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In agricultural regions prone to dust storms, heavy metal contamination of soil and crops from airborne particulates poses significant risks to food safety and public health. This study has assessed the potential of machine learning models for predicting concentrations of toxic heavy metals like arsenic, chromium, and lead in dust from the agricultural Sistan region of southeastern Iran. This region experiences frequent dust storms mobilizing particulates from local dried lakes onto agricultural lands. The metals including nickel, copper, magnesium, cobalt, zinc, chromium, arsenic, and lead were measured in summer dust samples during 2012-2018 across 15 stations. Two hybrid models were developed combining group method of data handling (GMDH) and support vector regression (SVR) machine learning with harmony search optimization (H) so as to predict toxic metals arsenic, chromium, and lead using nickel, copper, magnesium, cobalt, and zinc inputs. Standard error maps were uncertainty higher in southern and western areas, and they are most impacted by dust storms. Results demonstrated that the hybrid GMDH + H and SVR + H models improved the accuracy of individual GMDH and SVR models in predicting heavy metals. The GMDH + H model performed the best for the lead with an agreement index (d-index) of 0.98, root mean square error (RMSE) of 2.87 ppm, normalized RMSE (NRMSE) of 0.12, and coefficient of determination (RR) of 0.96. The SVR + H model showed the highest accuracy for arsenic and chromium, obtaining d-index 0.96, RMSE 0.47 ppm, NRMSE 0.09, and RR 0.92 for arsenic, and d-index 0.96, RMSE 11.24 ppm, NRMSE 0.16, and RR 0.93 for chromium. Taylor's diagram and heatmap analysis confirmed the superiority of the hybrid techniques. This work demonstrates the utility of state-of-the-art computing for addressing complex environmental health challenges.

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Sodium montmorillonite nanoclay particles were modified by 3-aminopropyltrimetoxysilane using a physical vapor deposition method and applied for the simultaneous removal of Cd, Zn, Pb and Ni heavy metals from wastewater samples. Experiments were performed in both batch and column systems. Several parameters that influence the removal efficiency, such as the pH, contact time, amount of adsorbent, interfering ions, flow rate of sample passage and eluent solvent, were evaluated and optimized. The results showed that the adsorption isotherms obeyed Langmuir isotherms for all heavy metals. Also, interfering ions did not influenced the removal efficiency. Finally, modified nanoclay successfully could remove all heavy metals from real wastewater samples to near completeness.

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The ZnS quantum dots decorated SnO2 nanosheets were prepared by a hydrothermal synthesis method. The characteristic structure of ZnS QDs/SnO2 nanocomposites was analyzed using several instruments such as X-ray diffraction, transmittance electron microscopy, atomic force microscopy, X-ray photoelectron and UV-vis and photoluminescence spectroscopy. The average diameters of SnO2 nanosheets and ZnS QDs/SnO2 nanocomposites were 12.5 and 3.6 nm, respectively. The merits of sono-photo-Fenton treatment process were investigated using degradation of Roxithromycin. The process involved ultrasound and UV irradiation, and hydrogen peroxide generated in situ. The treatment performance of the US/UV/catalyst process was superior to the constituent processes and synergistic mechanisms in the US/UV/catalyst process were the result of the promotion of hydroxyl radical generation. For the constituent processes, the US/catalyst system showed to the best efficiency with used catalyst compared to the conventional Fenton reaction. It was also observed that the addition of catalyst to the test solution undergoing UV irradiation substantially improved Roxithromycin and clarithromycin degradation. The best experimental conditions for efficient CLA and RXM degradation in the US/UV/catalyst/H2O2 system were pH0 3, hydrogen peroxide concentration of 6 mmol L-1, ZnS QDs/SnO2 nanocomposites dose of 0.3 g L-1 and ultrasonic power of 75 W. The antibacterial experiment was investigated under visible light illumination and the ZnS QDs/SnO2 nanocomposite showed good efficiency as antibacterial.

