Revealing the Role of Mo Doping in Promoting Oxygen Reduction Reaction Performance of Pt<sub>3</sub>Co Nanowires
Bibliographic record
Abstract
Highly active and durable electrocatalysts towards oxygen reduction reaction (ORR) are imperative for the commercialization application of proton exchange membrane fuel cells. By manipulating ligand effect, structural control, and strain effect, we report here the precise preparation of Mo-doped Pt3Co alloy nanowires (Pt3Co-Mo NWs) as the efficient catalyst towards ORR. The as-prepared Pt3Co-Mo NWs deliver high specific activity (0.596 mA cm-2) and mass activity (MA, 0.84 A mg -1Pt), much higher than those of undoped counterparts. Besides activity, Pt3Co-Mo NWs also demonstrate excellent structural stability and cyclic durability even after 50,000 cycles (76% MA retain), again surpassing control samples without Mo dopants. The superior catalytic performance can be attributed to several structural advantages. First, both ultrafine nanowire morphology and controlled growth ensure the exposure of abundant highly active high-index facets, resulting in increased electrochemical active surface area (ECSA, 141 m2 g−1 Pt). Second, one dimensional (1D) Pt nanostructure, particularly ultrafine Pt nanowires, are less subject to dissolution, aggregation, and even Ostwald ripening than nanoparticles, leading to much-improved durability. Third and most importantly, the Mo doping directly changes the local electronic structure of both element Pt and Co, not only effectively endowing the Pt3Co electrocatalysts with an enhanced activity but also efficiently preventing element Co from leaching leading to improved durability. The geometric phase analysis (GPA) strain maps and the density function theory (DFT)calculations were conducted to further analyze the role of Mo dopants. GPA strain maps reveal that Mo dopants are inclined to be the tensile strain cores, generating local lattice expansion to adjust the excessively compressive strain effect introduced by Co alloying. Benefiting from the optimized d-band center, the oxygen binding energy (Eo) on Pt3Co-Mo slabs is closest to the optimal value compared with those of undoped counterparts. Moreover, the energy required to remove Co atoms from Pt3Co slab with Mo doping is increased by 0.195 eV compared to the slab without Mo doping, indicating the electronic effect of Mo dopants to stabilize Co. This work provides not only a facile methodology but also an in-depth investigation of the relationship between structure and properties to provide general guidance for future design and optimization.
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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".