MétaCan
Menu
Back to cohort
Record W4403588195 · doi:10.1021/acs.nanolett.4c04014

Immobilization of Metal Nanoparticles to an Ultrathin Two-Dimensional Conjugated Metal–Organic Framework for Synergistic Electrocatalysis

2024· article· en· W4403588195 on OpenAlexaff
Yecan Pi, Ziming Qiu, Fan Yu, Qixin Mao, Guangxun Zhang, Xuemei Wang, Hao‐Hsiang Chang, Hsiang-Jung Chen, Tsung‐Yi Chen, Han‐Yi Chen, Songtao Zhang, Mohsen Shakouri, Huan Pang

Bibliographic record

VenueNano Letters · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersYangzhou UniversityNational Science and Technology CouncilNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsElectrocatalystNanoparticleConjugated systemMetalMetal-organic frameworkNanotechnologyMaterials scienceChemical engineeringChemistryElectrochemistryPolymerElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Metal-organic frameworks (MOFs) have been considered as promising hosts for immobilizing ultrafine metal nanoparticles (MNPs) due to their high surface area and porosity. However, electrochemical applications of such emerging composites are severely limited by the poor electrical conductivity and large size of the MOFs. Herein, we report the general synthesis of incorporating various MNPs into a conjugated MOF ultrathin nanosheet (Cu-TCPP UNS) matrix, which not only prevents agglomeration and restricts the growth of MNPs but also benefits the exposure of active sites and the transport of electrons. Specifically, the obtained PtCu@Cu-TCPP UNSs exhibited nearly two times higher mass activity for the methanol oxidation reaction (MOR) than the commercial Pt/C catalyst. Mechanistic studies reveal that the strong interaction between MNPs and Cu-TCPP promotes the oxidation of the CO intermediate. Moreover, the PtCu@Cu-TCPP UNSs can be employed as bifunctional electrocatalysts to couple MOR with the hydrogen evolution reaction for highly efficient hydrogen production.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Citations19
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueNano LettersSame topicElectrocatalysts for Energy ConversionFrench-language works237,207