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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 OSTI2021

Nanoporous Dielectric Resistive Memories Using Sequential Infiltration Synthesis

Resistance switching in metal–insulator–metal structures has been extensively studied in recent years for use as synaptic elements for neuromorphic computing and as nonvolatile memory elements. However, high switching power requirements, device variabilities, and considerable trade-offs between low operating voltages, high on/off ratios, and low leakage have limited their utility. Here, we have addressed these issues by demonstrating the use of ultraporous dielectrics as a pathway for high-performance resistive memory devices. Using a modified atomic layer deposition based technique known as sequential infiltration synthesis, which was developed originally for improving polymer properties such as enhanced etch resistance of electron-beam resists and for the creation of films for filtration and oleophilic applications, we are able to create ~15 nm thick ultraporous (pore size ~5 nm) oxide dielectrics with up to 73% porosity as the medium for filament formation. We show, using the Ag/Al 2 O 3 system, that the ultraporous films result in ultrahigh on/off ratio (>10 9 ) at ultralow switching voltages (~±600 mV) that are 10× smaller than those for the bulk case. In addition, the devices demonstrate fast switching, pulsed endurance up to 1 million cycles. and high temperature (125 °C) retention up to 10 4 s, making this approach highly promising for large-scale neuromorphic and memory applications. Additionally, this synthesis methodology provides a compatible, inexpensive route that is scalable and compatible with existing semiconductor nanofabrication methods and materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Resolving the Atomic Structure of Sequential Infiltration Synthesis Derived Inorganic Clusters

Sequential infiltration synthesis (SIS) is a route to the precision deposition of inorganic solids in analogy to atomic layer deposition but occurs within (vs upon) a soft material template. SIS has enabled exquisite nanoscale morphological complexity in various oxides through selective nucleation in block copolymers templates. However, the earliest stages of SIS growth remain unresolved, including the atomic structure of nuclei and the evolution of local coordination environments, before and after polymer template removal. We employed In K-edge extended X-ray absorption fine structure and atomic pair distribution function analysis of high-energy X-ray scattering to unravel (1) the structural evolution of InO x H y clusters inside a poly(methyl methacrylate) (PMMA) host matrix and (2) the formation of porous In 2 O 3 solids (obtained after annealing) as a function of SIS cycle number. Early SIS cycles result in InO x H y cluster growth with high aspect ratio, followed by the formation of a three-dimensional network with additional SIS cycles. That the atomic structures of the InO x H y clusters can be modeled as multinuclear clusters with bonding patterns related to those in In 2 O 3 and In(OH) 3 crystal structures suggests that SIS may be an efficient route to 3D arrays of discrete-atom-number clusters. As a result, annealing the mixed inorganic/polymer films in air removes the PMMA template and consolidates the as-grown clusters into cubic In 2 O 3 nanocrystals with structural details that also depend on SIS cycle number.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Selective sequential infiltration synthesis of ZnO in the liquid crystalline phase of silicon-containing rod-coil block copolymers

The combination of block copolymer (BCP) thin film self-assembly and selective infiltration synthesis of inorganic materials into one BCP block provides access to various organic–inorganic hybrids. Here, we apply sequential infiltration synthesis, a vapor-phase hybridization technique, to selectively introduce ZnO into the organic microdomains of silicon-containing rod-coil diblock copolymers and a triblock terpolymer, polydimethylsiloxane (PDMS)-b-poly{2,5-bis[(4-methoxyphenyl)-oxycarbonyl]styrene} (PDMS-b-PMPCS) and PDMS-b-polystyrene-b-PMPCS (PDMS-b-PS-b-PMPCS), in which the PMPCS rod block is a liquid crystalline polymer. The in-plane cylindrical PDMS-b-PMPCS and core–shell cylindrical and hexagonally perforated lamellar PDMS-b-PS-b-PMPCS films were infiltrated with ZnO with high selectivity to the PMPCS. The etching contrast between PDMS, PS and the ZnO-infused PMPCS enables the fabrication of ZnO/SiO x binary composites by plasma etching and reveals the core–shell morphology of the triblock terpolymer.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Sequential Kalman tuning of the t -preconditioned Crank-Nicolson algorithm: efficient, adaptive and gradient-free inference for Bayesian inverse problems

Ensemble Kalman Inversion (EKI) has been proposed as an efficient method for the approximate solution of Bayesian inverse problems with expensive forward models. However, when applied to the Bayesian inverse problem EKI is only exact in the regime of Gaussian target measures and linear forward models. Here, in this work we propose embedding EKI and Flow Annealed Kalman Inversion, its normalizing flow (NF) preconditioned variant, within a Bayesian annealing scheme as part of an adaptive implementation of the t-preconditioned Crank-Nicolson (tpCN) sampler. The tpCN sampler differs from standard pCN in that its proposal is reversible with respect to the multivariate t-distribution. The more flexible tail behaviour allows for better adaptation to sampling from non-Gaussian targets. Within our Sequential Kalman Tuning (SKT) adaptation scheme, EKI is used to initialize and precondition the tpCN sampler for each annealed target. The subsequent tpCN iterations ensure particles are correctly distributed according to each annealed target, avoiding the accumulation of errors that would otherwise impact EKI. We demonstrate the performance of SKT for tpCN on three challenging numerical benchmarks, showing significant improvements in the rate of convergence compared to adaptation within standard SMC with importance weighted resampling at each temperature level, and compared to similar adaptive implementations of standard pCN. The SKT scheme applied to tpCN offers an efficient, practical solution for solving the Bayesian inverse problem when gradients of the forward model are not available. Code implementing the SKT schemes for tpCN is available at https://github.com/RichardGrumitt/KalmanMC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Multi-Armed Bayesian Ordinal Outcome Utility-Based Sequential Trial with a Pairwise Null Clustering Prior

A multi-armed trial based on ordinal outcomes is proposed that leverages a flexible non-proportional odds cumulative logit model and numerical utility scores for each outcome to determine treatment optimality. This trial design uses a Bayesian clustering prior on the treatment effects that encourages the pairwise null hypothesis of no differences between treatments. A group sequential design is proposed to determine which treatments are clinically different with an adaptive decision boundary that becomes more aggressive as the sample size or clinical significance grows, or the number of active treatments decreases. A simulation study is conducted for 3 and 5 treatment arms, which shows that the design has superior operating characteristics (family wise error rate, generalized power, average sample size) compared to utility designs that do not allow clustering, a frequentist proportional odds model, or a permutation test based on empirical mean utilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO 2 capture process

In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO 2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO 2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

20 FOSSIL-FUELED POWER PLANTS↗
NASA NTRS1966

Electrical power and sequential systems

Power sources for Gemini spacecraft electrical systems, load sharing of fuel cells during flight, and sequential system for electrical control of spacecraft

POWER SUPPLY↗