Search NASA⌕ Search

DOE OSTI · 3005423

Implementation of Johnson Noise Thermometry into Radiation Hardened Electronics

Abstract

The random motion of charge carriers in an electrical conductor is a function of its temperature. This phenomenon generates an electrical signal, referred to as Johnson noise, that is present in all analog signals. Conversely, the temperature dependence of the Johnson noise spectrum also enables a direct measurement of absolute temperature, which can provide a secondary measurement of temperature when quantified using sensors such as resistive temperature devices (RTDs) in a process called Johnson noise thermometry (JNT). For nuclear environments, this is of particular interest in correcting for RTDs signal drift caused by accumulation, transmutation, and displacement damage caused by neutron and gamma irradiation.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Sweeney, Daniel C. [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000212891627), Reed, F. Kyle [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000222804574), Ezell, N. Dianne Bull [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000193345822). 2025-10-01. Implementation of Johnson Noise Thermometry into Radiation Hardened Electronics. https://doi.org/10.2172/3005423

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