in 17 years. The total cost saving was estimated at $\$$327 326, with the highest potential savings in May ($\$$215 100), followed by April ($\$$65 465), and June ($\$$46 761). When factoring in buffer installation, cropland loss, nitrate removal, and cost associated with buffer harvest for biofuel in an established biomass market, the benefit to the entire watershed community would be $\$$2.63 million annually. The results underscore the significant cost benefits and environmental benefits associated with cropland riparian buffers in a watershed community. The approach employed in this study holds promise for assessing riparian buffer benefits in other watersheds, contributes to an understanding of sustainable water management practices, and provides a basis for decision making in a wide range of agricultural regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Bayesian Adaptive Polynomial Chaos Expansions

Polynomial chaos expansions (PCEs) are widely used for uncertainty quantification (UQ) tasks, particularly in the applied mathematics community. However, PCE has received comparatively less attention in the statistics literature, and fully Bayesian formulations remain rare—especially with implementations in R. Motivated by the success of adaptive Bayesian machine learning models such as BART, BASS and BPPR, we develop a new fully Bayesian adaptive PCE method with an efficient and accessible R implementation: khaos. Our approach includes a novel proposal distribution that enables data-driven interaction selection and supports a modified g-prior tailored to PCE structure. Through simulation studies and real-world UQ applications, we demonstrate that the Bayesian adaptive PCE provides competitive performance for surrogate modeling, global sensitivity analysis and ordinal regression tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Reader agreement and accuracy of ultrasound features for hepatic steatosis

Purpose: The purpose of the study is to assess the reader agreement and accuracy of eight ultrasound imaging features for classifying hepatic steatosis in adults with known or suspected hepatic steatosis. Methods: This was an IRB-approved, HIPAA-compliant prospective study of adult patients with known or suspected hepatic steatosis. All patients signed written informed consent. Ultrasound images (Siemens S3000, 6C1HD, and 4C1 transducers) were acquired by experienced sonographers following a standard protocol. Eight readers independently graded eight features and their overall impression of hepatic steatosis on ordinal scales using an electronic case report form. Duplicated images from the 6C1HD transducer were read twice to assess intra-reader agreement. Intra-reader, inter-transducer, and inter-reader agreement were assessed using intraclass correlation coefficients (ICC). Features with the highest intra-reader agreement were selected as predictors for dichotomized histological steatosis using Classification and Regression Tree (CART) analysis, and the accuracy of the decision rule was compared to the accuracy of the radiologists’ overall impression. Results: 45 patients (18 males, 27 females; mean age 56 ± 12 years) scanned from September 2015 to July 2016 were included. Mean intra-reader ICCs ranged from 0.430 to 0.777, inter-transducer ICCs ranged from 0.228 to 0.640, and inter-reader ICCs ranged from 0.014 to 0.561. The CART decision rule selected only large hepatic vein blurring and achieved similar accuracy to the overall impression (74% to 75% and 68% to 72%, respectively). Conclusions: Large hepatic vein blurring, liver–kidney contrast, and overall impression provided the highest reader agreement. Large hepatic vein blurring may provide the highest classification accuracy for dichotomized grading of hepatic steatosis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2020

A spatial-statistical investigation of surface expressions associated with cyclic steaming in the Midway-Sunset Oil Field, California

In the Midway Sunset Oil Field in Central California, operators inject steam into the shallow diatomite formation to enhance heavy oil recovery through imbibition, wettability alteration, and viscosity reduction, among other mechanisms. The injected steam, however, does not always remain in the reservoir or return through the wells. In two zones in the study area, the steam comes out at the surface, creating sinkholes, seeps, and steam outlets. These phenomena, called “surface expressions,” pose safety and environmental hazards. Even though these surface expressions are a widespread problem in Central California, they are not well documented and understood. Possible causes of the surface expressions include: high injection pressure, structurally controlled flow patterns, leakage of steam through old improperly abandoned wells, high injection volumes, or flow along naturally occurring faults, among other possible factors. This work examines attributes of the zones with surface expressions in order to determine factors that may contribute to their occurrence. Spatial statistical analysis using logistic regression, random forests, and classification trees is used to explore the relationship between the surface expressions and geological and production-related attributes. The results point to a significant spatial correlation between the surface expressions and two predictors: concentration of plugged wells and geologic seal thickness. The results guide follow-up studies to further investigate the role of well abandonment and seal thickness in the occurrence of surface expressions.

