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Nitrite monitoring serves as a fundamental practice for protecting public health, preserving environmental quality, ensuring food safety, maintaining industrial safety standards, and optimizing agricultural practices. Although many nitrite sensing methods have been recently developed, the quantification of nitrite remains challenging due to sensitivity and selectivity limitations. In this context, we present the fabrication of enzymeless iron oxide nanoparticle-modified zinc oxide nanorod (α-Fe2O3-ZnO NR) hybrid nanostructure-based nitrite sensor fabrication. The α-Fe2O3-ZnO NR hybrid nanostructure was synthesized using a two-step hydrothermal method and characterized in detail utilizing x-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). These analyses confirm the successful synthesis of an α-Fe2O3-ZnO NR hybrid nanostructure, highlighting its morphology, purity, crystallinity, and elemental constituents. The α-Fe2O3-ZnO NR hybrid nanostructure was used to modify the SPCE (screen-printed carbon electrode) for enzymeless nitrite sensor fabrication. The voltammetric methods (i.e., cyclic voltammetry (CV) and differential pulse voltammetry (DPV)) were employed to explore the electrochemical characteristics of α-Fe2O3-ZnO NR/SPCE sensors for nitrite. Upon examination of the sensor’s electrochemical behavior across a range of nitrite concentrations (0 to 500 µM), it is evident that the α-Fe2O3-ZnO NR hybrid nanostructure shows an increased response with increasing nitrite concentration. The sensor demonstrates a linear response to nitrite concentrations up to 400 µM, a remarkable sensitivity of 18.10 µA µM−1 cm−2, and a notably low detection threshold of 0.16 µM. Furthermore, its exceptional selectivity, stability, and reproducibility make it an ideal tool for accurately measuring nitrite levels in serum, yielding reliable outcomes. This advancement heralds a significant step forward in the field of environmental monitoring, offering a potent solution for the precise assessment of nitrite pollution.

The study utilizes a dataset with seven critical constraints and creates models that are estimated based on various metrics. The goal is to categorize and properly predict the water quality index (WQI) using the suggested models. The outcomes show that the implied models can accurately assess water quality and forecast WQI with high rates of success. Temperature, pH, dissolved oxygen (DO), conductivity, total dissolved solids (TDS), turbidity, and chlorides (Cl-) are some of the six crucial factors used in the study’s dataset. The mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2) are some of the metrics used to develop and assess the Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) models. The study also makes use of heat maps and correlation graphs to shed further light on the connections between various water quality measures. The color-coded values of the seven parameters, which represent the water quality level of the sample, are displayed on the heat map. The link between the two parameters is shown by the correlation graph between TDS and turbidity, which depicts their correlation coefficient. The study’s results show how effective machine learning algorithms may be as a tool for observing surface water quality. Himachal Pradesh is the tourist hub, so with the rapid increase in the volume of surface water contamination, the application of artificial intelligence will give a better view of data analytics and help with prediction and modeling. It was obtained from the study that the mean square error and root mean square error of ANN and LSTM lie between 0.52–6.0 and 0.04–0.21, respectively. However, the LSTM model’s accuracy is 95%, which is higher than the ANN model. The study highlights the importance of leveraging machine learning techniques in water quality monitoring to ensure the protection and management of water resources. With advancements in machine learning, artificial intelligence (AI) techniques have emerged as a promising tool for surface water quality monitoring. The major goal of the study is to explore the potential of two types of machine learning algorithms, namely artificial neural networks (ANNs) and long short-term memory (LSTM) models, for surface water quality monitoring.

