On the Rapid Synthesis of Reduced Graphene Oxide via Ultrasound-Promoted Eco-Friendly Reduction Approach and Its Effects on Physicochemical Properties.

The common synthesis approach of reduced graphene oxide (rGO) using toxic reducing agents poses a threat to environmental pollution. This study used banana peel extract as a green reducing agent for the synthesis of rGO. Ultrasonication was assimilated to expedite the synthesis process. For comparison, rGO was also produced by reducing GO with hydrazine treatment under conventional stirring. Both morphological (SEM) and physicochemical (FTIR and XRD) studies have revealed that banana peel extract can reduce GO to rGO, although its reducing effect is much weaker compared to hydrazine. Despite this, the rGO produced using banana peel extract with the assimilation of ultrasonication technique has a greater interlayer spacing than that formed under the conventional stirring method. In terms of electrical properties, the electrical conductance of hydrazine-produced rGO (5.69 × 10-6 S) is higher than the banana peel extract-produced rGO (3.55 × 10-6 S - 4.56 × 10-6 S). Interestingly, it was found that most of the rGO produced by banana peel extract under ultrasound assistance has higher or comparable electrical conductance compared to the rGO produced by banana peel extract under stirring method. This implies the feasibility of using short-period ultrasound to replace conventional stirring in rGO synthesis.

Further to quantification of content, can reflectance spectroscopy determine the speciation of cobalt and nickel on a mine waste dump surface?

Toxic elements released due to mining activities are of the most important environmental concerns, characterised not only by their concentration, but also by their distribution among different chemical species, known as speciation. These are conventionally determined using chemical analysis and sequential extraction, which are expensive and time-demanding. In this study, the possibility of using visible-near-infrared-shortwave infrared (VNIR-SWIR) reflectance spectroscopy was investigated as an alternative technique to quantify the contents of cobalt (Co) and nickel (Ni) in soil samples collected from Sarcheshmeh copper mine waste dump surface, in Iran. As a novel approach, the capability of VNIR-SWIR spectroscopy was also investigated in speciation of those elements. Three machine learning (ML) techniques (i.e., extreme gradient boosting (EGB), random forest (RF) and support vector regression (SVR)) were used to make relationships between soil spectral responses and Co and Ni contents of the samples. For all ML algorithms, the best prediction accuracies were obtained by the models developed on the first derivative (FD) spectra (for Co: RMSEp values of 7.82, 8.03 and 9.22 mg·kg-1, and for Ni: RMSEp values of 9.88, 10.32 and 11.02 mg·kg-1, using EGB, RF and SVR, respectively). Spatial variability maps of elements showed relatively similar patterns between observed and predicted values. Correlation and ML (EGB, RF, SVR)-based methods revealed that the most important wavelengths for Co and Ni prediction were those related to iron oxides/hydroxides and clay minerals, as two main soil properties responsible for controlling their speciation. This study demonstrated that the EGB technique was successful at indirect quantification and spatial variability mapping of Co and Ni on the mine waste dump surface. In addition, it provided an inspiration for implementation of the VNIR-SWIR reflectance spectroscopy as a potentially fast and cost-effective method for speciation studies of toxic elements, especially in heterogeneous soil environments.

Prediction of the concentration of cadmium in agricultural soil in the Czech Republic using legacy data, preferential sampling, Sentinel-2, Landsat-8, and ensemble models.

The current study assesses and predicts cadmium (Cd) concentration in agricultural soil using two Cd datasets, namely legacy data (LD) and preferential sampling-legacy data (PS-LD), along with four streams of auxiliary datasets extracted from Sentinel-2 (S2) and Landsat-8 (L8) bands. The study was divided into two contexts: Cd prediction in agricultural soil using LD, ensemble models, 10 and 20 m spatial resolution of S2 and L8 (context 1), and Cd prediction in agricultural soil using PS-LD, ensemble models and 10 and 20 m spatial resolution of S2 and L8 (context 2). In context 1, ensemble 1, L8 with PS-LD was the cumulative optimal approach that predicted Cd in agricultural soil with a higher R2 value of 0.76, root mean square error (RMSE) of 0.66, mean absolute error (MAE) of 0.35, and median absolute error (MdAE) of 0.13. However, with R2 = 0.78, RMSE = 0.63, MAE = 0.34, and MdAE = 0.15, ensemble 1, S2 of PS-LD was the best prediction approach in predicting Cd concentration in agricultural soil in context 2. Overall, the predictions from both contexts indicated that ensemble 1 of S2 combined with PS-LD was the most appropriate and best model for Cd prediction in agricultural soil. The modeling approaches' uncertainty in both contexts was assessed using ensemble-sequential gaussian simulation (EnSGS), which revealed that the degree of uncertainty propagated in the study area was within 5% in both contexts. The combination of the PS dataset and the LD along with ensemble models and the remote sensing dataset, produced promising results. Nonetheless, the results demonstrated that the 20 m spatial resolution band dataset used in the prediction of Cd in agricultural soil outperformed the 10 m spatial resolution. When PS is combined with LD, an appropriate modeling approach, and a well-correlated remote sensing dataset are used, good results are obtained.

