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Air pollution in industrial environments, particularly in the chrome plating process, poses significant health risks to workers due to high concentrations of hazardous pollutants. Exposure to substances like hexavalent chromium, volatile organic compounds (VOCs), and particulate matter can lead to severe health issues, including respiratory problems and lung cancer. Continuous monitoring and timely intervention are crucial to mitigate these risks. Traditional air quality monitoring methods often lack real-time data analysis and predictive capabilities, limiting their effectiveness in addressing pollution hazards proactively. This paper introduces a real-time air pollution monitoring and forecasting system specifically designed for the chrome plating industry. The system, supported by Internet of Things (IoT) sensors and AI approaches, detects a wide range of air pollutants, including NH3, CO, NO2, CH4, CO2, SO2, O3, PM2.5, and PM10, and provides real-time data on pollutant concentration levels. Data collected by the sensors are processed using LSTM, Random Forest, and Linear Regression models to predict pollution levels. The LSTM model achieved a coefficient of variation (R²) of 99 % and a mean absolute percentage error (MAE) of 0.33 for temperature and humidity forecasting. For PM2.5, the Random Forest model outperformed others, achieving an R² of 84 % and an MAE of 10.11. The system activates factory exhaust fans to circulate air when high pollution levels are predicted to occur in the next hours, allowing for proactive measures to improve air quality before issues arise. This innovative approach demonstrates significant advancements in industrial environmental monitoring, enabling dynamic responses to pollution and improving air quality in industrial settings.

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Stress corrosion cracking (SCC) under harsh environmental conditions still poses a significant challenge, despite extensive research efforts. The intricate interplay among mechanical, chemical, and electrochemical factors hinders the accurate prognosis of material degradation and remaining service life. Furthermore, the demand for real-time monitoring and early detection of SCC defects adds further complexity to the prognostication process. Therefore, there is an urgent need for comprehensive review papers that consolidate current knowledge and advancements in prognosis methods. Such reviews would facilitate a better understanding and resolution of the challenges associated with SCC under harsh environmental conditions. This work aims to provide a comprehensive overview of various prognosis methods utilized for the assessment and prediction of SCC in such environments. The paper will delve into the following sections: exacerbating harsh environmental conditions, non-destructive testing (NDT) techniques, electrochemical techniques, numerical modeling, and machine learning. This review is inclined to serve as a valuable resource for researchers and practitioners working in the field, facilitating the development of effective strategies to mitigate SCC and ensure the integrity and reliability of materials operating in challenging environments. Despite considerable research, stress corrosion cracking in harsh environments remains a critical issue, complicated by the interplay of mechanical, chemical, and electrochemical factors. This review aims to consolidate current prognosis methods, including non-destructive testing, electrochemical techniques, numerical modeling, and machine learning. Key findings indicate that while traditional methods offer limited reliability, emerging computational approaches show promise for real-time, accurate predictions. The paper also briefly discusses notable SCC failure cases to underscore the urgency for improved prognosis techniques. This work aspires to fill knowledge gaps and serve as a resource for developing effective SCC mitigation strategies, thereby ensuring material integrity in challenging operational conditions.

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This study reports first-principles predictions as well as experimental synthesis of manganese oxide nanoparticles under different conditions. The theoretical part of the work comprised density functional theory (DFT)-based calculations and first-principles molecular dynamics (MD) simulations. The extensive research efforts and the current challenges in enhancing the performance of the lithium-ion battery (LIB) provided motivation to explore the potential of these materials for use as an anode in the battery. The structural analysis of the synthesized samples carried out using X-ray diffraction (XRD) confirmed the tetragonal structure of Mn3O4 on heating at 450 and 550 °C and the cubic structure of Mn2O3 on heating at 650 °C. The structures are found in the form of nanoparticles at 450 and 550 °C, but at 650 °C, the material appeared in the form of a nanoporous structure. Further, we investigated the electrochemical functionality of Mn2O3 and Mn3O4 as anode materials for utilization in LIBs via MD simulations. Based on the investigations of their electrical, structural, diffusion, and storage behavior, the anodic character of Mn2O3 and Mn3O4 is predicted. The findings indicated that 10 lithium atoms adsorb on Mn2O3, whereas 5 lithium atoms adsorb on Mn3O4 when saturation is taken into account. The storage capacities of Mn2O3 and Mn3O4 are estimated to be 1697 and 585 mAh g-1, respectively. The maximum value of lithium insertion voltage per Li in Mn2O3 is 0.93 and 0.22 V in Mn3O4. Further, the diffusion coefficient values are found as 2.69 × 10-9 and 2.65 × 10-10 m2 s-1 for Mn2O3 and Mn3O4, respectively, at 300 K. The climbing image nudged elastic band method (Cl-NEB) was implemented, which revealed activation energy barriers of Li as 0.30 and 0.75 eV for Mn2O3 and Mn3O4, respectively. The findings of the work revealed high specific capacity, low Li diffusion energy barrier, and low open circuit voltage for the Mn2O3-based anode for use in LIBs.

