Highly Sensitive and Selective Non-Enzymatic Measurement of Glucose Using Arraying of Two Sweat Sensors Modified By Controlled Growth of Co/Cu and Functionalized Carbon Nanotubes
Bibliographic record
Abstract
Due to a relation between glucose in sweat and blood, there is an opportunity to monitor patients' glucose levels non-invasively through sweat. There is a high demand for developing highly selective and sensitive biosensors in order to sense biomarkers like glucose and, therefore, the diseases based on those biomarkers. However, enzymatic sensors are selective to specific biomarkers; they are suffering from high sensitivity to the fluctuation of temperature, oxygen, pH, humidity, detergents, organic reagents, and toxic chemicals, affecting their stability and sensitivity, and reproducibility. Therefore, developing non-enzymatic glucose (or other biomarkers) sensors is getting significant attention to fulfill higher sensitivity and selectivity as well as minimized susceptibility to fouling by enzyme-ageing and adsorbed intermediates. Here we have proposed a combination of two sensors that can help us improve these non-enzymatic sensors' selectivity. Two electrochemical arrayed sensors have been developed. The first electrochemical sensor has been achieved by controlled growth of cobalt nanowire and copper nanoparticles on carbon substrate in order to measure the glucose level at low concentrations, and the second electrochemical sensor has been modified by MWCNT-CO-NH-cyanuric-NH2 and Fe3O4 in order to measure the uric acid and eliminate the interference of it in glucose measurement results. In order to show the morphology of the glucose sensor, the SEM and EDX have been conducted. Also, the FT-IR test has been shown to confirm the functionalization of MWCNT. The electrocatalytic and electrochemical performance of each sensor have been evaluated in the presence of the various contaminants of sweat. The glucose sensor showed less than 5% interference toward ascorbic acid, sodium bicarbonate, and the lactic acid at their max range of presence in the sweat. For eliminating the interference of uric acid, the second sensor has developed, which has no response to the glucose and high sensitivity to the uric acid. The calibration curve of each sensor has been provided in the 3D form, and a simple method of arraying has been applied to improve the sensors' selectivity. The arrayed sensors showed a highly glucose-selective sweat-based sensor with minimized error imposed by the uric acid interference. The glucose sensor's reproducibility and durability have also been tested, which showed less than 5% and 10% variations, respectively. In the end, the arrayed sensor's performance has been evaluated by real sweat samples of a male and a female, analyzed by Clarke’s error grid analysis showing less than 20% deviation from the glucose levels measured by the commercial glucometers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".