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This research utilizes a precursor solution of SnCl 2 and SC(NH 2 ) 2 at varying sulfur concentrations of 0.5, 0.6 and 0.7 M to fabricate Sn x S y films through the nebulizer spray technique to establish the role of sulfur concentration on its phase formation. The crystallographic structure, morphology, optical and electrical properties of the deposited films were analyzed using X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, UV-Vis spectroscopy and four probe measurements. X-ray diffraction analysis revealed the crystalline phases present in the films, with distinct peaks corresponding to various phases of Sn x S y , indicating the successful incorporation of sulfur at different concentrations. Scanning electron microscopy provided insights into the surface morphology, demonstrating uniform film deposition and varying grain sizes and shapes influenced by sulfur concentration. Energy dispersive X-ray spectroscopy confirmed the elemental composition of the films, with the ratio of tin to sulfur aligning with the initial concentrations in the precursor solution. Optical measurements through UV-Vis spectroscopy indicated enhanced light absorption properties with increasing sulfur content, the value of band gap reaching a minimum of 1.31 eV for higher molar concentration of sulfur which is found to be beneficial for photovoltaic applications. Finally, four probe measurements determined a maximum electrical conductivity of 2.18 × 10 − 8 Ω -1 cm -1 and a highest charge carrier mobility of 6.54 × 10 15 cm -1 for 0.7 M revealing the influence of sulfur concentration variation on the electrical properties of the prepared films. The findings suggest that tuning sulfur concentration can optimize the properties of Sn x S y films to enhance its performance in photovoltaic applications.

Portland cement is a critical material widely used in the construction industry, where its crystallization and microstructure are key factors determining its physical and mechanical properties. This study investigated the effect of sulfur on the crystallization and microstructure of Portland cement. Sulfur acts as either an additive or an impurity during the cement production process, influencing the crystal size, distribution, and microstructure formation of major hydration products such as C3S (tricalcium silicate), C2S (dicalcium silicate), C3A (tricalcium aluminate), and C4AF (tetracalcium aluminoferrite). Through quantitative and qualitative evaluation using XRD, SEM, and EPMA analytical techniques, this study examined changes in the hydration characteristics, crystal structure, and microstructure of Portland cement with varying sulfur concentrations. The results revealed that increased sulfur content promotes the crystal growth of C3A and the formation of ettringite, which alters the density of the structure during the early stages of hydration and affects its long-term strength properties. These findings suggest that controlling the sulfur content plays a significant role in optimizing the performance and durability of Portland cement. This study highlights the potential for developing high-performance cement by regulating sulfur levels during the production process, contributing to advancements in construction materials.

Sulphur dioxide is one of the most common air pollutants, forming acid rain and other harmful substances in the atmosphere, which can further damage our ecosystem and cause respiratory diseases in humans. Therefore, it is essential to monitor the concentration of sulphur dioxide produced in industrial processes in real-time to predict the concentration of sulphur dioxide emissions in the next few hours or days and to control them in advance. To address this problem, we propose an AR-LSTM analytical forecasting model based on ARIMA and LSTM. Based on the sensor’s time series data set, we preprocess the data set and then carry out the modeling and analysis work. We analyze and predict the proposed analysis and prediction model in two data sets and conduct comparative experiments with other comparison models based on the three evaluation indicators of R 2 , RMSE and MAE. The results demonstrated the effectiveness of the AR-LSTM analytical prediction model; Finally, a forecasting exercise was carried out for emissions in the coming weeks using our proposed AR-LSTM analytical forecasting model.

Soil samples from different locations with varied soybean cultivation histories were taken from arable fields in 2018 in East Germany and Poland (Lower Silesia) to evaluate the specific microsymbionts of the soybean, Bradyrhizobium japonicum, one to seven years after inoculation. Soybeans were grown in the selected farms between 2011 and 2017. The aim of the experiment was to investigate whether there is a difference in rhizobia contents in soils in which soybeans have been recultivated after one to seven years break, and whether this could lead to differences in soybean plant growth. The obtained soil samples were directly transferred into containers, then sterilized soybean seeds were sown into pots in the greenhouse. After 94 days of growth, the plants were harvested and various parameters such as the nodular mass, number of nodules, and dry matter in the individual plant parts were determined. In addition, the relative abundances of Bradyrhizobium sp. in soil samples were identified by sequencing. No major decline in Bradyrhizobium sp. concentration could be observed due to a longer interruption of soybean cultivation. Soil properties such as pH, P, and Mg contents did not show a significant influence on the nodule mass or number, but seem to have an influence on the relative abundance of Bradyrhizobium sp. The investigations have shown that Bradyrhizobium japonicum persists in arable soils even under Central European site conditions and enters into an effective symbiosis with soybeans for up to seven years.

A mathematical model is derived to predict the trajectories of pores and inclusions that are nucleated in the interdendritic region during the continuous casting of steel. Using basic fluid mechanics and heat transfer, scaling analysis, and asymptotic methods, the model accounts for the possible lateral drift of the pores as a result of the dependence of the surface tension on temperature and sulfur concentration. Moreover, the soluto–thermocapillary drift of such pores prior to final solidification, coupled to the fact that any inclusions present can only have a vertical trajectory, can help interpret recent experimental observations of pore-inclusion clusters in solidified steel castings.

