Unveiling the positive effect of mineral induced natural organic matter (NOM) on catalyst properties and catalytic dechlorination performance: An experiment and DFT study.

Herein, we report the significant effects of natural organic matter contained in natural zeolite (Z-NOM) on the physicochemical characteristics of a Ni/Fe@natural zeolite (NF@NZ) catalyst and its decontamination performance toward the dechlorination of trichloroethylene (TCE). Z-NOM predominantly consists of humic-like substances and has demonstrable utility in the synthesis of bimetallic catalysts. Compared to NF@NZ600C (devoid of Z-NOM), NF@NZ had increased dispersibility and mobility and showed significant enhancement in the catalytic dechlorination of TCE owing to the encapsulation of Ni0/Fe0 nanoparticles by Z-NOM. The results of corrosion experiments, spectroscopic analyses, and H2 production experiments confirmed that Ni0 acted as an efficient cocatalyst with Fe0 to enhance the dechlorination of TCE to ethane, and Z-NOM-capped Ni0 showed improved adsorption of TCE and atomic hydrogen on their reactive sites and oxidation resistance. The density functional theory (DFT) studies have substantiated the improved adsorption of TCE due to the presence of NOM (especially by COOH structure) and the enhanced charge density at the Ni site in the Ni/Fe bimetal alloy for the stronger adsorption of hydrogen atoms that ultimately enhanced the TCE reduction reaction. These findings illustrate the efficiency of NOM containing natural minerals toward the synthesis of bimetallic catalysts for practical applications.

Development of Temporal Model for Forecasting of Helicoverpa armigera (Noctuidae: Lepidopetra) Using Arima and Artificial Neural Networks.

Helicoverpa armigera (Hübner) (Noctuidae: Lepidopetra) is a polyphagous pest of major crops grown in India. To prevent the damage caused by H. armigera farmers rely heavily on insecticides of diverse groups on a regular basis which is not a benign practice, environmentally and economically. To provide more efficient and accurate information on timely application of insecticides, this research was aimed to develop a forecast model to predict population dynamics of pod borer using Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN). The data used in this study were collected from the randomly installed sex pheromone traps at International Crops Research Institute for the Semi-arid Tropics (ICRISAT), Patancheru, Hyderabad. Several ARIMA (p, d, q) (P, D, Q) and ANN models were developed using the historical trap catch data. ARIMA model (1,0,1), (1,0,2) with minimal BIC, RMSE, MAPE, MAE, and MASE values and higher R2 value (0.53) was selected as the best ARIMA fit model, and neural network (7-30-1) was found to be the best fit to predict the catches of male moths of pod borer from September 2021 to August 2023. A comparative analysis performed between the ARIMA and ANN, shows that the ANN based on feed forward neural networks is best suited for effective pest prediction. With the developed ARIMA model, it would be easier to predict H. armigera adult population dynamics round the year and timely intervention of control measures can be followed by appropriate decision-making schedule for insecticide application.

Recent advances in photocatalytic treatment of pollutants in aqueous media.

Photocatalysis can be an excellent solution for resolving the world's energy and environmental problems. It has a wide range of applications for the decontamination of diverse hazardous pollutants in aqueous media. Technological progress in this research field has been achieved toward the improvement of the solar sensitivity to enhance the efficiency of pollutant decontamination. As a result, various strategies have been introduced to upgrade photocatalytic performance with the modification of prototypical photocatalyst such as doping, dye sensitization, semiconductor coupling, mesoporous supports, single site, and nano-based catalysts. In this review, a brief survey is presented to describe those strategies based on the evaluation made against various pollutants (such as pharmaceuticals, pesticides, heavy metals, detergents, and dyes) in aqueous media.