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Record W2753006128 · doi:10.14447/jnmes.v16i2.29

Electrical and Dielectric Behavior of Nano-bio Ceramic Filler Incorporated Polymer Electrolytes for Rechargeable Lithium Batteries

2013· article· en· W2753006128 on OpenAlexvenueno aff
K. Karuppasamy, S. Thanikaikarasan, S. Balakumar, T. Mahalingam, P.J. Sebastián, X. Sahaya Shajan

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2013
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Materials and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceIonic conductivityElectrolytePolymerDielectric spectroscopyCeramicLithium (medication)NanocompositeChemical engineeringNanoindenterComposite materialFourier transform infrared spectroscopyDielectricElectrochemistryPolymer chemistryElastic modulusChemistryElectrode

Abstract

fetched live from OpenAlex

A series of nanocomposite solid polymer electrolytes (NCSPE) consisting of PMMA as host polymer, lithium bisoxalatoborate (LiBOB) as doping salt and nano-hydroxy apatite as filler have prepared by membrane hot-press method. To enhance the electrochemical properties and stiffness of polymer electrolyte film, a bioactive ceramic filler nano-hydroxy apatite is incorporated in the polymer matrix. The prepared different weight contents of NCSPE films are subjected to various electrochemical characterizations such as ionic conductivity, electric modulus and dielectric spectroscopy studies. The complexation behavior and structural reorganization in polymer electrolytes are confirmed by means of FT-IR (Fourier-Transform Infra Red Spectroscopy) analysis. The electrical response and relaxation of dipole in polymer electrolytes are investigated by means of electric modulus and dielectric studies. The addition of nano-hydroxy apatite significantly enhances the ionic conductivity and blocking the reorganizing tendency of polymer matrix. The maximum ionic conductivity is found to be in the range of 10-4.8 S/cm for sample containing 10 wt. % nanoceramic filler.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.216
Teacher spread0.206 · 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.

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

Citations5
Published2013
Admission routes1
Has abstractyes

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