Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Atlanta”

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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

DeSelenator: A Se-Removal Process for Environmental Decontamination of Wastewaters from Coal-Burning Power Plants

Selenium may become a toxic contaminant of freshwater systems when released into the environment through industrial wastewaters from mining, coal-burning power plants, or oil refining. Efficient and cost-effective Se-removal technologies are therefore necessary to reduce Se concentrations in these wastewaters to below the regulatory discharge limits. In this study, we have demonstrated an effective process that removes Se, mostly as selenate anions, from wastewaters generated by coal-burning power plants. This process, dubbed DeSelenator, leverages the high concentration of sulfate relative to selenate in the wastewater and the propensity of these oxyanions to cocrystallize with benzene-bis-iminoguanidinium (BBIG) cations into extremely insoluble salts (on par with BaSO 4 ). The SO 4 2− /SeO 4 2− cocrystallization with BBIG removes over 90% of S and Se from the wastewater. Following removal of the precipitate by filtration, the filtrate is passed over an anion-exchange resin that further reduces selenium concentration to 5 ppb, the EPA’s regulatory limit for freshwater systems. Finally, the effluent is passed over an activated carbon column, which removes 99.8% of the residual BBIG ligand remaining after crystallization, allowing for the safe discharge of the treated water into the environment. The Se-removal process was first optimized in the lab at the bench scale and then tested in the field at the Tennessee Valley Authority’s Bull Run coal-burning power plant. A technoeconomic assessment found the cost of water treatment with DeSelenator is on par with that of the active biological method, which is currently considered a state-of-the-art Se-removal technology.

anions↗
DOE OSTI2025

Cetyltrimethylammonium Bromide/Chloride on Gold Nanocrystals Can Be Directly Replaced with Tri-Citrate

This work demonstrates an effective method for directly exchanging the toxic cetyltrimethylammonium bromide/chloride (CTAB/C) on Au nanocrystals with tri-citrate. Our experimental and computational studies indicate that counterion plays a vital role in the exchange process. Specifically, when citrate species bind to Au surface, they all evolve into tri-citrate with different counterions. In the case of three H + counterions, tri-citrate could readily replace the CTAB/C due to a strong binding of the carboxylate group with the Au surface. The substitution of H + counterion by Na + or K + weakens the binding strength and thus compromises the exchange. Additionally, our quantitative measurements and theoretical calculations indicate that Au nanospheres encased by high-index facets are advantageous over their counterparts enclosed by {111} and/or {100} facets for the exchange owing to the difference in binding strength. The mechanistic insights and experimental control should be extendable to other combinations of surface ligands and metal nanocrystals.

adsorption↗
DOE OSTI2024

Model-Based Energy and Cost Analysis of Direct Air Capture Using ePTFE-Based Laminate-Structured Gas–Solid Contactors

Carbon dioxide removal (CDR) technologies will play a significant role in limiting global warming if implemented on a large scale. Direct air capture (DAC) is a scalable approach for removing atmospheric carbon, yet the true scope of its scalability remains unclear due to the early stage of technology development and high first plant costs. This study provides groundwork for understanding the technoeconomic trade-offs in developing DAC systems using laminate-structured gas–solid contactors, encompassing the analysis of both contactor and process design spaces. The robust mass transfer and process models outlined in this study provide tools for evaluating DAC processes and designing DAC plants based on cost and energy analysis. First, the key contactor geometrical parameters are identified to understand the CO 2 productivity–energy demand trade-offs, where geometries yielding higher mass transfer rates can achieve higher CO 2 productivities at the expense of energy consumption by fans and steam use. Next, a detailed process parametric study is conducted for DAC systems coupled with steam-assisted temperature-vacuum swing adsorption (S-TVSA) to visualize the trade-offs in the multidimensional design space. The main cost driver dramatically changes over different process conditions, but the operating cost prevailed on the Pareto front, with potential to operate as low as 150 $/tonne-CO 2 (within the cost range of 148–504 $/tonne-CO 2 in this study where the DAC system is coupled with industrial facilities for steam production).

