Conventional food supply chains are centralized in nature and possess challenges pertaining to a single point of failure, product irregularities, quality compromises, and loss of data. Numerous cases of food fraud, contamination, and adulteration are daily reported from multiple parts of India, suggesting the absolute need for an upgraded decentralized supply chain model. A country such as India, where its biggest strength is its demographic dividend, cannot afford to malnutrition a large population of its children by allowing them to consume contaminated and adulterated dairy products. In view of the gravity of the situation, we propose a blockchain-enabled supply chain platform for the dairy industry. With respect to the supply chain platform, the dairy products of choice include milk, cheese, and butter. Blockchain is one of the fastest growing technologies having widespread acceptance across multiple industry verticals. Blockchain possesses the power to transform traditional supply chains into decentralized, robust, transparent, tamper proof, and sustainable supply chains. The proposed supply chain platform goes beyond the aspect of food traceability and focuses on maintaining the nutritional values of dairy products, identification of adulteration and contamination in dairy products, the increasing economic viability of running a dairy farm, preventing counterfeit dairy products, and enhancing the revenue of the dairy company. The paper collates the mentioned functionalities into four distinct impact dimensions: social, economic, operations, and sustainability. The proposed blockchain-enabled dairy supply chain platform combines the use of smart contracts, quick response code (QR code) technology, and IoT and has the potential to redefine the dairy supply chains on socio-economic, operational, and sustainability parameters.

During the last few decades, ion exchange materials have evolved from laboratory tool to industrial products with significant technical and commercial impact. The current paper briefly summarizes the history of the development of the ion exchange materials. The paper defines the ion exchange materials and their types. The paper signifies the kinetics involved in the ion exchange process with description of factors affecting the rate of ion exchange. The mechanism of ion exchange has also been delineated through schematic diagram, illustrating that there are two types of diffusion, film and particle controlled diffusion. A brief of mathematical approach for kinetics of ion exchange has also been incorporated.

COVID-19, a disease caused by severe acute respiratory syndrome-corona virus-2 (SARS-CoV-2) has taken the lives of millions of people across the globe. Researchers are working on the development of different aids to cure and prevent the disease. Personal protection equipment (PPE) has become an essential prerequisite for healthcare workers. PPE kits are reported to provide sufficient protection against pathogens but their disposal can be devastating to environment. The proper disposal of PPE is being taken care of by the national authorities according to the guidelines provided by WHO.The plethora of disposed PPE kits will further increase the burden of polymer on our earth.This review presents a strategy to dispose the PPE kits by their conversion to alternative fuel.

In the past couple of years, the world has come to realize the importance of renewable sources of energy and the disadvantages of excessive use of fossil fuels. Numerous studies have been conducted to implicate the benefits of artificial intelligence in areas of green energy production. Artificial intelligence (AI) and machine learning algorithms are believed to be the driving forces behind the fourth industrial revolution and possess capabilities for interpreting non-linear relationships that exist in complex problems. Sustainable biofuels are derived from renewable resources such as plants, crops, and waste materials other than food crops. Unlike traditional fossil fuels such as coal and oil, biofuels are considered to be more sustainable and environmentally friendly. The work discusses the transesterification of jatropha oil into biodiesel using KOH and NaOH as alkaline catalysts. This research aims to examine and optimize the nonlinear relationship between transesterification process parameters (molar ratio, temperature, reaction time, and catalyst concentration) and biodiesel properties. The methodology employed in this study utilizes AI and machine learning algorithms to predict biodiesel properties and improve the yield and quality of biodiesel. Deep neural networks, linear regression, polynomial regression, and K-nearest neighbors are the algorithms implemented for prediction purposes. The research comprehensively examines the impact of individual transesterification process parameters on biodiesel properties, including yield, viscosity, and density. Furthermore, this research introduces the use of genetic algorithms for optimizing biodiesel production. The genetic algorithm (GA) generates optimal values for transesterification process parameters based on the desired biodiesel properties, such as yield, viscosity, and density. The results section presents the transesterification process parameters required for obtaining 72%, 85%, and 98% biodiesel yields. By leveraging AI and machine learning, this research aims to enhance the efficiency and sustainability of biodiesel production processes.

Chalcone is one of the most versatile scaffolds with wide range of applications. It is used as a precursor for diverse classes of organic molecules. Several methods are reported for the synthesis of chalcones using conventional catalysts. Nanocatalysts are gaining attention due to their efficiency, reproducibility, cost-effectiveness, reusability, etc. This review will give an epigrammatic status of the nanocatalyst used in the synthesis of chalcone. The review also discusses methods used for the synthesis of chalcones.