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Record W3092789490 · doi:10.1039/d0ra06160c

Recent developments in conducting polymers: applications for electrochemistry

2020· review· en· W3092789490 on OpenAlexaff
Somayeh Tajik, Hadi Beitollahi, Fariba Garkani Nejad, Iran Sheikh Shoaie, Mohammad A. Khalilzadeh, Mehdi Shahedi Asl, Quyet Van Le, Kaiqiang Zhang, Ho Won Jang, Mohammadreza Shokouhimehr

Bibliographic record

VenueRSC Advances · 2020
Typereview
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
FundersKerman University of Medical SciencesNational Research Foundation of KoreaNational Research FoundationGraduate University of Advanced TechnologyNational Foundation for Science and Technology Development
KeywordsConductive polymerMaterials scienceElectrochemistryElectrodeNanotechnologySupercapacitorPolymerLithium (medication)Chemically modified electrodeSubstrate (aquarium)Electrical conductorElectrochemical energy conversionReference electrodeChemistryComposite material

Abstract

fetched live from OpenAlex

, simplistic synthesis, acceptable environmental stability, beneficial optical, electronic, and mechanical features, researchers have largely considered them for diverse applications. Therefore, their capability of catalyzing several electrode reactions has been introduced as one of their significant features. A thin layer of the conducting polymer deposited on the substrate electrode surface can augment the electrode process kinetics of several solution species. Such electrocatalytic procedures with modified conducting polymer electrodes can create beneficial utilization in diverse fields of applied electrochemistry. This review article explores typical recent applications of conductive polymers (2016-2020) as active electrode materials for energy storage applications, electrochemical sensing, and conversion fields such as electrochemical supercapacitors, lithium-ion batteries, fuel cells, and solar cells.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.003

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 head0.092
GPT teacher head0.380
Teacher spread0.288 · 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

Citations241
Published2020
Admission routes1
Has abstractyes

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