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Record W3088249502 · doi:10.1002/adma.202004654

Mesoporous Nanoarchitectures for Electrochemical Energy Conversion and Storage

2020· review· en· W3088249502 on OpenAlexaff
Yuxing Yan, Guangrui Chen, Peihong She, Guiyuan Zhong, Wenfu Yan, Yusuke Yamauchi

Bibliographic record

VenueAdvanced Materials · 2020
Typereview
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsFuture Earth
FundersAustralian Research CouncilNational Natural Science Foundation of ChinaJapan Science and Technology AgencyHigher Education Discipline Innovation ProjectAustralian National Fabrication Facility
KeywordsMesoporous materialMaterials scienceNanomaterialsNanotechnologySupercapacitorEnergy storageElectrochemical energy conversionNanostructureLithium (medication)Electrochemical energy storageGrapheneElectrochemistryMesoporous organosilicaElectrodeMesoporous silicaCatalysisChemistry

Abstract

fetched live from OpenAlex

Mesoporous materials have attracted considerable attention because of their distinctive properties, including high surface areas, large pore sizes, tunable pore structures, controllable chemical compositions, and abundant forms of composite materials. During the last decade, there has been increasing research interest in constructing advanced mesoporous nanomaterials possessing short and open channels with efficient mass diffusion capability and rich accessible active sites for electrochemical energy conversion and storage. Here, the synthesis, structures, and energy-related applications of mesoporous nanomaterials are the main focus. After a brief summary of synthetic methods of mesoporous nanostructures, the delicate design and construction of mesoporous nanomaterials are described in detail through precise tailoring of the particle sizes, pore sizes, and nanostructures. Afterward, their applications as electrode materials for lithium-ion batteries, supercapacitors, water-splitting electrolyzers, and fuel cells are discussed. Finally, the possible development directions and challenges of mesoporous nanomaterials for electrochemical energy conversion and storage are proposed.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
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 head0.016
GPT teacher head0.275
Teacher spread0.259 · 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
GenreReview

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

Citations182
Published2020
Admission routes1
Has abstractyes

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