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The purpose of this paper is to study the effect of the nano tube fillers on the corrosion protection properties of the self-curing epoxy (SEP) coatings.

The self-curing epoxy (SEP) resin was synthesized via a reaction between diisopropoxy-bis ethylacetoacetato titanate and the epoxy resin. Halloysite nanotubes (HNTs) was surface modified by grafting (3-glycidoxypropyl) trimethoxysilane to obtain modified HNTs (mHNTs). The HNTs and mHNTs are used as nano tube fillers for the SEP coating. The thermal stability of the coatings was assessed via thermo-gravimetric analysis. The field-emission scanning electron microscopy (SEM) was conducted to analyze the surfaces and cross sections of the coatings. The anticorrosive efficiencies of the coatings were investigated by electrochemical measurements and a neutral salt spray test.

The results demonstrated that the additions of HNTs and mHNTs have little effect on the thermal degradation temperature of the SEP coating. However, the addition of the nanotubes reduced the corrosion resistance of the SEP coating.

The SEP coating itself showed excellent corrosion resistance without any reinforcement particles and is hence promising for application in the heavy-duty anticorrosion field of heat exchangers.

With the current pandemic situation, the world is shifting to online buying and therefore the purpose of this study is to understand how the industry can improve sales based on the product recommendations shown on their online platforms.

This paper has studied content-based filtering using decision trees algorithm and collaborative filtering using K-nearest neighbour algorithm and measured their impact on sales of product of different genres on e-commerce websites and if their recommendation causes a difference in sales.This paper has conducted a field experiment to analyse the customer frequency, change in sales caused by different algorithms and also tried analysing the change in buying preferences of customers in post-pandemic situation and how this paper can improve on the search results by incorporating them in the already used algorithms.

This study indicates that different algorithms cause differences in sales and score over each other depending upon the category of the product sold. It also suggests that post-Covid, the buying frequency and the preferences of consumers have changed significantly.

The study is limited to existing users of these sites, it also requires the sites to have a huge database of active users and products. Also, the preferences and likings of Indian subcontinent might not generally apply everywhere else.

This study enables better insight into consumer behaviour, thus enabling the data scientists to design better algorithms and help the companies improve their product sales.