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Record W2799735436 · doi:10.1063/1.5023396

Membrane isolation of repeated-use sweat stimulants for mitigating both direct dermal contact and sweat dilution

2018· article· en· W2799735436 on OpenAlexfundno aff
Phillip Simmers, Yuchan Yuan, Z. Sonner, Jason Heikenfeld

Bibliographic record

VenueBiomicrofluidics · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsnot available
FundersAir Force Research LaboratoryMcMaster University
KeywordsSWEATIontophoresisStimulantCarbacholChemistryPerspirationSweat testMembranePharmacologyStimulationMedicineEndocrinologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

With the device integration of sweat stimulation, sweat becomes a stronger candidate for non-invasive continuous biochemical sensing. However, sweat stimulants are cholinergenic agents and non-selective to just the sweat glands, and so, direct placement of sweat stimulants poses additional challenges in the possibility for uncontrollable transport of the stimulant into the body and challenges in contamination of the sweat sample. Reported here is membrane isolation of repeated-use sweat stimulants for mitigating direct dermal contact, dilution of the sweat stimulant, and contamination of the sweat sample. The membrane dramatically reduces passive diffusion of the sweat stimulant carbachol by roughly two orders of magnitude, while still allowing repeated sweat stimulation by iontophoretic delivery of the carbachol through the membrane and into the skin. Both in-vivo and in-vitro validation reveal feasibility for reliable integration of sweat stimulants within a wearable device for use periods of 24 h or more. In addition, advanced topics and confounding issues such as stimulant gel design, osmotic pressure, and ionic impurities are speculatively and theoretically discussed.

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)0.000
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 score0.015
Threshold uncertainty score0.644

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 head0.020
GPT teacher head0.240
Teacher spread0.220 · 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.

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

Citations10
Published2018
Admission routes1
Has abstractyes

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