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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Performance of a dynamic single bubbler in single and two-phase immiscible liquids

Ensuring nonproliferation and safeguards of special nuclear materials (SNM) is a critical aspect of advancing the nuclear fuel cycle. Traditional bubbler systems used to estimate liquid levels and densities in nuclear recycling processes have limitations, particularly in harsh environments where dip-tube corrosion and buildup necessitate frequent maintenance and recalibration. This study explores the Dynamic Single Bubbler (DSB) method, which utilizes a single dip-tube attached to a linear actuator to estimate liquid properties dynamically. This approach is extended to estimate liquid-liquid interfaces in immiscible liquids and employs a linear regression method to reduce uncertainties and improve accuracy. The DSB method achieved density estimate uncertainties of less than 0.5% and surface level estimate uncertainties typically under 0.5%, across various fluids including water, acetone, methanol, mineral oil, glycerol, and aqueous salt solutions. Results indicate that the DSB method provides accurate and robust estimates of liquid density and surface levels with minimal maintenance and without the need for calibration. Additionally, the method's applicability to immiscible liquids and various dip-tube geometries was demonstrated, showing promise for widespread use in nuclear and other industrial applications.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗
DOE OSTI2024

Monotonic testing of single shear-plane CLT-to-CLT joint with hardwood dowels

Reducing embodied carbon emissions in buildings and striving for carbon neutrality through sustainable design has become a primary goal in the construction industry. The industry is expanding its use of wood-engineered products, such as cross-laminated timber (CLT) to achieve those sustainability goals. Furthermore, replacing steel fasteners with lower embodied carbon alternatives is gaining increased attention in timber engineering. This paper aims to quantify the mechanical behavior of single shear-plane CLT-to-CLT joints using hardwood dowels. The study includes 154 single shear-plane experimental tests with four different CLT species, two hardwood dowel species, and two dowel diameters. Further, the test specimens were subjected to cyclic and monotonic loading until failure, and mechanical properties such as yielding and ultimate strength, serviceability and yielding stiffness, and ductility were quantified. The hardwood dowel CLT-to-CLT joints demonstrated high strength with up to 10.3 kN (2.3 kips) capacity per dowel and lateral stiffness of up to 5.2 kN/mm (30.8 k/in) per dowel. This strength and stiffness are roughly equivalent to or exceed values for mass timber wood screws, making hardwood dowels a good alternative. In addition, the observed mean ductility of joints ranged from 2.4 to 4.5. Finally, analytical equations for the joints' yielding strength, lateral serviceability stiffness, and yielding stiffness were derived with non-linear regression analysis and validated using the experimental test results.

42 ENGINEERING↗
DOE OSTI2023

Novel method for accurately estimating membrane transport properties and mass transfer coefficients in reverse osmosis

Here, we present a simple and robust method to simultaneously characterize the water and salt permeability (A, B) of reverse osmosis (RO) membranes and mass transfer coefficient (k) in membrane modules. The proposed methodology comprises a set of RO experiments performed at different operating pressures or stages. The measured water and salt fluxes in each stage are simultaneously fitted to the RO transport equations by performing a non-linear regression, using A, B, and k as regression parameters. We first perform a systematic accuracy analysis of the proposed method across the full operational range of RO. The assessment shows that the method accuracy is substantially higher than current methods and increases with number of experimental stages and driving forces. This assessment is used to inform the design of an experimental protocol that minimizes errors in estimated A, B, and k. We then evaluate two commercial RO membranes following the new protocol. For both membranes, A and B parameters decrease by 17% and 15% from the dilute solution to seawater concentrations, whereas the k parameter remains constant. Our study demonstrates that the proposed method, informed by data-driven experimental designs, provides a new approach for accurately characterizing transport phenomenon in membrane processes with feeds of less than 100 g/L total dissolved solids.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Relationship between ductility and the porosity of additively manufactured AlSi10Mg