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This paper reviews and analyzes the innovations and advances in using algae and their derivatives in different parts of Li-ion batteries. Applications in Li-ion battery anodes, electrolytes, binders, and separators were discussed. Algae provides a sustainable feedstock for different materials that can be used in Li-ion batteries, such as carbonaceous material, biosilica, biopolymers, and other materials that have unique micro- and nano-structures that act as biotemplates for composites structure design. Natural materials and biotemplates provided by algae have various advantages, such as electrochemical and thermal stability, porosity that allows higher storage capacity, nontoxicity, and other properties discussed in the paper. Results reveal that despite algae and its derivatives being a promising renewable feedstock for different applications in Li-ion batteries, more research is yet to be performed to evaluate its feasibility of being used in the industry.

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Stress Corrosion Cracking (SCC) is a failure mechanism that occurs when certain materials are subjected to both external or residual stresses and corrosion. This combined effect leads to the development of cracks in the susceptible materials. Submerged steel pipelines in the petroleum sector are built of low-alloy steels having a ferrite-cementite composition, including API 5L X70. Such materials are sensitive to SCC damage in aqueous systems. The film rupture dissolution repassivation (FRDR) process is used in this study to evaluate the cracks and pits growth in oil and gas pipelines in the Gulf area under diverse SCC environmental conditions. The SCC crack propagation and pit growth under near-neutral environmental conditions were analyzed using phase field modelling. X70 steel under NS4 the solution was used for the analysis to represent the anodic dissolution film rupture mechanism. A parametric study was done to study the impact of different electrochemistry and phase field parameters on crack growth behaviour. The study assess the susceptibility to SCC caused by an pit by employing diverse settings to evaluate the impact of corrosion parameters and the interaction among the FRDR mechanism. The corrosion rates are influenced by the interface kinetics coefficient (L), which exhibits an accelerated effect as L increases. This transition from fracture-controlled to dissolution-controlled SCC growth occurs until the system reaches the diffusion limit, beyond which further increases in L do not significantly impact corrosion rates. Moreover, higher values of the kinetic coefficient (k) advance the creation of SCC cracks at the crack front, resulting from corrosion originating from pitting at the crack mouth. This process leads to the refinement of the pit and its transformation into a crack. A comparison analysis was utilized to validate our simulation under a near-neutral NS4 solution for X70 steel by correlating the findings with other numerical methods for crack growth utilizing the same material and environmental parameters. The results show decent agreement with the comparative study.

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Atmospheric pressure plasma jets are gaining a lot of attention due to their widespread applications in the field of bio-decontamination, polymer modification, material processing, deposition of thin film, and nanoparticle fabrication. Herein, we are reporting the disinfection of Pseudomonas aeruginosa, Staphylococcus aureus, and Escherichia coli bacteria using plasma jet. In this regard, Ar-O2, Ar-N2, and Ar-O2-N2 mixture plasma is generated and characterized using optical and electrical characterization. Variation in plasma parameters like electron temperature, electron density, and reactive species production is monitored with discharge parameters such as applied voltage and feed gas concentration. Results show that the peak average power consumed in Ar-O2, Ar-N2, and Ar-O2-N2 mixture plasma is found to be 4.45, 2.93, and 4.35 W respectively, at 8 kV. Moreover, it is noted that by increasing applied voltage, the electron temperature, electron density, and reactive species production also increases. It is worth noting that electron temperature increases with increase in oxygen concentration in the mixture (, while it decreases with increase in nitrogen concentration in the mixture (Ar-N2). Similarly, a decreasing trend in electron temperature is noted for Ar-O2-N2 mixture plasma. On the other hand, a decreasing trend in electron density is noted for all the mixtures. Reduction in viable colonies of Pseudomonas aeruginosa, Staphylococcus Aureus, and Escherichia coli were confirmed by the serial dilution method. The inactivation efficiency of pulsed DC plasma generated, in the Ar-N2 mixture at 8 kV and 6 KHz, was evaluated against P. aeruginosa, S. aureus and E. coli bacteria by measuring the number of surviving cells versus plasma treatment time. Results showed that after 240 s of plasma treatment, the number of survival colonies of the mentioned bacteria was reduced to less than 30 CFU/mL.

