Energy-resolved imaging and tomography with compact neutron systems—application to novel construction materials for thermal-energy storage
Bibliographic record
Abstract
Herein, we have used so-called Compact Accelerator-driven Neutron Systems (CANS) to explore the properties and performance of composite cement foams for thermal-energy storage. To this end, energy-resolved neutron imaging and tomography were implemented on RIKEN RANS-I, and these data were complemented by thermal conductivity measurements. The ability to pulse this CANS down to tens of microseconds enables data collection across the available neutron energy spectrum for different foam formulations. With relatively modest L/D ratios of ∼35, it is possible to attain a spatial resolution in the millimeter range, and useful images can be collected in as little time as 3 min. Volume reconstruction was performed via angular scans from 0° to 180° in 10° steps, corresponding to a total of 19 images collected over a period of less than an hour. Key to the above has been the deployment of recently developed algorithms and techniques for the analysis of sparse data. On the basis of these results, the existing capabilities of RIKEN RANS-I and similar CANS provide us with new, nonintrusive, and quantitative measures of key performance parameters, including the degree of porosity over relevant length scales typical in these construction materials. In combination with thermal-conductivity data, these results also enable an assessment of insulating performance, with a view to its further optimization.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".