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Record W4402137998 · doi:10.3390/mi15091118

A Novel Device for Micro-Droplets Generation Based on the Stepwise Membrane Emulsification Principle

2024· article· en· W4402137998 on OpenAlexafffund
Sven Achenbach, Garth Wells, Hongbo Zhang, Wenjun Zhang

Bibliographic record

VenueMicromachines · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersCanadian Institutes of Health ResearchWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaNational Research Council CanadaNational Natural Science Foundation of ChinaCanadian Light Source
KeywordsMembraneMembrane emulsificationFabricationMaterials scienceNanotechnologyMicrosphereEngineeringChemical engineeringChemistry

Abstract

fetched live from OpenAlex

This paper presents a novel design of the device to generate microspheres or micro-droplets based on the membrane emulsification principle. Specifically, the novelty of the device lies in a proposed two-layer or stepwise (by generalization) membrane structure. An important benefit of the stepwise membrane is that it can be fabricated with the low-cost material (SU-8) and using the conventional lithography technology along with a conventional image-based alignment technique. The experiment to examine the effectiveness of the proposed membrane was conducted, and the result shows that microspheres with the size of 2.3 μm and with the size uniformity of 0.8 μm can be achieved, which meets the requirements for most applications in industries. It is noted that the traditional membrane emulsification method can only produce microspheres of around 20 μm. The main contribution of this paper is thus the new design principle of membranes (i.e., stepwise structure), which can be made by the cost-effective fabrication technique, for high performance of droplets production.

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: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.482

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 head0.033
GPT teacher head0.287
Teacher spread0.255 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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