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Record W2462308730 · doi:10.1002/anie.201604833

The Strong Influence of Structure Polymorphism on the Conductivity of Peptide Fibrils

2016· article· en· W2462308730 on OpenAlexaff
Denis Ivnitski, Moran Amit, Ohad Silberbush, Yoav Atsmon‐Raz, Jayanta Nanda, Rivka Cohen‐Luria, Yifat Miller, Gonen Ashkenasy, Nurit Ashkenasy

Bibliographic record

VenueAngewandte Chemie International Edition · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of Calgary
FundersEuropean Cooperation in Science and Technology
KeywordsFibrilStackingPeptideDelocalized electronPolymorphism (computer science)Hydrogen bondChemistryMaterials scienceConductivityCrystallographyNanotechnologyChemical physicsMoleculeOrganic chemistryBiochemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Peptide fibril nanostructures have been advocated as components of future biotechnology and nanotechnology devices. However, the ability to exploit the fibril functionality for applications, such as catalysis or electron transfer, depends on the formation of well-defined architectures. Fibrils made of peptides substituted with aromatic groups are described presenting efficient electron delocalization. Peptide self-assembly under various conditions produced polymorphic fibril products presenting distinctly different conductivities. This process is driven by a collective set of hydrogen bonding, electrostatic, and π-stacking interactions, and as a result it can be directed towards formation of a distinct polymorph by using the medium to enhance specific interactions rather than the others. This method facilitates the detailed characterization of different polymorphs, and allows specific conditions to be established that lead to the polymorph with the highest conductivity.

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)0.001
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 score0.582

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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

Citations55
Published2016
Admission routes1
Has abstractyes

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