Mechanism of polypyrrole and silver nanorod formation in lauric acid–cetyl trimethyl ammonium bromide coacervate gel template: Physical and conductivity properties

Polypyrrole (PPy) and silver (Ag) nanorods are synthesized in cetyl trimethylammonium bromide–lauric acid (CTAB–LA) complex coacervate gel template. When PPy–CTAB–LA system is polymerized with AgNO₃, Ag nanorods are produced while use of ammonium persulphate (APS) as initiator yields PPy nanorods. Ag-nanorods are produced from the initial stage while PPy nanorods take a longer time. The average diameter of Ag nanorods varies from 60 to 145 nm by increasing AgNO₃ concentration from 0.27 M to 1.08 M and that of PPy varies from 145 nm to 345 nm by changing pyrrole concentration from 1 × 10^?4 to 2 × 10^?4 M, respectively. Fourier transformed infrared (FTIR) spectra indicate stabilization of Ag nanorods through complexation of PPy with adsorbed Ag⁺ ions. PPy nanoparticles are stabilized by adsorbed sulphate ions and lauric acid, both are acting as dopant to it. FFT pattern and EDX spectra clearly indicate the presence of Ag nanocrystals and PPy on the surface of Ag nanorods, respectively. The mechanism of nanorod formation is attributed from UV–Vis spectra showing a red shift of surface plasmon band of Ag and π–π* transition band of PPy with time. The highest dc conductivity of PPy–Ag composite is found to be 414.2 S/cm, 7 orders higher than that of PPy nanorods (9.3 × 10^?4 S/cm). PPy–Ag systems show Ohmic behavior while PPy nanorods exhibit semi-conducting behavior. The preferential formation of Ag nanorod in AgNO₃ initiated polymerization is attributed to the higher cohesive force of Ag than that of PPy. With two times higher LA and CTAB concentration in the gel the Ag nanorod diameter decreases only 12% while that of PPy nanorod decreases by 50%. Possible reasons are discussed from the hard and soft nature of the two nanorods and from the elasticity of the gel template.     

Optimisation of production and subcontracting strategies

We consider the problem of optimising production and subcontracting decisions in a supply chain manufacturing engineered products. The supply chain manager can use a combination of internal production capacities, available capacity at qualified subcontractors, make capital investments and process improvements to minimise costs associated with production and penalties for not meeting desired operational metrics. We formulate this as an optimisation problem that requires simultaneous solution of a mathematical programming problem and queuing network model. We propose an efficient iterative approach to solve this problem and conduct numerical studies to demonstrate the effectiveness of the approach.