02 PETROLEUM↗
DOE OSTI2017

Changes in global vegetation activity and its driving factors during 1982–2013

Vegetation activity plays a crucial role in the global carbon cycle and climate. Many studies have examined recent changes in vegetation growth and the associated local climatic drivers. They revealed a global greening trend during the recent decades. However, few studies have analyzed how remote oceanic conditions affect land vegetation growth through atmospheric teleconnection, and the causes of the recent greening needs further investigation. Here in this study, we investigate the spatio-temporal variations (including trends) of vegetation activity using satellite data of growing-season normalized difference vegetation index (NDVI gs ), and examine their relationship to local and remote climate oscillations and external anthropogenic forcing by statistical means. As expected, there is an increasing trend in global-mean NDVI gs from 1982–2013, with significant greening over Europe and many other land areas. NDVI gs is temperature-limited at northern high-latitudes, but water-limited in arid and semi-arid regions, and radiation-limited in the Amazon and eastern and southern Asia. Globally, El Niño-Southern Oscillation (ENSO) is the leading climatic driver of interannual variability of NDVI gs , especially over southern and eastern Africa, eastern Australia, northeastern Asia, and northern South America. Consistent with previous modeling studies, a regression-based attribution analysis suggests that historical anthropogenic forcing (mainly increases in greenhouse gases) explains about two thirds of the NDVI gs trend from 1982 to 2013, with the rest coming mainly from the Atlantic Multi-decadal Oscillation (AMO). Contributions to the recent NDVI gs trend from ENSO and Pacific decadal variability and Artctic Oscillation appear to be small.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Circular RNA circ{sub 0}001649 acts as a prognostic biomarker and inhibits NSCLC progression via sponging miR-331-3p and miR-338-5p

Accumulating evidence documented the key functions of circular RNAs (circRNAs) in various malignancies. However, the study regarding the involvement of circRNAs in non-small cell lung cancer (NSCLC) has just begun. In the present study, qRT-PCR was used to determine the expression of circ{sub 0}001649 in NSCLC tissues and cells. Its clinical significance was further assessed by Fisher's exact test, Kaplan-Meier analysis and Cox regression model. Additionally, loss-of-function and gain-of-function experiments were carried out to detect the functional role of circ{sub 0}001649 in cell proliferation, migration and invasion. Furthermore, animal study was performed to confirm the in vitro results. Importantly, luciferase reporter assay was induced to reveal the underlying mechanism of circ{sub 0}001649. As a result, circ{sub 0}001649 was decreased in NSCLC tissues and cells and this downregulation is correlated with advanced TNM stage, positive lymph node metastasis and unfavorable prognosis. Additionally, circ{sub 0}001649 inhibited cell growth and metastasis both in vitro and in vivo. In mechanism, circ{sub 0}001649 was identified as the sponge of miR-331-3p and miR-338-5p. Moreover, the biological functions of circ{sub 0}001649 is partly dependent on its regulation on miR-331-3p and miR-338-5p. Collectively, this study suggested that circ{sub 0}001649/miR-331-3p/miR-338-5p regulatory signaling might be a potential target for NSCLC therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Using PCA and PLS on publicly available data to predict the extractability of hydrocarbons from shales

Prediction of hydrocarbon extraction from shale requires specialized knowledge of shale play characteristics and analysis to assess effective, economical, and sustainable implementation of oil and natural gas production. In this paper, we present a statistical approach that can be used as a preliminary investigation into the hydrocarbon resource potential of a shale play based on limited data. Statistical algorithms for Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS) were used to determine if depositional environments and lithographic boundary characteristics of different plays allowed prediction of specific production parameters. This project characterizes Eagle Ford and Utica formations—two high-producing shale plays in the United States—and Banff/Exshaw and Colorado formations—two recently assessed shale plays in Alberta, Canada. Partial Least Squares Regression models were unable to model gas production parameters from predictor variables, highlighting the complexity of gas formations and the need for data on microscale petrophysical characteristics. In contrast, oil production parameters were better predicted, because bulk variables such as mineral composition appeared to correlate with oil location in mineral interfaces. As expected, a PLS model's predictive capabilities increased with specificity of data sets to particular regions of a shale play. Finally, this study indicates how PCA and PLS modeling could assist stakeholders to make preliminary decisions regarding hydrocarbon extraction, especially when limited to publicly available petrophysical data.

