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Record W2333953251 · doi:10.1021/acs.biomac.5b00018

Criteria for Quick and Consistent Synthesis of Poly(glycerol sebacate) for Tailored Mechanical Properties

2015· article· en· W2333953251 on OpenAlexafffund
Xinda Li, Albert Hong, Nilanjon Naskar, Hyun‐Joong Chung

Bibliographic record

VenueBiomacromolecules · 2015
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacsUniversity of Alberta
KeywordsGlycerolChemistryChemical engineeringPolymer sciencePolymer chemistryMaterials scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Poly(glycerol sebacate) (PGS) and its derivatives make up an attractive class of biomaterial owing to their tunable mechanical properties with programmable biodegradability. In practice, however, the application of PGS is often hampered by frequent inconsistency in reproducing process conditions. The inconsistency stems from the volatile nature of glycerol during the esterification process. In this study, we suggest that the degree of esterification (DE) can be used to predict precisely the physical status, the mechanical properties, and the degradation of the PGS materials. Young's modulus is shown to linearly increase with DE, which is in agreement with an entropic spring theory of rubbers. To provide a processing guideline for researchers, we also provide a physical status map as a function of curing temperature and time. The amount of glycerol loss, obtainable by monitoring the evolution of the total mass loss and the DE during synthesis, is shown to make the predictions even more precise. We expect that these strategies can be applicable to different categories of polymers that involve condensation polymerization with the volatility of the reactants. In addition, we demonstrate that microwave-assisted prepolymerization is a time- and energy-efficient pathway to obtain PGS. For example, 15 min of microwave time is shown to be as efficient as prepolymerization in nitrogen atmosphere for 6 h at 130 °C. The quick synthesis method, however, causes a severe evaporation of glycerol, resulting in a large distortion in the monomer ratio between glycerol and sebacic acid. Consequently, more rigid PGS is produced under a similar curing condition compared to the conventional prepolymerization method. Finally, we demonstrate that the addition of molecularly rigid cross-linking agents and network-structured inorganic nanoparticles are also effective in enhancing the mechanical properties of the PGS-derived materials.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.077
GPT teacher head0.275
Teacher spread0.198 · 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

Citations109
Published2015
Admission routes2
Has abstractyes

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