Nanomaterials Characterization Using Hybrid Genetic ... B. Finite Element Methods . Finite element methods

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  • Nanomaterials Characterization Using Hybrid

    Genetic Algorithm Based Support Vector

    Machines

    Odedele Timothy Oladele Raw Materials Research and Development Council, Abuja Nigeria

    E-mail: odedelermrdc@yahoo.com

    Abstract—Nanotechnology is the process that develops novel

    materials at size of 100 nm or less and has become one of the

    most promising areas of human endeavor. Because of their

    intrinsic properties, nano-particles are commonly employed

    in electronics, photovoltaic, catalysis, environmental and

    space engineering, cosmetic industry and even in medicine

    and pharmacy. However, recent toxicological studies have

    shown evident toxicity of some nano-particles to living

    organisms (toxicity), and their potentially negative impact

    on environmental ecosystems (ecotoxicity). Characterization

    is the connection between an abstract material model and its

    real world behavior. Until recently, relatively simple testing

    procedures are available for the characterization of

    engineering materials. However, the large number of

    nanoparticles and the variety of their characteristics

    including sizes and coatings show that it is only rational to

    develop an approach that avoids testing every single

    nanoparticle produced. The modeling of the material is

    becoming increasingly difficult and complex such that it

    requires the use of complex numerical models. A trend is

    being established where characterization is accomplished

    through a combination of numerical modeling and

    experimental testing. Several researchers have carried out

    analytical and numerical studies on modeling of materials

    but failed to give a simple model to predict the physico-

    chemical properties of nano-materials. Computational

    intelligent techniques such as artificial neural network

    (ANN), fuzzy logic, genetic algorithm and support vector

    machine (SVM) are successfully used to solve complex

    problems. In this paper, a hybrid genetic algorithm tuned

    support vector machine classifier (GA-SVMC) model is

    developed to predict the toxicity of nano-materials. 

    Index Terms—nanotecnology, nanomaterials, support vector

    machines, genetic algorithm, toxicity, characterization

    I. INTRODUCTION

    The term “nanotechnology” covers processes

    associated with the creation and utilization of structures

    in the 1 nanometer (nm) to 100nm range. Nanofabrication

    involves engineering at the atomic length scale.

    Engineering at this scale makes it feasible to create, atom

    by atom, fibers which are very small in diameter but

    extremely strong. In the health care domain,

    nanofabrication can be utilized to fabricate extremely

    minute probes that can detect disease by examining

    Manuscript received November 1, 2013; revised February 25, 2014.

    individual strands of DNA. Nanofabrication makes it

    possible to manufacture capillary systems for providing

    nutrients to man-made replacement organs. The unique

    properties of these [nanotechnology] materials are a

    double edged sword because they can be tailored for

    beneficial properties and at the time may also have

    unknown consequences, such as new toxicological and

    environmental effects. The following examples illustrate

    how the same nanotechnology material may be both

    potentially beneficial and potentially harmful to human

    health and environment.

    Nanoscale silver is highly effective as an antibacterial

    agent in wound dressings, clothing, and washing

    machines, but there are general concerns that widespread

    dispersion of nanoscale silver in the environment could

    kill microbes that are vital to waste water treatment plants

    and to ecosystems. Some beneficial bacteria, for example,

    break down organic matter, remove nitrogen from water,

    aid in animal digestion, protect against fungal infestations,

    and even aid some animals in defense against predators

    [1]-[3].

    Due to size of nanoscale particles they may have the

    potential to penetrate the blood-brain barrier, a structure

    that protects the brain from harmful substances in the

    blood but also are such that they may affect the delivery

    of therapeutic agents. The characteristics of certain

    nanoscale materials may assist the development of

    pharmaceuticals to purposefully and beneficially cross

    this barrier and deliver medicine directly to the brain to

    treat, for example, a brain tumor. The harmful aspect,

    however, is the possibility of nanoscale particles to

    unintentionally pass through the blood-brain barrier

    causing harm to humans and animals [1]-[3].

    It is also generally believed that certain nanoscale

    materials are highly chemically reactive due to their high

    surface-to-volume ratio. This is a property which could

    be positively applied in catalysis, treatment of

    groundwater contamination, and site remediation. This

    property which is also being explored for use in

    protective masks and clothing as defense against

    chemical and biological agents can potentially result in

    cell damage in animals. Carbon nanotubes (CNTs) despite potential uses in a

    wide range of applications (e.g., materials, batteries,

    memory devices, electronic displays, transparent

    conductors, sensors, medical imaging), can exhibit

    107

    International Journal of Materials Science and Engineering Vol. 2, No. 2 December 2014

    ©2014 Engineering and Technology Publishing doi: 10.12720/ijmse.2.2.107-114

  • properties similar to asbestos fibers and might become

    lodged in organs (e.g., lungs, kidneys, livers), harming

    humans and animals [1]-[3].

    Considering the Environmental, Health, Safety (EHS)

    issues, nanomaterials play decisive roles in their

    distribution through environment, ecosystem and human

    body. Due to their biological activities/or unique

    properties they may gain access into human body through

    the main ports of entry such as the skin, lungs,

    gastrointestinal tract. Several toxicological studies have

    reported that nanomaterials can be cytotoxic, neurotoxic,

    genotoxic and ecotoxic [4].

    These apprehensions of the potential EHS effects of

    nanomaterials constitute serious barrier to

    nanotechnology transfer towards business perspectives. It

    is quite obvious that the current body of knowledge of

    how nanoscale materials might affect humans and the

    environment is insufficient to assess, address, and

    manage the potential risks. While there is agreement on

    the need for more EHS research, there are differing views

    on the level of funding required, how it should be

    managed, and related issues. To accomplish this, there is

    need to develop sensitive analytical methodologies, tools

    and an acceptable protocol for screening, characterization

    and monitoring of nanomaterials in the work place,

    laboratory, homes and environment. Therefore,

    considering the EHS issues there is serious need to

    develop and design predictive models for nanomaterials

    toxicity using computational intelligent systems

    (Artificial neural network, neuro-fuzzy systems, hybrid

    support vector machines and fuzzy inference systems).

    The objective is to develop computational/predictive

    model used to establish knowledge domains, risk

    modeling and nano-informatics capabilities to reliably

    assist decision making. Therefore, in order to accomplish this, the following

    are necessary:

     Development of computational intelligent predictive models for nanomaterials toxicity.

     Development of standardized methods, risk evaluation, risk assessment and management

    protocol.

     Information sharing, common database for research that uses standard protocols to generate

    knowledge

    Recent advances in machine learning methods have

    provided attractive alternatives for constructing

    interpretation models of complex nanomaterials. Here,

    Support Vector Machines (SVMs), a class of a learning

    machine that is formulated to output regression models

    and classifiers of competitive generalization capability,

    has been explored to determine its capabilities for

    determining the relationship, both in regression and

    classification, between physicochemical properties and

    human health effect which is the main focus. This paper

    will therefore focus on the capability of SVMs to model

    physicochemical properties and toxic effect of

    nanomaterials [4]. Specifically, the capabilities of GA tuned SVM

    regression and classification will be examined for

    appropriate prediction purposes on the beneficial and

    toxic effects of specific nano-particles.

    Section I gives a brief introduction. Section II

    highlights the existing computational model approach for

    nanomaterial characterization. Section III describes the

    proposed SVM technique and approach for estimatin