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Record W4239627160 · doi:10.1149/ma2019-04/5/290

(Invited) Green Materials for Sustainable Supercapacitors

2019· article· en· W4239627160 on OpenAlexaboutno aff
Federico Poli, Giovanni Spina, Antonio Terella, Mehrdad Mashkour, Maria Letizia Focarete, Davide Fabiani, Clara Santato, Damilola Momodu, Ncholu Manyala, Francesca Soavi

Bibliographic record

VenueECS Meeting Abstracts · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
Fundersnot available
KeywordsSupercapacitorElectrospinningMaterials scienceSeparator (oil production)NanofiberNanotechnologyWaste managementPolymerPolymer scienceElectrodeComposite materialEngineeringChemistryElectrochemistry

Abstract

fetched live from OpenAlex

The exponential increase of energy storage systems like batteries and supercapacitors is paralleled by the growing need of minimizing the environmental and economic impact of disassembly and recycling of waste devices. Today, electrode and separator processing greatly contribute to device cost and environmental footprint. The use of water-processable, biodegradable electrodes and separators is a viable approach to develop green and easy-to-dispose devices. Natural polymers processed by aqueous solutions are a very promising alternative to fluorinated polymers like polyvinylidene difluoride that also requires the use of the toxic N-Methyl-2-pyrrolidone solvent. Electrospinning is an emerging technology for the preparation of free-standing fiber mats to be used as electrode materials and separators in supercapacitors. Here, the strategies to develop green supercapacitors making use of natural binders and separators and bio-derived electrodes are presented. In particular, the performance of supercapacitors making use of cellulose and pullulan membranes prepared by electrospinning are reported and compared to those of devices featuring cellulose separators produced by bacteria. Acknowledgments The research has been carried out under the Italy-South Africa joint Research Programme 2018-2020 and the Executive Bilateral Program Italy-Quebec 2017-2019, Italian Ministers of Foreign Affairs and of the Environment. References [1] M. Yassine, D. Fabris, Energies, 10 (2017) 1340 [2] B. Dyatkin, V. Presser, M. Heon, M. R. Lukatskaya, M. Beidaghi, Y. Gogotsi, ChemSusChem, 6 (2013) 2269 -2280. [3] D. Bresser, D. Buchholz, A. Moretti, A. Varzi, S. Passerini, Energy & Environmental Science, 11 (2018) 3096-3127 [4] P. Kumar, E. Di Mauro, S. Zhang, A. Pezzella, F. Soavi, C. Santato, F. Cicoira, J. Mater. Chem. C, 4 (2016) 9516. [5] S. Chen, S. He, H. Hou, Current Organic Chemistry, 17 (2013) 1402-1410 [6] F. Poli, D. Momodu, A. Terella, M. L. Focarete, N. Manyala, F. Soavi, Energy Storage Materials, submitted.

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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.115
Threshold uncertainty score0.384

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1150.067

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 head0.015
GPT teacher head0.237
Teacher spread0.221 · 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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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