Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Guide”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Synthesis and Characterization of Magnesium Vanadates as Potential Magnesium-Ion Cathode Materials through an Ab Initio Guided Carbothermal Reduction Approach**

Many technologically relevant materials for advanced energy storage and catalysis feature reduced transition-metal (TM) oxides that are often nontrivial to prepare because of the need to control the reducing nature of the atmosphere in which they are synthesized. In this report, we show that an ab initio predictive synthesis strategy can be used to produce multi-gram-scale products of various MgV x O y -type phases (δ-MgV 2 O 5 , spinel MgV 2 O 4 , and MgVO 3 ) containing V 3+ or V 4+ relevant for Mg-ion battery cathodes. Characterization of these phases using 25 Mg solid-state NMR spectroscopy illustrates the potential of 25 Mg NMR for studying reversible magnesiation and local charge distributions. Rotor-assisted population transfer (RAPT) is used as a much-needed signal-to-noise enhancement technique. The ab initio guided synthesis method is seen as a step forward towards a predictive synthesis strategy for targeting specific complex TM oxides with variable oxidation states of technological importance.

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

A GPT‐4 Reticular Chemist for Guiding MOF Discovery**

Abstract We present a new framework integrating the AI model GPT‐4 into the iterative process of reticular chemistry experimentation, leveraging a cooperative workflow of interaction between AI and a human researcher. This GPT‐4 Reticular Chemist is an integrated system composed of three phases. Each of these utilizes GPT‐4 in various capacities, wherein GPT‐4 provides detailed instructions for chemical experimentation and the human provides feedback on the experimental outcomes, including both success and failures, for the in‐context learning of AI in the next iteration. This iterative human‐AI interaction enabled GPT‐4 to learn from the outcomes, much like an experienced chemist, by a prompt‐learning strategy. Importantly, the system is based on natural language for both development and operation, eliminating the need for coding skills, and thus, make it accessible to all chemists. Our collaboration with GPT‐4 Reticular Chemist guided the discovery of an isoreticular series of MOFs, with each synthesis fine‐tuned through iterative feedback and expert suggestions. This workflow presents a potential for broader applications in scientific research by harnessing the capability of large language models like GPT‐4 to enhance the feasibility and efficiency of research activities.

Chemistry↗
DOE OSTI2026

Electronegativity-Guided Site Differentiation in High-Entropy Alloy for pH-universal Hydrogen Evolution Reactions

Enhancing the intrinsic activity of transition metal catalysts for the hydrogen evolution reaction (HER) remains a critical challenge in sustainable energy conversion. Herein, we report an electronegativity-guided site differentiation strategy in a single-phase CoNiCuMoW high-entropy alloy (HEA) via electrodeposition by incorporating high-electronegativity 4d/5d orbital transition metals (Mo, W) into the face-centered cubic (fcc) matrix (CoNiCu). The as-synthesized HEA demonstrates exceptional HER performance in all pH conditions, delivering an outstanding overpotential of 65 mV (alkaline), 28 mV (acidic), and 155 mV (neutral) at a current density of 100 mA cm−2, showing performance comparable to commercial Pt/C and has excellent long-term stability at high current density (1 A cm−2, 1000 h). X-Ray absorption spectroscopy (XAS) and density functional theory (DFT) calculations reveal that the incorporation of Mo/W simultaneously alters the local coordination environment and induces element-dependent charge redistribution, accompanied by a system-level d-band center downshift, thereby optimizing the hydrogen binding strength across multimetallic sites. Meanwhile, oxophilic Mo/W sites lower the water dissociation energy barrier. These synergistic effects collectively enable efficient and durable pH-universal HER performance.

Wu, Yutong↗
DOE OSTI2020

A Systematic Assessment of US Food and Drug Administration Dosing Recommendations For Drug Development Programs Amenable to Response‐Guided Titration

