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Record W4231783099 · doi:10.26434/chemrxiv.14541909.v1

Chitosan Nanocrystals Synthesis via Aging and Application Towards Alginate Hydrogels for Sustainable Drug Release

2021· preprint· en· W4231783099 on OpenAlexafffund
Tony Jin, Tracy Liu, Shuaibing Jiang, Vladimir K. Michaelis, David Kurdyla, Brittney A. Klein, Edmond Lam, Jianyu Li, Audrey Moores

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsNational Research Council CanadaUniversity of AlbertaMcGill University
FundersFonds de recherche du Québec – Nature et technologiesCentre in Green Chemistry and CatalysisNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsChitosanSelf-healing hydrogelsNanocrystalNanotechnologyFabricationMaterials scienceDrug deliveryDrugChemical engineeringPolymer chemistryEngineering

Abstract

fetched live from OpenAlex

In this article, we demonstrate a new and clean method for the fabrication of chitosan nanocrystals relying on aging. We provide metrics to showcase the greeness of this method. We are then using these materials as building block to fabricate alginate hydrogels, and demonstrated that they have superior properties for gelation and drug release.

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 categoriesMeta-epidemiology (narrow)
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 score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
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 head0.037
GPT teacher head0.368
Teacher spread0.332 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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