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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».