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Record W7015684877

Use of (Super)Hydrophobic Surfaces in Digital Microfluidics

2018· dissertation· en· W7015684877 on OpenAlexfundno aff

Bibliographic record

VenueQSpace (Queen's University Library) · 2018
Typedissertation
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsnot available
FundersQueen's University
KeywordsMicrofluidicsDigital microfluidicsWettingContact anglePolymerSurface energySuperparamagnetismParticle (ecology)
DOInot available

Abstract

fetched live from OpenAlex

A superhydrophobic (SH) surface is characterized by a water contact angle of >150 and a sliding angle <10. The water fearing properties result from significantly stronger cohesive forces of the resting liquid compared to weak adhesive forces with the surface material. Microfluidics is a discipline in which minute liquid volumes (10–9 to 10–18 litres) are actuated, mixed, merged, split, and/or analyzed on uniquely engineered devices. SH surfaces have aided digital microfluidics by facilitating magnetic actuation of droplets and the spontaneous formation of droplet microarrays. SH surfaces offer low friction between a liquid droplet and the surface itself, allowing for actuation using minimal force. Magnetically susceptible material (paramagnetic salts or superparamagnetic particles) can be added to the droplet, making it possible to manipulate the liquid droplet with an external magnetic field. 
\nThis thesis focuses on the magnetic actuation performance of aqueous droplets on different SH coatings including Ultra-Ever Dry, (a commercial coating), fluorine-containing and more environmentally friendly “fluorine-free” SH porous polymer monolith (PPM). Droplet kinematic parameters are explored (e.g. volume, acceleration/deceleration, particle concentration etc.) SH surfaces can be patterned with hydrophilic regions to create surface energy traps (SETs). In this way, the liquid can be pinned to the SETs for small volume deposition and droplet anchoring, respectively. We utilize the contrasting wetting behavior to conduct a droplet-based quantitative DNA assay using fluorescence detection. Furthermore, we examine the use of patterned hydrophobic PPM surfaces to conduct an ice recrystallization inhibition assay that allows the side-by-side comparison of up to a dozen samples treated in an identical, higher throughput manner. A novel method of droplet manipulation is explored termed biologically-driven magnetic actuation (BDMA), which uses magnetotactic bacteria (MTB). BMDA is used to transport droplets along various trajectories (e.g. square and eight-shaped tracks). We further demonstrate the use of BMDA for sequential merging and mixing of multiple droplets. SH surfaces are typically limited to aqueous droplet manipulation.\tAlternatively, slippery liquid-infused porous (SLIPS) surfaces have been shown to exhibit low friction for both organic and aqueous droplets. We demonstrate the magnetic actuation of aqueous and organic (hexadecane) droplets on SLIPS using paramagnetic particles. The sixth chapter of the thesis demonstrates the proof of principal application involving functionalized superparamagnetic particles for the extraction of asphaltenes from crude oil on omniphobic surfaces. This thesis successfully explains different ways (super)hydrophobic and SLIPS surfaces can be used for performing different DMF operations on aqueous and organic droplets.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.006
GPT teacher head0.168
Teacher spread0.162 · 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.

Study designNot applicable
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
Published2018
Admission routes1
Has abstractyes

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