Niobium Oxide Coated Mesoporous Platinum Nanoparticles for the Oxygen Reduction Reaction
Bibliographic record
Abstract
Effective and economic methods of converting chemical energy into electricity or vice versa (i.e., through the use of fuel cells) must be developed to aid in the transition to renewable energy sources. One of the major barriers in the commercialization of fuel cells is the disparity in speed and efficiency between the two fuel cell reactions: the hydrogen oxidation reaction (HOR) and the oxygen reduction reaction (ORR) of which the latter is orders of magnitude slower.1 Mesoporous Pt prepared by electrodeposition has been shown to have increased activity towards the ORR than the industry standard Pt nanoparticles (NPs) supported on carbon.2 Platinum based catalysts, however, lack long term stability under acidic fuel cell conditions. One method that has been found to mitigate the long-term loss of Pt catalyst stability and activity is coating platinum with nanoscale thin films of niobium oxide.3 In this work, niobium oxide was investigated as a stabilizing coating for mesoporous Pt NP catalysts. Mesoporous Pt NPs were synthesized via electrodeposition to form ~50 nm diameter NPs. The Pt NPs were then coated with niobium oxide films with a varying nanoscale thickness that ranged from 3 to 50 nm. The niobium oxide films were fabricated using atomic layer deposition (ALD) or electrodeposition.3,4 Scanning electron microscopy and transmission electron tomography were used to evaluate the porosity and surface morphology of the Pt NPs before and after coating with niobium oxide. X-ray photoelectron spectroscopy and energy dispersive X-ray spectroscopy were used to confirm the presence and composition of the niobium oxide films. Electrochemical characterization by cyclic voltammetry and electrochemical impedance spectroscopy under acidic conditions was used to determine an optimal thickness of the niobium oxide film that stabilizes the size and morphology of the Pt NPs while maintaining catalyst activity. The Pt NPs coated with <10-nm thick niobium oxide films were observed to have an increased long-term performance in comparison to their uncoated counterparts. Stabilizing NP catalysts for improving their long-term activity is a vital step towards creating affordable alternatives to fossil fuels by maximizing renewable energy resources. Debe, M. K. Electrocatalyst Approaches and Challenges for Automotive Fuel Cells. Nature 2012, 486, 43–51. Paul, M. T. Y.; Gates, B. D. Mesoporous Platinum Prepared by Electrodeposition for Ultralow Loading Proton Exchange Membrane Fuel Cells. Sci. Rep. 2019, 9, 4161. Eastcott, J.; Gates, B. D. Nanoscale Thin Films of Niobium Oxide on Platinum Surfaces: Creating a Platform for Optimizing Material Composition and Electrochemical Stability. Can. J. Chem. 2017, 96, 260–266. Zhitomirsky, I. Electrolytic Deposition of Niobium Oxide Films. Mater. Lett. 1998, 35, 188–193.
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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) | 0.002 | 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".