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Record W2035411647 · doi:10.3390/nano2020187

Amino Acid and Peptide Immobilization on Oxidized Nanocellulose: Spectroscopic Characterization

2012· article· en· W2035411647 on OpenAlexafffund
Saïd Barazzouk, Claude Daneault

Bibliographic record

VenueNanomaterials · 2012
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanocelluloseAmino acidFourier transform infrared spectroscopyPeptideMoleculeChemistryPeptide bondAmideInfrared spectroscopyPolymer chemistryCarbodiimideX-ray photoelectron spectroscopyOrganic chemistryCombinatorial chemistryCelluloseChemical engineeringBiochemistry

Abstract

fetched live from OpenAlex

In this work, oxidized nanocellulose (ONC) was synthesized and chemically coupled with amino acids and peptides using a two step coupling method at room temperature. First, ONC was activated by N-ethyl-N'-(3-dimethylaminopropyl) carbodiimide hydrochloride, forming a stable active ester in the presence of N-hydroxysuccinimide. Second, the active ester was reacted with the amino group of the amino acid or peptide, forming an amide bond between ONC and the grafted molecule. Using this method, the intermolecular interaction of amino acids and peptides was avoided and uniform coupling of these molecules on ONC was achieved. The coupling reaction was very fast in mild conditions and without alteration of the polysaccharide. The coupling products (ONC-amino acids and ONC-peptides) were characterized by transmission electron microscopy and by the absorption, emission, Fourier transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS) spectroscopic techniques.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.021
GPT teacher head0.284
Teacher spread0.263 · 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

Citations94
Published2012
Admission routes2
Has abstractyes

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