g-C3N4/Ag/TiO2 Nanocomposites For Enhanced Photoelectrochemical Water Splitting Under Visible Light
Bibliographic record
Abstract
Recently, one of the concerns that human beings are facing is energy crisis The non-renewable energy sources such as fossil energies such as coal, petroleum and natural gas are reducing progressively Hydrogen is growing attention as one of the most ideal fuel owing to its superior energy conversion performance and zero-carbon emission So, the photoelectrochemical (PEC) water splitting of semiconductor-based photoelectrodes which can transform solar energy into renewable hydrogen energy is a encouraging strategy to influentially use solar energy Metal oxide semiconductors are ideal materials for PEC generation of hydrogen owing to their optical and electrical features, ease of production and stability. Different metal oxides semiconductors such as an electrode, including TiO2, ZnO, Fe2O3, BiOI and WO3 have been utilized in PEC water splitting. However, the wide band gap (3.2 eV) of TiO2 limits the absorption of the solar light because it only absorbs the photons in the ultraviolet part of the solar Energy Some methods such as noble metal doping, surface photosensitization and combining with narrow band semiconductors were applied to develop the photocatalytic performance. In recent years, metal free graphitic like carbon nitride (g-C3N4) nanomaterials have attracted a great deal of attention in the production H2 from water under visible light irradiation. Conduction band and valence band potentials of g-C3N4 with a band gap of ~2.7 eV, are above the values of H20 reduction and oxidation potentials, respectively
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".