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Record W2425211565 · doi:10.1002/aenm.201600490

Novel Pliable Electrodes for Flexible Electrochemical Energy Storage Devices: Recent Progress and Challenges

2016· article· en· W2425211565 on OpenAlexafffund
Muhammad Yousaf, Hao Shi, Yunsong Wang, Yijun Chen, Zhimin Ma, Anyuan Cao, Hani E. Naguib, Ray P. S. Han

Bibliographic record

VenueAdvanced Energy Materials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaCanada Research ChairsCanada Foundation for InnovationNational Science Foundation
KeywordsMaterials scienceElectrodeSupercapacitorNanotechnologyGrapheneCarbon nanotubeEnergy storageFabricationFlexible electronicsPolyethylene terephthalateComposite materialElectrochemistry

Abstract

fetched live from OpenAlex

With wearable electronics rapidly coming into fashion, research into flexible energy storage devices and in particular, pliable electrodes, is attracting a lot of attention. Pliable electrodes are usually fabricated by intercalating an active material in a flexible matrix with superior mechano‐electrical properties, and can be grouped either as substrate‐supported or free‐standing. Depending on their mode of deformation the electrodes can be labeled as bendable, compressible or stretchable. Recent progress and challenges in the design and fabrication of pliable electrodes for constructing flexible lithium ion batteries and flexible supercapacitors are highlighted. 2 pliable core‐shell structure electrodes fabricated from: a) carbon nanotube sponge embedded with MoS 2 nanoparticles and b) electrospun polyethylene terephthalate fibers impregnated with graphene nanoplatelets are also presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.026
GPT teacher head0.250
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations167
Published2016
Admission routes2
Has abstractyes

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