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Record W4285497515 · doi:10.1149/ma2022-014571mtgabs

Apple Pectin Based Hydrogel Electrolyte for Energy Storage Application

2022· article· en· W4285497515 on OpenAlexaffabout
Nora Chelfouh, Gaël Coquil, Steeve Rousselot, Elsa Briqueleur, Gabrielle Foran, Mickaël Dollé

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsElectrolyteSelf-healing hydrogelsDielectric spectroscopyEnergy storageMaterials scienceIonic conductivityElectronicsBattery (electricity)NanotechnologyProcess engineeringChemical engineeringElectrochemistryChemistryElectrical engineeringElectrodePolymer chemistryEngineering

Abstract

fetched live from OpenAlex

With the increase of portable power sources demand, new technologies, e.g. wearable and flexible electronics, are projected to generate $1.25 billion market by 2022. [1] New storage energy devices are more than ever in demand which requires new specifications in order to be used in those future applications. To achieve this development, we have to minimize the environmental impact in the whole battery life cycle, from conception to degradation of the system, and reduce production costs. Polymer hydrogel electrolyte are one of the promising alternative for processing new flexible batteries. [2] A great hydrogel electrolyte should promise excellent ionic transport pathways and sufficient mechanical strength, not to cause short-circuits. As a matter of fact, hydrogel electrolytes are well-known for their good ionic conductivity. Nevertheless, the original polymers used in these systems don’t take into account the cost of the environmental impact and safety due to the processing or biodegradability of those hydrogels. [3] In this study, we report a new hydrogel-based electrolyte material made by apple pectin. This presentation will mainly focus on the interactions between pectin functional groups, water and ions using solid NMR spectroscopy. Thermal properties will be discussed based on differential scanning calorimetry analysis. Electrical and electrochemical characterisctics obtained by electrochemical impedance spectroscopy, galvanostatic cycling and cyclic voltametry will demonstrate the applicability of such hydrogel electrolyte. This study could promote a great innovation in the energy storage field, by recycling one of apple peel’s component (which is the main waste in preserves manufacturing [4] ) into a hydrogel electrolyte. References [1] N. R. C. Canada in Environmentally friendly printed batteries , Vol. Boucherville, Quebec, 2021 . [2] C. Y. Chan, Z. Wang, H. Jia, P. F. Ng, L. Chow and B. Fei, Journal of Materials Chemistry A 2021 , 9 , 2043-2069. [3] Y. Huang, M. Zhong, F. Shi, X. Liu, Z. Tang, Y. Wang, Y. Huang, H. Hou, X. Xie and C. Zhi, Angewandte Chemie International Edition 2017 , 56 , 9141-9145. [4] B. S. Virk and D. S. Sogi, International Journal of Food Properties 2004 , 7 , 693-703.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.563

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.239
Teacher spread0.223 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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