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Record W2008702629 · doi:10.1021/jp5016214

Donor–Acceptor Polymers for Electrochemical Supercapacitors: Synthesis, Testing, and Theory

2014· article· en· W2008702629 on OpenAlexaff
Paul M. DiCarmine, Tyler B. Schon, Theresa M. McCormick, Philipp Klein, Dwight S. Seferos

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSupercapacitorAcceptorPolymerConjugated systemElectrochemistryMaterials scienceElectron acceptorEnergy storageElectrodeNanotechnologyElectrochemical energy conversionElectron donorChemical engineeringPhotochemistryChemistryOrganic chemistryPower (physics)Physical chemistryCatalysisPhysicsThermodynamicsComposite material

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Donor–acceptor polymers can store both a positive and negative charge allowing them to function as both the positive and negative charge storage material in a supercapacitor device, however few have been explored for this application. Here, we describe the synthesis of several donor–acceptor polymers and their electrodeposited polymer electrodes. We use differing molecular structures to examine the effect of electron acceptor concentration and show that device stability can be improved significantly by increasing the acceptor concentration. Further, we provide computational insight into the important chemical requirements for achieving even higher performance supercapacitors based on donor–acceptor conjugated polymers. Supercapacitor devices with specific energy and specific power as high as 11 Wh kg –1 (at 0.5 A g –1 ) and 20 kW kg –1 (at 50 A g –1 with an energy of 3.6 Wh kg –1 ) are reported, which are some of the highest values achieved to date.

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 categoriesnone
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 score0.003
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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.

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

Citations89
Published2014
Admission routes1
Has abstractyes

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