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Ascorbic acid is an important antioxidant agent that acts as an electron donor and is involved in many physiological processes. Structural modification in ascorbic acid is a subject of extensive biochemical research due to its involvement in a variety of relevant phenomena including electron transport, complex redox reactions, neurochemical reactions, enzymatic reactions, and chemotherapeutic potential. In this work, the structure of ascorbic acid is modified via doping with the first three members of the halogen group to investigate the changes in the electronic structure and spectroscopic parameters using first-principles methods. To obtain the lowest-energy structures, different basis sets in density functional theory (DFT) and Hartree-Fock approaches were employed in the geometry optimization process. The potential energy maps of the structures were computed to study the molecular orientations and their optical and electrical properties. The spectroscopic properties were computed via UV-vis and nuclear magnetic resonance (NMR) spectroscopies to study the effects of doping into the compound. To obtain further insights into the chemical structure, the Fourier transform infrared (FT-IR) spectra of the materials were theoretically investigated. It was found that the band gap is sensitive to doping as we moved from fluorine to chlorine and then to bromine.

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This work was carried out to explore the compounds of bismuth with carbon using density functional theory (DFT)-based computations. The structures of the compounds BiC, BiC2, BiC3, Bi2C3, BiC5, and Bi2C5 were predicted at a generalized gradient approximation (GGA-PBE) level of theory. The calculations were carried out on the structures in unit cell and supercell geometries in slab and bulk periodicities. The structural and electronic properties of the mentioned compounds were investigated in detail. The calculations of the structures revealed lattice constants of the compounds for cubic unit cell as 212.2 pm for BiC, 176.9 pm for BiC2, 240.5 pm for BiC3, 232.4 pm for Bi2C3, and 354.5 pm for Bi2C5. The compounds BiC, BiC2, BiC3, BiC5, and Bi2C5 were found to be metallic, whereas Bi2C3 exhibited semiconducting character with a band gap of 0.305 eV. This work provides an initial framework for preparing new 2D materials from BixCy.

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The excellent optical properties of gold nanoparticles (AuNPs) make them promising for numerous applications. Herein, we present a facile synthesis of both surfactant-free (SF-AuNPs) and non-toxic D-fructose (DF)-coated gold nanoparticles (DF-AuNPs) via the plasma-liquid interactions (PLIs) method. Moreover, we demonstrate that both SF-AuNPs and DF-AuNPs are potential candidates for trace detection via surface-enhanced Raman scattering (SERS) and catalytic degradation of toxic dyes. However, SF-AuNPs have superior SERS and catalytic performance compared to the DF-AuNPs due to their surfactant-free nature. Moreover, SF-AuNPs have also been shown to quench the fluorescence of analyte molecules, making their SERS-based trace detection more efficient. In particular, SERS enhancement of rhodamine 6G (R6G) and catalytic reduction of a toxic dye methylene blue (MB) have been explored.

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This is the first investigation of yttrium (Y) and erbium (Er) co-doped hydroxyapatite (HAp) structures, conducted using theoretical and experimental procedures. By using a wet chemical method, the materials were synthesized by varying the concentration of Y amounts of 0.13, 0.26, 0.39, 0.52, 0.65, and 0.78 at.% every virtual 10 atoms of calcium, whereas Er was kept fixed at 0.39 at.%. Spectroscopic, thermal, and in vitro biocompatibility testing were performed on the generated samples. Theoretical calculations were carried out to compute the energy bandgap, density of states, and linear absorption coefficient. The effects of Y concentration on thermal, morphological, and structural parameters were investigated in detail. Raman and Infrared (FTIR) spectroscopies confirmed the formation of the HAp structure in the samples. Theoretical investigations indicated that the increasing amount of Y increased the density from 3.1724 g cm-3 to 3.1824 g cm-3 and decreased the bandgap energy from 4.196 eV to 4.156 eV, except for the sample containing 0.39 at. % of the dopant, which exhibited a decrease in the bandgap. The values of linear absorption appeared reduced with an increase in photon energy. The samples exhibited cell viability higher than 110%, which revealed excellent biocompatibility for biological applications of the prepared samples.

