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Record W2014592648 · doi:10.1163/092050611x587510

Synthesis and<i>In Vitro</i>Characterization of Poly(Ethylene Glycol)–Albumin Hydrogel Microparticles

2012· article· en· W2014592648 on OpenAlex
Ping He, Jacques Jean‐François, Guy Fortier

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Biomaterials Science Polymer Edition · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsEthylene glycolMaterials scienceSelf-healing hydrogelsCharacterization (materials science)Chemical engineeringPolymer chemistryPEG ratioMicroparticleAlbuminIn vitroNanotechnologyChemistryBiochemistry

Abstract

fetched live from OpenAlex

High water content hydrogel microparticles based on the cross-linking of albumin with activated poly(ethylene glycol) were synthesized. The influence of different synthesis parameters on the physicochemical characteristics of the microparticles, such as the type of oil and of albumin, and the molecular weight of PEG, was evaluated. The water content of the microparticles ranged from 95 to 98%, increasing with an increase of the molecular weight of PEG. At optimal conditions, microparticles with sizes ranging from 3 to 50 μm were prepared. These microparticles showed a negatively charged surface. They were freely dispersed in PBS buffer and they were stable at 4°C for times varying from 0.5 to 10 months. Initial stirring speed and molecular weight of PEG were the 2 main factors that significantly affected microparticle size. High hydrophilicity, good stability and modulable size make this hydrogel an attractive matrix for protein or cell immobilization for biomedical applications.

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.

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 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 score0.008
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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