National Institute of Technology, Karnataka

About: National Institute of Technology, Karnataka is a education organization based out in Mangalore, Karnataka, India. It is known for research contribution in the topics: Corrosion & Cloud computing. The organization has 5017 authors who have published 7057 publications receiving 70367 citations.

Structure-Sensitive Electrocatalytic Reduction of CO2 to Methanol over Carbon-Supported Intermetallic PtZn Nano-Alloys.

Abstract: The electrochemical reduction of CO2 (CO2RR) to produce valuable synthetic fuel like CH3OH not only mitigates the accumulated greenhouse gas from the environment but is also a promising direction toward attenuating our continuous reliance on fossil fuels. However, CO2RR to yield CH3OH suffers because of large overpotential, competitive H2 evolution reaction (HER), and poor product selectivity. In this regard, intermetallic alloy catalysts open up a wide possibility of fine-tuning the electronic property and attain appropriate structures that facilitate selective CO2RR. Here, we report for the first time the CO2RR over carbon-supported PtZn nano-alloys and probed the crucial role of structures and interfaces as active sites. PtZn/C, Pt3Zn/C, and PtxZn/C (1 < x < 3) synthesized from the metal-organic framework material were characterized structurally and morphologically. The catalysts demonstrated structure dependency toward CH3OH selectivity, as the mixed-phase PtxZn/C outperformed the phase-pure PtZn/C and Pt3Zn/C. The structure-dependent reaction mechanism and the kinetics were elucidated over the synthesized catalysts with the help of detail experiments and associated density functional theory calculations. Results showed that in spite of low electrochemically active surface area, PtxZn could not only have facilitated the single electron transfer to adsorbed CO2 but also showed better binding of the intermediate CO2•- over its surface. Moreover, the lower bond energy between the mixed-phase surface and -OCH3 compared to the phase-pure catalysts has enabled higher CH3OH selectivity over PtxZn. This work opens a wide possibility of studying the role of interfaces between phase-pure nano-alloys toward CO2RR.

NLP based sentiment analysis on Twitter data using ensemble classifiers

Abstract: Most sentiment analysis systems use bag-of-words approach for mining sentiments from the online reviews and social media data. Rather considering the whole sentence/ paragraph for analysis, the bag-of-words approach considers only individual words and their count as the feature vectors. This may mislead the classification algorithm especially when used for problems like sentiment classification. Traditional machine learning algorithms like Naive Bayes, Maximum Entropy, SVM etc. are widely used to solve the classification problems. These machine learning algorithms often suffer from biasness towards a particular class. In this paper, we propose Natural Language (NLP) based approach to enhance the sentiment classification by adding semantics in feature vectors and thereby using ensemble methods for classification. Adding semantically similar words and context-sense identities to the feature vectors will increase the accuracy of prediction. Experiments conducted demonstrate that the semantics based feature vector with ensemble classifier outperforms the traditional bag-of-words approach with single machine learning classifier by 3–5%.