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In this study, silver nanoparticles modified choline chloride functionalized graphene oxide (AgNPs-ChCl-GO) was synthesized using sonochemical method and utilized as a bioelectrochemical sensor for detection of celecoxib (CEL). The characterization studies were ultimately performed in order to acheive a more complete understanding of the morphological and structural features of the AgNPs-ChCl-GO using different techniques including FT-IR, AFM, FE-SEM, EDX, and XRD. AgNPs-ChCl-GO demonstrated a significant improvement in the reduction activity of CEL due to the enhancement in the current response compared to the bare carbon paste electrode (CPE). The optimum experimental conditions, were optimized using central composite design (CCD) methodology. The differential pulse voltammetry (DPVs) showed an expanded linear dynamic ranges of 9.6 × 10-9-7.4 × 10-7 M for celecoxib in Britton-Robinson buffer in pH 5.0 with. LOD (S/N = 3) and LOQ (S/N = 10) were obtained 2.51 × 10-9 M and 6.58 × 10-9 M respectively. AgNPs-ChCl-GO-carbon paste electrode exhibited suitable properties and high accuracy determination of celecoxib in the human plasma sample.

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The present study has reported an optimized fabrication and application of a novel PVA/TEOS/Schiff base nanofibers membrane as a highly sensitive copper (II) ions in aqueous environment. Here in, for first time, an ultrasound-assisted synthesized symmetric Schiff base has been immobilized on a hybrid polyvinyl alcohol (PVA) and TEOS using electrospinning technique for detection and filtration of copper ions. For this purpose, various working parameters were evaluated and finally the optimized nano fibers membrane was synthesized with 72 nm thickness and PVA/TEOS/Schiff base ratio of (wt%) 8:6:1. The optimized sample named PTLNFM has been employed successfully as an ultra sensitive chemosensor for Cu (II) detection in real samples. The immobilized Schiff base used as a chelating agent could detect copper (II) in the range from 9.34 × 10-8 to 1.15 × 10-5 mol L-1 with the following correlation equation: Absorbance = 0.066 [Cu2+] × 10-6 + 0.095 and R2 = 0.992 and LOD of 1.27 × 10-8 mol L-1 which was lower than most of the reported detection limits in the previous literatures. Validity of this method has been carried out by analysis of Cu2+ in real samples with satisfying recoveries of over 96.11-99.24%.The developed membrane could be offered for diverse use such as medical or industrial applications.

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The applicability of C44, B22N22, Ge44, and Al22P22 nanocages, as well as variants of those nanocages with an adsorbed halogen atom, as high-performance anode materials in Li-ion, Na-ion, and K-ion batteries was investigated theoretically via density functional theory. The results obtained indicate that, among the nanocages with no adsorbed halogen atom, Al22P22 would be the best candidate for a novel anode material for use in metal-ion batteries. Calculations also suggest that K-ion batteries which utilize these nanocages as anode materials would give better performance and would yield higher cell voltages than the corresponding Li-ion and Na-ion batteries with nanocage-based anodes. Also, the results for the nanocages with an adsorbed halogen atom imply that employing them as anode materials would lead to higher cell voltages and better metal-ion battery performance than if the nanocages with no adsorbed halogen atom were to be used as anode materials instead. Results further implied that nanocages with an adsorbed F atom would give higher cell voltages and better battery performance than nanocages with an adsorbed Cl or Br atom. We were ultimately able to conclude that a K-ion battery that utilized Al21P22 with an adsorbed F atom as its anode material would afford the best metal-ion battery performance; we therefore propose this as a novel highly efficient metal-ion battery. Graphical abstract The results of a theoretical investigation indicated that Al22P22 is a better candidate for a high-performance anode material in metal-ion batteries than Ge44 is. Calculations also showed that K-ion batteries with nanocage-based anodes would produce higher cell voltages and perform better than the equivalent Li-ion and Na-ion batteries with nanocage-based anodes, and that anodes based on nanocages with an adsorbed F atom would perform better than anodes based on nanocages with an adsorbed Cl or Br atom.

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Plants are unique sources of useful metabolites. Plant essential oils display a wide range of antimicrobial effects against various pathogens. Here, we studied the essential oil from the seeds of Carum copticum. We monitored aflatoxin by high-performance liquid chromatography. Results show that Carum copticum essential oil inhibits Asergillus parasiticus growth and prevents aflatoxin production. The half-maximal inhibitory concentration (IC50) is 127.5 µg mL-1 for aflatoxin B1 and 23.22 µg mL-1 for aflatoxin G1. Our findings show that Carum copticum essential oil is a potential candidate for the protection of foodstuff and feeds from toxigenic fungus growth and their subsequent aflatoxin contamination.