High-loaded and transparent La<sub><i>x</i></sub>Ce<sub>1-</sub><sub><i>x</i></sub>F<sub>3</sub> — polystyrene nanocomposite scintillators for radiation detection
Bibliographic record
Abstract
For radiation detection, sensitivity, response time, and energy resolution are important. Scintillating nanoparticles, in principle, can have enhanced light output in comparison with their bulk materials due to quantum size confinement and increased overlap of electron and hole wave function. However, the aggregation and the loss of transparency at high loading into polymers are the challenging issues for practical applications. Here, for the first time, we report a new method to fabricate the blue-emitting nanocomposites with up to 30 wt% nanoparticle loading. First, the polymerizable surfactant coated La 0.6 Ce 0.4 F 3 nanoparticles are synthesized, and then, they are loaded up to 30 wt% in a polystyrene matrix. The nanocomposites show an intense luminescence and a good transparency, performing much better than the commercial EJ-200 plastic scintillator. The gamma spectra acquired with the nanocomposite show a photopeak for the Co-57 isotope. The gamma spectra of Cs-137 show a full energy peak at around 622 keV, due to the escape of La and Ce Kα X-rays. The observation of the photopeaks is attributed to the enhanced photoelectric effect as a result of increased effective atomic number (Z eff ). All these indicate that the high loaded nanocomposites are promising for radiation detection and nuclear material surveillance for homeland security.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".