03 NATURAL GAS↗
DOE OSTI2016

Using individual patient anatomy to predict protocol compliance for prostate intensity-modulated radiotherapy

If a prostate intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) plan has protocol violations, it is often a challenge knowing whether this is due to unfavorable anatomy or suboptimal planning. This study aimed to create a model to predict protocol violations based on patient anatomical variables and their potential relationship to target and organ at risk (OAR) end points in the setting of definitive, dose-escalated IMRT/VMAT prostate planning. Radiotherapy plans from 200 consecutive patients treated with definitive radiation for prostate cancer using IMRT or VMAT were analyzed. The first 100 patient plans (hypothesis-generating cohort) were examined to identify anatomical variables that predict for dosimetric outcome, in particular OAR end points. Variables that scored significance were further assessed for their ability to predict protocol violations using a Classification and Regression Tree (CART) analysis. These results were then validated in a second group of 100 patients (validation cohort). In the initial analysis of the hypothesis-generating cohort, percentage of rectum overlap in the planning target volume (PTV) (%OR) and percentage of bladder overlap in the PTV (%OB) were highlighted as significant predictors of rectal and bladder dosimetry. Lymph node treatment was also significant for bladder outcomes. For the validation cohort, CART analysis showed that %OR of < 6%, 6% to 9% and > 9% predicted a 13%, 63%, and 100% rate of rectal protocol violations respectively. For the bladder, %OB of < 9% vs > 9% is associated with 13% vs 88% rate of bladder constraint violations when lymph nodes were not treated. If nodal irradiation was delivered, plans with a %OB of < 9% had a 59% risk of violations. Percentage of rectum and bladder within the PTV can be used to identify individual plan potential to achieve dose-volume histogram (DVH) constraints. A model based on these factors could be used to reduce planning time, improve work flow, and strengthen plan quality and consistency.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Deforestation decreases spatial turnover and alters the network interactions in soil bacterial communities

Despite important progress in understanding the influence of deforestation on the bacterial α diversity and community structure at local scales, little is known about deforestation impacts in terms of spatial turnover and soil bacterial community network interactions, especially at regional or global scales. In this study, to address this research gap, we examined the bacterial spatial turnover rate and the species networks in paired primary and secondary forest soils along a 3700-km north-south transect in eastern China using high-throughput 16S rRNA gene sequencing. The spatial turnover rate of bacterial communities was higher in primary forests than in secondary, suggesting deforestation increased biotic homogenization at a large geographic scale. Multiple regression on matrices analysis revealed that both geographic distance and soil properties (especially soil pH and organic matter availability) strongly affected bacterial spatial turnover. Through the phylogenetic molecular ecological network approach, we demonstrate that the bacterial network of primary forests was more intricate than in secondary forests. This suggests that microbial species have greater niche-sharing and more interactions in primary forests as compared to secondary forests. On the other hand, the bacterial network in secondary forests was more modular, and the taxa tended to co-occur, with positive correlations accounting for 82% of all potential interactions. Lastly, our findings demonstrate that anthropogenic deforestation has clear effects on bacterial spatial turnover and network interactions, with potential for serious consequences such as microbial diversity loss in primary forests.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Unraveling Fundamental Activity–Stability Relationships in Rutile Oxides

The oxygen evolution reaction (OER) is a key anodic half-cell reaction that accompanies several critical electrochemical reduction reactions of interest to a variety of applications. Despite steady advances in understanding and qualitatively predicting OER activity and selectivity trends, a comprehensive description or prediction of material aqueous (in)stability and degradation mechanisms remains elusive, even though these processes critically influence device lifetime and economic feasibility. In this work, we investigate the interplay, or lack thereof, between OER activity and material aqueous stability across rutile oxides, with a particular focus on iridium oxide (IrO 2 ). By applying a Born–Haber cycle, we calculate the thermodynamic driving force for metal dissolution as a function of the applied bias and electrolyte conditions. We apply interpretable machine learning techniques, including principal component analysis and symbolic regression, to analyze trends across rutile oxides and find that key thermodynamic descriptors for OER activity and surface stability are only very weakly correlated. Instead, the local atomic environment─especially electronic structure signatures for interactions between the active site and its neighbors─plays a more important role in predicting material stability. Leveraging these insights, we investigate the impact of doping IrO 2 with a range of transition metals and show that the stability of Ir active sites can be tuned largely independently of its predicted OER activity. These insights lay the foundation for material design to improve stability with respect to corrosion, with the ultimate aim to enhance long-term stability without sacrificing catalytic performance in the OER.

