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Record W4200242177 · doi:10.3390/nano11123462

Ordered Porous TiO2@C Layer as an Electrocatalyst Support for Improved Stability in PEMFCs

2021· article· en· W4200242177 on OpenAlexaff
Gaoyang Liu, Zhaoyi Yang, Xindong Wang, Baizeng Fang

Bibliographic record

VenueNanomaterials · 2021
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsProton exchange membrane fuel cellElectrocatalystMaterials scienceElectrochemistryPorosityCathodeChemical engineeringElectrodeCatalysisLayer (electronics)Carbon fibersComposite materialFuel cellsChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Proton exchange membrane fuel cells (PEMFCs) are the most promising clean energy source in the 21st century. In order to achieve a high power density, electrocatalytic performance, and electrochemical stability, an ordered array structure membrane electrode is highly desired. In this paper, a new porous Pt-TiO2@C ordered integrated electrode was prepared and applied to the cathode of a PEMFC. The utilization of the TiO2@C support can significantly decrease the loss of catalyst caused by the oxidation of the carbon from the conventional carbon layer due to the strong interaction of TiO2 and C. Furthermore, the thin carbon layer coated on TiO2 provides the rich active sites for the Pt growth, and the ordered support and catalyst structure reduces the mass transport resistance and improves the stability of the electrode. Due to its unique structural characteristics, the ordered porous Pt-TiO2@C array structure shows an excellent catalytic activity and improved Pt utilization. In addition, the as-developed porous ordered structure exhibits superior stability after 3000 cycles of accelerated durability test, which reveals an electrochemical surface area decay of less than 30%, considerably lower than that (i.e., 80%) observed for the commercial Pt/C.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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 teacher head, not a consensus.

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

Citations12
Published2021
Admission routes1
Has abstractyes

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