Additive manufacturing via selective laser melting can result in variable levels of internal porosity both between build plates and within components from the same build. In this work, sample porosity levels were compared to tensile properties for 176 samples spanning eight different build plates. Sample porosity was measured both by Archimedes density, which provided an estimation of overall porosity, and by observation of voids in the fracture surface, which provided an estimation of the porosity at the failure plane. The porosity observed at the fracture surface consistently demonstrated higher porosity than that suggested by Archimedes density. The porosity values obtained from both methods were compared against the mechanical results. Sample porosity appears to have some correlation to the ultimate tensile strength, yield strength, and modulus, but the strongest relationship is observed between porosity and ductility. Three different models were used to relate the fracture surface porosity to the ductility. The first method was a simple linear regression analysis, while the other two models have been used to relate porosity to ductility in cast alloys. It is shown that all three models fit the data well over the observed porosity ranges, suggesting that the models taken from casting theory can extend to additively manufactured metals. Finally, it is proposed that the non-destructive Archimedes method could be used to estimate an approximate sample ductility through the use of correlations realized here. Such a relationship could prove useful for design and for a deeper understanding of the impact of pores on tensile behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Measuring Success: A Refined Methodology for Estimating Long-term Continuous Improvement

Successful resource management systems require current and detailed feedback on operational and corporate-level performance. As corporate accountability concerns intensify, the precision and reliability of these performance metrics have become crucial. Traditional savings estimation methods can be difficult to understand, particularly linear regression, and can provide varying results. This paper reviews common efficiency metrics and highlights underlying mathematical inconsistencies when estimating total and percent savings with current methods. A refined approach to calculating long-term utility savings is proposed that simplifies current methodologies utilizing ratios to define an adjusted baseline, allowing for consistent and fair aggregation of results across multiple scales from resources to corporate performance. A simplified example demonstrates how the proposed methodology improves upon existing methods, especially in intermediate years. This paper’s major contributions are the simplified approach for converting modeled utility usage into estimated savings and the consistent roll-up methodology enabling more comparable, aggregable, and actionable results across scales.

Price, Chris [ORNL] (ORCID:0000000202007906)↗
DOE OSTI2025

Predictive modeling of Néel temperature in austenitic alloys using CALPHAD and data analytics

The Néel temperature is a crucial yet often overlooked parameter in calculating the stacking fault energy (SFE) of austenitic alloys. Several empirical equations have been proposed to estimate the Néel temperature of austenitic alloys, which are then used to calculate the SFE and explain deformation mechanisms. However, these empirical equations, typically derived using linear regression algorithms, are often simplistic and may fail to capture the complex interactions among multiple alloying elements that influence the Néel temperature. Moreover, their applicability is usually limited to specific compositional ranges. In this study, we propose a CALPHAD based approach and develop a surrogate decision tree based regression model capable of capturing the interactions among multiple alloying elements to predict the Néel temperature. Predictions from both the CALPHAD approach and the regression model show close agreement with experimental measurements reported in the literature. In conclusion, the implications of accurate Néel temperature predictions on the calculated SFE and deformation mechanisms are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microbial extracellular polysaccharide production and aggregate stability controlled by switchgrass ( Panicum virgatum ) root biomass and soil water potential

Deep-rooting perennial grasses are promising feedstocks for biofuel production, especially in marginal soils lacking organic material, nutrients, and/or that experience significant water stress. Perennial grass roots influence surrounding soil conditions and microbial activities, and produce extracellular polymeric substances (EPS) composed primarily of extracellular polysaccharides (EPSac). These polymers can alleviate microbial moisture and nutrient stress, and enhance soil characteristics through improved water retention and aggregate stability—which may in turn enhance carbon persistence. Here, we used a 13 CO 2 greenhouse tracer experiment to examine the effect of switchgrass cultivation on EPSac production and origin in a marginal soil with five fertilization/water treatments (control, +N, +NP, +P, low water), and compared these results with measurements of field soils collected after long-term switchgrass cultivation. Soils with added nitrogen and phosphorus (+NP) had the highest root biomass, EPSac and percentage of water-stable soil aggregates. Multiple linear regression analyses revealed that root biomass and soil water potential were important determinants of soil EPSac production, potentially by controlling carbon supply and diurnal changes in moisture stress. Path analysis showed that soil aggregation was positively correlated with bulk soil EPSac content and also regulated by soil water potential. High mannose content indicated the majority of EPSac was of microbial origin and 13 CO 2 labeling indicated that 0.18% of newly fixed plant carbon was incorporated into EPSac. Analysis of field soils suggests that EPSac is significantly enhanced after long-term switchgrass cultivation. Taken as a whole, our greenhouse and field results demonstrate that switchgrass cultivation can promote microbial production of EPSac, providing a mechanism to enhance aggregation in marginal soils.

