Encapsulating Pt Nanoparticles inside a Derived Two-Dimensional Metal–Organic Frameworks for the Enhancement of Catalytic Activity
Bibliographic record
Abstract
The development of highly active and stable electrocatalysts toward oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER) is a key for commercial application of fuel cells and water splitting. Here, we report a highly active and stable Pt nanoparticles (NPs) encapsulated in ultrathin two-dimensional (2D) carbon layers derived from the ultrathin 2D metal–organic framework precursor (ZIF-67). Electrochemical tests reveal that our approach not only stabilized Pt NPs successfully but also boosted Pt activities toward ORR and HER. We found that our Pt catalysts encapsulated in ultrathin 2D carbon layers exhibited an ORR activity of 5.9 and 12 times greater than those of the commercial Pt/C and Pt/RGO without 2D carbon layer protection. Our encapsulated Pt catalysts also show more than nine times higher stability than those of Pt/C catalysts. In addition to ORR, our novel encapsulated Pt catalysts display an extraordinary stability and activity toward HER, with a lower overpotential (14.3 mV in acidic media and 37.2 mV in alkaline media) at a current density of 10 mA cm–2 than Pt/C catalysts (23.1 mV in acidic media and 92.1 mV in alkaline media). The enhanced electrochemical activities and stability of our encapsulated Pt catalysts are attributed to the synergistic effect of Pt-based NPs and ultrathin 2D carbon layers derived from ZIF-67 with enriched active sites Co–Nx. First-principles simulations reveal that the synergistic catalysis of Pt-based NPs and Co–Nx derived from ZIF-67 improves the activity for ORR and HER.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".