High Efficiency Solar-to-Hydrogen Conversion on a Monolithically Integrated InGaN/GaN/Si Adaptive Tunnel Junction Photocathode
Bibliographic record
Abstract
H2 generation under sunlight offers great potential for a sustainable fuel production system. To achieve high efficiency solar-to-hydrogen conversion, multijunction photoelectrodes have been commonly employed to absorb a large portion of the solar spectrum and to provide energetic charge carriers for water splitting. However, the design and performance of such tandem devices has been fundamentally limited by the current matching between various absorbing layers. Here, by exploiting the lateral carrier extraction scheme of one-dimensional nanowire structures, we have demonstrated that a dual absorber photocathode, consisting of p-InGaN/tunnel junction/n-GaN nanowire arrays and a Si solar cell wafer, can operate efficiently without the strict current matching requirement. The monolithically integrated photocathode exhibits an applied bias photon-to-current efficiency of 8.7% at a potential of 0.33 V versus normal hydrogen electrode and nearly unity Faradaic efficiency for H2 generation. Such an adaptive multijunction architecture can surpass the design and performance restrictions of conventional tandem photoelectrodes.
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".