Molecular Trapping Strategy To Stabilize Subnanometric Pt Clusters for Highly Active Electrocatalysis
Bibliographic record
Abstract
Structure engineering is an effective way to substantially adjust the chemical and physical properties of materials. However, the effects of structure engineering of carbon hosts on the catalytic properties of Pt-based catalysts at the molecular scale are poorly understood. Herein, we report a molecular-level strategy to anchor and stabilize subnanometric Pt clusters on a covalently coupled host of graphitic carbon nitride (g-C3N4) and carbon nanotubes (CNT) for the development of electrocatalysts with high activities toward methanol oxidation reactions. Theoretical evaluation and experimental validation identified that the chemical integration of g-C3N4 on CNT is critical in optimizing the electronic structures and catalytic properties of Pt catalysts. As a result, the Pt-g-C3N4-CNT possesses a high energy level of d-band position, significantly strengthening its adsorption behaviors for the key reaction intermediates during the methanol electrooxidation process and energetically decreasing the energy barriers in the multistep reaction pathways. Combining with the strong catalyst–support interactions afforded by the adaptive coordination environment of g-C3N4 with Pt clusters as well as the unimpeded electron transfer via a σ-orbital overlap between CNTs and g-C3N4, the as-obtained Pt-g-C3N4-CNT possesses prodigious electrocatalytic properties including high activities, unusual poison tolerance, and reliable long-term discharge stabilities toward methanol oxidation reactions, in comparison to commercial Pt/activated carbon (Pt-AC) and Pt-CNT catalysts. This molecular-level finding opens up a new avenue to design and develop more efficient and effective carbon-based supports for fabricating advanced heterogeneous catalysts and could also be extended to more applications, such as lithium-ion batteries, lithium-sulfur batteries, supercapacitors, and sensors.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".