Computational evaluation of a novel beta radiation probe design using integrated circuits
Bibliographic record
Abstract
Abstract Researchers at Texas A&M University (TAMU) have designed the radiation integrated circuit (RIC) for deployment as a new radiation detection system. Most integrated circuits are susceptible to radiation-induced failures, and decades of research have gone into solving this problem. Research at TAMU has led to a novel integrated circuit design that utilizes both radiation-hardened areas (RHAs) and radiation-sensitive areas (RSAs) to take advantage of these failures. The RSAs are susceptible to charged particle interactions, allowing the RIC to detect alpha and beta particles. However, beta particles are more penetrating compared to alpha particles, resulting in a lower interaction probability for beta particles incident on a bare RIC. In any material, the higher the beta energy, the deeper the beta particle can penetrate; therefore, the use of a wedge-shaped attenuator for beta particle detection not only increases interaction probability, but also provides the capability to perform maximum beta energy discrimination in the field. The objective of this research was to optimize the design of the RIC. Monte Carlo N-particle radiation transport code (MCNP) simulations assessed the beta particle detection and maximum energy discrimination performance of plate glass, borosilicate (Pyrex®) glass, acrylic (Lucite®), and natural rubber attenuators. In this proof-of-concept analysis, natural rubber was observed to be the optimal attenuating material for the beta probe with respect to maximum energy discrimination capability and weight, but all materials considered proved to be good candidates. The results of this study are promising and indicate the potential to achieve maximum beta particle energy discrimination of 50 keV using a wedged, natural rubber attenuator on the RIC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".