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

Cell-Free-Based Thermophilic Biocatalyst for the Synthesis of Amino Acids from One-Carbon Feedstocks

Bioproduction from one-carbon compounds, such as formate, is an attractive prospect due to reduced energy requirements and the possibility for using CO 2 as a sustainable feedstock. Formate-fixing pathways engineered using Escherichia coli lysate-based cell-free expression (CFE) biocatalysts have the potential to route 100% of feedstock carbon toward chemical synthesis but are undermined by siphoning of in-pathway metabolites and cofactors by the CFE background metabolism. To address this limitation, we engineer a CFE-based thermophilic multienzyme biocatalyst for the synthesis of serine and glycine from formate, bicarbonate, and ammonia. After expression of the thermophilic formate-to-serine pathway in a one-pot reaction, the mesophilic E. coli CFE background machinery is removed by simple heat denaturation, eliminating the siphoning of cofactors, inpathway metabolites, and products. After bioprocess optimization, including pathway gene expression duration and chemical synthesis temperature, we achieve near stoichiometric conversion of formate and bicarbonate to serine and glycine, reaching 97% of stoichiometric yield. The use of a moderately thermophilic biocatalyst allowed chemical synthesis to take place at mesophilic temperatures, enabling the balance of optimal enzyme activity with minimal metabolite/cofactor thermal degradation. In a fed-batch experiment, the biocatalyst shows sustained chemical synthesis rates for 8 h, paving the way toward a continuous bioprocess. Finally, a sensitivity analysis of cofactor usage revealed that the most expensive cofactors, THF and NADPH, can be reduced by 5-fold without significantly lowering product yields. To the best of our knowledge, this is the first instance of expressing a thermophilic pathway in an E. coli lysate-based CFE system to generate a thermophilic biocatalyst for use at mesophilic temperatures. The CFEbased thermophilic formate-to-serine biocatalyst triples the combined serine and glycine yield previously obtained by a CFE-based mesophilic formate-to-serine biocatalyst (30%), and quadruple the yield obtained by a purified enzyme system (22%). Ultimately, this work opens the door to using E. coli lysate-based CFE for thermophilic biocatalyst generation to achieve high chemical synthesis yields.

bacteria↗
DOE OSTI2025

Fano Resonance in CO 2 Reduction Catalyst Functionalized Quantum Dots

Molecular catalyst functionalized semiconductor quantum dots (QDs) are a promising modular platform for developing novel hybrid photocatalysts. The interaction between adsorbed catalyst vibrations and the QD electron intraband absorption can influence the photophysical properties of both the QD and the catalysts and potentially their photocatalysis. In CdSe QDs functionalized by the CO 2 reduction catalyst, Re(CO) 3 (4,4’-bipyridine-COOH)Cl, we observe that the transient Fano resonance signal resulting from coupling of the catalyst CO stretching mode and the QD conduction band electron mid-IR intraband absorption appears on an ultrafast time scale and decays with the electron population, irrespective of the occurrence of photoreduced catalysts. The Fano asymmetry factor increases with an increase in the adsorbed catalyst number and a decrease in QD sizes. The latter can be attributed to an enhanced charge transfer interaction between the more strongly quantum-confined QD conduction band and catalyst LUMO levels. These results provide a more in-depth understanding of interactions in excited QD-catalyst hybrid photocatalysts.

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

What Makes Au Nanospheres Superior to Octahedral and Cubic Counterparts for the Deposition of a Pt Monolayer Shell?

