Haynes-Shockley experiment analogs in surface and optoelectronics: Tunable surface electric field extracting nearly all photocarriers
Bibliographic record
Abstract
Photocarriers predominantly recombine at semiconductor surfaces and interfaces, assuming high bulk carrier lifetime. Consequently, understanding the extraction of photocarriers via surfaces is critical to optoelectronics. Here, we propose Haynes-Shockley experiment analogs to investigate photocarrier surface extraction. A Schottky junction is used to tune the silicon near-surface electric field strength that varies over several orders of magnitude and simultaneously observe variations in broadband photocarrier extraction. Schottky barrier height and surface potential are both modulated. Work function tunable indium tin oxide (ITO) is developed to precisely regulate the barrier height and collect photocarriers at 0 V bias, thus avoiding the photocurrent gain effect. All experiments demonstrate >98% broadband internal quantum efficiency. The experiments are further extended to wave interference photonic crystals and random pyramids, paving a way to estimate the photogeneration rate of diverse surface light-trapping topologies by collecting nearly all photocarriers. The insights reported here provide a systematic experimental basis to investigate interfacial effects on photocarrier spatial generation and collection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".