Robust Porous TiN Layer for Improved Oxygen Evolution Reaction Performance
Bibliographic record
Abstract
The poor reversibility and slow reaction kinetics of catalytic materials seriously hinder the industrialization process of proton exchange membrane (PEM) water electrolysis. It is necessary to develop high-performance and low-cost electrocatalysts to reduce the loss of reaction kinetics. In this study, a novel catalyst support featured with porous surface structure and good electronic conductivity was successfully prepared by surface modification via a thermal nitriding method under ammonia atmosphere. The morphology and composition characterization-confirmed that a TiN layer with granular porous structure and internal pore-like defects was established on the Ti sheet. Meanwhile, the conductivity measurements showed that the in-plane electronic conductivity of the as-developed material increased significantly to 120.8 S cm−1. After IrOx was loaded on the prepared TiN-Ti support, better dispersion of the active phase IrOx, lower ohmic resistance, and faster charge transfer resistance were verified, and accordingly, more accessible catalytic active sites on the catalytic interface were developed as revealed by the electrochemical characterizations. Compared with the IrOx/Ti, the as-obtained IrOx/TiN-Ti catalyst demonstrated remarkable electrocatalytic activity (η10 mA cm−2 = 302 mV) and superior stability (overpotential degradation rate: 0.067 mV h−1) probably due to the enhanced mass adsorption and transport, good dispersion of the supported active phase IrOx, increased electronic conductivity and improved corrosion resistance provided by the TiN-Ti support.
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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".