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Record W2958988116 · doi:10.1021/acssuschemeng.9b01830

Experimental Design of Sustainable 3D-Printed Poly(Lactic Acid)/Biobased Poly(Butylene Succinate) Blends via Fused Deposition Modeling

2019· article· en· W2958988116 on OpenAlexafffund
Mawath Qahtani, Feng Wu, Manjusri Misra, Stefano Gregori, Deborah F. Mielewski, Amar K. Mohanty

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2019
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Economic Development and InnovationNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural AffairsU.S. Department of Energy
KeywordsRheologyMaterials scienceFused deposition modelingPolylactic acidScanning electron microscopeViscosityComposite materialThermal stabilityPolymerRheometryLactic acid3D printingPolymer blendChemical engineeringCopolymer

Abstract

fetched live from OpenAlex

A mixture design of experiment (DoE) was used to guide the fabrication and analysis of sustainable poly(lactic acid) (PLA) and biobased poly(butylene succinate) (BioPBS) 3D-printing filaments. The statistical DoE approach was employed to investigate the correlation between the mechanical properties of the PLA/BioPBS blends at different PLA and BioPBS wt % and to obtain the linear regression models of the mechanical properties. The statistical models help to design PLA/BioPBS blends with the desired mechanical properties. The PLA/BioPBS filaments with different composition ratios were 3D-printed via fused deposition modeling (FDM). The 3D-printability of the polymer blends was determined by the flowability and dimensional stability of the filaments, provided by fundamental rheological and coefficient of linear thermal expansion (CLTE) studies. Preliminary research found that the 3D-printability of PLA/BioPBS filaments with BioPBS content higher than 50 wt % was unsuccessful due to high viscosity and low thermal stability. These findings were verified with rheological tests for a range of PLA/BioPBS blend ratios and thermomechanical studies. Rheological results show a significant increase of the blend viscosity when BioPBS content in the blend was >50%. Additionally, the CLTE drastically increased with higher contents of BioPBS, making the PLA/BioPBS filaments thermally unstable during FDM processing. These results confirmed that the 3D-printability of PLA/BioPBS filaments is greatly influenced by the blend viscosity and the printing temperature. Rheological studies revealed that the viscosity range of a 3D-printable PLA/BioPBS filament lies within 1000–100 Pa·s. Scanning electron microscopy (SEM) and polarized optical microscopy (POM) images confirmed that PLA and BioPBS are immiscible. However, the addition of BioPBS improved the ductility and the crystallinity of PLA. The 3D printed PLA/BioPBS (90/10) blend showed an interesting result in that it obtained higher tensile and impact strengths than the neat PLA, which was attributed to crystallinity and morphological factors.

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 score0.118
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 communication0.0000.001
Open science0.0010.000
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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations83
Published2019
Admission routes2
Has abstractyes

Explore more

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