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Record W2942142681 · doi:10.1016/j.actbio.2019.04.050

Micro-injection molded, poly(vinyl alcohol)-calcium salt templates for precise customization of 3D hydrogel internal architecture

2019· article· en· W2942142681 on OpenAlexfundno aff
Jason D. McNulty, Carlos R. Marti-Figueroa, Frank Seipel, Joshua Z. Plantz, Thomas Ellingham, Lukas Duddleston, Sebastian Goris, Benjamin L. Cox, Tim A. Osswald, Lih‐Sheng Turng, Randolph S. Ashton

Bibliographic record

VenueActa Biomaterialia · 2019
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersMcMaster UniversityWisconsin Alumni Research FoundationNational Science Foundation
KeywordsVinyl alcoholMaterials scienceTemplateSalt (chemistry)Composite materialCalciumChemical engineeringPolymerNanotechnologyOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

In tissue engineering applications, sacrificial molding of hydrogel monoliths is a versatile technique for creating 3D molds to control tissue morphology. Previous sacrificial templates fabricated by serial processes such as solvent casting and thermal extrusion/fiber drawing can be used to effectively mold internal geometries within rapidly polymerizing, bulk curing hydrogels. However, they display poorer performance in controlling the geometry of diffusion limited, ionically cross-linked hydrogels, such as alginate. Here, we describe the use of poly(vinyl alcohol)-calcium salt templates (PVOH-Ca) fabricated by micro-injection molding, a parallel mass-production process, to conveniently cast internal geometries within both bulk curing hydrogels and ionically cross-linked alginate hydrogels. Calcium salt solubility was discovered to be a critical factor in optimizing the polymer composite's manufacturability, mechanical properties, and the quantity of calcium released upon template dissolution. Metrological and computed tomography (CT) analysis showed that the template's calcium release enables precise casting of microscale channel geometries within alginate hydrogels (6.4 ± 7.2% average error). Assembly of modular PVOH-Ca templates to mold 3D channel networks within alginate hydrogels is presented to demonstrate engineering scalability. Moreover, the platform is used to create hydrogel molds for engineering human embryonic stem cell (hESC)-derived neuroepithelial organoids of a microscale, biomimetic cylindrical morphology. Thus, injection molded PVOH-Ca templates facilitate customization of hydrogel sacrificial molding, which can be used to generate 3D hydrogels with complex internal microscale architecture for diverse tissue engineering applications. STATEMENT OF SIGNIFICANCE: Sacrificial molding of hydrogel monoliths is a versatile technique for creating 3D molds for tissue engineering applications. Previous sacrificial materials fabricated by serial processes have been used to effectively mold internal geometries within rapidly polymerizing, bulk curing hydrogels. However, they display poor performance in molding geometry within diffusion limited, ionically cross-linked hydrogels, e.g. alginate. We describe the use of poly(vinyl alcohol)-calcium salt templates (PVOH-Ca) fabricated by micro-injection molding, an unparalleled mass-production process, to conveniently cast internal geometries within both bulk curing hydrogels and ionically cross-linked alginate hydrogels. Calcium release from the PVOH-Ca templates enables precise sacrificial molding of alginate hydrogels and the process is biocompatible. Moreover, we demonstrate its use to engineer the morphology of hPSC-derived neuroepithelial organoids, and modular PVOH-Ca template designs can be assembled to enable scalable 3D customization of hydrogel internal architecture.

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)0.000
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 score0.012
Threshold uncertainty score0.820

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 head0.015
GPT teacher head0.272
Teacher spread0.256 · 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.

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

Citations29
Published2019
Admission routes1
Has abstractyes

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