NiO Modified CN Film As Photoanodes for Photoelectrochemical Water Oxidation
Bibliographic record
Abstract
The utilization of solar energy, by far the most promising renewable energy resource, remains one of the hottest topics in the 21st century. Metal-free carbon nitride (CN) material emerged as a promising water splitting photocatalyst in 2009 due to its appropriate visible light absorption, suitable optical band gap energy (2.7 eV), and facile synthesis. Though CN is one of the leading materials for solar energy conversion, this material is limited by light absorption, rapid charge recombination, and low charge carrier mobility. Metal oxide are thus investigated for counterbalancing CN’s inherent drawbacks. Interfacial CN/metal oxide heterostructures facilitate charge transportation, resulting in improved sunlight-driven water splitting process. In this work, low-cost NiO transition-metal oxide material was applied to speed up the sluggish kinetics of the oxygen evolution reaction (OER). Typically, CN modification is achieved by traditional hydrothermal approach, while its disadvantages such as uneven coating particle size and heterogenous distribution are becoming increasingly apparent. Aiming at a conformal morphology, atomic layer deposition (ALD) is developed as the state-of-the-art technique. It has boosted the depositing accuracy by achieving precisely depositing thickness and extremely homogenous surface. In our process, a uniform CN film was deposited on FTO substrates using a dipping-drying technique with a hot saturated thiourea aqueous solution followed by a thermal treatment. Then plasma-enhanced atomic layer deposition (PEALD) was used to modify the CN film with a thin layer of NiO. PEALD controls the NiO modification on a fine-scale, allowing to with deposit a nanoscale NiO layer on the CN surface while exposing adequate CN photoreactive sites. According to our results, the modified NiO/CN heterostructure has the potential to improve photoelectrochemical water oxidation as a photoanode in alkaline solution. From the morphology side, the NiO loaded flake-like CN creates relatively high specific area for OER. Then we investigated the photo-process kinetic using a series of optical and spectroelectrochemcial techniques. Optical absorption is enhanced, showing stronger visible light absorption up to 700 nm. Fast charge separation is suggested in photoluminescence (PL) characterization. Superior (photo)electrochemical (PEC) activity is foreseen through PEC and EC measurements. To conclude, this is the first time we succeed to modify the CN film with the ALD technique for solar-driven water oxidation, and has the highly possibility of reaching the photo(electro)chemical performance new level.
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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 | 0.001 |
| 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".