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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Wireless Frequency‐Multiplexed Acoustic Array‐Based Acoustofluidics

Abstract Acoustofluidics has shown great potential in enabling on‐chip technologies for driving liquid flows and manipulating particles and cells for engineering, chemical, and biomedical applications. To introduce on‐demand liquid sample processing and micro/nano‐object manipulation functions to wearable and embeddable electronics, wireless acoustofluidic chips are highly desired. This paper presents wireless acoustofluidic chips to generate acoustic waves carrying sufficient energy and achieve key acoustofluidic functions, including arranging particles and cells, generating fluid streaming, and enriching in‐droplet particles. To enable these functions, the wireless acoustofluidic chips leverage mechanisms, including inductive coupling‐based wireless power transfer (WPT), frequency multiplexing‐based control of multiple acoustic waves, and the resultant acoustic radiation and drag forces. For validation, the wirelessly generated acoustic waves are measured using laser vibrometry when different materials (e.g., bone, tissue, and hand) are inserted between the WPT transmitter and receiver. Moreover, the wireless acoustofluidic chips successfully arrange nanoparticles into different patterns, align cells into parallel pearl chains, generate streaming, and enrich in‐droplet microparticles. This research is anticipated to facilitate the development of embeddable wireless on‐chip flow generators, wearable sensors with liquid sample processing functions, and implantable devices with flow generation and acoustic stimulation abilities for engineering, veterinary, and biomedical applications.

Li, Jiali↗
DOE OSTI2026

Wireless Bioelectronic Modulation of Membrane Potential in Glioblastoma Using Carbon Nanotube Porins

Disruption of membrane potential (V mem ) can activate pathways associated with cancer proliferation. Manipulating ion channels may therefore present an effective strategy for treating cancers that fail to respond to conventional therapies. One approach to target these channels is to manipulate the membrane charge, which involves the use of wireless bipolar electrodes such as carbon nanotube porins (CNTPs) inserted into cell membranes to modulate membrane charge and ionic flux. By utilizing membrane dyes, we observed alterations in V mem induced by CNTPs and externally applied voltages. Analyses of cellular behaviors and processes indicated that V mem is more receptive to stimuli in invasive cancers, while it leads to increased metabolism in less invasive cancers, with notable changes in the cell cycle occurring at approximately 48 h post-treatment in Glioblastoma (GB) cell lines. This work shows that CNTPs, in combination and with externally applied voltages, can modulate V mem and alter cancer cell processes, supporting their potential as a therapeutic.

bioelectricity↗
DOE OSTI2024

Vacancy Mediated Electrooxidation of 5‐Hydroxymethyl Furfuryl Using Defect Engineered Layered Double Hydroxide Electrocatalysts

Abstract Electrochemical biomass oxidation coupled with hydrogen evolution offers a promising route to generate value‐added chemicals and clean energy. The complex adsorption behavior of 5‐hydroxymethyl furfural (HMF) and hydroxyl ions (OH − ) on the electrocatalyst surface during HMF electrooxidation reaction (HMFOR) necessitates an in‐depth understanding of active sites available for adsorption. Herein, oxygen vacancy (V O ) defects are introduced in NiFe layered double hydroxide (LDH) using Ce dopants to manipulate electronic structure. Synchrotron‐based HE‐XRD and XAS indicate negligible V O in La‐doped NiFe while Ce doping leads to V O defects due to flexible Ce redox (Ce 3+ ↔ Ce 4+ ). The V O ‐rich Ce‐NiFe exhibits higher Faradic efficiency of ≈90% to produce 2,5‐furan dicarboxylic acid (FDCA), far greater than ≈60% for NiFe V O in Ce‐NiFe act as alternative active sites for OH − adsorption, hence reducing adsorption competition for the same metal sites. DFT calculation results corroborate experimental findings by showcasing that the presence of V O in Ce‐NiFe manipulates the adsorption energies and facilitates the chemical adsorption OH − in V O to improve HMFOR. In situ HE‐XRD derived pair distribution function coupled to RMC simulations confirm OH − trapping in V O and HMF adsorption on metal centers as evident by interlayer distance evolution. Taken together, this work showcases routes for dual‐site electrocatalyst design for improved biomass electrooxidation.

