A review of the prospects, efficacy and sustainability of nanotechnology-based approaches for oil spill remediation.

Numerous marine oil spill incidents and their environmental catastrophe have raised the concern of the research community and environmental agencies on the topic of the offshore crude oil spill. The oil transport through oil tankers and pipelines has further aggravated the risk of the oil spill. This has led to the necessity to develop an effective, environment-friendly, versatile oil spill clean-up strategy. The current review article analyses various nanotechnology-based methods for marine oil spill clean-up, focusing on their recovery rate, reusability and cost. The authors weighed the three primary factors recovery, reusability and cost distinctively for the analysis based on their significance in various contexts. The findings and analysis suggest that magnetic nanomaterials and nano-sorbent have been the most effective nanotechnology-based marine oil spill remediation techniques, with the magnetic paper based on ultralong hydroxyapatite nanowires standing out with a recovery rate of over 99%. The chitosan-silica hybrid nano-sorbent and multi-wall carbon nanotubes are also promising options with high recovery rates of up to 95-98% and the ability to be reused multiple times. Although the photocatalytic biodegradation approach and the nano-dispersion method do not offer benefits for recovery or reusability, they can nevertheless help lessen the negative ecological effects of marine oil spills. Therefore, careful evaluation and selection of the most appropriate method for each marine oil spill situation is crucial. The current review article provides valuable insights into the current state of nanotechnology-based marine oil spill clean-up methods and their potential applications.

Thermal degradation model of used surgical masks based on machine learning methodology.

Background: The COVID-19 pandemic has leveraged facial masks to be one of the most effective measures to prevent the spread of the virus, which thereby has exponentially increased the usage of facial masks that lead to medical waste mismanagements which pose a serious threat to life. Thermal degradation or pyrolysis is an effective treatment method for the used facial mask wastes and this study aims to investigate the thermal degradation of the same. Methods: Predicted the TGA experimental curves of the mask components using a Machine Learning model known as Artificial Neural Network (ANN). Significant findings: Three different parts of the mask namely- ribbon, body, and corner were separated and used for the analysis. The thermal degradation behavior is studied using Thermogravimetric Analysis (TGA) and this is crucial for determining the reactivity of the individual mask components as they are subjected to a range of temperatures. Using the curves obtained from TGA, kinetic parameters such as Activation energy (E) and Pre-exponential factor (A) were estimated using the Coats-Redfern model-fitting method. Using the determined kinetic parameters, thermodynamic quantities such as a change in Enthalpy (ΔH), Entropy (ΔS), and Gibbs-Free energy (ΔG) were also calculated. Since TGA is a costly and time-consuming process, this study attempted to predict the TGA experimental curves of the mask components using a Machine Learning model known as Artificial Neural Network (ANN). The dataset obtained at a heating rate of 10°C/min was used to train the 3 different neural networks corresponding to the mask components and it showed an excellent agreement with experimental data (R2 > 0.99). Through this study, a complex chemical process such as thermal degradation was modelled using Machine Learning based on available experimental parameters without delving into the intricacies and complexities of the process.