Oxygen Reduction Reaction (ORR) on a Mixed Titanium and Tantalum Oxy-nitride Catalyst Prepared by the Urea-based Sol-gel Method
Bibliographic record
Abstract
The electrochemical stability and activity of different compositions of titanium and tantalum oxy-nitride nano-catalysts were investigated for the oxygen reduction reaction (ORR). A new sol-gel method was used to produce a nano-powder mixture of Ti and Ta oxynitride from their alkoxides using urea as a nitrogen source. The precursors prepared by the sol-gel method were annealed in a N2 + 3% H2 atmosphere at determined temperatures (500, 700 and 900 °C) inside a silica tube furnace. X-ray diffraction results proved that by using this method a considerable amount of nitrogen was inserted into the catalyst structure at a relatively low temperature. Energy dispersive spectroscopy showed that the prepared catalyst should be oxidized carbonitride of titanium and/or tantalum. Heat treatment had a major effect on the onset potential by changing the crystallinity of the catalyst, so that the onset potential of titanium oxynitride increased from ca. 0.05 V to 0.65 V vs. NHE by increasing the temperature from 500 to 700 °C. Increasing the Ta concentration also led to a higher onset potential but lower ORR current. For instance, the onset potential for the ORR for tantalum oxynitride heat treated at 700 °C was ca. 0.85 V vs. NHE while this value was ca. 0.65 V vs. NHE for titanium oxynitride. However, the ORR current was 100 times smaller in tantalum oxynitride, most likely because of a low electrochemically active surface area. Electrochemical measurements suggested that an appropriate composition of titanium and tantalum was required to have both a good onset potential and ORR current by improving the catalytic activity and increasing the active surface area and electrical conductivity.
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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".