(Invited) Multifunctional Membrane Coated Electrocatalysts
Bibliographic record
Abstract
Electrocatalysts are essential components in many emerging electrochemical technologies due to their ability to efficiently facilitate the interconversion between electrical and chemical energy. However, significant improvements in the stability, activity, and selectivity of state-of-the-art electrocatalysts must be made if these technologies are going to play a major role in a sustainable energy future. The vast majority of electrocatalysts used in today’s commercial devices are comprised of metallic nanoparticles or thin films that are deposited onto a conductive support and partially exposed to the bulk electrolyte. By contrast, this work has explored an alternate electrocatalyst architecture in which the active electrocatalyst has been encapsulated by an ultrathin permeable overlayer. Specifically, we encapsulate Pt nanoparticle and thin film electrocatalysts with 2-20 nm thick layers of silicon oxide (SiOx) fabricated using a room temperature deposition process.[1] Through a combination of physical characterization and electroanalytical measurements, we show that these permeable overlayers can serve as nano-scale membranes that provide significant benefits for stabilizing Pt nanoparticles and imparting advanced catalytic functionalities such as poison-resistance. This work has focused on SiOx-encapsulated Pt thin films electrocatalysts for the hydrogen evolution reaction, but the membrane coated electrocatalyst architecture also has great potential as a tunable platform that can be extended to many other materials and chemistries. [1] N. Y. Labrador, X. Li, Y. Liu, J. T. Koberstein, R. Wang, H. Tan, T. P. Moffat, and D. V. Esposito, Nano Letters, 16, 6452-6459, 2016.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.038 | 0.016 |
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".