@article {46704, title = {Bimetallic palladium nickel nanoparticles anchored on carbon as high-performance electrocatalysts for oxygen reduction and formic acid oxidation reactions}, journal = {ACS Applied Energy Materials}, volume = {3}, year = {2020}, month = {SEP }, pages = {9285-9295}, type = {Article}, abstract = {

{Herein, we report the synthesis of carbon-supported palladium-nickel electrocatalysts (ECs) (Pd4-xNix/C ECs

}, keywords = {electrocatalysts, Formic Acid Oxidation, fuel cells, oxygen reduction reaction, palladium-nickel alloy}, issn = {2574-0962}, doi = {10.1021/acsaem.0c01622}, author = {Goswami, Chiranjita and Saikia, Himadri and Tada, Kohei and Tanaka, Shingo and Sudarsanam, Putla and Bhargava, Suresh K. and Bharali, Pankaj} } @article {47390, title = {Insights from a pan India Sero-epidemiological survey (Phenome-India Cohort) for SARS-CoV2}, journal = {eLife}, volume = {10}, year = {2021}, month = {APR }, pages = {e66537}, type = {Article}, abstract = {

To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14\%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95\% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10\% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95\% confidence interval, p value: 2.23, 1.92-2.59, \<0.0001), use of public transport (1.79, 1.43-2.24, \<0.0001), not smoking (1.52, 1.16-1.99, 0.0257), non-vegetarian diet (1.67, 1.41-1.99, \<0.0001), and B blood group (1.36, 1.15-1.61, 0.001).

}, issn = {2050-084X}, doi = {10.7554/eLife.66537}, author = {Naushin, Salwa and Sardana, Viren and Ujjainiya, Rajat and Bhatheja, Nitin and Kutum, Rintu and Bhaskar, Akash Kumar and Pradhan, Shalini and Prakash, Satyartha and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Chandak, Giriraj Ratan and Lahiri, Amit and Kar, Susanta and Mulay, Shrikant Ramesh and Mugale, Madhav Nilakanth and Srivastava, Mrigank and Khan, Shaziya and Srivastava, Anjali and Tomar, Bhawana and Veerapandian, Murugan and Venkatachalam, Ganesh and Vijayakumar, Selvamani Raja and Agarwal, Ajay and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Sundaram, Gopinath M. and Veeranna, Ravindra P. and Pal, Anirban and Agarwal, Vinay Kumar and Maurya, Anil Ku and Singh, Ranvijay Kumar and Raman, Ashok Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Venkatesan, Subramanian and Sardana, Harish Kumar and Kothari, Anamika and Jain, Rishabh and Thakur, Anupama and Parihar, Devendra Singh and Saifi, Anas and Kaur, Jasleen and Kumar, Virendra and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Singh, Praveen and Chakraborty, Rahul and Chaturvedi, Gaura and Karunakar, Pinreddy and Yadav, Rohit and Singhmar, Sunanda and Singh, Dayanidhi and Sarkar, Sharmistha and Bhattacharya, Purbasha and Acharya, Sundaram and Singh, Vandana and Verma, Shweta and Soni, Drishti and Seth, Surabhi and Vashisht, Sakshi and Thakran, Sarita and Fatima, Firdaus and Singh, Akash Pratap and Sharma, Akanksha and Sharma, Babita and Subramanian, Manikandan and Padwad, Yogendra S. and Hallan, Vipin and Patial, Vikram and Singh, Damanpreet and Tripude, Narendra Vijay and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sarkar, Jit and Ramakrishna, Sistla and Kumar, Balthu Narender and Kumar, Kiran A. and Gandhi, Sumit G. and Jamwal, Piyush Singh and Chouhan, Rekha and Jamwal, Vijay Lakshmi and Kapoor, Nitika and Ghosh, Debashish and Thakkar, Ghanshyam and Subudhi, Umakanta and Sen, Pradip and Chaudhury, Saumya Ray and Kumar, Rashmi and Gupta, Pawan and Tuli, Amit and Sharma, Deepak and Ringe, Rajesh P. and Amarnarayan, D. and Kulkarni, Mahesh and Shanmugam, Dhansekaran and Dharne, Mahesh S. and Dastager, Sayed G. and Joshi, Rakesh and Patil, Amita P. and Mahajan, Sachin N. and Khan, Abujunaid Habib and Wagh, Vasudev and Yadav, Rakesh Kumar and Khilari, Ajinkya and Bhadange, Mayuri and Chaurasiya, Arvindkumar H. and Kulsange, Shabda E. and Khairnar, Krishna and Paranjape, Shilpa and Kalita, Jatin and Sastry, Narahari G. and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, Ravi Kumar and Babu, Elapavalooru Vssk and Sukumaran, Rajeev and Nair, Aiswarya R. and Valappil, Prajeesh Kooloth and Puthiyamadam, Anoop and Velayudhanpillai, Adarsh and Chodankar, Kalpana and Damare, Samir and Madhavi, Yennapu and Aggarwal, Ved Varun and Dahiya, Sumit and Agrawal, Anurag and Dash, Debasis and Sengupta, Shantanu} } @article {48236, title = {Machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys}, journal = {Computers in Biology and Medicine}, volume = {146}, year = {2022}, month = {JUL}, pages = {105419}, type = {Article}, abstract = {

