Urine Glucose Detection Via Gold Nanoparticle Formation Using 3D-Connector Microfluidic Paper Based Analytical Devices


Krista Firdaus Suwarno Putri(1), Hermin Sulistyarti(2), Akhmad Sabarudin(3*)

(1) Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Brawijaya, Indonesia
(2) Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Brawijaya, Indonesia
(3) Department of Chemistry, Faculty of Mathematics and Natural Science, Universitas Brawijaya, Indonesia
(*) Corresponding Author

Abstract


A metabolic disorders that have experienced a significant increase in the world are diabetes mellitus. Diabetes is caused by two main factors: the first is damage to pancreatic beta cells, which prevents insulin from being produced, and the second is impaired insulin secretion and function. Chronic diabetes, if not treated properly, can lead to acute complications including eye, kidney, lung, nerve, and even death. Diabetes can be diagnosed through blood and urine. In general, glucose detection is carried out using invasive methods that use blood samples, which can cause pain and discomfort for users. Current research is developing non-invasive glucose detection using urine samples. This research aims to develop non-invasive glucose detection technology using 3D-connector μPADs (Microfluidic Paper Based Analytical Devices) which have the advantages of being safe, easy, and simple. The three-dimensional connector on the device functions as a connector to facilitate the coordination of fluid flow in the sample zone and detection zone. The glucose detection method uses gold (III) chloride as a gold nanoparticle (AuNPs) precursor, an aqueous extract of Acalypha indica Linn as a stabilizing agent, sodium hydroxide (NaOH) as a catalyst, and glucose in artificial urine as a sample. Method validation results using imageJ software indicated linearity with a coefficient of determination value (R2) of 0.9714, precision with a %RSD value (Relative Standard Deviation) of 2.69, and an accuracy level ranging from 92.22-99.23%.

Keywords


Diabetes mellitus; glucose; μPADs; AuNPs; non-invasive

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DOI: https://doi.org/10.15575/ak.v11i1.35245

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