Ti/TiOxNy thin films as highly efficient photoelectrode via engineered interfacial facet on plasmonic schottky barriers
Bibliographic record
Abstract
Titanium nitride (TiN) is recognized for enhancing semiconductor photocatalytic activity due to its plasmonic properties, similar to those of gold and silver nanoparticles. Changing the stoichiometry of TiN enhances free carrier density, resulting in metallic characteristics and optical properties resembling gold in the visible spectrum. New Ti/TiO x N y thin films were synthesized with the aid of the Arc-PVD technique under some conditions, including low temperatures and various concentrations of N 2 gas. Under zero and maximum amounts of N 2 gas, nonstoichiometric and highly conductive N-rich phases of TiN were created, respectively. A TiO 2 semiconductor interlayer was specifically coated on a Ti substrate which served both as a co-catalyst layer and as a means to reduce residual stress in the TiN film. Our results demonstrate that the defects and resistance to oxidation in the TiN structure, dependent on the N 2 input, may significantly influence the orientation of stable crystal facet ( [111]) and the excitation of plasmonic-photonic hybrid modes. The highest photocurrent was recorded at about 8.2 mA cm ‒2 at 1.4 V vs. RHE for gold-colored Ti/TiO x N y oriented along [111] facet, which is 4.3 and 54.6 times more than samples adjusted along [110] and [001] facets, respectively. The employed PVD method at low temperatures as a strong designing technique for tailoring the metal-semiconductor interface simplifies the creation of highly activated photoelectrodes within the water-splitting performance. This applicable technique brings interesting eco-friendly benefits, including energy efficiency, low environmental footprint, and optimized material utilization, in accordance with green chemistry principles.
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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 | 0.001 | 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".