Visible Light Driven CO<sub>2</sub> Photoreduction Using TiO<sub>2</sub> Nanotube Arrays Embedded with Low Bandgap Carbon Nitride Nanoparticles
Bibliographic record
Abstract
The extraordinary thermal and photochemical stability, superior charge transport, and tunable band positions of graphitic carbon nitride (g-CN), which is constituted of elements that are plentiful on Earth, renders g-CN an important semiconductor photocatalyst for heterogeneous catalysis [1,2]. Despite these advantages, carbon nitride-based semiconductors do not function effectively as freestanding photocatalysts or photoelectrodes due to a rapid carrier recombination rate and a slightly wide bandgap that only enables them to capture blue and UV photons [3,4]. Anodically formed TiO2 nanotube arrays (TNTAs) are semiconducting wide bandgap scaffolds with excellent photocatalytic properties due to the intrinsic orthogonalization of charge generation/transport and charge transfer processes. Herein, we use a novel in situ electrophoretic anodization to embed low bandgap carbon nitride nanoparticles (CNNPs) in the walls of titania nanotubes. The likelihood of the CNNPs leaching off the TNTA photoanode during photoelectrochemical processes was eliminated by encapsulating CN inside a TiO2 matrix. CNNPs were formed by the thermal condensation polymerization of carbon nitride utilizing citric acid and urea as the precursors, and exhibited some unusual properties, including a lower bandgap of 2.1 eV, a highly redshifted fluorescence emission maximum at 2.35 eV, surface carboxylate groups, and the emergence of unique structural characteristics corresponding to amorphous yet graphitic carbon [5]. In contrast to bulk g-CN, which has a C:N ratio of 0.75, the CNNPs possessed an elevated C:N ratio as high as 1.87 at the surface. The additional carbon was found to be both amorphous and graphitic, although the structural characteristics of g-CN were mostly unaffected, as validated by diffractometric and spectroscopic data. Even in the absence of a sacrificial agent, the CNNP@TNT nanocomposite demonstrated enhanced performance in sunlight-driven CO2 photoreduction. When compared to the freestanding TNT photocatalyst, the CO yield of photoreduction for the CN@TNT hybrid was more than three times higher. UV-filtered illumination of the CNNP@TNT heterojunction photocatalyst generated appreciable quantities of methane and CO (3.41 and 8.78 μmolg–1h–1 respectively). In situ electrophoretic anodization is an innovative approach to incorporate semiconductor quantum dots into TiO2 nanotubes or other electrochemically grown nanostructures. REFERENCES 1. Kessler, F. K. et al., Nature Reviews Materials (2017) 2 (6), 1. 2. Chaulagain, N. et al., ACS Applied Materials & Interfaces (2022) 14 (21), pp. 24309-24320. 3. Kumar, P. et al., Advanced Optical Materials (2020) 8 (4), Art. No. 1901275. 4. Fu, J. et al., Advanced Energy Materials (2018) 8 (3), Art. No. 1701503. 5. Alam, K.M. et al., Chemical Engineering Journal (2023) 456, Art. No. 141067.
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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".