We report on the performance of solar cells with stacked self-assembled Ge dots in the intrinsic region of Si-based $\mathrm{p-i-n}$ diode. These dots were epitaxially grown on $p$ -type Si(100) substrate via the Stranski–Krastanov growth mode by gas-source molecular beam epitaxy. Enhanced external quantum efficiency (EQE) in the infrared region up to 1.45 μm was observed for the solar cells with stacked self-assembled Ge dots compared with that without Ge dots. Furthermore, the EQE was found to increase with increasing number of stacking. These results show that electron-hole pairs generated in Ge dots can be efficiently separated by the internal electric field, and can contribute to the photocurrent without considerable recombination in Ge dots or at Ge/Si interfaces.

Innovative technologies enable businesses to stay competitive in marketplace while also increasing profits in manufacturing of nanocomposites for water treatment. Aforementioned driving factors resulted in adoption of a number of innovative technologies, and no other trend has had a greater impact in recent years than Industry 4.0. Industry 4.0 is a comprehensive term that encompasses data management, manufacturing competitiveness, manufacturing processes, and efficiency. The term “Industry 4.0” refers to a group of key enabler technologies, such as cyber physical models, Internet of Things (IoT), artificial intelligence (AI), and big data analytics, including embedded devices which are all significant components to the mechanized and digitized industrial environments. AI approaches have been used for water treatment processes as well as desalination in recent years for optimizing the process along with providing realistic answers to water scarcity and water pollution‐related issues. AI applications have been used to predict and minimize water treatment process operational costs by lowering costs and optimizing chemical utilization. Several AI models are successful and accurate in predicting effectiveness of various adsorbents used in the removal process of a variety of contaminants from water. To identify the current level of research and future development prospects of smart manufacturing, this study uses a comprehensive literature review technique for manufacturing sustainability of nanocomposite fabrication for water treatment applications. The model provided will help to create a baseline for AI and hybrid models in the water treatment and wastewater management sectors, allowing for the increased performance and innovative growth. It will serve to provide the framework and give guidance for researchers interested in creating superior nanocomposites for waste and fresh water treatment and management using Industry 4.0. This study looks at a variety of AI approaches as well as how they may be used in water treatment, with an emphasis on pollutant adsorption. This assessment also identified certain obstacles and research gaps in the field of AI applications in water treatment processes.

For the safe and economical construction of embankment dams, the mechanical behaviour of the rockfill materials used in the dam’s shell must be analyzed. The characterization of rockfill materials with specified shear strength is difficult and expensive due to the presence of particles greater than 500 mm in diameter. This work investigates the feasibility of using an extreme gradient boosting (XGBoost) computing paradigm to estimate the shear strength of rockfill materials. To train and validate the proposed XGBoost model, a total of 165 databases obtained from the literature are chosen. The XGBoost model was compared against support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K‐nearest neighbor (KNN) models described in the literature. XGBoost beats SVM, RF, AdaBoost, and KNN models in terms of performance evaluation metrics such as coefficient of determination (R²), Nash–Sutcliffe coefficient (NSE), and error in the root mean square ratio (RMSE) to the standard deviation of the measured data (RSR). The results demonstrated that the XGBoost model has the highest prediction performance with (R² = 0.9707, NSE = 0.9701, and RSR = 0.1729), followed by the SVM model with (R² = 0.9655, NSE = 0.9639, and RSR = 0.1899), RF (R² = 0.9545, NSE = 0.9542, and RSR = 0.2140), the AdaBoost model with (R² = 0.9390, NSE = 0.9388, and RSR = 0.2474) and the KNN model with (R² = 0.6233, NSE = 0.6180, and RSR = 0.6181). A sensitivity analysis has been conducted to ascertain the impact of each investigated input parameter. This study demonstrates that the established XGBoost model for estimating the shear strength of rockfill materials is reliable.

