Sensing of ultraviolet light: a transition from conventional to self-powered photodetector
Bibliographic record
Abstract
Clouds in the sky pass almost 80% of ultraviolet (UV) radiation to the earth's surface, which has a significant impact on humankind. Conventional UV photodetectors (PDs) require an external battery, which not only increases the device size but also has a limited life span and maintenance costs can be prohibitively expensive. An alternative and more technically-sound solution would be the use of self-powered UV PDs that can operate independently, eliminating the need for an external source. Although many exciting studies have been done and state-of-the-art research is underway to successfully fabricate self-powered UV PDs, periodic reviews on this topic are deemed essential so that the technology's readiness can be properly evaluated and critical challenges can be addressed in a timely manner. In this article, the key issues and most exciting developments made in recent years on built-in electric field assisted self-powered UV PDs based on p-n homojunctions, p-n heterojunctions, and Schottky junctions followed by energy harvester integrated UV PDs are extensively reviewed. Finally, a summary and comparison of different types of self-powered UV PDs as well as future challenges that need to be addressed are discussed. This review sets a foundation providing essential insights into the present status of self-powered UV PDs with which researchers can engage and deal with the major challenges.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".