A key goal in drug development is optimized dosing for patients. Interactions between drug developers and regulatory scientists throughout development are important for the optimization of dosing and serve as a forum to discuss approaches for optimal dosing, such as precision or individualized dosing. To date, there has not been a systematic assessment of the advice provided by the US Food and Drug Administration (FDA) to drug developers from an individualized dosing perspective. Here, we reviewed FDA recommendations on dose selection for efficacy trials at end‐of‐phase meetings between the FDA and drug developers for 76 new molecular entities approved between 2013 and 2017 that are considered amenable for an individualized dosing method, response‐guided titration. Forty FDA dosing recommendations were identified as specific to dose selection and design of the respective efficacy trials and subsequently: (i) characterized based on if they were supportive of individualized dosing and (ii) compared with dosing regimens used in efficacy trials and labeling at approval to evaluate if FDA recommendations were implemented. Of these 40 recommendations for efficacy trials, 35 (88%) were considered supportive of individualized dosing. Eighteen of these 40 recommendations (45%) were incorporated into efficacy trials and 11 (28%) were incorporated into labeling. This research suggests that early FDA‐sponsor interactions can support the study of doses in efficacy trials that may lead to individualized dosing strategies in labeling.

Wang, Lingshan↗
DOE OSTI2024

Predicting responses to climate change using a joint species, spatially dependent physiologically guided abundance model

Abstract Predicting the effects of warming temperatures on the abundance and distribution of organisms under future climate scenarios often requires extrapolating species–environment correlations to climatic conditions not currently experienced by a species, which can result in unrealistic predictions. For poikilotherms, incorporating species' thermal physiology to inform extrapolations under novel thermal conditions can result in more realistic predictions. Furthermore, models that incorporate species and spatial dependencies may improve predictions by capturing correlations present in ecological data that are not accounted for by predictor variables. Here, we present a joint species, spatially dependent physiologically guided abundance (jsPGA) model for predicting multispecies responses to climate warming. The jsPGA model uses a basis function approach to capture both species and spatial dependencies. We apply the jsPGA model to predict the response of eight fish species to projected climate warming in thousands of lakes in Minnesota, USA. By the end of the century, the cold‐adapted species was predicted to have high probabilities of extirpation across its current range—with 10% of lakes currently inhabited by this species having an extirpation probability >0.90. The remaining species had varying levels of predicted changes in abundance, reflecting differences in their thermal physiology. Though the model did not identify many strong species dependencies, the variation in estimated spatial dependence across species suggested that accounting for both dependencies was important for predicting the abundance of these fishes. The jsPGA model provides a new tool for predicting changes in the abundance, distribution, and extirpation probability of poikilotherms under novel thermal conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bond Energies of UO + and UC + : Guided Ion Beam and Quantum Chemical Studies of the Reactions of Uranium Cation with O 2 and CO

Here, guided ion beam tandem mass spectrometry was used to examine the kinetic energy dependent reactions of U + with O 2 and CO. In the reaction of U + with O 2 , the UO + product is formed in a barrierless and exothermic process with a reaction efficiency at low energies of k/k col =1.1±0.2, but increases at higher collision energies. Formation of both UO + and UC + in the reaction of U + with CO is endothermic. 0 K bond dissociation energies (BDEs) of D 0 (U + -O)=7.88±0.09 eV and D 0 (U + -C)=4.03±0.13 eV were determined by analyzing the kinetic energy dependent cross sections in the latter endothermic reactions. These values are within experimental uncertainty of previously reported experimental values. Additionally, the electronic states of UO + and UC + and the potential energy surfaces for the reactions were explored by quantum chemical calculations. The former include a full Feller-Peterson-Dixon composite approach with correlation contributions up to CCSDT(Q) for UO and UO + , yielding D 0 (U-O)=7.82 eV and D 0 (U + -O)=7.99 eV, as well as more approximate CCSD(T) calculations where a semi-empirical model was used to estimate spin-orbit energy contributions, which are generally found to improve the agreement with experiment. Both experimental BDEs are observed to be close to those of their transition metal congeners, ScL + , YL + , and GdL + (L=O and C).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The microbiologist's guide to metaproteomics

Metaproteomics is an emerging approach for studying microbiomes, offering the ability to characterize proteins that underpin microbial functionality within diverse ecosystems. As the primary catalytic and structural components of microbiomes, proteins provide unique insights into the active processes and ecological roles of microbial communities. By integrating metaproteomics with other omics disciplines, researchers can gain a comprehensive understanding of microbial ecology, interactions, and functional dynamics. This review, developed by the Metaproteomics Initiative (www.metaproteomics.org), serves as a practical guide for both microbiome and proteomics researchers, presenting key principles, state-of-the-art methodologies, and analytical workflows essential to metaproteomics. Topics covered include experimental design, sample preparation, mass spectrometry techniques, data analysis strategies, and statistical approaches.

