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Record W2053486736 · doi:10.1007/s00542-009-0883-5

Design and fabrication of MEMS-based microneedle arrays for medical applications

2009· article· en· W2053486736 on OpenAlexafffund
Peiyu Zhang, Colin Dalton, G.A. Jullien

Bibliographic record

VenueMicrosystem Technologies · 2009
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of AlbertaCMC Microsystems
KeywordsFabricationMicroelectromechanical systemsMaterials scienceRobustness (evolution)Biomedical engineeringOptoelectronicsNanotechnologyEngineering

Abstract

fetched live from OpenAlex

Fabrication results for MEMS-based microneedle arrays are presented in this paper. The microneedle array was fabricated by employing a bi-mask technique to facilitate sharp tips, a cylindrical body and side openings. The presented array has advantages over previously published microneedle arrays in terms of ease of fabrication and bonding; high needle density and robustness; and side openings, which are expected to minimize the potential for clogging from skin debris during insertion. In addition, control over the process via etch-stop markers employed as stop layers, which assure the depth of long blind holes and the structure of the needle top, allows for different needle lengths and needle top structures to be easily implemented. The preliminary fluid flow and insertion experiments were performed to demonstrate the efficiency of the microneedle arrays.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.068
GPT teacher head0.400
Teacher spread0.332 · 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

Citations82
Published2009
Admission routes2
Has abstractyes

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