MétaCan
Menu
Back to cohort
Record W4309644049 · doi:10.1088/1361-6439/aca4da

High density cleanroom-free microneedle arrays for pain-free drug delivery

2022· article· en· W4309644049 on OpenAlexafffund
Thomas Lijnse, Kazim Haider, Catherine Betancourt Lee, Colin Dalton

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2022
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesCMC Microsystems
KeywordsCleanroomFabricationMaterials scienceBiomedical engineeringNanotechnologyOptoelectronicsEngineeringMedicine

Abstract

fetched live from OpenAlex

Abstract The purpose of this work is to demonstrate the fabrication process for cleanroom-free solid metal microneedles and perform quantification of insertion profiles. Metal microneedles were created using a modified wirebonding process and inserted into porcine tissue to determine design efficacy. Microneedle arrays were analyzed through optical imaging and scanning electron microscopy. Insertion forces were measured using combined uniaxial load cell and resistance measurement data. Microneedle arrays were successfully inserted into porcine tissue with high repeatability and reliability. These arrays demonstrate lower or equivalent insertion forces (less than 3 N) to other forms of microneedles in the literature without the need for complex cleanroom fabrication processes. The microneedle fabrication method presented here rapidly produces mass manufacturable, high-quality microneedle arrays with minimal insertion forces, able to reliably penetrate tissue samples. The manufacturing method presented here achieved array densities as high as 3200 needles cm −2 . These microneedle arrays demonstrate simple fabrication of a reliable, high-density, pain-free drug delivery system, with potential applications in biosensing and electric field modulated drug delivery.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.028
GPT teacher head0.297
Teacher spread0.269 · 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.

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

Citations11
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Micromechanics and MicroengineeringSame topicAdvancements in Transdermal Drug DeliveryFrench-language works237,207