Efficient Hole Extraction and *OH Alleviation by Pd Nanoparticles on GaN Nanowires in Seawater for Solar‐Driven H <sub>2</sub> and H <sub>2</sub> O <sub>2</sub> Generation
Bibliographic record
Abstract
Abstract Photocatalytic seawater splitting into hydrogen and hydrogen peroxide (2H 2 O→H 2 ↑ + H 2 O 2 ) offers an ultimate solution for simultaneously generating green fuel and value‐added chemicals by the two most earth‐abundant resources i.e., solar energy and natural seawater. In this study, Pd nanoparticles are integrated with one‐dimensional gallium nitride nanowires (Pd NPs/GaN NWs) on a silicon wafer to produce H 2 and H 2 O 2 from seawater powered by sunlight. In situ spectroscopic characterizations combined with computational investigations reveal that in this nanohybrid, Pd NPs function as an efficient hole extractor and *OH alleviator during photocatalysis. Meanwhile, the chloride ions in seawater facilitate the H 2 O→ H 2 + H 2 O 2 conversion by improving the charge dynamics and lowering the energy barrier of the key *OH self‐coupling step over Pd sites in the catalytic system. As a result, the photocatalyst delivers an appreciable hydrogen production rate of 2.5 mmol⋅cm −2 ⋅h −1 with a light‐to‐hydrogen (LTH) efficiency of 4.38 % in natural seawater under concentrated light irradiation of 3 W⋅cm −2 without sacrificial agents and external energies. Notably, the water oxidation reaction produces 300 μmol/L of valuable H 2 O 2 over a duration of 2 hours under a light intensity of 3 W/cm 2 using a 20 mL water sample, achieving a light‐to‐chemical efficiency of 0.53 %. The photocatalyst shows excellent stability for up to 60 hours with a considerable turnover number of 1.42×10 7 moles H 2 per mole of Pd. The outdoor test further suggests the great potential for solar‐driven seawater splitting into green fuels and chemicals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".