Prediction of the concentration of antimony in agricultural soil using data fusion, terrain attributes combined with regression kriging.

Potentially toxic elements in agricultural soils are primarily derived from anthropogenic and geogenic sources. This study aims to predict and map antimony (Sb) concentration in soil using multiple regression kriging in two distinct modeling approaches, namely Sb prediction using data fusion coupled with regression kriging (scenario 1) and Sb prediction using data fusion, terrain attributes, and regression kriging (scenario 2). Cubist regression kriging (cubist_RK), conditional inference forest regression kriging (CIF_RK), extreme gradient boosting regression kriging (EGB_RK) and random forest regression kriging (RF_RK) were the modeling techniques used in the estimation of Sb concentration in agricultural soil. The validation results suggested that in scenario 1, EGB_RK was the optimal modeling approach for Sb prediction in agricultural soil with root mean square error (RMSE) = 1.31 and mean absolute error (MAE) = 0.61, bias = 0.37, and high coefficient of determination R2 = 0.81. Similarly, the EGB_RK was also the optimal modeling approach in scenario 2, with the highest R2 = 0.76, RMSE = 0.90, bias = 0.06, and MAE = 0.48 values than the other regression kriging modeling approaches. The cumulative assessment suggested that the EGB_RK in scenario 2 yielded optimal results compared to the respective modeling approach in scenario 1. The uncertainty propagated by the modeling approaches in both scenarios indicated that the degree of uncertainty during the modeling process was distributed across the study area from a low to a moderate uncertainty level. However, cubist_RK in scenario 2 exhibited some elevated spots of uncertainty levels. As a result, the combination of data fusion, terrain attributes, and regression kriging modeling approaches produces optimal results with a high R2 value, minimal errors as well as bias. Furthermore, combining terrain attributes with data fusion is promising for reducing model error, bias and yielding high-accuracy predictions.

Optimal zinc level and uncertainty quantification in agricultural soils via visible near-infrared reflectance and soil chemical properties.

Zinc (Zn) is a vital element required by all living creatures for optimal health and ecosystem functioning. Therefore, several researchers have modeled and mapped its occurrence and distribution in soils. Nonetheless, leveraging model predictive performances while coupling information derived from visible near-infrared (Vis-NIR) and soils (i.e. chemical properties) to estimate potential toxic elements (PTEs) like Zn in agricultural soils is largely untapped. This study applies two methods to rapidly monitor Zn concentration in agricultural soil. Firstly, employing Vis-NIR and machine learning algorithms (MLAs) (Context 1) and secondly, applying Vis-NIR, soil chemical properties (SCP), and MLAs (Context 2). For the Vis-NIR information, single and combined pretreatment methods were applied. The following MLAs were used: conditional inference forest (CIF), partial least squares regression (PLSR), M5 tree model (M5), extreme gradient boosting (EGB), and support vector machine regression (SVMR) respectively. For context 1, the results indicated that M5-MSC (M5 tree model-multiplicative scatter correction) with coefficient of determination (R2) = 0.72, root mean square error (RMSE) = 21.08 (mg/kg), median absolute error (MdAE) = 13.69 and ratio of performance to interquartile range (RPIQ) = 1.63 was promising. Regarding context 2, CIF with spectral pretreatment and soil properties [CIF-DWTLOGMSC + SCP (conditional inference forest-discrete wavelet transformation-logarithmic transformation-multiplicative scatter correction-soil chemical properties)] yielded the best performance of R2 = 0.86, RMSE = 14.52 (mg/kg), MdAE = 6.25 and RPIQ = 1.78. Altogether, for contexts 1 and 2, the CIF-DWTLOGMSC + SCP approach (context 2) was the best Zn model outcome for the agricultural soil. The uncertainty map revealed a low to high error distribution in context 1, and a low to moderate distribution in context 2 for all models except CIF, which had some patches with high uncertainty. We conclude that a multiple optimization approach for modeling Zn levels in agricultural soils is invaluable and may provide fast and reliable information needed for area-specific decision-making.

