Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Patterns”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Predicting U.S. federal fleet electric vehicle charging patterns using internal combustion engine vehicle fueling transaction statistics

Utilizing fueling transactions from internal combustion engine vehicles (ICEVs), the authors estimated how frequently midday public charging would be required for U.S. federal fleet battery electric vehicles (BEVs). Fueling transaction summary statistics are more widely available than trip-level telematics data, making this methodology more accessible and transferable to other researchers and fleet managers considering BEV replacements. For example, readers can easily apply a linear model using only the count of back-to-back fueling events at gas stations over 57 straight-line miles apart to predict days exceeding range. This linear regression predicted binned days exceeding 250 miles at 80% accuracy on a hold-out test set from the same fleet as the training data and 66 % accuracy on a new fleet displaying different driving behaviors. The authors additionally provide linear equations for days exceeding 200 and 300 miles as alternative range estimates to account for differences in BEV range and temperature impacts. Beyond the single-feature linear models which readers can apply, the authors tuned and trained other machine learning models on a variety of fueling transaction statistics including consecutive transaction distances, transaction distance from garage, estimated miles traveled from fuel economy and fuel quantity, and transaction periodicity. Utilizing a subset of 1678 light-duty federal fleet vehicles which contained daily vehicle miles traveled (VMT) in addition to fueling statistics, the authors determined which fueling transaction statistics were most relevant in predicting driving days exceeding 250 miles (an approximation of BEV rated driving range). In support of the U.S. federal fleet transition to zero-emission vehicles (ZEVs), the authors used these statistics and machine learning models to predict the frequency of BEV midday charging. After training models on the subset with VMT, the authors predicted days exceeding rated range for 112,902 light-duty vehicles operating in similar circumstances in the federal fleet using a Support Vector Regressor (SVR). In conclusion, they then used the projections as part of the ZEV Planning and Charging (ZPAC) tool to identify optimal candidates for BEVs for the federal fleet. An anonymized version of ZPAC is included in the supplementary materials.

25 ENERGY STORAGE↗
DOE OSTI2024

A high-throughput workflow to analyze sequence-conformation relationships and explore hydrophobic patterning in disordered peptoids

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. Herein, we introduce a high-throughput workflow that implements a practical colorimetric conformational assay, introduces a semi-automated sequencing protocol using matrix-assisted laser desorption/ionization and tandem mass spectrometry (MALDI-MS/MS), and develops a generalizable sequence-structure algorithm. Using a model system of 20mer peptidomimetics containing polar glycine and hydrophobic N-butylglycine residues, we identified nine classifications of conformational disorder and isolated 122 unique sequences across varied compositions and conformations. Conformational distributions of three compositionally identical library sequences were corroborated through atomistic simulations and ion mobility spectrometry coupled with liquid chromatography. A data-driven strategy was developed using existing sequence variables and data-derived “motifs” to inform a machine-learning algorithm toward conformation prediction. Here, this multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery of materials with conformational control.

data-driven analysis↗
DOE OSTI2025

Quantitative multiplexed analysis of gene and protein expression patterns in Yarrowia lipolytica

In this report, we present coordinated observations of protein and mRNA transcript counts at the single-cell level in the oleaginous yeast model Yarrowia lipolytica. The transcription factor Xbp1p regulates entry into a quiescent state, representing a shift of resources to sequestration of nutrients rather than cell division. We observed the responses of wild-type and Δxbp1 cells to protein (by fluorescence) and transcript quantification and localization at both single-cell and population-averaged levels. Data were collected via single-molecule fluorescence in situ hybridization (smFISH) and qPCR under nitrogen depletion, a condition that drives lipid accumulation. These techniques reveal a complex and heterogeneous population of Xbp1p dynamics and downstream regulation. Our findings highlight the need for single-cell resolution analyses to describe cellular dynamics and regulatory processes.

Yarrowia lipolytica↗
DOE OSTI2026

Enhanced Manganese Oxidation at the Biofilm–Fluid Interface Drives Pore-Scale Patterns in Mineral Precipitation

Microbial oxidation of manganese (Mn) from aqueous Mn(II) to solid-phase Mn(III, IV) minerals catalyzes Mn(II) removal in natural and engineered porous systems. However, little is known about the spatiotemporal evolution of Mn biomineralization in confined spaces that experience simultaneous Mn(II) delivery and Mn oxide precipitation. Here, we combine time-lapse microscopy, image analysis, and mass spectrometry to quantify the extent and rate of Mn biomineralization by Pseudomonas putida GB-1 in an optically transparent two-dimensional porous medium. We found that Mn(II) oxidation initially occurred within biofilms but shifted over time toward the edges of biofilms in contact with pore fluid. Minerals precipitated outside of the initial biofilm footprint likely due to surface-mediated oxidation of Mn(II) by nascent biogenic Mn oxides, reinforcing a gradient in mineral accumulation from the Mn(II) source near the reactor inlet to the outlet. The rate of mineral precipitation outside the biofilm footprint surpassed the rate of mineral accumulation inside biofilms within 6 h and accounted for two-thirds of the total Mn oxide mass in the pore space at the end of the experiment. This work advances a mechanistic understanding of coupled biotic and abiotic Mn oxidation in porous environments while providing a novel platform to quantify microbe-mineral-fluid interactions.

