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Record W4311106641 · doi:10.3389/fchem.2022.1104992

Editorial: Interfacial engineering of carbon-based materials for efficient energy conversion

2022· editorial· en· W4311106641 on OpenAlexaff
Ming Ge, Xiaona Li, Minmin Wang, Qian Tao, Yulin Min, Xiaolei Yuan

Bibliographic record

VenueFrontiers in Chemistry · 2022
Typeeditorial
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsWestern University
Fundersnot available
KeywordsHeteroatomCarbon fibersNanotechnologyMaterials scienceInterface (matter)NanomaterialsEnergy storageEnergy transformationFabricationChemistryComposite materialComposite numberPower (physics)PhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Over the past decade, carbon-based materials, such as simplex carbon materials, heteroatom-doped 14 carbon materials, carbon-transition metal composites, have received the increasing attentions for 15 energy conversion owning to their low cost, good electrical conductivity, and stable structure. Specially, 16 the diversity in their structure and composition could significantly enhance their applications in various 17 fields, including nanocatalysis, energy conversion, and energy storage. In general, catalytic reactions 18 usually occur on the surface or interface of electrodes. The interface structure is usually formed 19 between carbon-based materials and other components and can be theoretically used as a channel for 20 the transportation of electrons or intermediates. Therefore, interface engineering is one of the feasible 21 and effective strategies to enhance the performance of nanomaterials, which is the cornerstone for the 22 practical applications. We would like to thank all the authors for their meaningful work and all the reviewers for their valuable 71 contributions to this special issue. We expect that these endeavors will pave the way for further 72 advancements in the design and fabrication of carbon-based materials and their derivatives by using 73 interface engineering.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.090
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 science0.0010.000
Research integrity0.0010.000
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 head0.004
GPT teacher head0.200
Teacher spread0.196 · 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
GenreEditorial

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
Published2022
Admission routes1
Has abstractyes

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