Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Coding, Ranking and Optimum selection of Nanomaterials

Version 1 : Received: 15 January 2021 / Approved: 18 January 2021 / Online: 18 January 2021 (11:51:50 CET)

How to cite: Singh, T. Coding, Ranking and Optimum selection of Nanomaterials. Preprints 2021, 2021010322 (doi: 10.20944/preprints202101.0322.v1). Singh, T. Coding, Ranking and Optimum selection of Nanomaterials. Preprints 2021, 2021010322 (doi: 10.20944/preprints202101.0322.v1).

Abstract

The paper presents attribute based characterization of nanomaterials method for computer storage and retrieval as knowledgebase. The knowledgebase permits indepth understanding and comparison between nanomaterials available with the scientists and product developers to satisfy their research and development (R & D) needs. Techniques for order preference by similarity to ideal solution (TOPSIS) is proposed to evaluate nanomaterials in the presence of multiple attributes. The method normalizes attributes to nullify the effect of different units and their values in the range of 0 to 1. The relative importance of different attributes for different applications is considered. The weight vector is derived using Eigen value formulation. The positive and negative benchmark solutions are derived. Euclidean distance of alternatives from these best and worst solution leads to the development of proximity /goodness/suitability index for ranking. Final decision is taken by decision makers by SWOT analysis and short and long term strategies of the organisation. The methodology is illustrated with the help of an example and step-by-step procedure. Results, discussion and conclusion highlights the importance and practical application.

Subject Areas

Nanomaterial Selection; Pertinent attributes; MADM; TOPSIS; Normalization; Weighted normalization; Coefficient of similarity; Ranking

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