Woven electrochemical fabric-based test sensors (WEFTS): a new class of multiplexed electrochemical sensors
Bibliographic record
Abstract
We present textile weaving as a new technique for the manufacture of miniature electrochemical sensors with significant advantages over current fabrication techniques. Biocompatible silk yarn is used as the material for fabrication instead of plastics and ceramics used in commercial sensors. Silk yarns are coated with conducting inks and reagents before being handloom-woven as electrodes into patches of fabric to create arrays of sensors, which are then laminated, cut and packaged into individual sensors. Unlike the conventionally used screen-printing, which results in wastage of reagents, yarn coating uses only as much reagent and ink as required. Hydrophilic and hydrophobic yarns are used for patterning so that sample flow is restricted to a small area of the sensor. This simple fluidic control is achieved with readily available materials. We have fabricated and validated individual sensors for glucose and hemoglobin and a multiplexed sensor, which can detect both analytes. Chronoamperometry and differential pulse voltammetry (DPV) were used to detect glucose and hemoglobin, respectively. Industrial quantities of these sensors can be fabricated at distributed locations in the developing world using existing skills and manufacturing facilities. We believe such sensors could find applications in the emerging area of wearable sensors for chemical testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".