evolution reactions↗
DOE OSTI2023

Response of Ecosystem Productivity to High Vapor Pressure Deficit and Low Soil Moisture: Lessons Learned From the Global Eddy-Covariance Observations

Although there is mounting concern about how high vapor pressure deficit (VPD) and low soil moisture (SM) affect ecosystem productivity, their relative importance is still under debate. Here, we comprehensively quantified the relative impacts of these two factors on ecosystem gross primary production (GPP) using observations from a global network of eddy-covariance towers and two approaches (sensitivity analysis and linear regression model). Both approaches agree that a higher percentage of sites experience GPP reduction from high VPD than from low SM over the growing season. However, the constraint of high VPD and low SM on GPP reduction is tightly linked with climates and plant functional types. Humid and mesic ecosystems including forests and grasslands are dominated by VPD, while the semi-arid and arid ecosystems including shrublands and savannas are dominated by SM. The varying dominant role of these two factors on GPP is closely related to plant stomatal behavior, as predicted by a stomatal conductance model. Additionally, we highlight the non-linear impact of SM on GPP during droughts and the possible underestimation of the SM effects for deep-rooted plants when only using surface-layer SM. Our results shed light on a better understanding of the impacts of VPD and SM on vegetation productivity, with important implications for modeling the response and feedback of ecosystem dynamics to current and future climates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

The Role of Wind Speed in Prolonging Large Fire Durations in the Western US

Abstract The duration of large wildfires in the western US has increased significantly from 1992 to 2020 in the two fire seasons, by 0.76 days yr −1 in summer and 0.55 days yr −1 in fall. The factors driving the trend and variability were analyzed using multiple linear regression models. Our analysis identified the maximum daily wind speed during large fires, which has also increased during the study period, as the primary predictor for the large fire duration. Despite the observed rise in maximum wind speed specifically during large fires, there are no corresponding trends in the average daily mean wind speed throughout the fire seasons. The mechanisms underlying the increase in maximum wind speed during large fires remain unclear and warrant further investigation, as they may pose growing challenges for fire control.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Active Learning-driven Quantitative Synthesis-Structure-Property Relations for Improving Performance and Revealing Active Sites of Nitrogen-Doped Carbon for the Hydrogen Evolution Reaction

While quantitative structure-properties relations (QSPRs) have been developed successfully in multiple fields, catalyst synthesis affects structure and in turn performance, making simple QSPRs inadequate. Furthermore, catalysts often have multiple active sites preventing one from obtaining insights into structure-property relations. Here, we develop a data-driven quantitative synthesis-structure-property relation (QS2PRs) methodology to elucidate correlations between catalyst synthesis conditions, structural properties as well as observed performance and to provide fundamental insights into active sites and a systematic way to optimize practical catalysts. Here, we demonstrate the approach to the synthesis of nitrogen-doped catalysts (NDC) made via pyrolysis for the performance of the electrochemical hydrogen evolution reaction (HER), quantified by the onset potential and the current density. We determine crystallinity, nitrogen species type and fraction, surface area, and pore structure of the NDC’s using XRD, XPS, and BET characterization. We demonstrated that an active learning-based optimization combined with various elementary machine learning tools (regression, principal component analysis, partial least squares) can efficiently identify optimum pyrolysis conditions to tune structural characteristics and performance with concomitant savings in materials and experimental time. Unlike previous reports on the importance of pyridinic or graphitic nitrogen, we discover that the electrochemical performance is not driven by a single catalyst property; rather, it arises from a multivariate influence of nitrogen dopants, pore structure and disorder in the NDC materials. Identification of active sites can help mechanistic understanding and further catalyst improvement.

42 ENGINEERING↗