Gamma, X-ray, and neutron shielding properties of silicon–germanium alloys
Bibliographic record
Abstract
This work focuses on the gamma/X-ray and neutron shielding properties of silicon–germanium alloys in different compositions such as Si0.1Ge0.9 (SG1), Si0.2Ge0.8, Si0.4Ge0.6, Si0.6Ge0.4, Si0.8Ge0.2, and Si0.9Ge0.1. Among all the selected alloys, SG1 has less half-value thickness, tenth-value thickness, and penetration depth, whereas specific gamma-ray constant, Zeff, electron density, radiation protection efficiency, and kinetic energy released in matter have larger values compared to the other studied alloys. The neutron shielding parameters, such as coherent neutron scattering length, incoherent neutron scattering length, coherent neutron scattering cross-section, and incoherent neutron scattering cross-sections are smaller for the SG1 alloy than the other studied alloys. Furthermore, total neutron scattering cross-section and neutron absorption cross-section are found to be larger for SG1 than the other studied alloys. Based on gamma/X-ray and neutron shielding parameter analysis, we have suggested a shielding material consisting of ordinary concrete and SG1 alloy of particular thickness to cease X-ray/gamma-ray and neutron radiation. By analyzing all these parameters, we suggest that the SG1 alloy might be a good shielding material for X-rays/gamma-rays and for neutrons compared to other selected alloys.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".