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Two-dimensional (2D) SnO is a p-type semiconductor that has received research and industrial attention for device-grade applications due to its bipolar conductivity and transparent semiconductor nature. The first-principles investigations based on the generalized gradient approximation (GGA) level of theory often failed to accurately model its structure due to interlayer Van der Waals interactions. This study is carried out to calculate structural and electronic properties of bulk and layered structures of SnO using dispersion correction scheme DFT+D3 with GGA-PBE to deal with the interactions which revealed good agreement of the results with reported data. The material in three-dimensional bulk happened to be an indirect gap semiconductor with a band gap of 0.6 eV which is increased to 2.85 eV for a two-dimensional monolayer structure. The detailed analysis of the properties demonstrated that the SnO monolayer is a promising candidate for future optoelectronics and spintronics devices, especially thin film transistors.

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The electrochemical deposition of the composites polyaniline (PANI):polypyrrole (PPy)/activated carbon (AC) and polyaniline (PANI): 3, 4-polyethylenedioxythiophene (PEDOT)/AC films is carried out in this work. The electrochemical character of the fabricated samples is investigated via cyclic voltammetry (CV), galvanostatic charge-discharge (GCD) and electrochemical impedance spectroscopy (EIS) using a three-electrode setup. The values of the specific capacitance of the composites PANI:PPy/AC and PANI:PEDOT/AC at a current density of 1 Ag-1 are evaluated as 586 Fg-1 and 611 Fg-1, respectively. The values of energy density are 40 Whkg-1 and 2094 Wkg-1, whereas power density is recorded as 44 Whkg-1 and 2160 Wkg-1 for respective composites PANI:PPy/AC and PANI:PEDOT/AC. Moreover, the respective composites appeared to retain cyclic stabilities of 92% and 90%. This study points to the potential of the prepared composites for application as electrodes in supercapacitors.

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The changes in properties of materials upon introduction of impurities is well documented but less is known about the location of foreign atoms in different hosts. This study is carried out with the motivation to explore dopant location in hexagonal GaN using density functional theory based calculations. The dopant site location of the individual dopants Ti, Ce, and Ti-Ce codoped wurtzite GaN was investigated by placing the dopants at cationic lattice sites as well as off-cationic sites along the c-axis. The geometry optimization relaxed individual dopants on cationic Ga sites but in the case of codoping Ce settled at site 7.8% away along [0001 ¯] and Ti adjusted itself at site 14% away along [0001] from regular cationic sites. The analysis of the results indicates that optimized geometry is sensitive to the starting position of the dopants. The magnetic exchange interactions between Ti and Ce ions are responsible for their structural relaxation in the matrix.

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The current paper is aimed to investigate the effects of waviness, random orientation, and agglomeration factor of nanoreinforcements on wave propagation in fluid-conveying multi-walled carbon nanotubes (MWCNTs)-reinforced nanocomposite cylindrical shell based on first-order shear deformable theory (FSDT). The effective mechanical properties of the nanocomposite cylindrical shell are estimated employing a combination of a novel form of Halpin-Tsai homogenization model and rule of mixture. Utilized fluid flow obeys Newtonian fluid law and it is axially symmetric and laminar flow and it is considered to be fully developed. The effect of flow velocity is explored by implementing Navier-Stokes equation. The kinetic relations of nanocomposite shell are calculated via FSDT. Moreover, the governing equations are derived using the Hamiltonian approach. Afterward, a method which solves problems analytically is applied to solve the obtained governing equations. Effects of a wide range of variants such as volume fraction of MWCNTs, radius to thickness ratio, flow velocity, waviness factor, random orientation factor, and agglomeration factor on the phase velocity and wave frequency of a fluid conveying MWCNTs-reinforced nanocomposite cylindrical shell were comparatively illustrated and the results were discussed in detail.

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Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.