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Record W2791085736 · doi:10.1109/jsen.2018.2801459

A 3-D-Printed Integrated PCB-Based Electrochemical Sensor System

2018· article· en· W2791085736 on OpenAlexafffund
Yue Dong, Xin Min, Woo Soo Kim

Bibliographic record

VenueIEEE Sensors Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPotentiostatPrinted circuit boardPrinted electronicsScreen printingMaterials scienceElectrodeAmperometryConductive inkInkwellSubstrate (aquarium)OptoelectronicsElectrical engineeringNanotechnologyElectronic engineeringLayer (electronics)ElectrochemistryEngineeringChemistryComposite material

Abstract

fetched live from OpenAlex

In this paper, we report a fully 3-D-printed, flexible, amperometric lactate sensor system with a 3-D-printed, integrated electrochemical circuit platform, a so-called potentiostat, realized via a novel 3-D printing method. First, a lactate sensor was designed in a three-electrode configuration with a layer of deposited enzyme on the conductive silver electrodes. The thin silver electrodes were printed on a flexible substrate. Second, a highly integrated potentiostat circuit prototype was designed and printed on a rigid epoxy board. Both sensor electrodes and printed circuit board (PCB) were fabricated using a novel direct ink writing (DIW) technology with highly viscous silver nanoparticle ink. The fully 3-D-printed potentiostat system along with the sensor demonstrated reliable lactate detection for concentrations in the range of 0-20 mM. The purely additive manufacturing strategy of DIW demonstrated the capability of 3-D printing double-sided circuits with high-density chip-on-board designs as well as the ability to significantly reduce the lead time of PCB prototyping. The 3-D-printed electrochemical sensor system is suitable for wearable devices for continuous monitoring of human metabolisms.

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 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 score0.054
Threshold uncertainty score1.000

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 head0.011
GPT teacher head0.223
Teacher spread0.212 · 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

Citations58
Published2018
Admission routes2
Has abstractyes

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