13c labeling↗
DOE OSTI2021

Air separation via a two-step solar thermochemical cycle based on (Ba,La) x Sr 1-x FeO 3-δ : Thermodynamic analysis

A two-step solar thermochemical cycle was considered for air separation to produce N2 based on (Ba,La) x Sr 1-x FeO 3-δ perovskite reduction/oxidation (redox) reactions for A-site fractions of 0 ≤ x ≤ 0.2. The cycle steps encompassed (1) thermal reduction and O 2 release via concentrated solar input and (2) re-oxidation with air to uptake O 2 and produce high-purity N 2 . Thermogravimetry at temperatures between 400 and 1100 °C in atmospheres of 0.005 to 90% O 2 /Ar at 1 bar was performed to measure equilibrium nonstoichiometries. The compound energy formalism was applied to model redox thermodynamics for both Ba 2+ and La 3+ substitution. Non-linear regression was used to determine the empirical parameters based on the thermogravimetric measurements. The model was used to define partial molar reaction enthalpies and entropies and predicted equilibrium oxygen nonstoichiometry as functions of oxide stoichiometry, site fraction, temperature, and O 2 partial pressure. The thermodynamic analysis showed the materials are appealing for air separation at temperatures below 800 °C.

42 ENGINEERING↗
DOE OSTI2024

A modeling pipeline to relate municipal wastewater surveillance and regional public health data

As COVID-19 becomes endemic, public health departments require improved passive indicators, which are independent of voluntary testing data, to estimate the prevalence of COVID-19 in local communities. Quantification of SARS-CoV-2 RNA from wastewater has the potential to be a powerful passive indicator. However, connecting measured SARS-CoV-2 RNA to community prevalence is challenging. We have developed a generalized methodology to improve the predictive power of wastewater measurements and applied it to data collected from treatment plants in the Chicago area. We built and compared a set of multi-linear regression models, which incorporate pepper mild mottle virus (PMMoV) as a population biomarker, Bovine coronavirus (BCoV) as a recovery control, and wastewater system flow rate into a corrected estimate for SARS-CoV-2 RNA concentration. For our data, models with BCoV performed better than those with PMMoV, but all model estimates of prevalence significantly improved correlation compared to doing no correction. We also investigate the utility of RNA measurements in wastewater as a leading indicator of COVID-19 trends. We do this in a rolling manner for corrected wastewater data and for other prevalence indicators, and statistically compare the temporal relationship between new increases in the wastewater data and those in other prevalence indicators. We find that wastewater trends often lead other COVID-19 indicators in predicting new surges.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Temporal variabilities of soil carbon dioxide fluxes from cornfield impacted by temperature and precipitation changes through high-frequent measurement and DAYCENT modelling

Soil carbon dioxide (CO 2 ) emissions from the field of corn (Zea mays L.) play an important role in global warming. This study investigated temporal variability of soil CO 2 fluxes (R s ) with soil temperature (T s ) and moisture (θ) and built DAYCENT models for predicting future impacts of climate changes on R s using the measured high-frequency data. R s trend was tested by Mann–Kendall and Sen Estimator. Predicted R s s under different climate scenarios were compared using Parallel-line Analysis. Herein, the findings indicated that daily R s exponentially increased with T s constrained by θ. During the θ of 27–31%, there was a strong exponential relationship between R s and T s , but the relationship was weaker for the θ of 38–41% and 22–26%. Soil environmental index (SEI, T s × θ) significantly impacted R s with linear regression R s 0.5 = 0.4599 + 0.002059 × SEI in 2008, 2009 and 2011. At the diurnal scale, there were different trends in R s s and relationships among R s and T s and θ in different years. Predicted yearly R s s, root R s s and corn yield in 2014–2049 increased with an increase in temperature scenarios, but the R s s significantly increased as temperature rose by 1°C or higher. Predicted yearly R s s, root R s s and yield reduced with precipitation scenario increase, and the root R s s and yield significantly diminished as precipitation increased by 15 and 30%. Predicted yearly R s from cornfields had a significantly increasing trend. Future research is needed to explore methods for mitigating cornfield R s and evaluating sensitivities of different cropland R s s to temperature changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Single-Particle Isotopic Analysis Using the Liquid Sampling-Atmospheric Pressure Glow Discharge Microplasma Coupled to an Orbitrap Mass Spectrometer