Soil toxic elements determination using integration of Sentinel-2 and Landsat-8 images: Effect of fusion techniques on model performance.

Finding an appropriate satellite image as simultaneous as possible with the sampling time campaigns is challenging. Fusion can be considered as a method of integrating images and obtaining more pixels with higher spatial, spectral and temporal resolutions. This paper investigated the impact of Landsat 8-OLI and Sentinel-2A data fusion on prediction of several toxic elements at a mine waste dump. The 30 m spatial resolution Landsat 8-OLI bands were fused with the 10 m Sentinel-2A bands using various fusion techniques namely hue-saturation-value (HSV), Brovey, principal component analysis (PCA), Gram-Schmidt (GS), wavelet, and area-to-point regression kriging (ATPRK). ATPRK was the best method preserving both spectral and spatial features of Landsat 8-OLI and Sentinel-2A after fusion. Furthermore, the partial least squares regression (PLSR) model developed on genetic algorithm (GA)-selected laboratory visible-near infrared-shortwave infrared (VNIR-SWIR) spectra yielded more accurate prediction results compared to the PLSR model calibrated on the entire spectra. It was hence, applied to both individual sensors and their ATPRK-fused image. In case of the individual sensors, except for As, Sentinel-2A provided more robust prediction models than Landsat 8-OLI. However, the best performances were obtained using the fused images, highlighting the potential of data fusion to enhance the toxic elements' prediction models.

Predictors of Persistence of Anxiety, Hyperarousal Stress, and Resilience During the COVID-19 Epidemic: A National Study in Iran.

Background: The coronavirus pandemic can cause unprecedented global anxiety, and, in contrast, resilience can help the mental health of people in stressful situations. This study aimed to assess anxiety, hyperarousal stress, the resilience of the Iranian population, and their related factors during the coronavirus disease 2019 (COVID-19) epidemic. Methods: A cross-sectional study was conducted in 31 provinces in Iran between March 18 and 25, 2020. A four-part questionnaire, including the demographic information, the State-Trait Anxiety Inventory (STAI-y1-a 20-item standard questionnaire for obvious anxiety), the Connor-Davidson Resilience Scale (CD-RISC-a 25 item standard questionnaire), and the stress hyperarousal subscale from the Impact of Event Scale-Revised (IES-R), was used to collect data. The ordinal multivariable generalized estimating equation (GEE) model was used to identify correlates of the psychological factors mentioned above. The Fisher exact test was used to investigate the relationship between anxiety, stress, resilience, and the COVID-19 outbreak. All analyses were conducted with SPSS 26 and GIS 10.71. Results: The findings show that most people had moderate-to-severe anxiety (80.17%) and a high level of resilience (96.4%) during the COVID-19 epidemic. The majority of participants had a moderate level of stress (58.9%). The lowest and highest prevalences of psychiatric disorders were in Sistan and Baluchestan (3.14 cases per 100,000 people) and Semnan (75.9 cases per 100,000 people) provinces, respectively. Men and unmarried people were the only variables significantly associated with anxiety and resilience. Age, gender, and education were significantly associated with hyperarousal stress. Conclusion: The high and moderate levels of anxiety and stress in Iranians can have negative effects on the well-being and performance of the people and can lead to serious problems. Also, high resilience during negative life events (such as the COVID-19 pandemic) is associated with the well-being in the lives of people. The results of this study can be used in interventions and other psychological studies.

Investigating the Applicability of Molecular Dynamics Simulation for Estimating the Wettability of Sandstone Hydrocarbon Formations.

