Application of bimetallic Al-doped ZnO nano-assembly for heavy metal removal and decontamination of wastewater

Breaking boundaries: Artificial intelligence for pesticide detection and eco-friendly degradation

Pesticides are extensively used agrochemicals across the world to control pest populations. However, irrational application of pesticides leads to contamination of various components of the environment, like air, soil, water, and vegetation, all of which build up significant levels of pesticide residues. Further, these environmental contaminants fuel objectionable human toxicity and impose a greater risk to the ecosystem. Therefore, search of methodologies having potential to detect and degrade pesticides in different environmental media is currently receiving profound global attention. Beyond the conventional approaches, Artificial Intelligence (AI) coupled with machine learning and artificial neural networks are rapidly growing branches of science that enable quick data analysis and precise detection of pesticides in various environmental components. Interestingly, nanoparticle (NP)-mediated detection and degradation of pesticides could be linked to AI algorithms to achieve superior performance. NP-based sensors stand out for their operational simplicity as well as their high sensitivity and low detection limits when compared to conventional, time-consuming spectrophotometric assays. NPs coated with fluorophores or conjugated with antibody or enzyme-anchored sensors can be used through Surface-Enhanced Raman Spectrometry, fluorescence, or chemiluminescence methodologies for selective and more precise detection of pesticides. Moreover, NPs assist in the photocatalytic breakdown of various organic and inorganic pesticides. Here, AI models are ideal means to identify, classify, characterize, and even predict the data of pesticides obtained through NP sensors. The present study aims to discuss the environmental contamination and negative impacts of pesticides on the ecosystem. The article also elaborates the AI and NP-assisted approaches for detecting and degrading a wide range of pesticide residues in various environmental and agrecultural sources including fruits and vegetables. Finally, the prevailing limitations and future goals of AI-NP-assisted techniques have also been dissected.

Synthesis of iron oxide nanoparticles mediated by Camellia sinensis var. Assamica for Cr(VI) adsorption and detoxification

Environment-benign synthesis of nanoparticles (NPs) are of great importance. Plant-based polyphenols (PPs) are electron donor analytes for the synthesis of metal and metal oxide NPs. This work produced and investigated iron oxide nanoparticles (IONPs) from PPs of tea leaves of Camellia sinensis var. assamica for Cr(VI) removal. The conditions for IONPs synthesis were using RSM CCD and found to be optimum at a time of 48 min, temperature of 26 °C, and iron precursors/leaves extract ratio (v/v) of 0.36. Further, these synthesized IONPs at a dosage of 0.75 g/L, temperature of 25 °C, and pH 2 achieved a maximum of 96% Cr(VI) removal from 40 mg/L of Cr(VI) concentration. The exothermic adsorption process followed the pseudo-second-order model, and Langmuir isotherm estimated a remarkable maximum adsorption capacity (Q_m) of 1272 mg g^-1 of IONPs. The proposed mechanistic for Cr(VI) removal and detoxification involved adsorption and its reduction to Cr(III), followed by Cr(III)/Fe(III) co-precipitation.

Novel metal based nanocomposite for rapid and efficient removal of lead from contaminated wastewater sorption kinetics, thermodynamics and mechanisms

A sol–gel method was utilized to prepare a novel nanocomposite adsorbent (nMgO/bentonite) and was tested for Pb(II) removal from aqueous solutions. The produced nanocomposite was investigated using, SEM–EDX, XRD, and FTIR analyses before and after Pb adsorption. Adsorption equilibrium and kinetic experiments were run in batch system under different conditions of pH, adsorbent dose, competitive cations, contact time and temperature. The results exhibited rapid Pb(II) adsorption by the nanocomposite in the first five min. Experimental lead adsorption equilibrium and kinetics data fitted well to Langmuir and power function models, respectively as indicated from the lowest standard error (SE) values. The calculated Langmuir maximum adsorption capacity (q_max) value of nanocomposite (75 mg g⁻¹) was 4.5 times higher than that of bentonite (16.66 mg g⁻¹). Moreover, the highest quantity of Pb(II) uptake was achieved at temperature of 307 K and pH 9. The Langmuir sorption capacity of the nanocomposite for Pb(II) increased from 75 to 145 mg g⁻¹ with increasing temperature from 287 to 307 K. The thermodynamic parameters of Pb(II) adsorption by the nanocomposite affirm the spontaneous and endothermic nature of the adsorption process. Lead adsorption mechanisms by the nanocomposite were proposed and discussed.