Isotope ratio (IR) determinations on individual particles can provide valuable information relative to the sample, including processing and dating, which could be valuable to geological and nuclear material analysis communities. In comparison to “bulk” measurements, in which all particle information is lost and homogenized within a sample, “particle” measurements allow for fingerprinting and can provide unique insights related to the sample’s nano- and micro- compositions. Furthermore, the complex nature of particles, with potential isobaric interferences from either the matrix or other elements within the same particle, poses a significant analytical challenge for conventional mass spectrometry platforms employed for elemental analysis due to their limited mass resolution. Presented here is the first demonstration of an ultrahigh resolution method for single particle (SP) isotopic analysis using the liquid sampling-atmospheric pressure glow discharge (LS-APGD) microplasma ionization source coupled to an Orbitrap mass spectrometer and FTMS Booster X2T acquisition/processing system. For proof of concept, well-characterized CeO 2 microparticles with a nominal diameter of 1.0 μm were analyzed at a mass resolution of ∼330,000. Important instrumental parameters were optimized to enable the detection of single particles. The 142 Ce/ 140 Ce ratio of individual particles and the population average were determined and compared to an SP inductively coupled plasma time-of-flight mass spectrometry (ICP-TOF-MS) analysis. The isotopic accuracy and precision were determined by two methods and found to be in good agreement with the SP-ICP-TOF-MS method, with the linear regression slope method providing a more accurate ratio, showing a ∼1.4% relative difference from the ratio determined from a particle digest. The encouraging performance of the method was further supported by a determined detection limit of 2.9 fg ( 142 Ce). The developed method is anticipated to overcome many of the challenges posed by isobaric interferences encountered in conventional mass spectrometry techniques when analyzing real-world samples for particle isotopic composition.

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

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t – 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.

42 ENGINEERING↗
DOE OSTI2020

Trivalent f-Element Squarates, Squarate-Oxalates, and Cationic Materials, and the Determination of the Nine-Coordinate Ionic Radius of Cf(III)

The synthesis, structure, and solid-state UV–vis–NIR spectroscopy of four new f-element squarates, M 2 (C 4 O 4 ) 3 (H 2 O) 4 (M = Eu, Am, Cf) and Sm(C 4 O 4 )(C 4 O 3 OH)(H 2 O) 2 ·0.5H 2 O, four new cationic lanthanide squarate chlorides, [M 4 (C 4 O 4 ) 5 (H 2 O) 12 ]Cl 2 ·5H 2 O (M = Eu, Dy, Ho Er), and two new actinide squarate oxalates, M 2 (C 4 O 4 ) 2 (C 2 O 4 )(H 2 O) 4 (M = Am, Cf), are presented. All of the metal centers are trivalent. Single-crystal X-ray diffraction analysis reveals that M 2 (C 4 O 4 ) 3 (H 2 O) 4 and Sm(C 4 O 4 )(C 4 O 3 OH)(H 2 O) 2 ·0.5H 2 O have a two-dimensional sheet structure constructed from MO 7 (H 2 O) 2 monocapped square-antiprismatic (coordination number (CN) = 9) metal centers and SmO 6 (H 2 O) 2 square-antiprismatic (CN = 8) metal centers, respectively, whereas M 2 (C 4 O 4 ) 2 (C 2 O 4 )(H 2 O) 4 have a three-dimensional (3D) structure constructed from MO 7 (H 2 O) 2 monocapped square-antiprismatic (CN = 9) metal centers. Additionally, the cationic framework materials [M 4 (C 4 O 4 ) 5 (H 2 O) 12 ]Cl 2 ·5H 2 O have a 3D structure constructed from two crystallographically unique MO 5 (H 2 O) 3 square-antiprismatic (CN = 8) metal centers. In these structures, the squarate ligands bind to the metal centers with varying coordination modes and denticities. The results of this study provide another example of the nonparallel chemistry between the lanthanides and transplutonium elements. From the crystallographic data for the isotypic series M 2 (C 4 O 4 ) 3 (H 2 O) 4 (M = La–Nd, Sm, Eu) and the linear regression fit to a plot of the unit cell volume as a function of the cube of the ionic radius, the nine-coordinate ionic radius of Cf 3+ was determined to be 1.127 ± 0.003 Å. Lastly, computational analysis of the americium and californium complexes M 2 (C 4 O 4 ) 3 (H 2 O) 4 and M 2 (C 4 O 4 ) 2 (C 2 O 4 )(H 2 O) 4 reveals three important attributes: (i) the 5f orbitals are nonbonding in all cases, with the bonding differences occurring with the empty 6d orbitals; (ii) the Cf complexes exhibit more covalent character than their Am counterparts; and (iii) there is more covalent character in the squarate-oxalate complexes than in the squarate complexes.

