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Record W1988794717 · doi:10.1088/0960-1317/20/1/015026

Microwave-induced, thermally assisted solvent bonding for low-cost PMMA microfluidic devices

2009· article· en· W1988794717 on OpenAlexafffund
Mona Rahbar, Sumanpreet K. Chhina, Dan Sameoto, M. Parameswaran

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSynthesis and properties of polymers
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsMaterials scienceMicrowaveAnodic bondingSolventNanotechnologyChemistryLayer (electronics)Computer scienceOrganic chemistry

Abstract

fetched live from OpenAlex

We present a low-cost bonding method for polymethylmethacrylate (PMMA) microfluidics that combines elements of solvent bonding, thermal bonding and microwave bonding. Rather than using specialized equipment, we take household equipment and combine it to produce an effective bonding method that borrows from food packaging technologies for selective heating in a microwave. A poor solvent for PMMA is applied between two halves of a microfluidic system and clamped together using miniature binder clips. Excess solvent from the channels is then drawn out via capillary action and avoids channel clogging during the bonding process. Placing the whole apparatus in a commercial microwave will heat up the thin metal clips and cause the solvent to dissolve and bond the PMMA interface. The whole bonding process takes only a few minutes, and results in high bond strengths.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.233
Teacher spread0.213 · 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

Citations45
Published2009
Admission routes2
Has abstractyes

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