bioinformatics↗
DOE OSTI2021

Operando XAS/SAXS: Guiding Design of Single-Atom and Subnanocluster Catalysts

Single-atom and subnanocluster catalysts (SSCs) represent a highly promising class of low-cost materials with high catalytic activity and high atom-utilization efficiency. However, SSCs are susceptible to undergo restructuring during the reactions. Exploring the active sites of catalysts through in situ characterization techniques plays a critical role in studying reaction mechanism and guiding the design of optimum catalysts. In situ X-ray absorption spectroscopy/small-angle X-ray scattering (XAS/SAXS) is promising and widely used for monitoring electronic structure, atomic configuration, and size changes of SSCs during real-time working conditions. Unfortunately, there is no detailed summary of XAS/SAXS characterization results of SSCs. The recent advances in applying in situ XAS/SAXS to SSCs are thoroughly summarized in this review, including the atomic structure and oxidation state variations under open circuit and realistic reaction conditions. Furthermore, the reversible transformation of single-atom catalysts (SACs) to subnanoclusters/nanoparticles and the application of in situ XAS/SAXS in subnanoclusters are discussed. To conlcude, the outlooks in modulating the SSCs and developing operando XAS/SAXS for SSCs are highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Enhancing Interpretability in Generative Modeling: Statistically Disentangled Latent Spaces Guided by Generative Factors in Scientific Datasets

This study addresses the challenge of statistically extracting generative factors from complex, high-dimensional datasets in unsupervised or semi-supervised settings. We investigate encoder-decoder-based generative models for nonlinear dimensionality reduction, focusing on disentangling low-dimensional latent variables corresponding to independent physical factors. Introducing Aux-VAE, a novel architecture within the classical Variational Autoencoder framework, we achieve disentanglement with minimal modifications to the standard VAE loss function by leveraging prior statistical knowledge through auxiliary variables. These variables guide the shaping of the latent space by aligning latent factors with learned auxiliary variables. We validate the efficacy of Aux-VAE through comparative assessments on multiple datasets, including astronomical simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A Novel Design of Guiding Stress Wave Propagation

Impact loads can induce a series of undesirable physical phenomena including vibration, acoustical shock, perforation, fracture and fragmentation, etc. The energy associated with the impact loads can lead to severe structure damage and human injuries. A design approach which effectively reduces these negative impacts through shock/stress wave diversion is highly needed. In this paper, a computational model which predicts stress wave propagation by considering different beam geometries and configurations is developed. Additionally, a novel concept of wave guide design which modifies the stress wave propagation path without disturbance is also presented. Furthermore, this design approach is not only useful for material property characterization particularly at intermediate or high strain rates, but also allows stress wave propagation in a desired direction as the shock/impact energy can be redistributed in controllable paths. The numerical results are experimentally verified through a Drop-Hopkinson bar apparatus at Sandia National Laboratories.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Causality guided machine learning model on wetland CH 4 emissions across global wetlands

Wetland CH 4 emissions are among the most uncertain components of the global CH 4 budget. The complex nature of wetland CH 4 processes makes it challenging to identify causal relationships for improving our understanding and predictability of CH 4 emissions. In this study, we used the flux measurements of CH 4 from eddy covariance towers (30 sites from 4 wetlands types: bog, fen, marsh, and wet tundra) to construct a causality-constrained machine learning (ML) framework to explain the regulative factors and to capture CH 4 emissions at sub-seasonal scale. We found that soil temperature is the dominant factor for CH 4 emissions in all studied wetland types. Ecosystem respiration (CO 2 ) and gross primary productivity exert controls at bog, fen, and marsh sites with lagged responses of days to weeks. Integrating these asynchronous environmental and biological causal relationships in predictive models significantly improved model performance. More importantly, modeled CH 4 emissions differed by up to a factor of 4 under a +1°C warming scenario when causality constraints were considered. These results highlight the significant role of causality in modeling wetland CH 4 emissions especially under future warming conditions, while traditional data-driven ML models may reproduce observations for the wrong reasons. Our proposed causality-guided model could benefit predictive modeling, large-scale upscaling, data gap-filling, and surrogate modeling of wetland CH 4 emissions within earth system land models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Amorphous Ag 2-x Cu x S quantum dots: “all-in-one” theranostic nanomedicines for near-infrared fluorescence/photoacoustics dual-modal-imaging-guided photothermal therapy

