LiF Nanoparticles Enhance Targeted Degradation of Organic Material under Low Dose X-ray Irradiation
Bibliographic record
Abstract
The targeted irradiation of structures by X-rays has seen application in a variety of fields. Herein, the use of 5–10 nm LiF nanoparticles to locally enhance the degradation of an organic thin film, diindenoperylene, under hard X-ray irradiation, at relatively low ionizing radiation doses, is shown. X-ray reflectivity analysis indicated that the film thickness increased 12.04 Å in air and 11.34 Å in a helium atmosphere, under a radiation dose of ∼65 J/cm2 for 3 h illumination with a bi-layer structure that contained submonolayer coverage of thermally evaporated LiF. This was accompanied by significant modification of the surface topography for the organic film, which initially formed large flat islands. Accelerated aging experiments suggested that localized heating was not a major mechanism for the observed changes, suggesting a photochemical mechanism due to the formation of reactive species from LiF under irradiation. As LiF has a tendency to form active defects under radiation across the energy spectrum, this could could open a new direction to explore the efficacy of LiF or similar optically active materials that form electrically active defects under irradiation in various applications that could benefit from enhanced activity, such as radiography or targeted X-ray irradiation therapies.
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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".