One of the techniques to increase oil recovery from hydrocarbon reservoirs is the injection of low salinity water. It is shown that the injection of low salinity water changes the wettability of the rock. However, there are argumentative debates concerning low salinity water effect on changing the wettability of the oil/brine/rock system in the oil reservoirs. In this regard, molecular dynamics simulation (MDS) as a tool to simulate the phenomena at the molecular level has been used for more than a decade. In this study, the Zisman plot (presented by KRUSS Company) was simulated through MDS, and then, contact angle experiments for n-decane interactions on the Bentheimer substrate in the presence of different concentrations of sodium ions were conducted. MDS was then used to simulate experiments and understand the wettability trend based on free-energy calculations. Hereafter, a new model was developed in this study to correlate free energies with contact angles. The developed model predicted the experimental results with high accuracy (R 2 ∼ 0.98). A direct relation was observed between free energy and water contact angle. In contrast, an inverse relation was noticed between the ion concentration and the contact angle such that an increase in the ion concentration resulted in a decrease in the contact angle and vice versa. In other terms, increasing brine ionic concentrations in the presence of n-decane is linked to a decrease in free energies and an increase in the wetting state of a sandstone. The comparison between the developed model's predicted contact angles and experimental observations showed a maximum deviation of 14.32%, which is in satisfactory agreement to conclude that MDS can be used as a valuable and economical tool to understand the wettability alteration process.

A review of copper concentrations in Iranian populations.

Copper contamination is increasing and can be a threat to human health. This review tries to summarize copper levels measured in humans in Iran. Persian databases such as SID, Magiran, and IranMedex and English databases such as Scopus, Pubmed, Science Direct, and the Google Scholar were searched using both English and Persian keywords. Twenty-six articles that measured the concentration of copper in human samples in Iran were included. According to the results of the reviewed studies, copper levels in some Iranian populations were higher than normal levels. These populations included pregnant women with preeclampsia, patients with oral cancer, patients with Giardiasis infection, patients with Parkinson's, children under the age of 12 years with ß-thalassemia major, pregnant women in the third trimester, and type 2 diabetic patients. Copper levels were less than normal, in patients with tuberculosis after treatment and post-menopausal women with osteopenia and osteoporosis. Also, copper concentrations in patients with tuberculosis, cutaneous leishmaniasis, brucellosis, and molybdenum unit workers were higher, and copper concentrations in patients with Pemphigus vulgaris and coronary artery disease were less than those of their controls, but all were in the normal range (70-140 µg/dL). The amount of copper adsorption in various teeth was different. High levels of copper have been reported in some Iranian populations and this can be a threat to human health. Monitoring copper levels in some Iranian populations is necessary.

Biological control of rice brown spot with native isolates of three Trichoderma species

Brown spot caused by Bipolaris oryzae is an important rice disease in Southern coast of Caspian Sea, the major rice growing region in Iran. A total of 45 Trichoderma isolates were obtained from rice paddy fields in Golestan and Mazandaran provinces which belonged to Trichoderma harzianum, T. virens and T. atroviride species. Initially, they were screened against B. oryzae by antagonism tests including dual culture, volatile and nonvolatile metabolites and hyperparasitism. Results showed that Trichoderma isolates can significantly inhibit mycelium growth of pathogen in vitro by producing volatile and nonvolatile metabolites Light microscopic observations showed no evidence of mycoparasitic behaviour of the tested isolates of Trichoderma spp. such as coiling around the B. oryzae. According to in vitro experiments, Trichoderma isolates were selected in order to evaluate their efficacy in controlling brown spot in glasshouse using seed treatment and foliar spray methods. Concerning the glasshouse tests, two strains of T. harzianum significantly controlled the disease and one strain of T. atroviride increased the seedling growth. It is the first time that the biological control of rice brown spot and increase of seedling growth with Trichoderma species have been studied in Iran.

Biological control of rice brown spot with native isolates of three Trichoderma species.

Brown spot caused by Bipolaris oryzae is an important rice disease in Southern coast of Caspian Sea, the major rice growing region in Iran. A total of 45 Trichoderma isolates were obtained from rice paddy fields in Golestan and Mazandaran provinces which belonged to Trichoderma harzianum, T. virens and T. atroviride species. Initially, they were screened against B. oryzae by antagonism tests including dual culture, volatile and nonvolatile metabolites and hyperparasitism. Results showed that Trichoderma isolates can significantly inhibit mycelium growth of pathogen in vitro by producing volatile and nonvolatile metabolites Light microscopic observations showed no evidence of mycoparasitic behaviour of the tested isolates of Trichoderma spp. such as coiling around the B. oryzae. According to in vitro experiments, Trichoderma isolates were selected in order to evaluate their efficacy in controlling brown spot in glasshouse using seed treatment and foliar spray methods. Concerning the glasshouse tests, two strains of T. harzianum significantly controlled the disease and one strain of T. atroviride increased the seedling growth. It is the first time that the biological control of rice brown spot and increase of seedling growth with Trichoderma species have been studied in Iran.