Chemistry↗
DOE OSTI2022

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Abstract Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

Ali, Asem M.↗
DOE OSTI2024

Ion‐Specific Interactions Engender Dynamic and Tailorable Properties in Biomimetic Cationic Polyelectrolytes

Abstract Biomaterials such as spider silk and mussel byssi are fabricated by the dynamic manipulation of intra‐ and intermolecular biopolymer interactions. Organisms modulate solution parameters, such as pH and ion co‐solute concentration, to effect these processes. These biofabrication schemes provide a conceptual framework to develop new dynamic and responsive abiotic soft material systems. Towards these ends, the chemical diversity of readily available ionic compounds offers a broad palette to manipulate the physicochemical properties of polyelectrolytes via ion‐specific interactions. In this study, we show for the first time that the ion‐specific interactions of biomimetic polyelectrolytes engenders a variety of phase separation behaviors, creating dynamic thermal‐ and ion‐responsive soft matter that exhibits a spectrum of physical properties, spanning viscous fluids to viscoelastic and viscoplastic solids. These ion‐dependent characteristics are further rendered general by the merger of lysine and phenylalanine into a single, amphiphilic vinyl monomer. The unprecedented breadth, precision, and dynamicity in the reported ion‐dependent phase behaviors thus introduce a broad array of opportunities for the future development of responsive soft matter; properties that are poised to drive developments in critical areas such as chemical sensing, soft robotics, and additive manufacturing.

Aubrecht, Filip J.↗
DOE OSTI2025

Electrical Energy Storage by Poly(ionic Liquids)

Abstract Manipulating van der Waals (vdW) and ionic interactions in polymers enable energy storage and formations of active or passive components of electrical circuits. The energy storage is achieved by electrically activating ion pairs containing polymers, which create ergotropically favorable non‐equilibrium gradient states. Molecular‐level events responsible for this behavior involve concurrent ion pairs' polarization‐depolarization gradients and conformational changes of aliphatic tails that collectively contribute to lowering local disorder states. Manipulating ionic and vdW interactions stabilizes polarized anion‐cation pairs, thus maintaining electrical energy storage for extended periods. These transparent and easily moldable materials require no multilayered assemblies, and their functional features depend upon polarization conditions and ionic‐vdW interactions, making them applicable in energy storage and other devices transcending classical time intricacy limits.

Liu, Jiahui [Department of Materials Science and E↗
DOE OSTI2025

Electrical Energy Storage by Poly(ionic Liquids)

Abstract Manipulating van der Waals (vdW) and ionic interactions in polymers enable energy storage and formations of active or passive components of electrical circuits. The energy storage is achieved by electrically activating ion pairs containing polymers, which create ergotropically favorable non‐equilibrium gradient states. Molecular‐level events responsible for this behavior involve concurrent ion pairs' polarization‐depolarization gradients and conformational changes of aliphatic tails that collectively contribute to lowering local disorder states. Manipulating ionic and vdW interactions stabilizes polarized anion‐cation pairs, thus maintaining electrical energy storage for extended periods. These transparent and easily moldable materials require no multilayered assemblies, and their functional features depend upon polarization conditions and ionic‐vdW interactions, making them applicable in energy storage and other devices transcending classical time intricacy limits.

Liu, Jiahui [Department of Materials Science and E↗
DOE OSTI2023

Using long‐term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models

Abstract Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea ‐ Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best‐performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea , the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO 2 parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type.

Schädel, Christina↗
DOE OSTI2024

Uncovering the Size-dependent Thermal Solid Transformation of Akaganéite

Investigating the structural evolution and phase transformation of iron oxides is crucial for gaining a deeper understanding of geological changes on diverse planets and preparing oxide materials suitable for industrial applications. In this study, we employed in-situ heating techniques in conjunction with transmission electron microscopy (TEM) observations and ex-situ characterization to thoroughly analyze the thermal solid-phase transformation of akaganéite one-dimensional (1D) nanostructures with varying diameters. Our findings offer compelling evidence for a size-dependent morphology evolution in akaganéite 1D nanostructures, which can be attributed to the transformation from akaganéite to maghemite (?-Fe2O3) and subsequent crystal growth. Specifically, we observed that akaganéite nanorods with a diameter of around 50 nm transformed into hollow polycrystalline maghemite nanorods, which demonstrated remarkable stability without arresting crystal growth under continuous heating. In contrast, smaller akaganéite nanoneedles or nanowires with a diameter ranging from 20 to 8 nm displayed a propensity for forming single-crystal nanoneedles or nanowires through phase transformation and densification. By manipulating the size of the precursors, we have developed a straightforward method for the synthesis of single-crystal and polycrystalline maghemite nanowires through solid-phase transformation. These significant findings provide new insights into the size-dependent structural evolution and phase transformation of iron oxides at the nanoscale.

