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.

Electrochemical-based approaches for the treatment of forever chemicals: Removal of perfluoroalkyl and polyfluoroalkyl substances (PFAS) from wastewater.

Electrochemical based approaches for the treatment of recalcitrant water borne pollutants are known to exhibit superior function in terms of efficiency and rate of treatment. Considering the stability of Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are designated as forever chemicals, which generating from various industrial activities. PFAS are contaminating the environment in small concentrations, yet exhibit severe environmental and health impacts. Electro-oxidation (EO) is a recent development that treats PFAS, in which different reactive species generates at anode due to oxidative reaction and reductive reactions at the cathode. Compared to water and wastewater treatment methods those being implemented, electrochemical approaches demonstrate superior function against PFAS. EO completely mineralizes (almost 100 %) non-biodegradable organic matter and eliminate some of the inorganic species, which proven as a robust and versatile technology. Electrode materials, electrolyte concentration pH and the current density applying for electrochemical processes determine the treatment efficiency. EO along with electrocoagulation (EC) treats PFAS along with other pollutants from variety of industries showed highest degradation of 7.69 mmol/g of PFAS. Integrated approach with other processes was found to exhibit improved efficiency in treating PFAS using several electrodes boron-doped diamond (BDD), zinc, titanium and lead based with efficiency the range of 64 to 97 %.

Chlorpyrifos and Endosulfan degradation studies in an annular slurry photo reactor.

TiO2 is one of those compounds which are highly used in photocatalytic degradation of substrates using UV radiation. The substrates are degraded oxidatively and hence finds an important position in advanced oxidation for water/wastewater treatment processes. The thrust of this research was to evaluate the effectiveness of Heterogeneous Photocatalysis (HP) technique, for the removal of pesticides from water/wastewater. The photo-catalytic degradation of two pesticides, widely used in India, viz., Endosulfan (ES) and Chlorpyriphos (CPS) was studied in an annular slurry photo reactor under UVillumination at 254nm. Results revealed that the degradation rate is significantly affected by the initial pesticide concentration, pH of the solution and catalyst concentration. Batch degradation studies on Endosulphan and Chlorpyrifos were conducted in the concentration range from 5 to 25mg/L at a pH ranging from 3.5 to 10.5 and at a catalyst loading of 0.5-2g/L. Endosulphan removal efficiency was about 80-99% and chlorpyrifos removal efficiency was about 84-94%. L-H rate constants were determined using L-H kinetics. High removal efficiencies obtained (80-99%) indicate the effectiveness of this process and its potential for practical application.