In the present study, the quality of the surface water of the Gomti river (Lucknow, India) was investigated. Lucknow is situated in the centre of Uttar Pradesh, which is most the populated state in India. The locality has experienced rapid, unregulated development activities and population growth in recent decades, both of which have had a negative impact on its ecosystem and environment. Continuous monitoring is required to maintain the ecosystem at the desired level. Nine samples of river water were collected from the Gomti River in Lucknow, and they were analysed for a total of nine different characteristics, including pH, turbidity (Tur), dissolved oxygen (DO), total dissolved solids (TDSs), chemical oxygen demand (COD), chloride ion (Cl-) concentration, temperature (T), biochemical oxygen demand (BOD5) and total hardness (TH). The observed data were analysed using multivariate statistical methods. A cluster analysis (CA) was used to sort the sampling locations into different groups, and a principal component analysis (PCA) was used to find the different sources of pollution. Using a cluster analysis, all the water quality parameters were divided into three groups. Cluster 1 represented the less polluted sites, cluster 2 represented the moderately polluted sites and cluster 3 represented the highly polluted sites. Sampling sites SS8, SS4, S99 and SS7 were highly polluted because of nearby pollution sources such as domestic wastewater and runoff storm water. The principal component analysis yielded two meaningful components that explained 82.4% of the total variation in the data. The first factor and second factor explained 59.022 and 23.363 percentages of the total variance, respectively. It was noticed that major sources of pollution for the Gomti river are storm water runoff and the release of domestic and industrial wastewater from residents and industries, respectively. This study will help policy makers to ensure sustainable practices and reduce negative impacts on the availability and quality of water, allowing for the most efficient use of the Gomti River.

The failure of an embankment causes loss of lives, massive damage to infrastructure and the interruption of basic facilities; it has thus drawn increasing attention from researchers. When compared to other types of embankment disasters, overtopping-related embankment breaches are much more frequent. The study of the breach mechanism of embankments due to overtopping is becoming more and more essential for developing evacuation plans, early warning systems and damage assessment. To recognize the breach activities of embankments, it is necessary to find out discrete breach considerations like breach depth, breach initiation, breach width, etc. In the present study, a total of six tests were performed in a narrow flume using an embankment model. By conducting different experiments, it was observed that embankment breaching may be described in three stages, i.e., initial erosion, headcut erosion and lateral erosion. Furthermore, erosion is a three-dimensional process that occurs during embankment breaching, with the majority of erosion movement being associated with lateral broadening. The rate of headcut migration also has an impact on the widening rate. Furthermore, it depends upon the type of fill material and dam geometry. Also, the observed effect of moisture content on breach widening proved that the rate of widening was strongly influenced by water content. A drop of about 50% in moisture content causes approximately a 20% decrease in time to failure. In the present study, it is observed that breach shape could not be assumed to be regular shape like rectangle or trapezoid, as described in the literature. The trials were carried out in a narrow flume under constant hydraulic conditions, which are two of the study’s limitations.

With increasing population and industrialization, the water quality of freshwater sources like rivers, lakes, and ponds is becoming increasingly degraded. Most of the rivers in India are becoming polluted, including the Godavari. With the construction of dams, new industries and unsustainable agricultural practices in the Godavari basin, the water characteristics are degrading spatially and temporally. The present study emphasizes the analysis of water quality parameters like temperature, pH, Dissolved Oxygen (DO), conductivity, Biological Oxygen Demand (BOD), nitrate, and faecal coliform concentration in the Godavari basin. This was achieved by analysis of data taken from the Central Pollution Control Board, India (CPCB) for 21 stations around the Godavari basin over a span of five years from 2015 to 2019. The Pearson Correlation coefficient for the water quality parameters was assessed to study the relationship among the parameters. Variation in the water quality parameter is observed from the graphs for each station for respective years. It was found that conductivity and DO, temperature and pH and DO and faecal coliform are negatively correlated. It was also observed that DO has a negative correlation with pH, BOD and faecal coliform, indicating the utilization of dissolved oxygen at higher rates due to increasing degradation of organic matter by aerobic microorganisms in the river. One-way ANOVA was applied to find out significant temporal variations and it was observed that temperature, pH, and faecal coliform level had significantly changed the overdue course of time (F(4, 115) = 2.451, p < 0.05). The obtained results from the analysis indicate that the selected water quality parameters have varied significantly spatially, whereas temporally, according to the ANOVA coefficient, only temperature, pH and faecal coliform had shown significant differences during the selected timeframe. Hence, the present study highlighted the deteriorating water quality of the Godavari River over time.