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8\% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6\%-80.2\%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

}, keywords = {BBV152, Covaxin, COVID-19, Ensemble methods, Infection, machine learning, SARS-CoV-2}, issn = {0010-4825}, doi = {10.1016/j.compbiomed.2022.105419}, author = {Singh, Prateek and Ujjainiya, Rajat and Prakash, Satyartha and Naushin, Salwa and Sardana, Viren and Bhatheja, Nitin and Singh, Ajay Pratap and Barman, Joydeb and Kumar, Kartik and Gayali, Saurabh and Khan, Raju and Rawat, Birendra Singh and Tallapaka, Karthik Bharadwaj and Anumalla, Mahesh and Lahiri, Amit and Kar, Susanta and Bhosale, Vivek and Srivastava, Mrigank and Mugale, Madhav Nilakanth and Pandey, C. P. and Khan, Shaziya and Katiyar, Shivani and Raj, Desh and Ishteyaque, Sharmeen and Khanka, Sonu and Rani, Ankita and Promila and Sharma, Jyotsna and Seth, Anuradha and Dutta, Mukul and Saurabh, Nishant and Veerapandian, Murugan and Venkatachalam, Ganesh and Bansal, Deepak and Gupta, Dinesh and Halami, Prakash M. and Peddha, Muthukumar Serva and Veeranna, Ravindra P. and Pal, Anirban and Singh, Ranvijay Kumar and Anandasadagopan, Suresh Kumar and Karuppanan, Parimala and Rahman, Syed Nasar and Selvakumar, Gopika and Venkatesan, Subramanian and Karmakar, Malay Kumar and Sardana, Harish Kumar and Kothari, Anamika and Parihar, Devendra Singh and Thakur, Anupma and Saifi, Anas and Gupta, Naman and Singh, Yogita and Reddu, Ritu and Gautam, Rizul and Mishra, Anuj and Mishra, Avinash and Gogeri, Iranna and Rayasam, Geethavani and Padwad, Yogendra and Patial, Vikram and Hallan, Vipin and Singh, Damanpreet and Tirpude, Narendra and Chakrabarti, Partha and Maity, Sujay Krishna and Ganguly, Dipyaman and Sistla, Ramakrishna and Balthu, Narender Kumar and Kumar, Kiran A. and Ranjith, Siva and Kumar, B. Vijay and Jamwal, Piyush Singh and Wali, Anshu and Ahmed, Sajad and Chouhan, Rekha and Gandhi, Sumit G. and Sharma, Nancy and Rai, Garima and Irshad, Faisal and Jamwal, Vijay Lakshmi and Paddar, Masroor Ahmad and Khan, Sameer Ullah and Malik, Fayaz and Ghosh, Debashish and Thakkar, Ghanshyam and Barik, S. K. and Tripathi, Prabhanshu and Satija, Yatendra Kumar and Mohanty, Sneha and Khan, Md Tauseef and Subudhi, Umakanta and Sen, Pradip and Kumar, Rashmi and Bhardwaj, Anshu and Gupta, Pawan and Sharma, Deepak and Tuli, Amit and Chaudhuri, Saumya Ray and Krishnamurthi, Srinivasan and Prakash, L. and Rao, V. Ch and Singh, B. N. and Chaurasiya, Arvindkumar and Chaurasiya, Meera and Bhadange, Mayuri and Likhitkar, Bhagyashree and Mohite, Sharada and Patil, Yogita and Kulkarni, Mahesh and Joshi, Rakesh and Pandya, Vaibhav and Mahajan, Sachin and Patil, Amita and Samson, Rachel and Vare, Tejas and Dharne, Mahesh and Giri, Ashok and Mahajan, Sachin and Paranjape, Shilpa and Sastry, G. Narahari and Kalita, Jatin and Phukan, Tridip and Manna, Prasenjit and Romi, Wahengbam and Bharali, Pankaj and Ozah, Dibyajyoti and Sahu, RaviKumar and Dutta, Prachurjya and Singh, Moirangthem Goutam and Gogoi, Gayatri and Tapadar, Yasmin Begam and Babu, Elapavalooru V. S. S. K. and Sukumaran, Rajeev K. and Nair, Aishwarya R. and Puthiyamadam, Anoop and Valappil, Prajeesh Kooloth and Prasannakumari, Adrash Velayudhan Pillai and Chodankar, Kalpana and Damare, Samir and Agrawal, Ved Varun and Chaudhary, Kumardeep and Agrawal, Anurag and Sengupta, Shantanu and Dash, Debasis} } @article {49119, title = {Durable and stable bifunctional Co3O4-based nanocatalyst for oxygen reduction/evolution reactions}, journal = {ACS Applied Nano Materials}, volume = {7}, year = {2024}, month = {FEB }, pages = {3620-3630}, type = {Article}, abstract = {

The oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) are recognized as the core reaction processes in regenerative energy storage and conversion systems. The design of cost-effective and high-performance bifunctional ORR/OER electrocatalysts (ECs) is very important for their substantial commercialization. Herein, sponge-like Co3O4 nanoparticles anchored on carbon (Sp-Co3O4/C) are successfully fabricated by a facile two-step solvothermal strategy for ORR/OER in an alkaline electrolyte. The Sp-Co3O4/C EC exhibits promising bifunctional ORR/OER activity with ORR onset potential (E onset = 0.88 V vs RHE), half-wave potential (E (1/2) = 0.75 V), limiting current density (j = -6.60 mA cm(-2)), OER onset potential (E-onset = 1.26 V), and OER overpotential for 10\% energy conversion (eta(10) = 0.38 V) in 0.1 M KOH. It demonstrates a significantly lower reversibility index (Delta E = E- j10 - E 1/2 = 0.86 V), comparable to standard Pt/C and RuO2 ECs. The superior ORR/OER performances of Sp-Co3O4/C EC can be ascribed to the synergistic contribution of a high electrochemically active surface area (48.33 m(2) g(-1)), BET surface area (131 m(2) g(-1)), the rich interfacial structure of the crystal facets (111), (220), and (311), and the abundant oxygen vacancies in the sponge-like morphology. Besides the methanol tolerance, accelerated durability and chronoamperometric test established excellent durability and stability in the electrocatalytic operation. This work offers insight into the development of high-performance ORR/OER ECs. [GRAPHICS]

}, keywords = {bifunctional, fuel cell, interface, oxygen evolution, oxygen reduction, Oxygen vacancy, Sp-Co3O4/C, sponge-like morphology}, doi = {10.1021/acsanm.3c04941}, author = {Chutia, Bhugendra and Chetry, Rashmi and Rao, Komateedi N. and Singh, Nittan and Sudarsanam, Putla and Bharali, Pankaj} } @article {49233, title = {Efficient glycolysis of used PET bottles into a high-quality valuable monomer using a shape-engineered MnO nanocatalyst}, journal = {CATALYSIS SCIENCE \& TECHNOLOGY}, volume = {14}, year = {2024}, month = {SEP }, type = {Journal Article}, keywords = {Catalyst, kinetics, Nanorods, Oxidation, Oxides, POLY(ETHYLENE-TEREPHTHALATE), WASTE}, issn = {2044-4753}, doi = {10.1039/d4cy00823e}, author = {Swapna, Bhattu and Singh, Nittan and Patowary, Suranjana and Bharali, Pankaj and Madras, Giridhar and Sudarsanam, Putla} }