Histamine is a well-known biogenic amine (BA) that is often associated with allergic reactions and is a significant cause of foodborne illnesses resulting from the consumption of spoiled food. Detecting histamine is essential for maintaining food safety standards and preserving the quality. In this work, we developed a simple, low-cost, and rapid colorimetric method for detecting histamine. Gold nanoparticles (AuNPs) of different sizes (16, 25, and 40 nm) were synthesized by using the citrate reduction method. The particle size was controlled by adjusting the precursor molar ratio (MR), with smaller ratios leading to larger particles and a red-shift in the surface plasmon resonance (SPR) peak (520, 524, and 528 nm). The nanoparticles were allowed to interact with increasing concentrations of histamine (ranging from 1 to 100 ppm), and the changes in the absorbance spectra and color of the solution were monitored. AuNP aggregation was induced by interaction with histamine through amino and imidazole groups that will coordinate with the AuNP's surface via electrostatic and hydrogen-bonding interactions, causing the solution to turn blue from red. The size variations of AuNPs significantly affected the colorimetric response to histamine. Among the varied sizes, 25 nm AuNPs exhibited the lowest detection limit of 0.72 μM and a linear detection range of 1-10 ppm. Notably, this sensor offered rapid detection (under 1 min) and a remarkable selectivity toward histamine analyte, highlighting its potential for practical applications.

This study presents a novel polyurethane-activated carbon composite (PACC) as an effective and sustainable adsorbent for treating lead-ion-contaminated waters. The PACC was characterized using Scanning Electron Microscopy with Energy Dispersive X-ray (SEM-EDX), Fourier Transform Infrared Spectroscopy (FTIR), Brunauer, Emmett, and Teller (BET) surface area analyzer, X-ray Diffraction (XRD), and Thermogravimetric Analyzer (TGA) to evaluate its physicochemical and thermal properties. Furthermore, the PACC was employed in an experimental column adsorption setup to investigate its adsorption performance and to develop a dynamic method suitable for industrial implementation. Parameters such as bed height (50, 100, 150 mm), flow rate (4, 6, 8 mL min⁻¹), pH (2, 4, 6), and initial metal-ion concentrations (10, 50, 100 mg L⁻¹) were examined. The experimental data exhibited strong agreement with the Thomas and Yoon–Nelson models (R² ≥ 0.96), indicating efficient adsorption mechanisms. Remarkably, the used adsorbent has the potential for facile regeneration without substantial loss in capacity. The PACC demonstrated excellent adsorption performance for lead ions in aqueous solutions in a fixed-bed column system. Thus, the novel PACC material holds the potential for scalable application in industrial settings to address water pollution challenges, especially in regions with uncontrolled effluent discharge.

Histamine is among the biogenic amines that are formed during the microbial decarboxylation of amino acids in various food products, posing a significant threat to both food safety and human health. Herein, we present a one-step synthesis of PEGylated gold nanoparticles (PEG-AuNPs) for rapid, simple, and cost-effective colorimetric histamine detection. PEG-AuNPs' surface plasmon resonance (SPR) range at 520-530 nm with a hydrodynamic size distribution of 20-40 nm. Fourier transform infrared (FT-IR) spectra confirmed the reduction of AuNPs at 1645 cm^-1 along with the other observed peaks at 2870, 1350, and 1100 cm^-1 as a strong evidence for the presence of PEG. Upon the addition of histamine to the PEG-AuNP solution, transmission electron microscopy (TEM) highlighted the aggregation of nanoparticles. In addition, red shifting and a decrease in the absorbance of the SPR peak along with the appearance of an additional peak at ∼690 nm was observed in the PEG-AuNP absorption spectra in the presence of histamine. Increasing the PEG concentration in the gold colloids leads to the formation of a protective barrier around the surface of nanoparticles, which influences the colloidal stability by impeding the aggregation of PEG-AuNPs upon histamine addition. The minimum colorimetric response of PEG-AuNPs to histamine concentration is 30 ppm, as assessed by the naked eye. The absorption ratio (A₆₉₀/A₅₂₆) showed a linear dynamic range from 20 to 100 ppm with a limit of detection of 9.357 μM. Additionally, the assay demonstrates a commendable selectivity toward histamine analyte.