A novel method development and validation for determination of 2,4,6-Trinitrotoluene and its metabolites on LC-MS/MS

Design and optimization of a cost-effective paper-based voltammetric sensor for the determination of trinitrotoluene (TNT) utilizing cysteamine-linked Fe3O4 @Au nanocomposite

This paper presents the development and optimization of a cost-effective paper electrochemical sensor for the detection of TNT using Fe3O4-Au core-shell nanoparticles modified with cysteamine (Fe3O4@Au/CA). The sensor was constructed by modifying a graphite paste with the aforementioned nanoparticles, which facilitated the formation of a Meisenheimer complex between cysteamine and TNT as an electron donor and an electron acceptor, respectively. The central composite design was employed to optimize four key parameters pH, modifier percentage, contact time, and buffer type to enhance the performance of the sensor. The detection limit was found to be 0.5 nM of TNT, while the linear range of the electrode response spanned from 0.002 μM to 10 μM. The simplicity and low cost of the sensor make it highly attractive for practical applications, particularly in scenarios where rapid and on-site TNT detection is required.

Biological 2,4,6-trinitrotoluene removal by extended aeration activated sludge: optimization using artificial neural network

Serious health issues can result from exposure to the nitrogenous pollutant like 2,4,6-trinitrotoluene (TNT), which is emitted into the environment by the munitions and military industries, as well as from TNT-contaminated wastewater. The TNT removal by extended aeration activated sludge (EAAS) was optimized in the current study using artificial neural network modeling. In order to achieve the best removal efficiency, 500 mg/L of chemical oxygen demand (COD), 4 and 6 h of hydraulic retention time (HRT), and 1-30 mg/L of TNT were used in this study. The kinetics of TNT removal by the EAAS system were described by the calculation of the kinetic coefficients K, Ks, Kd, max, MLSS, MLVSS, F/M, and SVI. Adaptive neuro fuzzy inference system (ANFIS) and genetic algorithms (GA) were used to optimize the data obtained through TNT elimination. ANFIS approach was used to analyze and interpret the given data, and its accuracy was around 97.93%. The most effective removal efficiency was determined using the GA method. Under ideal circumstances (10 mg/L TNT concentration and 6 h), the TNT removal effectiveness of the EAAS system was 84.25%. Our findings demonstrated that the artificial neural network system (ANFIS)-based EAAS optimization could enhance the effectiveness of TNT removal. Additionally, it can be claimed that the enhanced EAAS system has the ability to extract wastewaters with larger concentrations of TNT as compared to earlier experiments.