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Long-Term Performance of Electrodes Based on Vinyl Acetate Homo-Polymer Binder

2017· article· en· W2761596176 on OpenAlexvenueno aff
Pier Paolo Prosini, Mariasole Di Carli, Livia Della Seta, Maria Carewska, Ivan Fuso Nerini

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsnot available
FundersMinistero dello Sviluppo Economico
KeywordsMaterials scienceTriacetinVinyl acetateChemical engineeringLithium (medication)ElectrodePolymerElectrochemistryBattery (electricity)Composite materialOrganic chemistryCopolymerChemistry

Abstract

fetched live from OpenAlex

In this work we propose the use of a hydro-dispersible polymer such as the poly vinyl acetate as a binder for the production of electrodes for lithium-ion batteries. To increase the film forming properties of the polymer the poly vinyl was added with triacetin that acts as a plasticizer. The electrochemical stability of the polymer was tested by a polarizing electrode, formed by mixing the polymer with carbon. Subsequently, an electrode tape was prepared by using LiNi0.5Mn1.5O4 as the active material and characterized by SEM, EDS and TGA. Lithium metal cells were assembled and tested to evaluate specific capacity, power and energy density at various discharge rates. The cycle life of the cell was evaluated by galvanostatic charge/discharge cycles. The tests showed that the electrodes prepared with PVA plasticized with triacetin have very good electrochemical performance in terms of capacity retention as a function of the discharge rate and the cycle number. Our work demonstrates that the use of triacetin to plasticize the PVA allows to increase the electrochemical stability of the electrode likely due to an improvement of the slurry filmability. The proposed method could represent a promising technology for the production of long-term performance lithium batteries.

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)0.003
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.364
Teacher spread0.328 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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