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Record W1983180290 · doi:10.1117/12.582188

Human blood rheology in MEMS-based microneedles

2005· article· en· W1983180290 on OpenAlexafffund
Priyanka Aggarwal, C. R. Johnston

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvancements in Transdermal Drug Delivery
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Calgary
KeywordsMaterials scienceMicroelectromechanical systemsTransdermalBlood flowBiomedical engineeringMicrochannelFinite element methodEmbossingMicrofluidicsRheologyMechanicsMechanical engineeringComposite materialNanotechnologyPhysicsThermodynamics

Abstract

fetched live from OpenAlex

MEMS-based microneedles have the potential to revolutionize biomedical/biotechnology applications by providing precise transdermal drug delivery and localized blood sampling. In this paper, we propose a novel theory-based model that predicts drift velocity of blood-flow through the microchannels embedded in the microneedles. The profile of blood flow in the microneedles is determined by solving the conservation of momentum equation of the liquid phase, coupled with the force balance equations at the liquid-air interface. For the first time, this work enables accurate calculation/prediction of the velocity profile of the blood flow through a vertical in-plane microneedle, considering the effect of surface tension forces which are the most prominent forces. In order to withdraw blood samples from capillaries in the dermis layer, the length of our MEMS-based in-plane microneedle has been set at 600 μm with the micro-channel thickness chosen to be 35 μm, to avoid deformation of red blood cells. Blood flow through microneedles has been computed analytically using the proposed formulation. The results are then verified by a commercial finite element simulation tool "ANSYS".

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)0.001
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 score0.203
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.332
Teacher spread0.300 · 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

Citations3
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvancements in Transdermal Drug DeliveryFrench-language works237,207