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Background: Cancer till date remains one of the world's most life threatening disease accompanied by risk of secondary infections. Therefore formulations carrying anticancer drugs which can also decrease the risk of secondary infection are inevitable. Chemotherapeutic drug doxorubicin along with flavonoids quercetin and epigallocatechin gallate (EGCG) is simultaneously loaded on liposomal formulation exploiting the amphiphilic property of the liposomes. Results: Atomic force microscope imaging reveal the size of liposomal formulation loaded with doxorubicin, quercetin and EGCG to be greater than void liposome confirming the presence of drugs. Liposomal stability is improved by PEGylation; adding to the drug release time in vitro. The charge of phosphatidylcholine is rendered positive by coating the formulation with histone. The average size of the formulation is 342 nm. The encapsulation efficiency of doxorubicin, quercetin and EGCG is found to be 65.8%, 96.8% and 98% respectively. The above formulation demonstrated both anticancer and antimicrobial activity. Conclusion: The formulation will provide dual anticancer and antimicrobial therapy thereby evading secondary infection in cancer patients along with chemotherapy.

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Abstract:The manufacturing requirements of the aerospace industry makes it imperative to use thin wall machining techniques to machine parts that would otherwise have to be assembled from a number of parts. To achieve high productivity, there must be increase in material removal rate, which is constrained by the geometrical accuracy and surface finish requirements. Thus, a compromise must be made between productivity and product quality. This paper presents an optimisation scheme to improve the productivity while keeping the surface finish within acceptable limits during thin-wall machining operations. Initially full factorial experiments were carried out on machining of closed thin walled pocket by varying feed, cutting speed and tool diameter. Surface roughness and material removal values for all experiments were recorded. Analysis of variance was carried out to find out the most significant process parameter. Later firefly algorithm, a nature inspired swarm optimisation technique was employed to obtain the optimum process parameters for desired performance. A confirmation experiment was carried out which indicates an error of 1.27% and 1.03% between predicted and experimental results of surface roughness and material removal rate respectively.Keywords: thin wall machining; surface roughness; material removal rate; MRR; analysis of variance; ANOVA; interaction; optimisation; firefly algorithm.Reference to this paper should be made as follows: Dutta, A., Das, A.

Abstract:The manufacturing requirements of the aerospace industry makes it imperative to use thin wall machining techniques to machine parts that would otherwise have to be assembled from a number of parts. To achieve high productivity, there must be increase in material removal rate, which is constrained by the geometrical accuracy and surface finish requirements. Thus, a compromise must be made between productivity and product quality. This paper presents an optimisation scheme to improve the productivity while keeping the surface finish within acceptable limits during thin-wall machining operations. Initially full factorial experiments were carried out on machining of closed thin walled pocket by varying feed, cutting speed and tool diameter. Surface roughness and material removal values for all experiments were recorded. Analysis of variance was carried out to find out the most significant process parameter. Later firefly algorithm, a nature inspired swarm optimisation technique was employed to obtain the optimum process parameters for desired performance. A confirmation experiment was carried out which indicates an error of 1.27% and 1.03% between predicted and experimental results of surface roughness and material removal rate respectively.Keywords: thin wall machining; surface roughness; material removal rate; MRR; analysis of variance; ANOVA; interaction; optimisation; firefly algorithm.Reference to this paper should be made as follows: Dutta, A., Das, A.