FabSense - A DIY Approach for Development of Electrochemical Sweat Glucose Sensor
Bibliographic record
Abstract
Diabetes mellitus is one of the fastest growing chronic health conditions worldwide, projected to affect 643 million people by 2030. Despite 100 years since insulin's discovery, diabetes remains a leading cause of death globally, responsible for about 5 % of fatalities. Traditional invasive glucose monitoring methods, such as blood pricks and implantable sensors, face challenges like patient discomfort and frequent replacement due to biofouling. In this research, we propose a low-cost DIY (do-it-yourself) electrochemical sweat glucose sensor using a fabric-based three-electrode system. The sensor integrates the enzyme Glucose Oxidase (GOx) with commercially available conductive fabrics, allowing non-invasive glucose detection without complex laboratory infrastructure. Fabrication techniques using household devices like Cricut and steam irons were employed for their affordability and ease of use. The sensor's performance was validated through Cyclic Voltammetry (CV) and Electrochemical Impedance Spectroscopy (EIS). Differential Pulse Voltammetry (DPV) was used to detect glucose achieving a sensitivity of 0.16 μA μM-1. To evaluate the stability of the sensors, the prepared patch was subjected to repeated DPV measurements using a specific glucose concentration over a period of 48 hours. The Relative Standard Deviation (RSD) was calculated to be 2.59 %. Notably, sweat glucose levels have shown a significant correlation with blood glucose levels, enhancing the potential of our approach for diabetes management. This method demonstrates significant potential for advancing medical diagnostics and developing multifunctional, smart wearable devices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".