Plasmonic Titanium Nitride/g-C<sub>3</sub>N<sub>4</sub> with Inherent Interface Facilitates Photocatalytic CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Plasmonic metal nitride is a practical alternative for plasmonic gold nanoparticles owing to its low-cost, tunable plasmonic resonance in the visible-light and near-IR region. However, an efficient charge transfer between plasmonic metal nitride nanoparticles and graphitic carbon nitride g-C 3 N 4 through the formation of chemical bonds remains challenging. Herein, a facile strategy for the fabrication of plasmonic titanium nitride/g-C 3 N 4 with intimate contact toward an enhanced photocatalytic CO 2 reduction is proposed. The functionalization of TiN nanoparticles with amino groups enables the copolymerization with g-C 3 N 4 precursors, offering intimate contact between them by the covalent bonds. This intimate contact could facilitate the electron transfer between TiN nanoparticles and g-C 3 N 4 . Under optimized conditions, the representative g-C 3 N 4 -2.8TiN has the highest CO production rate of ∼820 μmol g –1 h –1 with an apparent quantum yield of 3.5% at 400 nm and even 0.43% at 550 nm, which are some of the highest reported values for g-C 3 N 4 -based materials. This work offers promising opportunities to fabricate low-cost plasmonic nanoparticle/semiconductor systems for solar energy applications.
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 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) | 0.001 | 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 | 0.001 | 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".