Development and application of a 30 MeV cyclotron-based neutron source for neutron imaging and verification of D1S activation analysis system
Bibliographic record
Abstract
A compact neutron source based on a 30 MeV cyclotron was successfully developed at the Korea Atomic Energy Research Institute and applied to neutron imaging and preliminary design studies for fusion-related activation analysis. The developed neutron source demonstrated stable neutron yields exceeding 1.6 × 10 12 n/s under proton beam conditions of 30 MeV and 10 µA. Neutron measurements using Bonner sphere spectrometers confirmed that the generated neutron energy spectrum closely matched Monte Carlo simulation results, validating the neutron source performance. A neutron imaging facility utilizing this cyclotron-based neutron source achieved a spatial resolution of approximately 0.3 mm, demonstrating its capability for various nondestructive testing applications, including defect inspection in industrial components and rotational imaging of cultural artifacts. Additionally, preliminary computational analyses and experimental designs for direct one-step activation analysis verified the neutron irradiation conditions necessary for future experimental validation studies. This study highlights the successful development and promising application potential of the cyclotron-based neutron source.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".