Integrating dual-modal imaging and photothermal effect within a single nanosystem is an enormous challenge for current nanomedicine application. In this study, non-toxic amorphous Ag 2-x Cu x S quantum dots (QDs) possessing near-infrared fluorescence (NIRF), enhanced photothermal and photoacoustic (PA) performance, as “one-in-all” nanomedicines, are designed and obtained. Cu doping in amorphous Ag 2 S QDs causes red-shift of NIRF to the lowest tissue absorption wavelength in so called “first biowindow” (~820 nm) and improves photothermal conversion efficiency to 44.0% simultaneously. Density functional theory simulation results suggest the fluorescence red-shift and enhanced photothermal/photoacoustics properties can be attributed to the generation of intragap states introduced by Cu doping. The amorphous Ag 2-x Cu x S QDs exhibit high long-term biocompatibility. Under a single laser irradiation with relatively low power density, an effective tumor ablation induced by high photothermal effect of amorphous Ag 2-x Cu x S QDs is achieved in vivo. Simultaneously, the PTT process can be guided by high quality NIRF/PA dual-modal-imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Mapping Neutralizing and Immunodominant Sites on the SARS-CoV-2 Spike Receptor-Binding Domain by Structure-Guided High-Resolution Serology

We report analysis of the specificity and kinetics of neutralizing antibodies (nAbs) elicited by SARS-CoV-2 infection is crucial for understanding immune protection and identifying targets for vaccine design. In a cohort of 647 SARS-CoV-2-infected subjects, we found that both the magnitude of Ab responses to SARS-CoV-2 spike (S) and nucleoprotein and nAb titers correlate with clinical scores. The receptor-binding domain (RBD) is immunodominant and the target of 90% of the neutralizing activity present in SARS-CoV-2 immune sera. Whereas overall RBD-specific serum IgG titers waned with a half-life of 49 days, nAb titers and avidity increased over time for some individuals, consistent with affinity maturation. We structurally defined an RBD antigenic map and serologically quantified serum Abs specific for distinct RBD epitopes leading to the identification of two major receptor-binding motif antigenic sites. Our results explain the immunodominance of the receptor-binding motif and will guide the design of COVID-19 vaccines and therapeutics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section

Paper drying is responsible for over two-thirds of energy consumption in the U.S. pulp and paper industry, presenting significant potential for energy savings through optimization of process parameters. Current approaches often assume fixed operating conditions, neglecting dynamic ambient and process variations that limit achievable savings and real-world applicability. To this end, we develop a physics-based simulation environment for a paper machine dryer section and propose a reinforcement learning (RL) framework to minimize overall energy consumption by optimizing drying process parameters under diverse operating conditions. To mitigate overdrying and numerical instabilities caused by suboptimal local RL actions, we introduce Reinforcement Learning-Guided Beam Search (RLGBS), which explores multiple action sequences in parallel using beam search. Instead of making step-by-step decisions, RLGBS prioritizes solutions based on cumulative probability, reducing the impact of individual suboptimal actions. Experiments demonstrate that RLGBS achieves consistent energy savings under unseen operating conditions not encountered during training, outperforming conventional RL methods. While validated in drying optimization, this framework is broadly applicable to other RL-based industrial process control problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Structure-guided design of direct-acting antivirals that exploit the gem-dimethyl effect and potently inhibit 3CL proteases of severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) and middle east respiratory syndrome coronavirus (MERS-CoV)

The high morbidity and mortality associated with SARS-CoV-2 infection, the etiological agent of COVID-19, has had a major impact on global public health. Significant progress has been made in the development of an array of vaccines and biologics, however, the emergence of SARS-CoV-2 variants and breakthrough infections are an ongoing major concern. Furthermore, there is an existing paucity of small-molecule host and virus-directed therapeutics and prophylactics that can be used to counter the spread of SARS-CoV-2, and any emerging and re-emerging coronaviruses. Here, we describe herein our efforts to address this urgent need by focusing on the structure-guided design of potent broad-spectrum inhibitors of SARS-CoV-2 3C-like protease (3CL pro or Main protease), an enzyme essential for viral replication. The inhibitors exploit the directional effects associated with the presence of a gem-dimethyl group that allow the inhibitors to optimally interact with the S4 subsite of the enzyme. Several compounds were found to potently inhibit SARS-CoV-2 and MERS-CoV 3CL proteases in biochemical and cell-based assays. Specifically, the EC50 values of aldehyde 1c and its corresponding bisulfite adduct 1d against SARS-CoV-2 were found to be 12 and 10 nM, respectively, and their CC50 values were >50 μM. Furthermore, deuteration of these compounds yielded compounds 2c/2d with EC50 values 11 and 12 nM, respectively. Replacement of the aldehyde warhead with a nitrile (CN) or an α-ketoamide warhead or its corresponding bisulfite adduct yielded compounds 1g, 1e and 1f with EC50 values 60, 50 and 70 nM, respectively. High-resolution cocrystal structures have identified the structural determinants associated with the binding of the inhibitors to the active site of the enzyme and, furthermore, have illuminated the mechanism of action of the inhibitors. Overall, the high Safety Index (SI) (SI=CC50/EC50) displayed by these compounds suggests that they are well-suited to conducting further preclinical studies.