This study demonstrates that Au nanospheres are advantageous over their octahedral and cubic counterparts as seeds in the synthesis of Au@Pt core−shell nanocrystals with a monolayer shell. In combination with experimental characterization, we show through training a machine-learned interatomic potential that the Au nanospheres exhibit a large fraction of lowcoordination atoms which are uniformly distributed over the surface. The corresponding high-index facets, including {211}, {311}, {331}, {210}, and {310}, on a spherical seed promote nucleation while greatly shortening the diffusion distance for adatoms. In addition, the high-index facets are instrumental in retaining the deposited Pt atoms on the outermost surface by retarding their inter-diffusional exchange with the underlying Au atoms. By switching from a monolayer made of pure Pt to those made of Pt−Au alloys, we can optimize both the activity and selectivity of the nanocrystals toward the two-electron oxygen reduction reaction for the electrochemical synthesis of H 2 O 2 . This method should be extendible to the fabrication of other core−shell nanocatalysts with desired monolayer shells for various catalytic reactions.

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

Atomic Evolution of Hydrogen Intercalation Wave Dynamics in Palladium Nanocrystals Revealed by Liquid-Phase Transmission Electron Microscopy

Solute-intercalation-induced phase separation creates spatial heterogeneities in host materials, a phenomenon ubiquitous in batteries, hydrogen storage, and other energy devices. Despite many efforts, probing intercalation processes at the atomic scale has been a significant challenge. By utilizing liquid-phase transmission electron microscopy (TEM), we study hydrogen (de)intercalation in palladium nanocrystals as a model system and have achieved unprecedented atomic-resolution imaging of hydrogen intercalation wave dynamics. Our observations reveal that intercalation wave mechanisms, instead of shrinking-core mechanisms, prevail at ambient temperature for palladium nanocubes ranging from ∼60 nm down to ∼10 nm. Systematic image analysis uncovers the atomic evolution of the hydrogen intercalation wave, transitioning from nonplanar and inclined boundaries to those closely aligned with {100} planes. Our kinetic Monte Carlo simulations demonstrate that the observed intercalation wave dynamics correspond to sorption pathways minimizing the lattice mismatch strain at the phase boundary. In conclusion, unveiling the atomic intercalation pathways holds profound implications for engineering intercalation-mediated devices and advancements in energy sciences.

Lee, Daewon [Lawrence Berkeley National Laboratory↗
DOE OSTI2026

Plant‐Induced Changes Mediate Belowground Carbon Cycling in an Experimentally Warmed Peatland

Warming and elevated atmospheric CO 2 profoundly impact peatland ecosystems, particularly through changes in plant species composition. Plants regulate the initial input of organic compounds to peatland belowground systems, controlling the availability of electron donors and electron acceptors that fuel microbially mediated organic matter decomposition to CO 2 and CH 4 . However, explicit links between porewater CO 2 and CH 4 dynamics and plant-derived chemical compounds remain relatively undefined. In a whole ecosystem warming experiment, we investigated how warming affects plant leaf chemical composition and species assemblages, and how the alteration of leaf-derived organic compounds supplied to the subsurface impacts belowground CO 2 and CH 4 production. While earlier studies at our site found no temperature-dependent changes in CH 4 production pathways, our extended timeseries has revealed increased acetoclastic methanogenesis at higher temperatures in certain peat depths, correlated with elevated porewater phenolics. These changes appear driven by the observed increased plant productivity and altered vegetation inputs, which accelerate decomposition and fuel CH 4 production through enhanced substrate availability. In conclusion, we observed warming-induced changes in molecular composition both between and within plant species, suggesting that plant-mediated controls on belowground carbon processing are more complex than previously recognized.

Wilson, Rachel M. [Florida State Univ., Tallahasse↗
DOE OSTI2025

ACES-GNN: can graph neural network learn to explain activity cliffs?

