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Record W2574667270 · doi:10.1002/adfm.201605142

Dielectric Properties of Sustainable Nanocomposites Based on Zein Protein and Lignin for Biodegradable Insulators

2017· article· en· W2574667270 on OpenAlexaff
Maria Oliviero, Reza Rizvi, Letizia Verdolotti, Salvatore Iannace, Hani E. Naguib, Ernesto Di Maio, Giovanni Landi

Bibliographic record

VenueAdvanced Functional Materials · 2017
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceNanocompositeLigninDielectricDissipation factorComposite materialThermoplasticPlasticizerDielectric lossChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Two types of lignin, alkali lignin and lignosulfonic acid sodium salt, are blended into thermoplastic zein through melt mixing in order to develop biodegradable insulator materials for multifunctional applications in electronics. The effects of lignin type and content on the dielectric properties of the resulting bio‐nanocomposites are investigated. The results indicate that, by modifying the structural arrangement of the zein with the use of lignin, it is possible to obtain bio‐nanocomposites characterized by tunable dielectric properties. The bio‐nanocomposites containing low amounts of lignin derivatives exhibit extensive protein structural changes together with a modification of the dielectric properties compared to the pristine thermoplastic zein. Changes in the dielectric properties of these systems are also observed to change over time, indicating a loss of plasticizer, as is evident by a decrease in the glass‐transition temperature. At high frequencies, the resulting values of the dielectric permittivity and of the loss tangent demonstrate that the bio‐nanocomposite can be used as biodegradable dielectric material for transient (temporary) electronics.

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 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 score0.001
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.191
Teacher spread0.182 · 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

Citations63
Published2017
Admission routes1
Has abstractyes

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