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Record W3139490521 · doi:10.1088/1361-665x/abee34

An electrochemically actuated drug delivery device with <i>in-situ</i> dosage sensing

2021· article· en· W3139490521 on OpenAlexaff
Ying Yi, Mu Chiao, Bo Wang

Bibliographic record

VenueSmart Materials and Structures · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPolydimethylsiloxaneMaterials sciencePiezoresistive effectFabricationElectrolyteNanotechnologyMembraneDrug deliveryGauge factorActuatorDisplacement (psychology)Biomedical engineeringElectrodeComposite materialElectrical engineeringChemistryEngineering

Abstract

fetched live from OpenAlex

Abstract Very few conventional micro-electro-mechanical systems as drug delivery devices have in-situ dosage monitoring sensors, this thus brings inaccurate released dose, which results in either inefficient pharmaceutical effects or over-dose induced side effects. In this work, we integrate a low-cost piezoresistive sensor with an electrochemically actuated drug delivery device, and investigate its dosage monitoring performance. Different from the conventional sensor fabrication based on mixing conductive particles into liquid polymer, our proposed sensor is constructed from solidified carbon ink film embedded in a polydimethylsiloxane (PDMS) membrane, which can obtain an optimum tradeoff between the gauge factor and maximum achievable displacement. An electrolytic reaction induces the electrolysis-bubble in the actuator chamber with an increase in pressure, which causes displacement of the PDMS sealing membrane. This provides the actuation force to deliver the drug solution. The displacement of the PDMS membrane that determines the pumped volume of the drug solution is quantified through a resistance change of the embedded piezoresistive sensor. We report a single pumping volume of up to 7 μ l, which is monitored by the resistance change ratio (Δ R / R ), ranging from 2% to 12% with a dosage sensing accuracy of ±6.5%.

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 score0.005
Threshold uncertainty score0.758

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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