Enhanced melanoidin removal by amine-modified Phyllanthus emblica leaf powder

Melanoidins are classified as hazardous colouring and polluting biopolymers, which are generated in very large amounts in molasses-based distillery effluent. In this study, melanoidin was removed through adsorption using amine surface-modified Phyllanthus emblica leaf powder (PELP) as a low-cost natural adsorbent. The amine-modified adsorbents were prepared by forming self-assembled monolayers (SAMs). The pzc of melanoidin and anime-modified PELP were found to be 6.9 and 3.8, respectively. RSM-CCD was used to optimize the environmental conditions considering adsorbent doses (0.2-2 % w/v), pH (3-11) and temperature (25-55 °C). A complete decolourization of melanoidin (98.50 ± 1 %) was observed at the optimized conditions (44.0 °C, pH = 5.93 and dose = 1.34 % w/v) along with 93.4 ± 0.2 % of COD reduction. The surface modification enhanced the maximum adsorption capacity to 616.2 mg g^-1 i.e. 2.5 folds. The modified adsorbent also resulted in colour removal and COD reduction as 91 ± 3 and 84 ± 2 %, respectively from a real spentwash sample.

The adsorptive behaviour of kaolinite to sodium dodecyl benzene sulphonate and the structural variation of kaolinite

Analysis of the adsorptive behaviour of kaolinite to sodium dodecyl benzene sulphonate (SDBS) at different concentrations can provides a basis for selecting the best concentration. The adsorptive capacity and adsorptive behaviour of kaolinite to SDBS at different concentrations were studied using ultraviolet spectrophotometer, pseudo-first-order adsorption kinetics model, and pseudo-second-order adsorption kinetics model. Scanning electron microscopy with energy dispersive spectrometry (SEM–EDS), X-ray diffraction (XRD), and infrared spectroscopy (FTIR) were used to study the variation characteristics of surface structure, crystallinity indices, and main functional groups on kaolinite before, and after, adsorption. The results show that as the SDBS concentration increase, the adsorptive capacity of kaolinite to SDBS increase. The adsorption process can be accurately fitted by the pseudo-secondary adsorption kinetic model, which means the adsorptive behaviour was mainly chemical in origin. The adsorption of SDBS by kaolinite mainly occurs on the surface. The solidification, lamellar aggregation, and crystallinity index of kaolinite are more obvious after the adsorption of SDBS, but the interlayer spacing of kaolinite did not change to any significant. After the adsorption of SDBS, the intensity ratio of 1000–1008 cm⁻¹ bands changed significantly, indicating the change of the chemical environment, and the adsorptive behaviour was chemical.

Evolution of adsorption process for manganese removal in water via agricultural waste adsorbents

Manganese has recently been a topic of interest among researchers, particularly when 1,752 million tonnes of manganese are expected to be produced by the steel industry in 2020. Manganese discharges from industrial effluents have increased manganese contamination in water sources. Its concentrations of more than 0.2 mg/L in the water sources could have negative impacts on human health and the aquatic ecosystem. Thereby, the available water treatment processes face challenges in effectively removing manganese at low cost. In response to these challenges, adsorption has emerged as one of the most practical water treatment processes for manganese removal. In particular, agricultural waste adsorbents received a lot of attention owing to their low cost and high efficiency (99%) in the removal of manganese. Therefore, this paper reviews the removal of manganese by adsorption process using agricultural waste adsorbents. The factors affecting the adsorption process, the mechanisms, and the performances of the adsorbents are elucidated in detail.