Ultraviolet irradiation penetration depth on TiO2
Bibliographic record
Abstract
High-energy ultraviolet (UVC) irradiation of metal oxides (MOs, e.g., TiO2) results in photoinduced surface oxygen vacancies (PI-SOVs), which can change the charge carrier (e.g., electrons and holes) migration dynamics. Although PI-SOVs alter the electronic and chemical properties of MOs, there is no consensus on the penetration depth of the UVC irradiation, which induces PI-SOVs and is an important variable for the design and operation of MO-based systems. Here, we performed optical transmission and time-resolved atomic force microscopy measurements on back-illuminated TiO2 samples. Our experiments show that the effect of UVC irradiation on MOs can be observed hundreds of micrometers across the bulk, i.e., orders of magnitude larger than previously postulated values. We believe that our findings would be important both for the fundamental understanding of UVC irradiation/penetration and for device design/fabrication processes. High-energy ultraviolet (UVC) irradiation induces surface oxygen vacancies in metal oxides used in energy, sensing, and biomedical applications, but there is no consensus on its penetration depth. Here, using optical transmission and time-resolved atomic force microscopy, the authors observe UVC irradiation hundreds of micrometers deep into the bulk of titanium dioxide samples – orders of magnitude larger than previously postulated values.
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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) | 0.003 | 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".