Progress in hydrazine oxidation-assisted hydrogen production
Bibliographic record
Abstract
Hydrogen production via electrochemical water splitting demands high working voltages (>1.23 V) and hence incurs high electricity costs, encumbering its large-scale applications. The development of more high-efficiency and electricity-saving systems for hydrogen production is of great significance. Hydrazine oxidation reaction (HzOR)-assisted hydrogen production technology, which is constructed by replacing the anodic oxygen evolution reaction in pure water electrolysis systems with the HzOR, can greatly reduce the working voltage and electricity consumption, and hence shows great application prospects. In recent years, numerous studies have focused on designing various bifunctional electrocatalysts to simultaneously catalyze the cathodic and anodic reactions in HzOR-assisted hydrogen production systems. However, comprehensive reviews summarizing and commenting on this field are scarce. This review provides a systematic and insightful overview of the developments in HzOR-assisted hydrogen production technology from 2017 to the present, primarily focusing on catalyst design strategies, catalytic mechanisms, and economic and application analysis. Additionally, this review discusses several challenges and outlines future research directions in this field to attract more researchers' attention and accelerate the research and potential applications of HzOR-assisted hydrogen production technology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".