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Record W2120705447 · doi:10.1002/chem.201501395

Amino Acid Chirality and Ferrocene Conformation Guided Self‐Assembly and Gelation of Ferrocene–Peptide Conjugates

2015· article· en· W2120705447 on OpenAlexafffund
Bimalendu Adhikari, Charanpreet Singh, Afzal Shah, Alan J. Lough, Heinz‐Bernhard Kraatz

Bibliographic record

VenueChemistry - A European Journal · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto ScarboroughNatural Sciences and Engineering Research Council of Canada
KeywordsFerroceneChirality (physics)DiastereomerChemistryIntramolecular forcePeptideConjugateEnantiomerStereochemistryAmino acidHydrogen bondOrganic chemistryMoleculeBiochemistryElectrochemistry

Abstract

fetched live from OpenAlex

The self-assembly and gelation behavior of a series of mono- and disubstituted ferrocene (Fc)-peptide conjugates as a function of ferrocene conformation and amino acid chirality are described. The results reveal that ferrocene-peptide conjugates self-assemble into organogels by controlling the conformation of the central ferrocene core, through inter- versus intramolecular hydrogen bonding in the attached peptide chain(s). The chirality controlled assembling studies showed that two monosubstituted Fc conjugates FcCO-LFLFLA-OMe and FcCO-LFLFDA-OMe form gels with nanofibrillar network structures, whereas the other two diastereomers FcCO-DFLFLA-OMe and FcCO-LFDFLA-OMe exclusively produced straight nanorods and non-interconnected small fibers, respectively. This suggests the potential tuning of gelation behavior and nanoscale morphology by altering the chirality of constituted amino acids. The current study confirms the profound effect of diastereomerism and no influence of enantiomers on gelation. Correspondingly, the diastereomeric and enantiomeric Fc[CO-FFA-OMe]2 were constructed for the study of chirality-organized structures.

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)0.002
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 score0.009
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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

Citations45
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueChemistry - A European JournalSame topicSupramolecular Self-Assembly in MaterialsFrench-language works237,207