Conducting polymers are attractive chemical sensing materials due to their outstanding characteristics including low cost, room-temperature operations, easy device fabrication, high sensitivity and short response time. The new nanowires architecture, with high surface-to-volume ratio, makes possible the conducting polymers an ultra fast detection of chemical at low concentrations. Polymer-coated nanowires are thus the potential cost effective solution for the new generation gas sensors. As a sensing material, the molecular design of the conducting polymer is utterly important. The conductive polymers can be tailored to fulfill the sensing requirement by its modifying functional groups in accordance to the applications. Molecular modeling which predicts the material properties of conductive polymers helps in the design of the sensor material. In this thesis, I present a molecular modeling approach to design and evaluate conducting polymer as chemical sensing material for polymer nanowire or polymer-coated nanowire carbon dioxide (CO2) sensors in greenhouse application. In order to provide an overview of the rapid progress in the application of chemical sensing materials with nanowire architecture, literature study on nanowire gas sensors has been presented in the Chapter 2. A comparison between the two basic approaches (top-down and bottom-up) in the nanowire synthesis is given. The sensing principles and configurations of nanowire gas sensors with their relevant assembly technologies are summarized. Based on the review work, a polyaniline-coated nanowire field-effect transistor (NanoFET) is proposed for CO2 sensing system in greenhouse. This sensor set combines the advantages of nanowire architecture, FET sensor configuration and conducting polymers. A crucial part of any molecular simulation study is the choice of forcefields. In Chapter 3, we evaluate the validity of COMPASS and PCFF forcefields in predicting the physical and thermophysical properties of amorphous polymer emeraldine based polyaniline (EB–PANI). A combination of molecular mechanics (MM) and molecular dynamics (MD) analysis is employed to determine the polymer’s properties, including density (?) and solubility parameter (?). The temperature dependence of specific volume (?), non-bond energy (Enon-bond) and solubility parameters are used to estimate the glass transition temperature (Tg). Comparing the simulation results with experimental data, the accuracy of forcefields (COMPASS and PCFF) is elucidated. The COMPASS forcefield has been demonstrated as a better forcefield which provides a closer agreement with experiment data than the PCFF. Thus, the molecular modeling design of PANI for CO2 sensing is conducted by using the COMPASS forcefield. For effective sensing, the dissolution of an analyte, as quantified as the solubility parameter ?, in the sensing materials is crucial. Understanding of the temperature dependence of solubility parameter can provide adequate information for the sensitivity issue induced as the temperature changes. In Chapter 4, I have developed a compact model to describe the solubility parameter change due to the temperature impacts. It is showing that in the working temperature range of greenhouse, the temperature impact on the solubility parameter is limited and can be neglected. To verify the accuracy of our calculation, two kind of analysis has been are performed: (i) the ? value at 298 K for EB–PANI is predicted and compared with the literature reported data; (ii) the Tg of the polymer is determined from the ?–T curve and compared with the experimental value. The temperature dependence of solubility parameter of the EB-PANI has been determined by molecular modeling approach. The sensing mechanism of the PANI for CO2 materials is based on protonic acid doping. Molecular modeling of the sensing mechanism can offer useful information for the sensitivity and the selectivity of PANI. In Chapter 5, a compact model has been developed to describe the protonic acid doping of PANI with reasonable accuracy. The atomistic model is developed by using a statistical thermodynamic analysis method. The molecular modeling method is comprised of three key steps: (i) developing the atomistic models; (ii) defining the doing criteria; and (iii) simulating the protonic acid doping. By using the molecular model, the relationships including pKa/pH and doping percentage/pH are established. The computed results compare favorably with the reported experimental data. The change of charge carrier density causes the changes in the conductivity of the gas-sensitive conducting polymers. Thus, the relationship between macroscopic conductivity and charge carrier density is very useful in the design and evaluation of PANI as chemical sensing materials. In Chapter 6, by using the molecular model derived from Chapter 5, the relationships include the charge carrier density/pH and the conductivity/charge carrier density of EB-PANI are established properly. It is to find that the conductivity has an exponential function relationship with the charge carrier density [? = (A*n)a] in PANI. Using the computing relationship of conductivity/charge carrier density, the sensitivity of EB-PANI and its derivative K-SPANI for the detection of HCl is evaluated. The finding shows that by introducing function groups (–SO3K), the sensitivity of K-SPANI is greatly improved by two times. Thus the conducting polymer K-SPANI is a good candidate for acidic gas sensing, such as HCl, H2S, or CO2 in high humidity conditions. With the fundamental knowledge established in Chapters 3-6, the molecular design of PANI for greenhouse CO2 gas sensing can be achieved. Chapter 7 investigates the effect of functional group on the working range of polyaniline sensors for CO2 in agriculture industry. The humidity, temprature and the concentration of CO2 in the tightly clad greenhouses have been considered in the molecular model. The work compares the response of the pure EB, the polymer mixture of EB-PANI and undoped sodium sulfonated polyaniline (NaSPANI) with sulfur to nitrogen ratio (S/N) of 0.6, 0.5 and 0.4 to CO2. Under the working condition in a greenhouse, the working range of NaSPANI has been estimated as ~ [102- 104] ppm which demonstrates it is a good candidate for CO2 detection in agricultural industry. In considering the synthetic difficulty, I propose the conducting polymer NaSPANI (S/N = 0.5) is a good candidate for agricultural CO2 sensing. In summary, a molecular modeling method which helps in the design and evaluation of conductive polymers for carbon dioxide sensing in greenhouses has been established. This thesis work contributes at use of computational approaches in designing and optimizing chemical sensing materials for various applications.