Organic–inorganic hybrid perovskite scintillators for mixed field radiation detection
Bibliographic record
Abstract
Abstract Sensitive and fast detection of neutrons and gamma rays is vital for homeland security, high‐energy physics, and proton therapy. Fast‐neutron detectors rely on light organic scintillators, and γ‐ray detectors use heavy inorganic scintillators and semiconductors. Efficient mixed‐field detection using a single material is highly challenging due to their contradictory requirements. Here we report hybrid perovskites (C8H12N)2Pb(Br1−xClx)4 that combine light organic cations and heavy inorganic skeletons at a molecular level to achieve unprecedented performance for mixed‐field radiation detection. High neutron absorption due to a high density of hydrogen, strong radiative recombination within the highly confined [PbX6]4− layer, and sub‐nanometer distance between absorption sites and radiative centers, enable a light yield of 41 000 photons/MeV, detection pulse width of 2.97 ns and extraordinary linearity response toward both fast neutrons and γ‐rays, outperforming commonly used fast‐neutron scintillators. Neutron energy spectrum, time‐of‐flight based fast‐neutron/γ‐ray discrimination and neutron yield monitoring were all successfully achieved using (C8H12N)2Pb(Br0.95Cl0.05)4 detectors. We further demonstrate the monitoring of reaction kinetics and total power of a nuclear fusion reaction. We envision that molecular hybridized scintillators open a new avenue for mixed‐field radiation detection and imaging. image
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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 | 0.000 | 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".