Crystallography↗
DOE OSTI2020

General Protocol for the Accurate Prediction of Molecular 13 C/ 1 H NMR Chemical Shifts via Machine Learning Augmented DFT

An accurate prediction of NMR chemical shifts at affordable computational cost is very important for different types of structural assignments in experimental studies. Density functional theory (DFT) and gauge-including atomic orbital (GIAO) are two of the most popular computational methods for NMR calculation, yet they often fail to resolve ambiguities in structural assignments. In this work, we present a new method that uses machine learning (ML) techniques (DFT + ML) that significantly increases the accuracy of 13 C/ 1 H NMR chemical shift prediction for a variety of organic molecules. The input of the generalizable DFT + ML model contains two critical parts: one is a vector providing insights into chemical environments, which can be evaluated without knowing the exact geometry of the molecule; the other one is the DFT-calculated isotropic shielding constant. The DFT + ML model was trained with a data set containing 476 13 C and 270 1 H experimental chemical shifts. For the DFT methods used here, the root mean square deviations (RMSDs) for the errors between predicted and experimental 13 C/ 1 H chemical shifts can be as small as 2.10/0.18 ppm, which is much lower than those from simple DFT (5.54/0.25 ppm), or DFT + linear regression (LR) (4.77/0.23 ppm) approaches. It also has a smaller maximum absolute error than two previously proposed NMR-predicting ML models. The robustness of the DFT + ML model is tested on two classes of organic molecules (TIC10 and hyacinthacines), where the correct isomers were unambiguously assigned to the experimental ones. Overall, the DFT + ML model shows promise for structural assignments in a variety of systems, including stereoisomers, that are often challenging to determine experimentally.

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

Local Excitations of a Charged Nitrogen Vacancy in Diamond with Multireference Density Matrix Embedding Theory

Here, we investigate the negatively charged nitrogen-vacancy center in diamond using periodic density matrix embedding theory (pDMET). To describe the strongly correlated excited states of this system, the complete active space self-consistent field (CASSCF) followed by n-electron valence state second-order perturbation theory (NEVPT2) was used as the impurity solver. Since the NEVPT2-DMET energies show a linear dependence on the inverse of the size of the embedding subspace, we performed an extrapolation of the excitation energies to the nonembedding limit using a linear regression. The extrapolated NEVPT2-DMET first triplet–triplet excitation energy is 2.31 eV and that for the optically inactive singlet–singlet transition is 1.02 eV, both in agreement with the experimentally observed vertical excitation energies of ∼2.18 eV and ∼1.26 eV, respectively. This is the first application of pDMET to a charged periodic system and the first investigation of the NV – defect using NEVPT2 for periodic supercell models.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Exploring the Effects of Node Topology, Connectivity, and Metal Identity on the Binding of Nerve Agents and Their Hydrolysis Products in Metal–Organic Frameworks

