Functionalisation of MWCNTs with piperazine and dopamine derivatives and their potential antibacterial activity

Micro & nano letters

article

Saghir Hussain¹  Tariq Mahmood Ansari¹  Hina Sahar¹  Shamsa Kanwal²  Farrukh Mansoor²  Timur Darak¹  M. Zubair Iqbal³  Fahim Khurshid Butt⁴  Ajaz Hussain¹  Aun Muhammad⁵  Sher Zaman⁶  Ghulam Hasnain Tariq²  Hafiz Muhammad Asif¹ 

[1] Institute of Chemical Sciences, Bahauddin Zakariya University;Department of Chemistry, Khwaja Fareed University of Engineering & Information Technology Abu Dhabi Road;Ningbo Institute of Materials Technology and Engineering (NIMTE);Department of Physics, University of Education;Institute of Molecular Biology and Bio-Technology, Bahauddin Zakariya University;Department of Physics, Karakorum International University

关键词: organic compounds;    microorganisms;    nanoparticles;    antibacterial activity;    oxidation;    biomedical materials;    surface treatment;    scanning electron microscopy;    transmission electron microscopy;    Raman spectra;    nanocomposites;    X-ray diffraction;    nanofabrication;    Fourier transform infrared spectra;    multi-wall carbon nanotubes;    piperazine;    dopamine derivatives;    potential antibacterial activity;    functionalised multiwalled carbon nanotubes;    high aspect ratio;    surface penetration characteristics;    functionalised MWCNTs;    Fourier-transform infrared spectroscopy;    nanoantibacterials;    Staphylococcus aureus;    Micrococcus luteus;    acid oxidation;    scanning electron microscopy;    Fourier transform infrared spectroscopy;    X-ray diffraction;    Raman spectroscopy;    dimethyl sulfoxide;    DMSO;    dimethyl formamide;    DMF;    time 10.0 hour;    time 8.0 hour;    temperature 75.0 degC;    C;   

DOI  :  10.1049/mnl.2020.0114

学科分类：计算机科学（综合）

来源: Wiley

【 摘 要 】

In the middle of this wonderful Internet technology, the rise and growth of Internet misuse is shocking which compromises the security of the computers in network. In doing so, the use of the Internet becomes very destructive for one and all. Any unauthorized person can steal private information by hacking computer. Anonymous attack has many causes, such as viruses, malware, misuse of privileges on the computer and unauthorized access to information systems. To reduce the exposure to such types of threats, organizations need a reliable, robust and fast computer network security mechanism. Intrusion detection is a mechanism which detects and prevents different intruders in internet. There are many techniques of machine learning which can to apply intrusion detection systems. Current, many researcher are using ensemble methof to implement IDS. The selection of base classifeirs In ensemble method, the selection of suitable selection of base classifiers is a very key process. This  paper propose a novel intrusion detection systems using ensemble of two well-known decision trees. C4.5 decision tree and Random Forest have selected as a base classifiers. Intrusion detection system is framed by combining the gains of both C4.5 and Random Forest decision trees. The working of the proposed ensemble for intrusion detection system has estimated in terms of classification accuracy, true positives and false positives. The experimental results show that the offered ensemble classifier for intrusion detection performs well in classification accuracy, true positive than individual decision trees on testing dataset. Other aspects of performance of classifiers are described in the paper.

【 授权许可】

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