MétaCan
Menu
Back to cohort
Record W2537252834 · doi:10.1039/c6an02208a

Creating compact and microscale features in paper-based devices by laser cutting

2016· article· en· W2537252834 on OpenAlex
Md. Almostasim Mahmud, Eric J. M. Blondeel, Moufeed Kaddoura, Brendan D. MacDonald

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

VenueThe Analyst · 2016
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsEmmanuel Bible CollegeOntario Tech UniversityLakeridge Health
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicroscale chemistryMicrofluidicsLaserFabricationMaterials scienceFOIL methodLaser power scalingSample (material)NanotechnologyOptoelectronicsComputer scienceOpticsChromatographyChemistryComposite material

Abstract

fetched live from OpenAlex

laser cutting/engraving machine. Using this method we are able to produce the smallest features with the narrowest barriers yet reported for paper-based microfluidic devices. The method uses foil backed paper as the base material and yields inexpensive paper-based devices capable of using small fluid sample volumes and thus small reagent volumes, which is also suitable for mass production. The laser parameters (power and laser head speed) were adjusted to minimize the width of hydrophobic barriers and we were able to create barriers with a width of 39 ± 15 μm that were capable of preventing cross-barrier bleeding. We generated channels with a width of 128 ± 30 μm, which we found to be the physical limit for small features in the chromatography paper we used. We demonstrate how miniaturizing of paper-based microfluidic devices enables eight tests on a single bioassay device using only 2 μL of sample fluid volume.

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 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 score0.330
Threshold uncertainty score0.162

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 head0.005
GPT teacher head0.207
Teacher spread0.202 · 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