Functionalization of CO<sub>2</sub>-Derived Carbon Support as a Pathway to Enhancing the Oxygen Reduction Reaction Performance of Pt Electrocatalysts
Bibliographic record
Abstract
Proton-exchange membrane fuel cells (PEMFCs) hold promise for clean energy generation, but their commercialization is partially hindered by the sluggish oxygen reduction reaction (ORR) at the cathode, which relies on costly Pt electrocatalysts supported by petroleum-derived carbon. This study investigates a CO 2 -derived carbon (CO 2 –C) material as a sustainable alternative to petroleum-derived carbon and attempts to enhance the ORR performance of Pt electrocatalyst with the CO 2 –C support by pretreatment with hydrogen peroxide (H 2 O 2 ) and potassium hydroxide (KOH) solutions. Based on physical characterization results, both KOH and H 2 O 2 pretreatments of CO 2 –C increased the Brunauer–Emmett–Teller (BET) surface area and improved the metal–support interaction compared to untreated CO 2 –C. Electrochemical characterization revealed superior ORR performance of Pt/H 2 O 2 –CO 2 –C, exhibiting higher mass activity (142.8 mA mg Pt –1 ) compared to Pt/CO 2 –C (102 mA mg Pt –1 ), while Pt/KOH–CO 2 –C showed the highest specific activity (1503.8 μA cm Pt –2 ) among the studied samples. Thus, Pt electrocatalysts with pretreated CO 2 –C support are presented as an alternative to conventional Pt/C catalysts toward sustainable and high-performance PEMFCs.
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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".