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Record W2104451738 · doi:10.1039/c5lc00722d

Supersoft lithography: candy-based fabrication of soft silicone microstructures

2015· article· en· W2104451738 on OpenAlexafffund
Christopher Moraes, Joseph M. Labuz, Yue Shao, Jianping Fu, Shuichi Takayama

Bibliographic record

VenueLab on a Chip · 2015
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill University
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Biomedical Imaging and BioengineeringNational Science FoundationNational Institutes of HealthNational Cancer InstituteNational Institute of Allergy and Infectious DiseasesNatural Sciences and Engineering Research Council of CanadaDirectorate for Biological Sciences
KeywordsSiliconeFabricationLithographyMicrostructureMaterials scienceSoft lithographyNanotechnologyComposite materialOptoelectronicsMedicine

Abstract

fetched live from OpenAlex

We designed a fabrication technique able to replicate microstructures in soft silicone materials (E < 1 kPa). Sugar-based 'hard candy' recipes from the confectionery industry were modified to be compatible with silicone processing conditions, and used as templates for replica molding. Microstructures fabricated in soft silicones can then be easily released by dissolving the template in water. We anticipate that this technique will be of particular importance in replicating physiologically soft, microstructured environments for cell culture, and demonstrate a first application in which intrinsically soft microstructures are used to measure forces generated by fibroblast-laden contractile tissues.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.265
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations47
Published2015
Admission routes2
Has abstractyes

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