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Record W4212924334 · doi:10.1201/b14671

Electrochemical Supercapacitors for Energy Storage and Delivery

2017· book· en· W4212924334 on OpenAlexaff
Aiping Yu, Victor Chabot, Jiujun Zhang

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsYork UniversityUniversity of WaterlooBC Innovation CouncilUniversity of British Columbia
Fundersnot available
KeywordsSupercapacitorElectrochemical energy storageEnergy storageElectrochemical energy conversionElectrochemistryMaterials scienceEnvironmental scienceBusinessChemistryPhysicsElectrodePower (physics)

Abstract

fetched live from OpenAlex

Although recognized as an important component of all energy storage and conversion technologies, electrochemical supercapacitators (ES) still face development challenges in order to reach their full potential. A thorough examination of development in the technology during the past decade, Electrochemical Supercapacitors for Energy Storage and Delivery: Fundamentals and Applications provides a comprehensive introduction to the ES from technical and practical aspects and crystallization of the technology, detailing the basics of ES as well as its components and characterization techniques. The book illuminates the practical aspects of understanding and applying the technology within the industry and provides sufficient technical detail of newer materials being developed by experts in the field which may surface in the future. The book discusses the technical challenges and the practical limitations and their associated parameters in ES technology. It also covers the structure and options for device packaging and materials choices such as electrode materials, electrolyte, current collector, and sealants based on comparison of available data. Supplying an in depth understanding of the components, design, and characterization of electrochemical supercapacitors, the book has wide-ranging appeal to industry experts and those new to the field. It can be used as a reference to apply to current work and a resource to foster ideas for new devices that will further the technology as it becomes a larger part of main stream energy storage.

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)0.000
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.042
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0420.034

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 head0.018
GPT teacher head0.225
Teacher spread0.208 · 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
GenreOther

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

Citations133
Published2017
Admission routes1
Has abstractyes

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