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Record W4248752019 · doi:10.1002/chem.201885463

Frontispiece: Supramolecular Assembly of Peptide and Metallopeptide Gelators and Their Stimuli‐Responsive Properties in Biomedical Applications

2018· paratext· en· W4248752019 on OpenAlexaff
Natashya Falcone, Heinz‐Bernhard Kraatz

Bibliographic record

VenueChemistry - A European Journal · 2018
Typeparatext
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsSupramolecular chemistryBiocompatibilityBiocompatible materialNanotechnologyPeptideDrug deliveryMaterials scienceCombinatorial chemistryChemistryBiochemistryMoleculeOrganic chemistryBiomedical engineering

Abstract

fetched live from OpenAlex

A fascinating class of soft materials are supramolecular gels which have attracted significant attention in recent years. They are composed of small molecule gelators that assemble into supramolecular network structures. Some gel materials are able to respond to various stimuli making them attractive drug delivery vehicles and as matrices for tissue regeneration. Peptide-based gel materials are particularly attractive as they possess numerous advantages including biocompatibility and biodegradability. Stimuli-responsive peptides that alter properties as a function of pH, redox, temperature, and enzymes offer the potential to create materials with tunable characteristics. Herein, the stimuli-responsive properties (pH, redox, temperature, and enzyme responsive properties), as well as the biocompatible/-degradable nature of the peptide gelators are highlighted, for more details, see the Minireview by H.-B. Kraatz and N. Falcone on page 14316 ff.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.244
Teacher spread0.229 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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