Recent studies have shown that metal–organic frameworks (MOFs) built from hexanuclear M(IV) oxide cluster nodes are effective catalysts for nerve agent hydrolysis, where the properties of the active sites on the nodes can strongly influence the reaction energetics. The connectivity and metal identity of these M6 nodes can be easily tuned, offering extensive opportunities for computational screening to predict promising new materials. Thus, we used density functional theory (DFT) to examine the effects of node topology, connectivity, and metal identity on the binding energies of multiple nerve agents and their corresponding hydrolysis products. By computing an optimization metric based on the relative binding strengths of key hydrolysis reaction species (water, agent, and bidentate-bound products), we predicted optimal M6 nodes for hydrolyzing specific nerve agent and simulant molecules, where our results are in qualitative agreement with observed experimental trends. This analysis highlighted the notion that no single metal or node topology is optimal for all possible organophosphates, suggesting that MOFs should be selected based on the agent of interest. Using the large amount of data generated from our DFT calculations, we then derived quantitative structure–activity relationship (QSAR) models to help explain the complex trends observed in the binding energies. Through linear regression, we identified the most important descriptors for describing the binding of nerve agents and their hydrolysis products to M6 nodes. These results suggested that both molecular and node properties, including both structural and chemical features, collectively contribute to the binding energetics. By performing a thorough statistical analysis, we showed that our QSAR models are capable of making quantitatively accurate binding energy predictions for nerve agents and their hydrolysis products in a wide variety of M(IV)-MOFs. In conclusion, the insights gained herein can be used to guide future experiments for the synthesis of MOFs with enhanced catalytic activity for organophosphate hydrolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ultrasonic-Assisted Extrusion Processing for Enhancing Physical Properties of High-Density Polyethylene by Flow-Induced Crystallization

The evolution of crystallinity resulting from stress imposed on a melt, known as flow-induced crystallinity, can strongly influence the mechanical and physical properties of semicrystalline polymers. This study investigates shear-induced crystallization by applying an ultrasonic field to the melt flow as it passes through dies with various geometries. A custom-built sonication die is employed for controlling the dynamic temperature and shear environment, resulting in molecular alignment and potential for flow-induced crystallization. Application of both conventional and ultrasonic shear rates at the equilibrium melt temperature of high-density polyethylene (HDPE) was investigated to accelerate crystallinity and manipulate the crystal morphology across the film in pursuit of improved mechanical and gas barrier properties without the need for additives or other polymer layers. The relationships among ultrasonic-assisted extrusion processing, polymer structure, and performance were analyzed using wide- and small-angle X-ray scattering (WAXS and SAXS), tensile testing, and oxygen transmission rate (OTR) analysis. Multiple linear regression models were implemented to predict the correlation among HDPE structure, process, and properties. Structural analysis revealed that both conventional and ultrasonic shear rates had the most significant influence on lamellar spacing and redistribution of rigid and soft amorphous fractions within the crystalline domains, ultimately dictating the mechanical and physical properties of the films. The goal is to explore the potential of the ultrasonic-assisted high crystallinity monolayer that can replace some of the functionality of complex, heterogeneous multilayer packaging with a single-material film having enhanced oxygen barrier properties.

crystallinity↗
DOE OSTI2020

Predicting the Activity and Selectivity of Bimetallic Metal Catalysts for Ethanol Reforming using Machine Learning

Machine learning is ideally suited for the pattern detection in large uniform datasets, but consistent experimental datasets on catalyst studies are often small. Here we demonstrate how a combination of machine learning and first-principles calculations can be used to extract knowledge from a relatively small set of experimental data. The approach is based on combining a complex machine-learning model trained on an extensive computational library of transition-state energies with simple linear regression models of experimental catalytic activities and selectivities from the literature. Using the combined model, we identify the key C–C bond scission reactions involved in ethanol reforming and perform a computational screening for ethanol reforming on monolayer bimetallic catalysts with architectures TM-Pt-Pt(111) and Pt-TM-Pt(111) (TM = 3d transition metals). The model also predicts four promising catalyst compositions for future experimental studies. In conclusion, the approach is not limited to ethanol reforming but is of general use for the interpretation of experimental observations as well as for the computational discovery of novel catalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