Embedded Platinum–Cobalt Nanoalloys in Biomass-Derived Laser-Induced Graphene as Stable, Air-Breathing Cathodes for Zinc–Air Batteries
Bibliographic record
Abstract
Fuel cells and metal–air batteries hold significant promise to help decarbonize transportation and the electricity grid. To encourage widespread adoption of these technologies, improvements to their air-breathing cathodes are required, which address problems such as the high cost of platinum (Pt) catalysts used to boost the kinetics of the oxygen reduction reaction (ORR) as well as the stability of Pt/C interfaces over long-term cycling. In this paper, we demonstrate a facile approach to reduce Pt content to less than 2 wt % by interfacing Pt with CoO x as well-dispersed nanoparticles entrapped within a highly conductive laser-induced graphene (LIG) matrix. Laser-induced carbonization of polymerized furfural alcohol preloaded with Co and Pt precursors resulted in the formation of a mixture of spherical nanoalloys PtCoO x and core (CoO x )–shell (Pt) structures. This LIG-PtCoO x electrode exhibited a low onset and half-wave potential in alkaline media, which closely approached a benchmark Pt/C. The effectiveness of LIG-PtCoO x was demonstrated by its performance in rotating disk and rotating ring disk electrode studies versus commercial Pt/C with the same concentration of the catalyst, which resulted in 4-fold greater mass activity and more than 6-fold higher specific activity, which are reflected in a high turnover frequency (TOF). The resulting material was tested as an air–cathode for zinc (Zn)–air batteries leading to improved stability (118 h of operation) and rechargeability (0.75 V voltage gap), exhibiting a higher peak power density compared to batteries assembled with the commercial benchmark Pt/C cathodes with similar composition.
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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) | 0.000 | 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".