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Record W2139958793 · doi:10.1039/c4lc00084f

Micropatterned biofilm formations by laminar flow-templating

2014· article· en· W2139958793 on OpenAlexafffund
Nahid Babaei Aznaveh, Muhammad Safdar, Gideon Wolfaardt, Jesse Greener

Bibliographic record

VenueLab on a Chip · 2014
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsToronto Metropolitan UniversityUniversité Laval
FundersFonds de recherche du Québec – Nature et technologies
KeywordsBiofilmLaminar flowBioreactorShear stressFlow (mathematics)MicrofluidicsMaterials scienceNanotechnologyChemistryMechanicsEngineeringGeologyAerospace engineeringComposite materialBacteriaPhysics

Abstract

fetched live from OpenAlex

We present a microfluidic device capable of patterning linear biofilm formations using a flow templating approach. We describe the design considerations and fabrication methodology of a two level flow-templating micro-bioreactor (FT-μBR), which generates a biofilm growth stream surrounded on 3 sides by a growth inhibiting confinement stream. Through a combination of experiments and simulations we comprehensively evaluate and exploit control parameters to manipulate the biofilm growth template stream dimensions. The FT-μBR is then used to grow biofilm patterns with controllable dimensions. A proof-of-principle study using the device demonstrates its utility in conducting biofilm growth rate measurements under different shear stress environments. This opens the way for quantitative studies into the effects of the local shear environment on biofilm properties and for the synthesis of a new generation of functional biomaterials with controllable properties.

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: none
Teacher disagreement score0.636
Threshold uncertainty score0.559

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 head0.217
Teacher spread0.208 · 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

Citations26
Published2014
Admission routes2
Has abstractyes

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