Titanium Nitride Films as Durable, Enhancing Windows for Electrochemical Infrared Spectroscopy
Bibliographic record
Abstract
As electrochemical reactions become more central to the green energy transition, spectroscopic means to characterize these reactions are becoming more valuable. Electrochemical attenuated total reflectance – surface enhanced infrared spectroscopy (ATR-SEIRAS) is of particular note for its ability to characterize molecules at the catalyst-electrolyte interface. However, extensive studies using electrochemical ATR-SEIRAS are limited by the poor chemical and mechanical stability or optical properties of window materials. In this work, we report titanium nitride (TiN) prepared in a single-step reactive sputtering process with Ar and N 2 plasma as a new material for ATR-SEIRAS on account of its ease of preparation, good conductivity, outstanding mechanical and chemical stability, and ability to acquire surface-enhanced spectra. After depositing Pt on the TiN surface, a CO probe is used to demonstrate the spectroscopic utility of the TiN layers. TiN also shows remarkable chemical stability in the same strong alkaline (1 M KOH) and acidic (0.5 M H 2 SO 4 ) conditions often used for energy-related studies, especially in comparison to the classic electroless deposited Au system typically utilized. Finally, we validate TiN for studying energy-relevant reactions, demonstrating that meaningful spectra can be collected in CO 2 reduction and O 2 evolution. These properties make TiN one of the most promising optical window materials yet reported for electrochemical ATR-SEIRAS.
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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 | 0.001 | 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".