Probing the Effects of Defects in Ultrathin Oxide Overlayers on the Selectivity of Encapsulated Electrocatalysts
Bibliographic record
Abstract
Application of ultrathin oxide encapsulation layers on co-catalysts employed in z-scheme photocatalysis has the potential to increase overall efficiency through the prevention of undesirable back reactions and increased charge separation. [1,2] However, defects within the semipermeable oxide coatings – such as pinholes, cracks or particle protrusions – have been postulated to facilitate locally high rates of undesirable reactions by creating pathways for facile transport of undesired reactants to exposed active sites.[3] Local probe measurements, such as scanning electrochemical microscopy (SECM), can estimate the relative rates of production of product species generated from competing electrochemical reactions with high spatial resolution over the electrode. This can be leveraged to determine the influence of local defects on global performance metrics, such as the apparent permeability of the overlayer and selectivity of the electrode. In this presentation, we report the use of SECM to determine the influence of overlayer defects on the performance of a model silicon oxide (SiOx) -encapsulated Pt thin film electrocatalyst when operated under conditions where two competing reactions can occur. Motivated by Z-scheme photocatalysis, the hydrogen evolution reaction (HER) and Fe(III)/Fe(II) redox reaction were studied. After introducing new methodology to determine local selectivity towards HER using SECM tip current, the resulting selectivity maps are compared against defects seen in optical images, scanning electron micrographs, and atomic force microscopy images. This analysis reveals that that certain types of defects in the oxide overlayer can be responsible for a large percentage of the partial current density towards the undesired Fe(III) reduction reaction. Correcting for the defect contributions to the undesired reaction, it is determined that the true Fe(III) permeability values for the SiOx overlayers are almost two orders of magnitude lower than permeabilities determined from conventional analysis that ignored the presence of defects. Finally, different types of defects were studied revealing that the defect morphology can have varying influence on both the selectivity and calculated permeability. This work highlights the need for local measurements in addition to macroscopic measurements of oxide encapsulated catalysts as they are applied to more complex geometries such as particle photocatalysts. References [1] Qi, Y. et al. Applied Catalysis B: Environmental 224, 579–585 (2018). [2] T. Zhao, et al., PNAS, 2021, 118 (7), [3] N. Y. Labrador, et al. , ACS Catalysis, 2018, vol. 8, 1767–1778.
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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".