Decoding Plasmonic Enhancement Pathways in Group 4 Metal Nitride‐TiO <sub>2</sub> Composites: Rhodamine B Dye Degradation Case Study
Bibliographic record
Abstract
ABSTRACT Traditional photocatalysis has predominantly focused on TiO 2 due its low cost, chemical stability, and non‐toxicity, however, its wide bandgap (>3 eV) limits solar absorption to under 5%. Plasmonic materials address this by extending light absorption into the visible and near‐infrared range, enhancing catalytic activity through localized electromagnetic fields, hot carriers, and photothermal effects. Plasmonic transition metal nitrides have emerged as promising, cost‐effective alternatives to noble metals due to their strong broadband absorption and chemical stability. While earlier studies attribute their photocatalytic enhancement mainly to hot carrier injection, a more detailed assessment is needed to properly understand the enhancement pathway. This study synthesizes composites of TiN, ZrN, and HfN with commercial P25 TiO 2 and evaluates their photocatalytic performance via Rhodamine B dye degradation. Under 100 mW cm −2 illumination, the 1 wt% ZrN/TiO 2 composite achieves over 99% dye degradation in 50 min, outperforming TiN and HfN, which require 10 wt% loading for similar results. By analyzing reaction temperature profiles and degradation kinetics under different light intensities, the study finds that hot carrier effects dominate in TiN/TiO 2 and ZrN/TiO 2 systems, while photothermal effects play a larger role in HfN/TiO 2 composites. This highlights the distinct mechanisms by which plasmonic nitrides enhance photocatalytic efficiency.
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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".