Soil is a heterogeneous material, and its properties are vital from an agricultural perspective and for groundwater management. However, limited studies have been performed on the soil characteristics (soil texture, water-holding capacity, and soil compositions) of a single pumping well, especially in Malaysia. This article focuses on the soil characteristics and elemental analysis of a single borehole with 11 samples collected around Labu Kubong, Perak. The soil properties were analyzed in the context of particle size distribution (PSD) using the laser diffraction method (LDM), as well as soil composition for elemental analysis using a scanning electron microscope coupled with an energy dispersive X-ray (SEM-EDX). The LDM results revealed the average percentage of clay, silt, and sand to be 0%, 6%, and 94%, respectively, indicating most particles comprised sand particles which in percentages demonstrated a sandy texture with less silt content. Additionally, the water holding capacity is low because of major coarse sand particles in alluvial formations. Moreover, SEM-EDX outcomes displayed an average percentage of elemental composition reported as follows: C (40.77%), O (34.33%), Si (10.66%), Al (5.82%), Fe (1.10%), K (1.10%), As (0.05%), Na (0.04%), and Be (5.62%). Consequently, SEM-EDX outcomes showed these elements were derived from silicified quartz, feldspar, and iron-bearing minerals that originated from shale formations, and the presence of carbon indicates peat formation. Therefore, this study provides information on a single pumping well from an irrigation practice, and this study also recommends regional to global scale studies for supporting sustainable groundwater development worldwide.

Early-stage uric acid (UA) abnormality detection is crucial for a healthy human. With the evolution of nanoscience, metal oxide nanostructure-based sensors have become a potential candidate for health monitoring due to their low-cost, easy-to-handle, and portability. Herein, we demonstrate the synthesis of puffy balls-like cobalt oxide nanostructure using a hydrothermal method and utilize them to modify the working electrode for non-enzymatic electrochemical sensor fabrication. The non-enzymatic electrochemical sensor was utilized for UA determination using cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The puffy balls-shaped cobalt oxide nanostructure-modified glassy carbon (GC) electrode exhibited excellent electro-catalytic activity during UA detection. Interestingly, when we compared the sensitivity of non-enzymatic electrochemical UA sensors, the DPV technique resulted in high sensitivity (2158 µA/mM.cm2) compared to the CV technique (sensitivity = 307 µA/mM.cm2). The developed non-enzymatic electrochemical UA sensor showed good selectivity, stability, reproducibility, and applicability in the human serum. Moreover, this study indicates that the puffy balls-shaped cobalt oxide nanostructure can be utilized as electrode material for designing (bio)sensors to detect a specific analyte.

Transition metal oxide (TMO)-based nanomaterials are effectively utilized to fabricate clinically useful ultra-sensitive sensors. Different nanostructured nanomaterials of TMO have attracted a lot of interest from researchers for diverse applications. Herein, we utilized a hydrothermal method to develop porous nanosheets of cobalt oxide. This synthesis method is simple and low temperature-based. The morphology of the porous nanosheets like cobalt oxide was investigated in detail using FESEM and TEM. The morphological investigation confirmed the successful formation of the porous nanosheet-like nanostructure. The crystal characteristic of porous cobalt oxide nanosheets was evaluated by XRD analysis, which confirmed the crystallinity of as-synthesized cobalt oxide nanosheets. The uric acid sensor fabrication involves the fixing of porous cobalt oxide nanosheets onto the GCE (glassy carbon electrode). The non-enzymatic electrochemical sensing was measured using CV and DPV analysis. The application of DPV technique during electrochemical testing for uric acid resulted in ultra-high sensitivity (3566.5 µAmM−1cm−2), which is ~7.58 times better than CV-based sensitivity (470.4 µAmM−1cm−2). Additionally, uric acid sensors were tested for their selectivity and storage ability. The applicability of the uric acid sensors was tested in the serum sample through standard addition and recovery of known uric acid concentration. This ultrasensitive nature of porous cobalt oxide nanosheets could be utilized to realize the sensing of other biomolecules.

The tunnels in present-day cities are experiencing varying degrees of loading conditions ranging from static to extreme loading. Therefore, the stability of underground tunnels needs to be analyzed and understood for safer and strengthened design. The present study was conducted to simulate the impact loading conditions due to a missile traveling at a velocity of 5 Mach for different rock tunnels. The nonlinear continuum finite element analysis has been carried out through Abaqus and Explicit. The four different types of sandstones considered in the present study include Kota, Jamrani, Singrauli, and Jhingurda sandstones. An elastoplastic Mohr–Coulomb constitutive material model has been considered to model the behavior of rock surrounding the tunnel opening. The tunnel has an opening of 7 m in diameter (d), and 50 m in height and breadth, with 50 m of longitudinal length. The deformation and stress in the rock and the damage to the concrete lining have been compared in different cases. The Concrete–Damage–Plasticity (CDP) model and the Johnson–Cook model were considered for modelling of the RC lining and steel reinforcement. It was concluded that Jhingurda sandstone has maximum deformations due to impacts caused by missiles.

