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Record W4393866062 · doi:10.3390/polym16070965

Photopolymerization of Limonene Dioxide and Vegetable Oils as Biobased 3D-Printing Stereolithographic Formulation

2024· article· en· W4393866062 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolymers · 2024
Typearticle
Languageen
FieldChemistry
TopicPhotopolymerization techniques and applications
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaMitacsCentre québécois sur les matériaux fonctionnels
KeywordsEpoxideEpoxidized soybean oilPhotopolymerLimoneneCuring (chemistry)Materials sciencePolymerizationVegetable oilTerpeneOrganic chemistryPolymerChemical engineeringChemistryPolymer chemistryEssential oilComposite materialCatalysisChromatography

Abstract

fetched live from OpenAlex

Epoxidized vegetable oils and limonene dioxide, a bis-epoxide derived from the terpene limonene, are photo-copolymerized to yield highly crosslinked networks with high conversion of all epoxide groups at ambient temperature. However, the slow polymerization of such biobased formulation polymerizes is not compatible for a use in a commercial SLA 3D printer. Adding an acrylated epoxidized vegetable oil to the bis-epoxide leads to a decrease of curing time and an increase in LDO conversion to polymer. For example, in a 60:40 wt:wt mixture of LDO and epoxidized soybean oil, the conversions of both exocyclic and endocyclic epoxide groups of LDO are ≥95%. These formulations were successfully used in SLA 3D printers, leading to generation of hard and dry complex objects using biobased formulations.

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)0.000
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 score0.031
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

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 head0.008
GPT teacher head0.250
Teacher spread0.241 · 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