Graph Neural Networks (GNNs) have revolutionized molecular property prediction by leveraging graph-based representations, yet their opaque decision-making processes hinder broader adoption in drug discovery. This study introduces the Activity-Cliff-Explanation-Supervised GNN (ACES-GNN) framework, designed to simultaneously improve predictive accuracy and interpretability by integrating explanation supervision for activity cliffs (ACs) into GNN training. ACs, defined by structurally similar molecules with significant potency differences, pose challenges for traditional models due to their reliance on shared structural features. By aligning model attributions with chemist-friendly interpretations, the ACES-GNN framework bridges the gap between prediction and explanation. Validated across 30 pharmacological targets, ACES-GNN consistently enhances both predictive accuracy and attribution quality for ACs compared to unsupervised GNNs. Our results demonstrate a positive correlation between improved predictions and accurate explanations, offering a robust and adaptable framework to better understand and interpret ACs. This work underscores the potential of explanation-guided learning to advance interpretable artificial intelligence in molecular modeling and drug discovery.

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

The levelized cost of exergy: a technoeconomic framework for energy system comparison

While the levelized costs of electricity and heat have been quantified before, these two metrics cannot be directly compared, due to the different exergy content of heat and work. To address this, we develop a levelized cost of exergy (LCOEx) framework that enables direct comparisons between energy sources and processes. We find that moderate- and high-grade heat have an LCOEx that is comparable to electricity (5–10 ¢ per kWh ex ), while low-grade heat sources have much higher LCOEx values (>50 ¢ per kWh ex ). The LCOEx of a system's output is affected by (i) the LCOEx of the system input, (ii) the CAPEX of the system, and (iii) the exergetic efficiency of the system. We use our framework to identify which processes are already achieved with relatively high cost effectiveness (production of fuels, hydrogen, and ammonia) and which have room for improvement (dehumidification, food production).

08 HYDROGEN↗
DOE OSTI2025

Wind-Driven Direct Air Capture System Using 3D Printed, Passive, Amine-Loaded Contactors (WEDAC)

This project advanced the development of a novel wind-driven direct air capture (WEDAC) system for removing carbon dioxide (CO 2 ) from the atmosphere. Our research focused on combining passive air contact with electrothermal desorption using specially designed carbon fiber modules, aiming to provide a scalable, low-cost, and energy-efficient alternative to conventional carbon capture systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Virtual Power Plant Architecture and Resilient Design

Virtual Power Plants (VPPs) represent a fundamental shift in electric grid operations, aggregating distributed energy resources (DERs) such as solar panels and battery storage to deliver utility-scale grid services traditionally provided by centralized power plants. This report examines the unique architectural, operational, and digital assurance considerations that distinguish VPPs from conventional utility infrastructure as they scale from pilot projects to mainstream deployment across the United States. While VPPs offer significant opportunities for grid modernization and enhanced flexibility, their distributed, multi-stakeholder architecture introduces distinct security challenges that differ fundamentally from traditional generation facilities. The analysis identifies risks in VPP operations, including device-level security gaps, platform vulnerabilities, and communication protocol weaknesses that create expanded attack surfaces compared to centralized power plants. Through examination of real-world incidents and emerging threat patterns, the report demonstrates how some VPPs' reliance on consumer-owned devices, public internet infrastructure, and complex vendor ecosystems require new approaches to digital assurance and operational security. The findings provide practical guidance for utilities, regulators, and aggregators to implement robust security frameworks and operational best practices essential for maintaining grid reliability as VPP deployment accelerates under the Federal Energy Regulatory Commission (FERC) Order 2222 and related regulatory initiatives.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Business Case Analysis for Artificial Intelligence-Large Language Model Technology Integration