Particulate matter studies have been conducted regularly in the capital city of Mongolia. In contrast, studies related to the source and general estimation of levels of sulfur dioxide (SO2) over whole years are lacking. To explore the yearly trend in SO2, whole-year data of air pollutants were obtained from the Air Pollution Reducing Department. The results showed that the annual average concentration of SO2 was 32.43 µg/m3 at the Amgalan official monitoring station in 2017, which changed from 53 µg/m3 in 2016, representing a reduction of around 40%. The back-trajectory model and the National Oceanic and Atmospheric Administration (NOAA)’s hybrid single particle Lagrangian integrated trajectory model (HYPSLIT) were used to determine the source of SO2. A total of 8760 backward trajectories were divided into eight groups. The results showed that 78.8% of the total trajectories in Ulaanbaatar came from an area inside Mongolia. The results showed that pollutants enter Ulaanbaatar mainly from the northwest and north during the winter season. There are industrial cities, such as Darkhan and Sukhbaatar, in North Mongolia. Air pollutants created in the industrial area traveled into Ulaanbaatar during the winter season.

The bioleaching process, including acidification and solubilization of heavy metals, is a promising method for removing heavy metals from industrial waste sludge. Solubilization of heavy metals in industrial waste sludge is governed by adding elemental sulfur. A sulfur concentration exceeding 0.5% (w/v) inhibits sulfate production and the activity of acidophilic bacteria. Sulfate production was described well by a substrate inhibition expression in Haldane's kinetics. After 15 days of bioleaching, 79 to 81% copper, 50 to 69% lead, and 49 to 69% nickel were solubilized from sludge with a sulfur concentration of 0.5 to 1.0% (w/v). Experimental results indicated that the optimal sulfur concentration for the maximum solubilization rate of copper and nickel was 0.5% (w/v) and 1.0% (w/v) for lead. The profiles of denaturing gradient gel electrophoresis confirmed that indigenous acidophilic Acidithiobacilli (A. thiooxidans and A. ferrooxidans) existed and were the dominant species in the bioleaching process.

In this study, pH, total organic carbon, sulphate concentration and mercury concentrations of sediment samples from the Volta Lake at Yeji in the northern part of Ghana were determined. The results indicate that pH ranged from 6.32 to 8.21, total organic carbon ranged from 0.17 to 3.02 g/kg and sulphate concentration from 10.00 to 57.51 mg/kg. Total mercury concentrations ranged from 32.61 to 700 ng/g which is below the International Atomic Energy Agency recommended value of 810 ng/g. Humic substance-bound mercury ranged from 81.15 to 481.31 mg/kg in sediments and its two fractions existed as humic acid-bound mercury > fulvic acid-bound mercury with the ratio of humic substance-bound mercury to fulvic acid-bound mercury as 1.62 on the average. Humic substance-bound mercury and the two fractions fulvic acid-bound mercury and humic substance-bound mercury in sediments were favorably determined and found to correlate significantly positive with total organic carbon (r = 0.538) and total mercury (r = 0.574). However, there were poor correlations between SO 4 2− concentrations and humic substance-bound mercury (r = −0.391) as well as the two fractions; fulvic acid (r = −0.406) and humic acid (r = −0.381). By assuming that methyl mercury is mostly formed in sediments, these significant relations suggest that the efficiency of mercury being methylated from a given inorganic form depends on the amount, and most likely biochemical composition of total organic carbon in the lake sediment but not the SO 4 2− concentration.

Understanding the depth and severity of corrosion is crucial for predicting the long-term durability and economic viability of Zn-based structures. This study investigates the relationship between meteorological and pollution parameters on the corrosion rate of zinc using an artificial neural network (ANN) model trained on global data. The model incorporates temperature, time of wetness (TOW), SO2 concentration, Cl− concentration, and exposure time as input variables, with corrosion depth as the output. The ANN model demonstrated high predictive accuracy, achieving correlation coefficients of 0.99 and 0.95 for the training and test datasets, respectively, indicating strong agreement with the experimental data. A graphical user interface was developed to facilitate the practical application of the model. Sensitivity analysis using the index of relative importance (IRI) identified the SO2 concentration and TOW as the most influential factors, emphasizing their critical role in zinc corrosion. These findings enhance our understanding of the Zn corrosion dynamics and provide valuable insights into corrosion prevention strategies. A user-friendly graphical user interface (GUI) was developed using Java, enabling accurate prediction of the corrosion depth in zinc with approximately 95% accuracy without requiring prior knowledge of neural networks or programming.

The construction of a calibration curve using least square linear regression is common in many analytical measurements, and it comprises an important uncertainty component of the whole analytical procedure uncertainty. In the present work, various methodologies are applied concerning the estimation of the standard uncertainty of a calibration curve used for the determination of sulfur mass concentration in fuels. The methodologies applied include the GUM uncertainty framework, the Kragten numerical method, the Monte Carlo method (MCM) as well as the approximate equation calculating the standard error of prediction. The standard uncertainty results obtained by all methodologies agree well (0.172–0.175 ng μL −1 ). Aspects of inappropriate use of the approximate equation of the standard error of prediction, which leads to overestimation or underestimation of calculated uncertainty, are discussed. Moreover, the importance of the correlation between calibration curve parameters (slope and intercept) within GUM, MCM and Kragten approaches is examined.