2023 roadmap on photocatalytic water splitting
Bibliographic record
Abstract
Abstract As a consequence of the issues resulting from global climate change many nations are starting to transition to being low or net zero carbon economies. To achieve this objective practical alternative fuels are urgently required and hydrogen gas is deemed one of the most desirable substitute fuels to traditional hydrocarbons. A significant challenge, however, is obtaining hydrogen from sources with low or zero carbon footprint i.e. so called ‘green’ hydrogen. Consequently, there are a number of strands of research into processes that are practical techniques for the production of this ‘green’ hydrogen. Over the past five decades there has been a significant body of research into photocatalytic (PC)/photoelectrocatalytic processes for hydrogen production through water splitting or water reduction. There have, however been significant issues faced in terms of the practical capability of this promising technology to produce hydrogen at scale. This road map article explores a range of issues related to both PC and photoelectrocatalytic hydrogen generation ranging from basic processes, materials science through to reactor engineering and applications for biomass reforming.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.093 | 0.038 |
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".