Integrating Multiple Advantages into 1 Nm Pt3Ni Bimetallic Alloy Nanowires for Oxygen Reduction Reaction
Bibliographic record
Abstract
Exploiting highly active and durable oxygen reduction reaction (ORR) electrocatalyst is still imperative for clean and efficient energy conversion device, such as fuel cells and metal-air batteries. For this purpose and maximize the utilization of noble Pt, we present here a facile, scalable strategy for the high-precise synthesis of 1 nm thick Pt3Ni bimetallic alloy nanowires (Pt3Ni BANWs). The seed-mediated growth mechanism of Pt3Ni BANWs is identified by examining the morphology and composition of the intermediate products collect at different reaction stages. As expected, the Pt3Ni BANWs delivered enhanced mass activity (0.546 A mg-1 Pt, exceeding the DOE 2020 target) in comparison to Pt nanowires assembly (Pt NWA, 0.098 A mg-1 Pt) and Pt/C (Pt, 0.135 A mg-1 Pt) due to the rational integration of multiple compositional and structural advantages, such as alloy feature, ultrathin 1D nanostructure and high-index facets. Moreover, the Pt3Ni BANWs displayed enhanced durability (37% MA retention) than Pt NWA and Pt after 50,000 potential cycles. All these results indicate that the ultrathin Pt3Ni BANWs are potential candidates for catalyzing ORR with acceptable activity and durability. The present work could not provide a facile strategy but also a general guidance for the design of superb performance Pt-based nanowire catalysts for ORR. Figure 1. Progressive formation mechanism of the ultrathin Pt3Ni BANWs. Keywords: Oxygen Reduction Reaction, Pt-Ni alloy, Nanowires, Seed-mediated growth References: [1] Stamenkovic, V. R.; Fowler, B.; Lucas, C. A.; Marković, N. M. Improved oxygen reduction activity on Pt3Ni (111) via increased surface site availability. Science 2007, 315, 493-497. [2] Xiao, W.; Gong, M.; Xin, H. L.; Wang, D. Recent advances of structurally ordered intermetallic nanoparticles for electrocatalysis. ACS Catal. 2018, 8, 3237-3256. Figure 1
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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 | 0.001 |
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 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".