Pt Hollow Nanospheres As an Electrocatalyst for the Oxygen Reduction Reaction
Bibliographic record
Abstract
H2-fed proton exchange membrane fuel cells (PEMFCs) represent the most advanced fuel cell technology and have a great deal of potential applications in low/zero-emission electric vehicles, distributed home power generators, and power sources for small and portable electronics. However, the commercialization of PEMFC technology has been greatly hindered by some challenges, mainly sluggish kinetics of the oxygen reduction reaction (ORR) at the cathode and the high cost of the noble metal Pt (1, 2). Alloying platinum with non-noble metals such as Co is an effective approach to improve the catalytic performance and reduce the usage of Pt. In addition, through transferring Pt nanoparticles (NPs) into a hollow nanostructure, electrocatalytic performance can be improved greatly due to its relatively lower density and higher surface area-to-volume ratio than its solid counterpart (i.e., NPs). In this work, Pt hollow nanospheres (HNSs) were fabricated through a replacement reaction of Co atoms by PtCl6 2- ions to reduce Pt usage and improve the catalytic activity towards the ORR. Carbon black Vulcan XC-72R (VC) was introduced into a solution prior to the addition of Co(II) and the formation of Co NPs and the replacement of Co by PtCl6 2-ions for a uniform dispersion of Co NPs and the Pt HNSs on the carbon support. Some Co atoms have alloyed Pt in the synthesis and exist in the Pt HNSs (3). The hollow mesoporous core in the Pt HNSs can be utilized as an electrolyte solution buffering reservoir to minimize the diffusion distance to the interior surface of the porous shell of Pt crystallites while the porous nanochannels (i.e., the micropores between the Pt crystallites) in the shell open to the mesoporous hollow core form fast mass transport networks providing more accessible sites for oxygen transfer. In addition, triple phase boundaries (i.e., gas-electrolyte-Pt NPs) can be developed more easily in the Pt HNS enabling individual Pt crystallites in the shell to be accessible to electrolyte ions and oxygen, and thus catalytically active. Furthermore, alloying Pt with Co might result in a significant lattice shrinking because of the change in Pt-Pt bond distance which also contributes to the improved electrocatalytic activity. In contrast, for the state-of-the-art Pt NPs/VC catalyst, the Pt NPs can agglomerate more easily to form larger particles, resulting in reduced active sites. Besides, the interior voids between Pt NPs may not be accessible to electrolyte ions and do not contribute to the ORR activity due to the lack of triple phase boundaries. As a result, the as-developed PtCo (20 wt%) HNS/VC catalyst outperforms significantly the state-of-the-art Pt(20 wt%)NP/VC catalyst. References 1. B. Fang, M. Kim, J. Kim, M. Song, Y. Wang, H. Wang, D. Wilkinson and J.-S. Yu, J. Mater. Chem., 21, 8066 (2011). 2. B. Fang, N. Chaudhari, M. Kim, J. Kim and J.-S. Yu, J. Am. Chem. Soc., 131,15330 (2009). 3. B. Fang, B. Pinaud and D. Wilkinson, Electrocatalysis, 7, 336 (2016). 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 | 0.000 |
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".