3-Chymotrypsin-like protease (3CLpro)↗
DOE OSTI2024

Stress intensity factor models using mechanics-guided decomposition and symbolic regression

The finite element method can be used to compute accurate stress intensity factors (SIFs) for cracks with complex geometries and boundary conditions. In contrast, handbook solutions act as surrogate SIF models that provide significantly faster evaluation times. However, the development of conventional surrogate SIF models relies on manual development based on low-order parameterizations. This limits surrogate model accuracy and generalizability. Here, in this paper, we develop a framework for the automated development of mechanics-guided handbook SIF solutions by using interpretable machine learning via genetic programming for symbolic regression (GPSR). Formalizing the mechanics-based approach of Raju and Newman, SIF training data is decomposed into multiple subsets. This decomposition enables parallel GPSR model development of subfunctions, each of which accounts for specific geometrical corrections with respect to a known analytical model. Using this mechanics-based approach with GPSR allows for equations to be learned with improved accuracy and reduced complexity relative to the Raju Newman equations while maintaining the inherent interpretability of mathematical expressions. In this paper, we present equations that match the complexity of the Raju Newman equations while having reduced error, as well as equations with similar errors and reduced complexity.

42 ENGINEERING↗
DOE OSTI2026

Elastic-wave sensitivity-guided adaptive seismic survey design for cost-effective monitoring of geological carbon storage

Effective seismic monitoring is essential for verifying CO₂ containment, detecting potential leakage, and optimizing operational decisions in geologic carbon storage. Here, this study presents a time-adaptive, elastic-wave sensitivity-guided framework for designing cost-effective seismic monitoring layouts for tracking CO₂ plume migration. The method is based on elastic-wave sensitivity analysis, which quantifies how variations in subsurface properties impact seismic wavefields. Two complementary design strategies are developed: one based on selecting a fixed number of seismic sources (Method A), and the other based on selecting source–receiver pairs contributing to a fixed fraction of cumulative elastic-wave sensitivity energy (Method B). The optimization workflow to identify source–receiver configurations with the highest detection potential is demonstrated using a hypothetical GCS scenario at the Kimberlina site in California using simulations of elastic-wave sensitivity data at multiple post-injection timesteps. Results show that both strategies adapt to evolving plume geometries and wavefield sensitivities, with Method B offering broader spatial coverage and Method A ensuring simpler deployment. This framework enables site-specific, cost-effective, and risk-informed seismic survey designs, enhancing the ability to monitor CO₂ migration over time in evolving geological environments

58 GEOSCIENCES↗
DOE OSTI2021

Life-cycle analysis of greenhouse gas emissions from hydrogen delivery: A cost-guided analysis

The cost of hydrogen delivery for transportation accounts for most of the current H 2 selling price; delivery also requires substantial amounts of energy. In this work, we developed harmonized techno-economic and life-cycle emissions models of current and future H 2 production and delivery pathways. Our techno-economic analysis of dispensed H 2 costs guided our selection of pathways for the life-cycle analysis. In this paper, we present the results of market expansion scenarios using existing capabilities (for example, those that use H 2 from steam methane reforming, chlor-alkali, and natural gas liquid cracker plants), as well as results for future electrolysis plants that use nuclear, solar, and hydroelectric power. Reductions in greenhouse gas emissions for fuel cell electric vehicles compared to conventional gasoline pathways vary from 40% reduction for fossil-derived H 2 to 20-fold for clean H 2 . Supplemental tables with greenhouse gas emissions data for each step in the H 2 pathways enable readers to evaluate additional scenarios.

08 HYDROGEN↗