Surface photovoltage spectroscopy observes junctions and carrier separation in gallium nitride nanowire arrays for overall water-splitting
Bibliographic record
Abstract
Gallium nitride (GaN) nanowire arrays on silicon are able to drive the overall water-splitting reaction with up to 3.3% solar-to-hydrogen efficiency. Photochemical charge separation is key to the operation of these devices, but details are difficult to observe experimentally because of the number of components and interfaces. Here, we use surface photovoltage spectroscopy to study charge transfer in i-, n-, and p-GaN nanowire arrays on n+-Si wafers in the presence and absence of Rh/Cr2O3 co-catalysts. The effect of the space charge layer and sub-bandgap defects on majority and minority carrier transport can be clearly observed, and estimates of the built-in potential of the junctions can be made. Transient illumination of the p-GaN/n+-Si junction generates up to −1.4 V surface photovoltage by carrier separation along the GaN nanowire axis. This process is central to the overall water-splitting function of the n+-Si/p-GaN/Rh/Cr2O3 nanowire array. These results improve our understanding of photochemical charge transfer and separation in group III–V semiconductor nanostructures for the conversion of solar energy into fuels.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".