MétaCan
Menu
Back to cohort
Record W4289262309 · doi:10.1073/pnas.2200058119

High conductivity Sepia melanin ink films for environmentally benign printed electronics

2022· article· en· W4289262309 on OpenAlexaff
Anthony Camus, Manuel Reali, Michael Rozel, Mariia Zhuldybina, Francesca Soavi, Clara Santato

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsPrintability And Graphic Communications InstitutePolytechnique Montréal
Fundersnot available
KeywordsSepiaInkwellElectronicsPrinted electronicsConductivityMelaninMaterials scienceCuttlefishNanotechnologyOfficinalisChemistryBiologyComposite materialBiochemistryEngineeringFood scienceElectrical engineeringBotany

Abstract

fetched live from OpenAlex

Melanins (from the Greek μέλας, mélas, black) are bio-pigments ubiquitous in flora and fauna. Eumelanin is an insoluble brown–black type of melanin, found in vertebrates and invertebrates alike, among which Sepia (cuttlefish) is noteworthy. Sepia melanin is a type of bio-sourced eumelanin that can readily be extracted from the ink sac of cuttlefish. Eumelanin features broadband optical absorption, metal-binding affinity and antioxidative and radical-scavenging properties. It is a prototype of benign material for sustainable organic electronics technologies. Here, we report on an electronic conductivity as high as 10 −3 S cm −1 in flexographically printed Sepia melanin films; such values for the conductivity are typical for well-established high-performance organic electronic polymers but quite uncommon for bio-sourced organic materials. Our studies show the potential of bio-sourced materials for emerging electronic technologies with low human- and eco-toxicity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.031
GPT teacher head0.282
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations38
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicSilk-based biomaterials and applicationsFrench-language works237,207