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Record W4294415018 · doi:10.1002/pat.5847

Biomedical applications of microfluidic devices: Achievements and challenges

2022· article· en· W4294415018 on OpenAlex

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.

Bibliographic record

VenuePolymers for Advanced Technologies · 2022
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMicrofluidicsNanotechnologyMicroscale chemistryFabricationMicroelectronicsMaterials scienceMicrofabrication

Abstract

fetched live from OpenAlex

Abstract There has been a recent interest in microfluidics due to their wide application and unique integration of concepts, including physics, materials science, chemistry, microelectronics, and biology. Microfluidic chips can be applied in different fields, particularly in the biomedical sector, such as drug delivery, diagnosis devices, cell culture, and scaffold fabrication. Various materials, including metals, polymers, and ceramics, can be manufactured into microscale chips with channels and chambers. Platforms of any required size, structure, or geometry can be fabricated using a wide range of fabrication techniques, for example, three‐dimensional printing. This manuscript assesses the microfluidic devices starting from their historical development, materials, fabrication methods and challenges, as well as biomedical applications.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.394

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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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