MétaCan
Menu
Back to cohort
Record W4322492601 · doi:10.1021/acs.biomac.2c01438

Semiconductive and Biocompatible Nanofibrils from the Self-Assembly of Amyloid π-Conjugated Peptides

2023· article· en· W4322492601 on OpenAlexafffund
Nadjib Kihal, Mélanie Côté‐Cyr, Ali Nazemi, Steve Bourgault

Bibliographic record

VenueBiomacromolecules · 2023
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsPROTEOUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSelf-assemblyPeptideBiocompatible materialDiimideConjugated systemChemistryAmyloid (mycology)PeryleneBiophysicsNanotechnologyMaterials sciencePolymerBiochemistryOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Owing to their capacity to self-assemble into organized nanostructures, amyloid polypeptides can serve as scaffolds for the design of biocompatible semiconductive materials. Herein, symmetric and asymmetric amyloid π-conjugated peptides were prepared through condensation of perylene diimide (PDI) with a natural amyloidogenic sequence derived from the islet amyloid polypeptide. These PDI-bioconjugates assembled into long and linear nanofilaments in aqueous solution, which were characterized by a cross-β-sheet quaternary organization. Current-voltage curves exhibited a clear signature of semiconductors, whereas the cellular assays revealed cytocompatibility and potential application in fluorescence microscopy. Although the incorporation of a single amyloid peptide appeared sufficient to drive the self-assembly into organized fibrils, the incorporation of two peptide sequences at the PDI's imide positions significantly enhanced the conductivity of nanofibril-based films. Overall, this study exposes a novel strategy based on amyloidogenic peptide to guide the self-assembly of π-conjugated systems into robust, biocompatible, and optoelectronic nanofilaments.

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 score0.012
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.252
Teacher spread0.236 · 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

Citations13
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueBiomacromoleculesSame topicSupramolecular Self-Assembly in MaterialsFrench-language works237,207