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Record W3000225496 · doi:10.1080/03639045.2019.1711389

Low-temperature solvent-based 3D printing of PLGA: a parametric printability study

2020· article· en· W3000225496 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.

Bibliographic record

VenueDrug Development and Industrial Pharmacy · 2020
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsPLGAGlycolic acidSolventMaterials scienceChemical engineeringChemistryNuclear chemistryPolymer chemistryNanotechnologyLactic acidOrganic chemistryNanoparticle

Abstract

fetched live from OpenAlex

In this paper, a novel low-temperature 3 D printing technique is introduced and characterized through a parametric printability study to fabricate poly-lactic-co-glycolic acid (PLGA) constructs using methyl ethyl ketone (MEK) as a solvent. The effects of varying concentrations of PLGA in MEK solvent, lactic to glycolic ratio of PLGA, the molecular weight of PLGA, and the scaling of PLGA constructs on the printability are investigated. PLGA concentrations of higher than 80% w/v, lactic to glycolic ratio more than 75%, molecular weight more than 100 kDa, and printing through nozzles smaller than 0.96 mm internal diameter are recommended for 3 D printing of PLGA constructs with high shape fidelity. Ultimately, a vacuum drying solvent removal process is implemented, and Proton Nuclear Magnetic Resonance (1H-NMR) spectroscopy is used to confirm complete removal of the solvent from PLGA constructs. The results of this study can be used for the development of drug-eluting implants.

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 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 score0.351
Threshold uncertainty score1.000

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)0.0000.000

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 head0.043
GPT teacher head0.254
Teacher spread0.211 · 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