The shortage of natural aggregates has compelled the developers to devote their efforts to finding alternative aggregates. On the other hand, demolition waste from old constructions creates huge land acquisition problems and environmental pollution. Both these problems can be solved by recycling waste materials. The current study aims to use recycled brick aggregates (RBA) to develop eco-friendly pervious concrete (PC) and investigate the new concrete’s structural performance and pore structure distributions. Through laboratory testing and image processing techniques, the effects of replacement ratio (0%, 20%, 40%, 60%, 80%, and 100%) and particle size (4.75 mm, 9.5 mm, and 12.5 mm) on both structural performance and pore feature were analyzed. The obtained results showed that the smallest aggregate size (size = 4.75 mm) provides the best strength compared to the large sizes. The image analysis method has shown the average pore sizes of PC mixes made with smaller aggregates (size = 4.75 mm) as 1.8–2 mm, whereas the mixes prepared with an aggregate size of 9.5 mm and 12.5 mm can provide pore sizes of 2.9–3.1 mm and 3.7–4.2 mm, respectively. In summary, the results confirmed that 40–60% of the natural aggregates could be replaced with RBA without influencing both strength and pore features.

By the year 2035 it is estimated that Delhi and Mumbai will become two of the most populous cities around the globe. The massive population growth rate has led to the rise of land scarcity, urbanization, and industrialization and developments for rapid transit systems have made accordingly. Modern rapid transit systems comprise Metro rails and subways etc., and increase underground-construction activities. Nowadays, the tunnel-construction process heavily relies on massive machineries such as tunnelling-boring machines (TBM) and operations that produce great hindrance in the soil mass resulting in ground settlement at the surface. This study aimed to address these issues through small-scale laboratory experiments and further amplification to real-valued problems utilizing numerical methods. A cubic box of edge length 1 m made up of mild steel was generated to simulate a tunnelling operation and aluminum-made lining were used to simulate concrete tunnel linings. A finite element-based numerical investigation was done for a 2D elastoplastic numerical tunnel model with dimensions of 42 m × 42 m. Analysis was carried out on Optum G2 software. The analyzed variations in lining shapes of lining included circular, horseshoe, arch, elliptical, and square. Results showed that elliptical-shaped linings experienced the least ground settlement and these are recommended for places where surface settlement may cause major damage. It is also recommended that square-shaped linings should not be used in such situations due their higher settlement values.

Aluminum-based composites with characteristics such as low density and high strength to weight ratio have been identified to be one of the best-emerging alternatives. The lightweight composite is gaining popularity, particularly in the automotive industry. The composite’s qualities make it a prospective material to replace significant materials that are now used in the automobile industry. For lightweight products, various weight reduction solutions were proposed. In the present work, one such lightweight composite was fabricated by using a stir casting process, which includes reinforcement powders viz. carbon nanotube and fly ash to pure aluminum. The use of fly ash helps in reducing the overall associated cost of the material as well as provides low density. The work aims to identify the amount of fly ash (by weight %) suitable to avail good mechanical properties. In concern with the mechanical properties, density, yield strength, ultimate tensile strength, and wear resistance of the composite specimen were examined. Moreover, the artificial neural network was adopted to identify minimum volumetric wear for a given set of conditions. From the results, it was perceived that with the increase in fly ash content, the volumetric wear of the fabricated composite decreases. However, with the increase in load and speed, the volumetric wear rate increases.

In a laboratory, a model of a mid-channel bar is built to study the turbulent flow structures in its vicinity. The present study on the turbulent flow structure around a mid-channel bar is based on unravelling the fluvial fluxes triggered by the bar’s 3D turbulent burst phenomenon. To this end, the three-dimensional velocity components are measured with the help of acoustic doppler velocimetry (ADV). The results indicate that the transverse component of turbulent kinetic energy cannot be neglected when analyzing turbulent burst processes, since the dominant flow is three-dimensional around the mid-channel bar. Due to the three-dimensionality of flow, the octant events approach is used for analyzing the flow in the vicinity of the mid-channel bar. The aim is to develop functional relationships between the stable movements that are modelled in the present study. To find the best Markov chain order to present experimental datasets, the zero-, first-, and second-order Markov chains are analyzed using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). The parameter transition ratio has evolved in this research to reflect the linkage of streambed elevation changes with stable transitional movements. For a better understanding of the temporal behaviors of stable transitional movements, the residence time vs. frequency graphs are also plotted for scouring as well as for depositional regions. The study outcome herein underlines the usefulness of the octant events approach for characterizing turbulent bursts around mid-channel bar formation, which is a precursor to the initiation of braiding configuration.

