Cu-doped titanium suboxide fuel cell catalyst support prepared by sol-gel method: Unveiling the role of Cu as a lone dopant
Bibliographic record
Abstract
• Sol-gel synthesis of Cu-doped titanium suboxide (TOC) with a high surface area. • Cu as a lone dopant effectively reduces the band gap but is susceptible to leaching. • Strong metal support interaction helps anchor Pt catalyst particles TOC support. • Pt/TOC has higher stability and oxygen reduction reaction activity compared to Pt/C. In this study, we demonstrate a facile synthetic approach for a bottom-up sol-gel method for the preparation of a non-carbonaceous Cu-doped Ti 4 O 7 suboxide (TOC) fuel cell catalyst support. In addition, we seek to achieve a better understanding of the role of Cu as a dopant and reveal its contribution to activity and stability in Pt/TOC during the oxygen reduction reaction (ORR) and corresponding accelerated stress tests (ASTs). The Pt/TOC catalyst showed an enhanced activity and stability over commercial Pt/C due to the strong metal support interaction between TOC and Pt. This enabled the catalyst to retain its oxygen reduction performance after extended load cycling tests. Thus, Cu as a lone dopant was effective in promoting oxygen vacancies and greatly reduces the electronic bandgap, however Cu was prone to leaching after repeated cycling protocols. Finally, we also observed a relatively decreased stability compared to our previous dual-doped systems, highlighting the importance of incorporating a secondary dopant species for synergistic effects in stability and activity.
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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".