Wang, Xiang↗
DOE OSTI2023

A Platform for Atomic Fabrication and In Situ Synthesis in a Scanning Transmission Electron Microscope

Here, the engineering of quantum materials requires the development of tools able to address various synthesis and characterization challenges. These include the establishment and refinement of growth methods, material manipulation, and defect engineering. Atomic-scale modification will be a key enabling factor for engineering quantum materials where desired phenomena are critically determined by atomic structures. Successful use of scanning transmission electron microscopes (STEMs) for atomic scale material manipulation has opened the door for a transformed view of what can be accomplished using electron-beam-based strategies. However, serious obstacles exist on the pathway from possibility to practical reality. One such obstacle is the in situ delivery of atomized material in the STEM to the region of interest for further fabrication processes. Here, progress on this front is presented with a view toward performing synthesis (deposition and growth) processes in a scanning transmission electron microscope in combination with top-down control over the reaction region. An in situ thermal deposition platform is presented, tested, and deposition and growth processes are demonstrated. In particular, it is shown that isolated Sn atoms can be evaporated from a filament and caught on the nearby sample, demonstrating atomized material delivery. This platform is envisioned to facilitate real-time atomic resolution imaging of growth processes and open new pathways toward atomic fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Thermomechanical Cleave of Polycrystalline CdTe Solar Cells and its Applications: A Review

One of the primary research challenges for cadmium telluride (CdTe) solar cells is addressing its open‐circuit voltage ( V OC ) deficit. While theoretical studies and single crystal work show V OC > 1 V is possible, devices remain stubbornly low at ≈800–900 mV. As absorber opto‐electronic properties (e.g., hole density, carrier lifetime) are improved, device modeling suggests that interfaces become limiting. Because CdTe‐based devices are typically grown in the superstrate configuration, the back interface is relatively accessible for manipulation and study, while the front interface (i.e., the heterojunction region) is buried under microns of material and inaccessible. NREL has developed a novel technique to thermomechanically cleave polycrystalline CdTe device stacks directly at the front interface, enabling characterization and controlled manipulation of this important region. Herein, recent work, primarily from NREL, will be reviewed, including considerations for achieving successful delamination; key scientific discoveries about the front interface that have been enabled by this technique; and practical applications, such as flexible, low‐cost solar with high power‐to‐weight ratio.

14 SOLAR ENERGY↗
DOE OSTI2023

Improving evapotranspiration computation with electrical resistivity tomography in a maize field

Abstract Hydrogeophysical methods have been increasingly used to study subsurface soil–water dynamics, yet their application beyond the soil compartment or the quantitative link to soil hydraulic properties remains limited. To examine how these methods can inform model‐based evapotranspiration (ET) calculation under varying soil water conditions, we conducted a pilot‐scale field study at an experimental maize plot with manipulated irrigation treatments. Our goal was to develop a workflow for (1) acquiring and inverting field electrical resistivity tomography (ERT) data, (2) correlating ERT to soil hydraulic properties, (3) spatially characterizing soil water stress that feeds into ET modeling (the FAO‐56 model), and (4) evaluating the performance of ERT‐based ET computation. Our results showed that ERT was able to capture decimeter‐scale soil water content (SWC) dynamics from root water uptake and irrigation manipulation and the contrast of soil water stress between deficiently and fully irrigated maize. We also demonstrated the flexibility of using ERT to spatially integrate soil water stress in the soil volume of interest, which could be adjusted based on different crops and plot layouts. The integration of the ERT datasets into ET modeling provided insights into the spatial heterogeneity of the subsurface that has been challenging for point‐based sensing, which can further our understanding of the hydraulic dynamics in the soil‐plant‐atmosphere continuum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning

