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Record W4378348841 · doi:10.1021/acsami.3c03421

Layer-by-Layer Deposition of Low-Solid Nanochitin Emulgels Creates Porous Structures for High Cell Attachment and Proliferation

2023· article· en· W4378348841 on OpenAlexafffund
Ya Zhu, Esko Kankuri, Xue Zhang, Zhangmin Wan, Xin Wang, Siqi Huan, Long Bai, Shouxin Liu, Orlando J. Rojas

Bibliographic record

VenueACS Applied Materials & Interfaces · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020 Framework ProgrammeNatural Science Foundation of Heilongjiang ProvinceChina Scholarship CouncilCanada Excellence Research Chairs, Government of CanadaCanada Foundation for Innovation
KeywordsMaterials scienceNanotechnologyPorosityAdhesionInkwellArea densityLayer (electronics)Composite material

Abstract

fetched live from OpenAlex

Direct ink writing (DIW) is a customizable platform to engineer complex constructs from biobased colloids. However, the latter usually display strong interactions with water and lack interparticle connectivity, limiting one-step processing into hierarchically porous structures. We overcome such challenges by using low-solid emulgel inks stabilized by chitin nanofibrils (nanochitin, NCh). By using complementary characterization platforms, we reveal NCh structuring into spatially controlled three-dimensional (3D) materials that generate multiscale porosities defined by emulsion droplet size, ice templating, and DIW infill density. The extrusion variables, key in the development of surface and mechanical features of printed architectures, are comprehensively analyzed by using molecular dynamics and other simulation approaches. The obtained scaffolds are shown for their hierarchical porous structures, high areal density, and surface stiffness, which lead to excellent modulation of cell adhesion, proliferation, and differentiation, as tested with mouse dermal fibroblast expressing green fluorescent proteins.

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 score0.004
Threshold uncertainty score0.919

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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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

Citations16
Published2023
Admission routes2
Has abstractyes

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