Biofilms↗
DOE OSTI2024

Moiré Patterns in Pt Overlayers on Gold: A Graph Neural Network Interatomic Potential Study

Overlayer structures in bimetallic catalysts are relevant to a variety of catalytic reactions, particularly in electrocatalysis for fuel cell applications. Previous computational studies largely consider these overlayer structures to be those of a pseudomorphic overlayer, where there is a 1:1 atomic ratio between the overlayer and the support metal. Our previous work based on density functional theory (DFT) has shown that there exist nonstoichiometric overlayer structures that are more stable than the stoichiometric ones. Here, in this work, we developed a graph neural network interatomic potential (GNN-IP) to analyze structures formed in Pt overlayers on Au(111). The GNN-IP was used to explore properties of nonstoichiometric overlayers at length scales that are prohibitively expensive to pursue with planewave DFT calculations. In particular, we examined large Pt islands on top of a 48 × 48 Au(111) unit cell to explore the influence of the rotational angle (α) between the Pt overlayer and support Au(111) on both the stability of and the preferred atomic density in the overlayer. Island structures with smaller rotational angles between the Pt overlayer and Au(111) tend to be more stable. Further, smaller rotational angles tend to result in lower atomic density in the Pt overlayer.

chemical structure↗
DOE OSTI2025

Evolution and Degradation Patterns of Electrochemical Cells Based on the Analysis of Interfacial Phenomena at Li Metal Anode/Electrolyte Interfaces

In this work, we report the results of a theoretical–computational analysis of the solid electrolyte interphase (SEI) growth and degradation dynamics occurring in lithium metal batteries during cycling. We use ab initio-kinetic Monte Carlo simulations to generate a synthetic data set, which is analyzed by machine learning methods. We aim to determine: (i) how modifications in interfacial interaction energies between solid electrolyte interphase (SEI) blocks and between Li ions and SEI facets impact the Coulombic efficiency (CE) of the battery and (ii) what factors, including reactions, microscopic transport, and other interfacial events, may lead to cell performance “failure” during prolonged charge and discharge cycles, signaled as a sharp decay in the CE over cycling. The demonstration of our approach is done on a cell including a Li metal surface interfacing with a previously introduced state-of-the-art electrolyte, and the idea can be applied to any electrochemical system. Outcomes include the identification of the leading chemical, physical, and structural variables causing cell failure and relating them to the electrolyte formulation, thus paving the way to future more refined analysis and electrolyte design.

batteries↗
DOE OSTI2026

Mechanism-Informed Breakdown: Understanding Degradation by Controlling Voltage-Hold Patterns in Proton Exchange Membrane Water Electrolyzers

Low catalyst loadings pose challenges to performance stability in proton exchange membrane (PEM) water electrolysis over extended operation. To study the impact of degradation mechanisms and voltage loss rates, different stress tests are applied to membrane electrode assemblies. Potential cycling conditions were observed to induce higher degrees of iridium (Ir) oxide crystallization, ionomer degradation, and catalyst layer (CL) thinning, which likely contributed to higher kinetic loss rates. On the other hand, while Ir migrating into the PEM (Ir band) generally impairs performance, the interconnected and more uniform Ir band formed under a constant 2 V hold may allow for Ir at the catalyst/membrane interface to remain electronically connected and kinetically accessible, as well as indicate greater Ir site access during the applied stressor. The 2 V hold also demonstrates improved kinetic durability through a lower Tafel slope, faster polarization kinetics, and reduced charge transfer resistance. In contrast, potential cycling caused the migration of disconnected Ir agglomerates into the membrane bulk and created a steady increase in charge transfer resistance, a more dramatic decrease in capacitance (46.7% loss), and significant damage to the surrounding ionomer, indicating a decline in both the quality and quantity of active sites in the anode CL. This work underscores the distinct degradation pathways associated with load holds versus cycling, highlighting the role of catalyst-ionomer interactions in kinetic performance and long-term stability. These insights can inform operational strategies for PEM electrolyzers powered by intermittent energy sources, aiming to minimize efficiency losses over extended operation.

36 MATERIALS SCIENCE↗