One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an AM model developed within the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, ultimately reducing the time/cost of AM control and optimization processes. Our adoption of the operator learning (OL) approach enabled us to learn a family of differential equations produced by altering process variables in the laser’s Gaussian point heat source. More specifically, we used the Fourier neural operator (FNO) and deep operator network (DeepONet) to develop ROMs for time-dependent responses. Furthermore, we benchmarked the performance of these OL methods against a conventional deep neural network (DNN)-based ROM. Ultimately, we found that OL methods offer comparable performance and, in terms of accuracy and generalizability, even outperform DNN at predicting scalar model responses. The DNN-based ROM afforded the fastest training time. Furthermore, all the ROMs were faster than the original MOOSE model yet still provided accurate predictions. FNO had a smaller mean prediction error than DeepONet, with a larger variance for time-dependent responses. Unlike DNN, both FNO and DeepONet were able to simulate time series data without the need for dimensionality reduction techniques. Finally, the present work can help facilitate the AM optimization process by enabling faster execution of simulation tools while still preserving evaluation accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficacy and Costs of Restoring Wetland Breeding Habitat for Imperiled Amphibians in the Southeastern U.S.

Herbaceous isolated wetlands in the North American Southeastern Coastal Plain are important breeding sites for many imperiled amphibians. However, most are degraded from alterations to historic fire disturbance and hydrologic regimes. Without fire, encroaching woody vegetation can transition wetlands to more terrestrial conditions and negatively impact amphibian breeding habitat, yet few studies have experimentally tested the efficacy, cost, or temporal requirement of current methods to restore herbaceous wetland vegetation. Here, we tested the interaction of manipulating wetland canopy and leaf litter/duff to promote herbaceous vegetation within one year (i.e., one breeding season) in degraded herbaceous wetlands in South Carolina. We assessed plant response via herbaceous cover, composition, and species similarity to the wetland seed bank and then related treatment performance to treatment cost. Removing trees combined with burning, disturbing, or removing duff significantly increased herbaceous cover and proportions of wetland plants and graminoids. Removing trees alone did not improve herbaceous cover compared to closed-canopy controls, and manipulating duff alone had limited positive effects on plant cover and composition. The most expensive yet effective treatment was Tree Removal-Duff Removal, while Tree Removal-Duff Disturbance was the most cost-effective. At a minimum, we recommend removing trees and burning to kickstart herbaceous recovery. Promisingly, comparisons of our data with previous seed bank studies from these same wetlands indicate there was limited seed bank attrition during 30 years of woody encroachment. Results from this study should aid practitioners in choosing wetland restoration techniques to better conserve at-risk species in the Southeastern Coastal Plain.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Travels, optimizations, and ordering

With all the necessary build pathing in place from the preceding steps, the final task is to organize the build pathing via directionality, ordering, and connectivity. Directionality controls closed-loop pathing by deciding whether construction occurs clockwise or counterclockwise. Ordering determines whether construction occurs outside-in or inside-out, by manipulating what path types will be printed first. Connectivity links all the pathing together, which is achieved through the insertion of travels. Connectivity has numerous approaches, but only the most common will be addressed in this chapter. In addition to these manipulations, other considerations, such as spiral pathing and unique cases for infill, will be discussed.

Borish, Michael↗
DOE OSTI2020

Geometry-independent microstructure optimization for electron beam powder bed fusion additive manufacturing

The ability to manipulate the formation of microstructure is one of the potential advantages of additive manufacturing. Yet, the additive manufacturing process is riddled with complex interactions between processing parameters, geometry, and spot melting sequence which makes the task of controlling microstructure challenging. This paper proposes a method to solve the microstructure texture optimization problem by leveraging established optimization techniques and increasingly fast and accurate analytical heat transfer models. Respectively, we apply two different optimization algorithms, genetic algorithm and concurrent greedy line search, in order to induce arbitrarily shaped microstructure regions and we use a heat transfer model to evaluate the fitness of solutions in an iterative approach that is far more efficient than pure trial and error. The objective of the optimization algorithm is to control local thermal conditions – hence microstructure formation – during the melting process by manipulating the order and the dwell time of all the points of the infill pattern sequence. We then analyze the optimized sequence to determine important path characteristics that affect the microstructure formation. Through examples, we show that relative order and frequency, as measured by weighted average and variance of spot order, are important factors that influence the final characteristics.

36 MATERIALS SCIENCE↗