Modelling Simulation of Organic and Inorganic Interaction Based on First Principle Calculation

International Conference on Materials Engineering and Science

Adam, Tijjani^1 ; Mohammed, Aeshah M.^2 ; Mohammed, Mohammed^3 ; Dahham, Omar S.^1 ; Hashim, U.^3 ; Noriman, Nik Z.^1,4

Faculty of Engineering Technology, Kampus Uniciti Alam Sg. Chuchuh, Universiti Malaysia Perlis (UniMAP), Padang Besar

02100, Malaysia^1

University of Bagdad, College of Education for Pure Science, Ibn, Alhaitham, Iraq^2

Center of Excellence Geopolymer and Green Technology, School of Materials Engineering, Universiti Malaysia Perlis, P.O. Box 77, Kangar, Perlis

01007, Malaysia^3

Institute of Nano Electronic Engineering (INEE), Department of Electronic Engineering, Kangar, Perlis

01000, Malaysia^4

关键词: Density functionals;    Electrical response;    Experimental modeling;    Exponential models;    First principle calculations;    Interactive modeling;    Prediction capability;    Silicon-based sensors;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/454/1/012090/pdf
DOI  :  10.1088/1757-899X/454/1/012090

来源: IOP

【 摘 要 】

The student presented theoretically interaction of organic and inorganic based on the electrostatic interaction created by the ssDNA interaction with sensor active surface, the ssDNA strand was immobilised on silicon-based sensor to create binding chemistry with subsequent target molecule. The sensor is influenced by the ability of the attached probe ssDNA to recognise it complements through matched and mismatched. Upon interaction, the electrical response of the sensor was calculated based on density functional. A growth exponential functional model was established with 5nA rate of growth indicating complementary reactive and the model equally generated revised degenerative mode with mismatched produce decaying exponential model, thus, with excellent prediction capability. The organic and in-organic material interactive model developed could be employed to test the experimental model to validate and predict the selectivity and sensitive of Nano based biosensors.