Batteries to Keep Solar‐Driven Water Splitting Running at Night: Performance of a Directly Coupled System
Bibliographic record
Abstract
Direct solar‐powered hydrogen generation (so‐called “green” hydrogen) is promising as a renewable fuel that can be generated anywhere there is sunshine and water. Many attempts are made to integrate a water electrolyzer (EC) and solar cell at different levels (a so‐called artificial leaf) to take advantage of the reduced losses from the lack of wiring and optionally increased portability afforded by an integrated unit. However, in many cases, EC catalysts degrade as electrodes depolarize when shut down at night. Much less attention is paid to the need for a minimum current across the EC under insufficient illumination to prevent excessive cyclic degradation. Directly coupling a battery to keep an artificial leaf running at night can address this need and, in theory, also increase solar‐to‐hydrogen (STH) efficiency. A seven‐cell silicon heterojunction module, two bifunctional NiFeMo ECs in series, and a commercial Li‐ion NMC battery are selected to provide the same amount of solar output power despite different working voltages and tested in a series of simulated diurnal cycles. The increased average STH efficiency per cycle (11.4% vs. 10.5% without the battery) is analyzed and discussed with implications for future artificial leaf design and implementation.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".