Protective coatings for harsh environments are always welcome, but they must overcome profound challenges, including corrosion and wear resistance. The purpose of this study is to look into the long-term potentiodynamic polarization measurements and dry tribometric behavior of plasma-sprayed amorphous coatings on AISI 1035 mild steel. To investigate the impact of unique active polarization potentials on the electrochemical studies of the iron-based amorphous layer, which compares favorably to AISI 1035 mild steel, the active potential polarization curve and friction coefficient tests were performed. Scanning electron microscopy (SEM) and energy dispersive x-ray (EDX) analyses were used to investigate the coating’s corrosion behavior. Their mechanical (Tribometric tests at higher sliding speeds) and chemical properties (electrochemical potentiodynamic polarization investigations) have also been thoroughly investigated. There is enough validation that these protective coatings can be used in hostile environments. The effects of long-term corrosion for 24 and 48 h were thoroughly examined. Tribometric examinations revealed that amorphous layers are highly resistant under dry conditions, as they offered a very low and stable friction coefficient less than 4 μ with micro Vickers hardness 1140 ± 22.14 HV, which is more than twice as compared to mild steel AISI 1035. The corrosion resistance of coatings in 3.5 wt % NaCl solution displays active transition characteristics of activation, passivation, over passivation, and pitting, as shown by the potentiodynamic polarization curves.

Increasing infrastructure growth has forced the construction industry to look for wasteful, cheap, and suitable materials for construction. An investigation into the geotechnical utilization of fly ash was carried out in the present study. Practical applications normally involve the use of large quantities of fly ash, so proper mixing of the fly ash with other materials may not be significantly achieved. Therefore, the present paper investigates the behaviour of a fly ash–bentonite layered system with different ratios. The physical properties and chemical composition of fly ash and bentonite were determined. SEM and energy dispersive X-ray experiments were also used to investigate the morphology and phase compositions of fly ash and bentonite. A series of consolidated undrained (CU) triaxial tests on fly ash–bentonite were carried out to investigate shear strength characteristics. Fly ash (F) and bentonite (B) were used in the following ratios: 1:1 (50% F:50% B), 2:1 (67% F:33% B), 3:1 (75% F:25% B), and 4:1 (80% F:20% B), with different numbers of interfaces (N), i.e., 1, 2, and 3 for each ratio. The deviator stress and cohesion value were found to increase with the number of interfaces for each ratio. The angle of shear resistance changed marginally with the increase in the fly ash–bentonite ratios and varying interfaces.

Cosmetic industries are highly committed to finding natural sources of functional active constituents preferable to safer materials to meet consumers’ demands. Marine macroalgae have diversified bioactive constituents and possess potential benefits in beauty care products. Hence, the present study was carried out to characterize the biochemical profile of marine macroalga Chaetomorpha crassa by using different techniques for revealing its cosmetic potentials. In results, the FTIR study characterized the presence of different bioactive functional groups that are responsible for many skin-beneficial compounds whereas six and fifteen different important phycocompounds were found in GCMS analysis of ethanolic and methanolic extracts, respectively. In the saccharide profile of C. crassa, a total of eight different carbohydrate derivatives were determined by the HRLCMS Q-TOF technique, which showed wide varieties of cosmetic interest. In ICP AES analysis, Si was found to be highest whereas Cu was found to be lowest among other elements. A total of twenty-one amino acids were measured by the HRLCMS-QTOF technique, which revealed the highest amount of the amino acid, Aspartic acid (1207.45 nmol/mL) and tyrosine (106.77 nmol/mL) was found to be the lowest in amount among other amino acids. Their cosmetic potentials have been studied based on previous research studies. The incorporation of seaweed-based bioactive components in cosmetics has been extensively growing due to its skin health-promoting effects.