AI-assisted processes are expected to enhance operational efficiency and improve decision-making, supporting the long-term economic viability of nuclear power plants. However, detailed business analyses of AI-generated cost savings are rarely performed. Given the recent industry interest in Large Language Model (LLM), the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has conducted a comprehensive business case analysis of LLM Artificial Intelligence (AI) implementation in nuclear plant engineering workflows. The research employed three complementary business case approaches to evaluate impact of an LLM, using three representative engineering processes as use-cases: Boric Acid Corrosion (BAC) Evaluations, Maintenance Rule Evaluations, and 10 CFR 50.59 Screenings. Through detailed workload analyses and structured interviews, the study quantified significant efficiency improvements ranging from 11% to 59% across these processes. The research further considers how these efficiency gains could translate into tangible reliability improvements through enhanced engineering capacity. Analysis of historical plant trip data indicates that enabling engineers to focus on proactive reliability activities could provide substantial financial benefits through avoided outages, potentially generating greater value than the direct efficiency improvements alone. By documenting successful applications, implementation challenges, and strategic opportunities, this research provides nuclear utilities with a practical framework for evaluating the value of AI technology to support long-term operations through advanced digital technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors

This R&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Cyber-Informed Engineering (CIE) Guide for States

The Cyber-Informed Engineering (CIE) Guide for States provides state energy offices, public utility commissions, and partner organizations with a structured framework for integrating cyber-resilient engineering practices into energy planning, grantmaking, interconnection processes, and workforce development. As grid digitalization and the adoption of distributed energy resources accelerate, states face expanding cyber-physical risks that traditional cybersecurity measures alone cannot fully address. CIE offers a proactive, consequence-focused engineering methodology that emphasizes eliminating or mitigating high-impact failure modes through design, physical controls, and operational safeguards. The guide outlines the 12 core CIE principles, demonstrates their application through state-focused use cases—including grant evaluation rubrics, interconnection reviews, allow-list development, and training programs—and provides practical tools such as scoring frameworks, impact assessment methods, and implementation checklists. It also highlights pathways for state–utility collaboration and opportunities for technical assistance from national laboratories. By adopting CIE, states can enhance grid reliability, reduce lifecycle costs, strengthen supply-chain assurance, and foster a security-aware engineering culture that aligns with broader resilience and modernization goals. November 2025

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Ultra‐Flexible Pixelated Perovskite Photodetectors Enabled by Honeycomb Polymer Grids for High‐Resolution Imaging

Abstract A nature‐inspired fabrication method based on a photolithography‐free flexible polymer grid is reported for high‐resolution pixelation of perovskite photodiode arrays with exceptional mechanical ductility and a morphology resembling that of natural compound eyes. The resulting pixelated perovskite photosensitive layer has a ≈1 µm pixel size with 2000 Pixels per inch (PPI) resolution when fully assembled as a photodetector array, delivering a detectivity of >10 13 Jones while providing cross‐talk free imaging. Using a polymer grid effectively releases stress on the perovskite platform, greatly increasing the mechanical agility of the otherwise brittle perovskite film. This novel fabrication methodology and device design offer new possibilities for applications in robotics, biomedical imaging, and virtual and augmented reality.

Zheng, Ding [Department of Chemistry and the Mater↗
DOE OSTI2025

Synergistic Tuning of Microstructure and Morphology in Carbon Molecular Sieve Hollow Fibers for Propylene/Propane Separation

Abstract Asymmetric carbon molecular sieve (CMS) hollow fiber membranes with tunable micro‐ and macro‐structural morphologies for energy efficient propylene‐propane separation are reported here. A sub‐glass transition temperature (sub‐Tg) thermal oxidative crosslinking strategy enables simultaneous optimization of the intrinsic molecular sieving properties while also reducing the thickness of the CMS “skin” derived from the 6FDA : BPDA/DAM polyimide precursors. Such synergistic tuning of CMS microstructure and macroscopic morphology of CMS hollow fibers enables significantly increased propylene permeance (reaching 186.5 GPU) while maintaining an appealing propylene/propane selectivity of 13.3 for 50/50 propylene/propane mixed gas feeds. Our findings reveal a more refined and versatile tool than available with previous O 2 ‐doping pretreatments. The advanced approach here should be broadly useful to other polyimide precursors and diverse gas pairs.

Liu, Zhongyun [School of Chemical &amp, Biomolecul↗