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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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402 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2020

One-dimensional twisted and tubular structures of zinc oxide by semiconductor-catalyzed vapor–liquid–solid synthesis

The exploration of unconventional catalysts for the vapor-liquid-solid synthesis of one-dimensional materials promises to yield new morphologies and functionality. Here, we show, for the model ZnO system, that unusual nanostructures can be produced via a semiconductor (Ge) catalyst. As well as the usual straight nanowires, we describe two other distinct morphologies: twisted nanowires and twisted nanotubes. The twisted nanotubes show large hollow cores and surprisingly high twisting rates, up to 9°/μm, that cannot be easily explained through the Eshelby twist model. A combination of ex situ and in situ transmission electron microscopy measurements suggest that the hollow core results from a competition between growth and etching at the Ge-ZnO interface during synthesis. The twisting rate is consistent with a softening of elastic rigidity. Finally, these results indicate that the use of unconventional, nonmetallic catalysts provides opportunities to synthesize unusual oxide nanostructures with potentially useful properties.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Surface plasmon driven near- and midinfrared photoconductivity in ligand-free ITO nanocrystal films

Semiconductor nanocrystals (NCs) offer exciting opportunities to deploy infrared optoelectronic technology in a variety of applications that are off-limits to conventional single-crystalline materials. However, the performance and stability of state-of-the-art NC-based devices strongly depend on the details of the NC surface and its passivating ligands. Here, the authors use indium tin oxide NC films stripped of organic ligands and conformally coated with Al 2 O 3 to show that absorption enabled by infrared localized surface plasmon resonances permit infrared photoconduction. For Au electrodes, these metallic NC films exhibit stable photoconductive performance over months in ambient and under electrical stress.

36 MATERIALS SCIENCE↗

DOE OSTI2025

OLCF Test Harness

Acceptance and regression testing of a High Performance Computing (HPC) system requires an automated and reproducible framework and tool for running and logging results. Manually running tests across a system is labor intensive and prone to reproducibility errors. The OLCF Test Harness (OTH) provides a framework in which to document required tests for a HPC system. The OTH then provides tools to execute and log results of these tests in an automated fashion.

Dietz, Dan [Oak Ridge National Laboratory (ORNL), ↗

DOE OSTI2023

A Succinct Study of Silastic J

Silastic TM RTV-4130-J Base and Curing agent, also known as Silastic J, is a two-part addition cure silicone commonly used for molding due to its dimensional stability. The material has a wide variety of applications that the paper will address.

36 MATERIALS SCIENCE↗

DOE OSTI2025

MSU IETC ML for Modbus (AN EDGE)

This study explores machine learning for decoding Modbus RTU data using K-Nearest Neighbors (KNN) models. An initial KNN model trained on 8,000 packets achieved 95.15% accuracy. Although ML improves generalization, accuracy still falls short of deterministic methods. These findings have implications for Modbus traffic analysis, intrusion detection in industrial networks, and adaptive error correction in real-time monitoring systems. By refining ML-based decoding, future work could enable more efficient anomaly detection and predictive maintenance in industrial automation and cybersecurity applications.

Communication Protocol↗

DOE OSTI2025

MSU IETC LSTM Ethernet Decode (AN EDGE)

This research explores the ability of machine learning to perform signal separation of an Ethernet style encoded, full-duplex communication. Typical signal separation currently requires an active tap of the communication line, followed by a recombination and retransmission of the data. The purpose of this research is to study a passive approach to data acquisition from a full-duplex signal. The machine learning model used in this research is a long-short-term memory recurrent neural network (LSTM-RNN). The results show that the LSTM was largely successful in recreating the transmission signal from the measured data points, though the separated signals have not yet been tested using a decoding method.

Full Duplex Signals↗