A study on managing plastic waste to tackle the worldwide plastic contamination and environmental remediation.

Over the past 50 years, the emergence of plastic waste as one of the most urgent environmental problems in the world has given rise to several proposals to address the rising levels of contaminants associated with plastic debris. Worldwide plastic production has increased significantly over the last 70 years, reaching a record high of 359 million tonnes in 2020. China is currently the world's largest plastic producer, with a share of 17.5%. Of the total marine waste, microplastics account for 75%, while land-based pollution accounts for responsible for 80-90%, and ocean-based pollution 10-20% only in overall pollution problems. Even at small dosages (10 µg/mL), microplastics have been found to cause toxic effects on human and animal health. This review examines the sources of microplastic contamination, the prevalent reaches of microplastics, their impacts, and the remediation methods for microplastic contamination. This review explains the relationship between the community composition and the presence of microplastic particulate matter in aquatic ecosystems. The interaction between microplastics and emerging pollutants, including heavy metals, has been linked to enhanced toxicity. The review article provided a comprehensive overview of microplastic, including its fate, environmental toxicity, and possible remediation strategies. The results of our study are of great value as they illustrate a current perspective and provide an in-depth analysis of the current status of microplastics in development, their test requirements, and remediation technologies suitable for various environments.

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.

Ziziphus mauritiana-derived nitrogen-doped biogenic carbon dots: Eco-friendly catalysts for dye degradation and antibacterial applications.

In this study, the naturally available Ziziphus Mauritiana was used as a bioresource for the preparation of bifunctional nitrogen doped carbon dots (N-CDs). The doping of nitrogen into the graphitic carbon skeleton and the in-situ formation of N-CDs were systematically identified by the various structural and morphological studies. The green fluorescent N-CDs were used as active catalysts for the removal of Safranin-O dye and achieved 79 % removal efficiency. Furthermore, the prepared N-CDs were used to evaluate antibacterial activity with four different bacterial species, such as Shigella flexneri, Staphylococcus aureus, Streptococcus pyogenes, and Klebsiella pneumoniae. Amongst these, the highest antimicrobial activity was achieved against Klebsiella pneumonia, with a maximum zone of inhibition of 14.6 ± 1.12 at a concentration of 100 g mL-1. Thus, the obtained results demonstrate the cost efficient bifunctional application prospects of N-CDs to achieve significant catalytic and antibacterial activities.

Characteristics estimation of natural fibre reinforced plastic composites using deep multi-layer perceptron (MLP) technique.

Polymer Matrix Composite (PMC/Plastic Composite) often referred to as Plastic Composite with Natural fibre reinforcement has a huge interest in industries to manufacture components for various applications including medical, transportation, sports equipment etc. In the universe, different types of natural fibres are available which can be used for the reinforcement in PMC/Plastic Composite. So, the selection of appropriate fibre for the PMC/Plastic Composite/Plastic composite is a challenging task, but it can be done using an effective metaheuristic or optimization techniques. But in this type of optimal reinforcement fibre or matrix material selection, the optimization is formulated based on any one of the parameters of the composition. Hence to analyse the various parameter of any PMC/Plastic Composite/Plastic Composite without real manufacturing, a machine learning technique is recommended. The conventional simple or single-layer machine learning techniques were not sufficient to emulate the exact real-time performance of the PMC/Plastic Composite. Thus, a deep multi-layer perceptron (Deep MLP) algorithm is proposed to analyse the various parameter of PMC/Plastic Composite with natural fibre reinforcement. In the proposed technique the MLP is modified by including around 50 hidden layers to enhance its performance. In every hidden layer, the basis function is evaluated and subsequently, the sigmodal function-based activation is calculated. The proposed Deep MLP is utilized to evaluate the various parameters of PMC/Plastic Composite Tensile Strength, Tensile Modulus, Flexural Yield Strength, Flexural Yield Modulus, Young's Modulus, Elastic Modulus and Density. Then the obtained parameter is compared with the actual value and the performance of the proposed Deep MLP is evaluated based on the accuracy, precision, and recall. The proposed Deep MLP attained 87.2%, 87.18%, and 87.22% of accuracy, precision, and recall. Ultimately the proposed system proves that the proposed Deep MLP can perform better for the prediction of various parameters of PMC/Plastic Composite with natural fibre reinforcement.

Assessment of Elastic-Plastic Fracture Behavior of Cortical Bone Using a Small Punch Testing Technique.

The fracture properties of cortical bone are directly coupled to its complex hierarchical structure. The limited availability of bone material from many anatomic locations creates challenges for assessing the effect of bone heterogeneity and anisotropy on fracture properties. The small punch technique was employed to examine the fracture behavior of cortical bone in terms of area under the curve values obtained from load-load point displacement behavior. Fracture toughness of cortical bone was also determined in terms of J-toughness values obtained using a compact tension (CT) test. Area under the curve values obtained from the small punch test were correlated with the J-toughness values of cortical bone. The effects of bone density and compositional parameters on area under the curve and Jtoughness values were also analyzed using linear and multiple regression analysis. Area under the curve and J-toughness values are strongly and positively correlated. Bone density and %mineral content are positively correlated with both area under the curve and J-toughness values. The multiple regression analysis outcomes support these results. Overall, the findings support the hypothesis that area under the curve values obtained from small punch tests can be used to assess the fracture behavior of cortical bone.