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Record W4399172793 · doi:10.1021/acscatal.4c02016

Direct Methanol Fuel Cell with Porous Carbon-Supported PtRu Single-Atom Catalysts for Coproduction of Electricity and Value-Added Formate

2024· article· en· W4399172793 on OpenAlexaff
Munir Ahmad, Muhammad Bilal Hussain, Jiahui Chen, Yang Yang, Xuexian Wu, Hao Chen, Shahzad Afzal, Waseem Raza, Zhengxin Zeng, Fei Ye, Xueyang Zhao, Jiujun Zhang, Renfei Feng, Xian‐Zhu Fu, Jing‐Li Luo

Bibliographic record

VenueACS Catalysis · 2024
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsCanadian Light Source (Canada)
FundersScience, Technology and Innovation Commission of Shenzhen MunicipalityNational Natural Science Foundation of China
KeywordsCatalysisMethanolCoproductionFormateChemical engineeringPorosityCarbon fibersMaterials scienceFuel cellsChemistryWaste managementOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Bottlenecks in direct methanol fuel cells (DMFCs) with conventional noble metals as anode catalysts involve the formation of valueless byproducts and carbon dioxide (CO 2 ) emissions. Carbon-supported Pt single atoms have demonstrated high performance in DMFCs. However, the adsorbed intermediates (CO ads ) strongly bind to Pt single-atom sites, resulting in complete methanol oxidation to CO 2 and low power densities. Herein, we have developed a DMFC for CO 2 -emission-free coproduction of electricity and valuable formate using metal organic framework (MOF)-derived N-doped porous carbon-supported PtRu single-atom (referred to as PtRu SA /NPC) catalysts. The DMFC produces current and power densities of 657 mA cm –2 and 97.4 mW cm –2, respectively, at a potential of 0.65 V with a 98.4% Faraday efficiency for formate at 80 °C. Density functional theory (DFT) calculations show that CH 3 OH molecules preferentially adsorb onto the PtRu single atoms, but their oxidation to CO 2 molecules on PtRu SA /NPC is kinetically unfavorable due to the large energy barrier. This study offers a pathway to developing high-performance and CO 2 -emission-free electrocatalysts for DMFCs.

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)0.000
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 score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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

Citations39
Published2024
Admission routes1
Has abstractyes

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