Nickel Phosphide Clusters Sensitized TiO<sub>2</sub> Nanotube Arrays as Highly Efficient Photoanode for Photoelectrocatalytic Urea Oxidation
Bibliographic record
Abstract
Abstract The photoelectrocatalytic urea oxidation reaction (PEC‐UOR) holds a great promise for the wastewater remediation and energy production. However, the low efficiency of semiconductor/cocatalysts type photoanodes for UOR restricts their applications in photoelectrocatalytic system. Herein, a new semiconductor/cocatalyst, Ni2P clusters sensitized TiO2 nanotube arrays photoanode (Ni2P/TiO2‐NTAs) for PEC‐UOR with high efficiency are developed. The 1D TiO2‐NTAs structure accelerates urea molecules diffusion and promotes CO2 gas release at the electrode interface. Meanwhile, Ni2P is also beneficial to urea molecule absorption and CO2 desorption and enable to lower the energy barrier for amine (NH) dehydrogenation. Furthermore, the robust interfacial charge transfer pathway between Ni2P and TiO2 interface promotes the separation of photogenerated electrons and holes and the transfer of photogenerated electrons from Ni2P to TiO2. Therefore, this photoanode shows excellent PEC‐UOR performance with the potential of 1.43 V versus reversible hydrogen electrode (RHE) when the current density reaches 10 mA cm−2, which is much lower than that of 2.24 V versus RHE and 1.58 V versus RHE for TiO2‐NTAs and Ni(OH)2/TiO2‐NTAs, 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 | 0.000 | 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".