Application of Energy‐Resolving Neutron Imaging to Major‐Component Analyses of Materials Using Four‐Channel Superconducting Detector
Bibliographic record
Abstract
We proposed a current‐biased kinetic inductance detector (CB‐KID) as a novel superconducting detector to construct a neutron transmission imager. The characteristics of a superconducting neutron detector have been systematically studied to improve a spatial resolution down to 10 in transmission imaging. In this study, we report the application of the energy‐resolving neutron imaging to investigate major components of materials by analyzing neutron transmission spectra from 1 meV to 500 keV. We succeeded in identifying that copper (Cu) and iron (Fe) are major components respectively in commercial nuts and screws as test samples with the aid of Rietveld imaging of transmission spectra (RITS) program in analyzing transmission spectra in longer wavelengths. The Ti screw was also confirmed by comparing the nuclear resonance absorption measurements and simulations in high‐energy regions. We demonstrated that our superconducting neutron detector is applicable to reveal the transmission spectra in the wide range from cold‐neutron energies to higher neutron energies even up to 500 keV. By selecting distinctive energy regions of pulsed neutrons, we succeeded in mapping the distribution of SmSn 3 compound using the strong neutron absorption in samarium (Sm) and the selective nuclear‐resonance dips in Sm. By taking advantage of using CB‐KID in conducting neutron imaging, the CB‐KID method is extensively useful for various purposes in material sciences through energy‐selective neutron spectroscopy from 1 meV to 500 keV. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".