Application of Nanocrystal Cellulose Based on Empty Palm Oil  Fruit Bunch as Glucose Biosensing

Sunu Herwi Pranolo; Joko Waluyo; Royhan Ikbar; Ramanda Ayu Damayanthy; Septy Lestary; Muhammad Luqman Qadarusman

Sunu Herwi Pranolo Study Program of Chemical Engineering, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta, Indonesia 57126
Joko Waluyo Study Program of Chemical Engineering, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta, Indonesia 57126
Royhan Ikbar Study Program of Chemical Engineering, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta, Indonesia 57126
Ramanda Ayu Damayanthy Study Program of Chemical Engineering, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta, Indonesia 57126
Septy Lestary Study Program of Chemical Engineering, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta, Indonesia 57126
Muhammad Luqman Qadarusman Study Program of Chemical Engineering, Faculty of Engineering, Universitas Sebelas Maret, Jl. Ir. Sutami 36A, Surakarta, Indonesia 57126

Keywords: Biosensor, Empty Palm Fruit Bunch, Glucose, Nanocrystal Cellulose

Abstract

Abstract. Biosensors for glucose sensing purposes are important since diabetes is a worldwide disease. One of the components of glucose biosensors is cellulose nanocrystals (CNCs). CNCs are cellulose derivatives that could be extracted from oil palm empty fruit bunch (OPEFB). Indonesia has a high potential for OPEFB due to its abundance of resources. CNCs have poor conductivity as biosensors, so adding supporting electro-conductor components such as graphene and carbon nanotubes (G-CNT) is necessary. In this research, the amount of bleaching agent of H2O2 in CNCs extraction varies between 1.5% and 10%, and the portion of CNCs in the composite varies between 5%, 15%, and 30%. The purpose of this research is to create an optimum biosensor composite based on its CNCs quality through particle size analysis (PSA) and X-ray diffraction (XRD) tests followed by cyclic voltammetry to determine biosensor’s impedance, limit of detection (LOD), and performance stability. Fourier transform infra red (FTIR) tests are also conducted as process control. The research shows the success of delignification in CNC extraction based on FTIR. Crystallinity enhancement up to 51% as delignification using 1.5% and 10% H2O2 yields CNC with a crystallinity index of 87.1% and 94.0%. The average size of CNCs with delignification by 1.5% and 10% H2O2 are 640.0 nm and 579.8 nm, respectively. Results of testing the biosensor glucose G-CNT/CNC showed the best composition is 5% CNCs that using 10% H2O2 which the highest oxidation peak is 0.00205 A and reduction peak is -0.00223 A. Data of variance composition show the difference of the data is significant by using ANOVA SPSS Test. The biosensor has an accuracy of 83.2% in a test for diabetic urine.

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