The present work proposed a novel approach for transferring high-risk heavy metals tometal complexes via green chemistry remediation. The method of remediation of heavy metals developed in the present work is a great challenge for global environmental sciences and engineering because it is a totally environmentally friendly procedure in which black tea extract solution is used. The FTIR study indicates that black tea contains enough functional groups (OH and NH), polyphenols and conjugated double bonds. The synthesis of copper complex was confirmed by the UV-vis, XRD and FTIR spectroscopic studies. The XRD and FTIR analysis reveals the formation of complexation between Cu metal complexes and Poly (Vinyl Alcohol) (PVA) host matrix. The study of optical parameters indicates that PVA-based hybrids exhibit a small optical band gap, which is close to inorganic-based materials. It was noted that the absorption edge shifted to lower photon energy. When Cu metal complexes were added to PVA polymer, the refractive index was significantly tuned. The band gap shifts from 6.2 eV to 1.4 eV for PVA incorporated with 45 mL of Cu metal complexes. The nature of the electronic transition in hybrid materials was examined based on the Taucs model, while a close inspection of the optical dielectric loss was also performed in order to estimate the optical band gap. The obtained band gaps of the present work reveal that polymer hybrids with sufficient film-forming capability could be useful to overcome the drawbacks associated with conjugated polymers. Based on the XRD results and band gap values, the structure-property relationships were discussed in detail.

Supplier selection is one of the most critical processes in supply chain management (SCM). Most small and medium enterprises (SMEs) face difficulties choosing the best supplier using conventional methods. A hybrid multi-criteria decision-making (MCDM) approach is proposed in supplier selection. This proposed framework integrates the Delphi technique as a data-gathering tool and Analytic Hierarchy Process (AHP) as the MCDM methodology for data analysis; both were used to select an effective supplier. This project applies the Delphi technique, allows experts to select the main criteria, and compares the trade-offs between the available alternatives depending on the main criteria. The criteria selected were price, delivery time, online ranking, rejection rate, and flexibility. Using the AHP approach, the criteria's weights were then assigned. The highest was for the price (43.84%), followed by the rejection rate (21.81%), online ranking (19.27%), delivery time (9.44%), and flexibility (5.64%). Lastly, a new framework was suggested using the weighted criteria collection for supplier selection.

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Alloys 617 and 276 are nickel-based super alloys with excellent mechanical properties, oxidation, creepresistance, and phase stability at high temperatures. These alloys are used in complex and stochastic applications. Thus, it is difficult to predict their output characteristics mathematically. Therefore, the non-conventional methods for modeling become more effective. These two alloys have been subjected to time-dependent deformation at high temperatures under sustained loading of different values. The creep results have been used to develop the new models. Artificial neural network (ANN) was applied to predict the creep rate and the anelastic elongation for the two alloys. The neural network contains twenty hidden layer with feed forward back propagation hierarchical. The neural network has been designed with MATLAB Neural Network Toolbox. The results show a high correlation between the predicted and the observed results which indicates the validity of the models. 

Alumina is a non-conductive ceramic material which can meet the high demand of industrial applications due to its excellent physical and chemical properties. However, machining of alumina is not possible by using the conventional machining methods due to its inherent brittleness. Recently, electro-discharge machining has been used for structuring alumina with assisting electrode to initiate the spark between the conductive tool electrode and the non-conductive work piece material. However, the effects of process parameters on material removal rate and surface roughness have not been investigated to formulate mathematical models. This study dealt with developing models for material removal rate and surface roughness correlating three process parameters which are peak current, pulse-on time and gap voltage using response surface methodology. The models were verified with 7% error between the results of empirical models and the experimental values.