Commercialization potential of PET (polyethylene terephthalate) recycled nanomaterials: A review on validation parameters.

Polyethylene Terephthalate (PET) is a polymer which is considered as one of the major contaminants to the environment. The PET waste materials can be recycled to produce value-added products. PET can be converted to nanoparticles, nanofibers, nanocomposites, and nano coatings. To extend the applications of PET nanomaterials, understanding its commercialization potential is important. In addition, knowledge about the factors affecting recycling of PET based nanomaterials is essential. The presented review is focused on understanding the PET commercialization aspects, keeping in mind market analysis, growth drivers, regulatory affairs, safety considerations, issues associated with scale-up, manufacturing challenges, economic viability, and cost-effectiveness. In addition, the paper elaborates the challenges associated with the use of PET based nanomaterials. These challenges include PET contamination to water, soil, sediments, and human exposure to PET nanomaterials. Moreover, the paper discusses in detail about the factors affecting PET recycling, commercialization, and circular economy with specific emphasis on life cycle assessment (LCA) of PET recycled nanomaterials.

Optimizing the selection of natural fibre reinforcement and polymer matrix for plastic composite using LS-SVM technique.

The manufacturing sector is paying close attention to plastic matrix composites (PMCs) reinforced with natural fibres for improving their products. Due to the fact that PMC reinforced with naturally occurring fibres is more affordable and has superior mechanical qualities. Based on the application material requirements, An important step in the production of PMC is choosing the right natural fibres for reinforcing and determining how much of each. This investigation aimed that Artificial Intelligence (AI) or soft computing based approaches are used to determine the right amount of natural fibres in PMCs to make the manufacturing process simpler. However, techniques in the literature are not concentrated on finding suitable material. Hence in this investigation, a local search with support vector machine (LS-SVM) optimization technique is proposed for the optimal selection of appropriate proportions of suitable fibres. Modelling of the Proposed LS-SVM Optimization was demonstrated. In this proposed technique around four kinds of polymers/plastics and 14 natural fibres are considered, which are optimized in various proportions. The optimization performance is evaluated based on the tensile strength, flexural yield strength and flexural yield modulus. The proposed LS-SVM Optimization was evacuated by developing solutions for medical applications (Case 1), Transportation applications (Case 2) and other notable applications (Case 3) in terms of tensile and flexural properties of the material. The maximum flexure stress in case 1, case 2, and case 3 is observed as 53 MPa, 45 MPa and 26 MPa respectively. Similarly, the maximum flexure stress in case 1, case 2, and case 3 is observed as 53 MPa, 45 MPa and 26 MPa respectively. Hence the proposed method recommended for choosing optimal decision on the choice of fiber and their quantity in the composite matrix.

Waste plastics derived nickel-palladium alloy filled carbon nanotubes for hydrogen evolution reaction.

Carbon nanotubes (CNTs) composed of bimetallic nickel-palladium (NiPd) nanoparticles encapsulated in graphitic carbon shells (NdPd@CNT) are prepared by the chemical vapour deposition method using waste polyethylene terephthalate (PET) plastic carbon sources and NiPd-decorated carbon sheets (NiPd@C) catalyst. The characterization results reveal that the face-centered cubic crystalline (fcc)-structured NiPd bimetallic alloy nanoparticles are encased by thin carbon nanotubes. The bimetallic synergism of NiPd nanoparticles actuates the outer CNT layers and accelerates the electrical conductivity, stimulating the electrochemical activity toward an effective hydrogen evolution reaction (HER). By virtue of the collective individualities of highly conductive aligned carbon walls and bimetallic active sites, the NiPd@CNT-equipped HER delivers a minimum overpotential of 87 mV and a Tafel slope value of 95 mV dec-1. The existing intact contact between NiPd and CNT facilitates continuous electron and ion transportation and firm stability toward long-term hydrogen production in HER. Notably, the NiPd@CNT reported here produces excellent electrochemical activity with minimal charge transference resistance, substantiating the efficacy of NiPd@CNT for futuristic green hydrogen production.

COVID-19: Role of Robotics, Artificial Intelligence and Machine Learning During the Pandemic.

The outbreak of COVID-19 has led to a global health emergency. Emerging from China, it has now been declared as a pandemic. Owing to the fast pace at which it spreads, its control and prevention have now become the greatest challenge. The inner structural analysis of the virus is an important area of research for the invention of the potential drug. The countries are following different strategies and policies to fight against COVID-19; various schemes have also been employed to cope up with the economic crisis. While the government is struggling to balance between the public health sector and the economic collapse, the researchers and medicine practitioners are inclined towards obtaining treatment and early detection of the deadly disease. Further, the impact of COVID-19 on Dentistry is alarming and posing severe threats to the professionals as well. Now, the technology is helping the countries fight against the disease. ML and AI based applications are substantially aiding the process of detection and diagnosis of novel coronavirus. Science of Robotics is another approach followed with an aim to improve patient care.