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Record W2734367372 · doi:10.1149/ma2017-02/41/1789

Poison-Resistant Electrocatalysis Enabled By Encapsulation of Platinum with Silicon Oxide Nanomembranes

2017· article· en· W2734367372 on OpenAlexaboutno aff
Natalie Yumiko Labrador, Eva Liza Songcuan, Chathuranga De Silva, Daniel V. Esposito

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
Fundersnot available
KeywordsElectrocatalystMaterials scienceCatalysisNanotechnologyElectrochemistryChemical engineeringElectrodePlatinumOxideNanoparticleSiliconChemistryOptoelectronicsMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Improvement in the efficiency and lifetime of electrochemical technologies such electrolyzers and photoelectrochemical cells are critically important for the realization of storable chemical fuels.[1-5] Typically, the device lifetime relies on the durability of electrocatalyst nanoparticles. In an effort to reduce costs and catalyst loading, catalyst particle sizes are kept small (< 5 nm). However, decreasing particle size often leads to increased rates degradation mechanisms that generally reduce the electrochemically active surface area (ECSA).[1,6,7] To prevent this degradation, previous studies have encapsulated the active electrocatalyst material with an ultrathin, permeable or porous silica layer.[8,9] These encapsulated electrocatalysts exhibited greatly enhanced stability compared to silica-free electrocatalysts while still maintaining high electrochemical activity. It was hypothesized that transport of reactant and product species occurred through the silica layers, although direct evidence and detailed understanding of transport through the silica was lacking. This understanding is complicated by the complex electrode geometries studied in both reports, where the silica coatings had varying thickness and did not uniformly coat the electroactive Pt. This study removes this complexity by investigating well-defined Pt thin film electrodes that are encapsulated with silicon oxide (SiOx) nanomembranes. By systematically changing the SiOx thickness and evaluating hydrogen evolution reaction (HER) performance, we seek to gain deeper understanding of the structure-property relationships that affect the transport properties through SiOx nanomembranes. This membrane coated electrocatalyst (MCEC) architecture provides a promising approach to enhance electrocatalyst stability, improve poison resistance, and/or tune reaction selectivity. We use a room-temperature UV ozone synthesis process to systematically control the thickness of SiOx overlayers with nanoscale precision and evaluate the effects on the ECSA and HER performance of the underlying Pt thin films. Through detailed characterization of the SiOx overlayers this study shows that proton and H2 transport occur primarily through the SiOx coating. Notably, the SiOx nano-membranes exhibit high selectivity for proton and H2 transport compared to a HER poison species such as copper ions. These results demonstrate that MCECs are capable of multifunctional catalysis with poisoning resistance, still a more complete understanding of the structure-property-performance relationships will enable design improvements to further minimize efficiency losses due to mass-transport overpotential losses. References: [1] Shao-Horn, Y.; Sheng, W. C.; Chen, S.; Ferreira, P. J.; Holby, E. F.; Morgan, D. Top. Catal. 2007, 46(3–4), 285. [2] Wang, Y.; Chen, K. S.; Mishler, J.; Cho, S. C.; Adroher, X. C. Appl. Energy 2011, 88(4), 981. [3] Paidar, M.; Fateev, V.; Bouzek, K. Electrochimica Acta. 2016, pp 737–756. [4] Debe, M. K. Nature 2012, 486(7401), 43. [5] Pinaud, B. A.; Benck, J. D.; Seitz, L. C.; Forman, A. J.; Chen, Z.; Deutsch, T. G.; James, B. D.; Baum, K. N.; Baum, G. N.; Ardo, S.; Wang, H.; Miller, E.; Jaramillo, T. F. Energy Environ. Sci. 2013, 6(7), 1983. [6] Ferreira, P. J.; la O’, G. J.; Shao-Horn, Y.; Morgan, D.; Makharia, R.; Kocha, S.; Gasteiger, H. A. J. Electrochem. Soc. 2005, 152(11), A2256. [7] Pavlišič, A.; Jovanovič, P.; Šelih, V. S.; Šala, M.; Hodnik, N.; Hočevar, S.; Gaberšček, M. Chem. Commun. (Camb). 2014, 50(28), 3732. [8] Takenaka, S.; Miyamoto, H.; Utsunomiya, Y.; Matsune, H.; Kishida, M. J. Phys. Chem. 2014, 118(2), 774. [9] Labrador, N. Y.; Li, X.; Liu, Y.; Tan, H.; Wang, R.; Koberstein, J. T.; Moffat, T. P.; Esposito, D. V. Nano Lett. 2016, 16 (10), 6452.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.214
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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