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Record W2741970576 · doi:10.1063/1.4991550

Generating 2-dimensional concentration gradients of biomolecules using a simple microfluidic design

2017· article· en· W2741970576 on OpenAlex
Amid Shakeri, Nick Sun, Maryam Badv, Tohid F. Didar

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomicrofluidics · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthMcMaster University
KeywordsMicrofluidicsBiomoleculeConcentration gradientThroughputBiological systemSimple (philosophy)NanotechnologyMaterials scienceLab-on-a-chipVolumetric flow rateComputer scienceChemistryMechanicsChromatographyPhysics

Abstract

fetched live from OpenAlex

This study reports a microfluidic device for generating 2-dimensional concentration gradients of biomolecules along the width and length of a chamber and conventional 1-dimensional gradients along the width of its lateral parallel channels. The gradient profile can be precisely controlled by the applied flow rate. The proposed design is simple and straightforward, has a small footprint size compared to previously reported devices such as tree-shape designs, and for the first time, provides capability of generating desired 2D and 1D gradients, simultaneously. The finite element simulation analysis proves the feasibility of the microfluidic device, and the fluorescently labelled IgG antibody is used to demonstrate generated chemical gradients. This simple microfluidic device can be implemented for a wide range of high-throughput concentration gradient applications such as chemotaxis, drug screening, and organs-on-chips.

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.

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 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 score0.100
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.026
GPT teacher head0.251
Teacher spread0.225 · 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