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Record W2921161705 · doi:10.7567/1347-4065/ab1061

On the interfaces between organic bio-sourced materials and metals for sustainable electronics: the eumelanin case

2019· article· en· W2921161705 on OpenAlexafffund
Eduardo Di Mauro, Emilie Hebrard, Yasmina Boulahia, Marco Rolandi, Clara Santato

Bibliographic record

VenueJapanese Journal of Applied Physics · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicmelanin and skin pigmentation
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversidade Estadual PaulistaMinistère de l'Économie, de la Science et de l'Innovation - Québec
KeywordsElectronicsNanotechnologyOrganic electronicsMaterials scienceEnvironmental chemistryChemistryEngineeringElectrical engineeringTransistorPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract Organic bio-sourced materials featuring charge transfer and transport properties are of high interest in the field of sustainable (green) electronics, which aims at alleviating the environmental impact of conventional electronics. Such materials may contain, after extraction from biological media, traces of salts that can dramatically affect their electrical response, because of electrochemical processes at organic bio-sourced material/metal contact interfaces. Eumelanin, a bio-sourced pigment, is an attractive candidate for sustainable electronics. This work reports on chemical and structural changes occurring at interfaces between metal electrodes (Pd, Cu, Fe, Ni and Au) and hydrated films of eumelanin, under bias. The parameters affecting such changes, i.e. the chloride content in eumelanin, the relative humidity of the environment and the type of eumelanin (synthetic versus natural), were investigated. Our work contributes to establish criteria for selecting metal contacts suitable for the fruitful exploration and exploitation of organic bio-sourced materials in sustainable 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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.217

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 studies0.0000.000
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 head0.008
GPT teacher head0.235
Teacher spread0.227 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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