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Record W4200026638 · doi:10.32920/16814698.v1

Fabrication Of Sieved-Microwells For Single Particle Capture

2021· preprint· en· W4200026638 on OpenAlexaff
Lauren Romita

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMicrofluidicsSettlingSeedingNanotechnologyFabricationParticle (ecology)Materials scienceSettling timeEnvironmental scienceEngineeringAerospace engineeringBiology

Abstract

fetched live from OpenAlex

The use of microwells is popular for a wide range of applications due to its’ ease of use. However, the seeding of conventional microwells, which are closed at the bottom, is restricted to gravitational sedimentation for cell or particle deposition and therefore require lengthy settling times to maximize well occupancy. Microfluidics has accelerated cell or particle capture through flow but is mostly limited to gravitationally-driven settling for capture into the wells. An alternative approach to conventional closed-microwells, sieved microwells supersedes reliance on gravity by using hydrodynamic forces through the open pores at the bottom of the microwells to draw targets into the wells. The aim of this thesis is to develop a rapid and high-throughput fabrication method for sieved microwells and integrate the microwells into a double-layered microfluidic device to enable crossflow trapping. The resulting device achieves an 87% well occupancy in under 10 seconds.

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)0.001
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.022
GPT teacher head0.217
Teacher spread0.195 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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