Search Results (113)

Machine learning (ML) is transforming computational chemistry by accelerating molecular simulations, property prediction, and inverse design. Central to this transformation is mathematical optimization, which underpins nearly every stage of model development, from training neural networks and tuning hyperparameters to navigating chemical space for molecular discovery. This review presents a structured overview of optimization techniques used in ML for computational chemistry, including gradient-based methods (e.g., SGD and Adam), probabilistic approaches (e.g., Monte Carlo sampling and Bayesian optimization), and spectral methods. We classify optimization targets into model parameter optimization, hyperparameter selection, and molecular optimization and analyze their application across supervised, unsupervised, and reinforcement learning frameworks. Additionally, we examine key challenges such as data scarcity, limited generalization, and computational cost, outlining how mathematical strategies like active learning, meta-learning, and hybrid physics-informed models can address these issues. By bridging optimization methodology with domain-specific challenges, this review highlights how tailored optimization strategies enhance the accuracy, efficiency, and scalability of ML models in computational chemistry. Full article

The safety performance of liquid hydrogen storage has a significant influence on its large-scale commercial application. Due to the complexity and costs of experimental investigation, computational fluid dynamics (CFD) simulations have been extensively applied to investigate the dynamic behaviors of liquid hydrogen release. The involved physical and chemical models, such as models of species thermodynamic properties and liquid–gas mass transfer, play a major role for the entire CFD model performance. However, comprehensive investigations into their impacts remain insufficient. In this study, CFD models of liquid hydrogen release were developed by using two widely used commercial simulation tools, Fluent and FLACS, and validated against experimental data available in the literature. Comparisons of the model results reveal strong discrepancies in the prediction accuracy of temperature and hydrogen volume fraction between the two models. The impact of the models of thermodynamic properties and liquid–gas mass transfer on the prediction results was subsequently explored by incorporating the FLACS sub-models to Fluent and evaluating the resulting prediction differences in temperatures and hydrogen volume fractions. The results show that the models of thermodynamic properties and liquid–gas mass transfer used in FLACS underestimate the vertical rise height and the highest hydrogen volume fraction of the cloud. Sensitivity analyses on the parameters in these sub-models indicate that the specific heats of hydrogen and nitrogen, in conjunction with the mass flow rate and outflow density of the mass transfer model, have a significant influence on model prediction of temperature. Full article

Fluorinated polyimide (FPI), renowned for its exceptional low-dielectric properties, colorless transparency, high-temperature resistance, and flexibility, has emerged as an ideal material for addressing challenges in 5G/6G high-frequency signal transmission and flexible electronic substrates. Nevertheless, the structure–property relationship between molecular architectures and the dielectric characteristics of FPI films remains insufficiently understood, necessitating urgent elucidation of the underlying mechanisms. In this study, a diamine monomer containing bis-amide bonds, 4-amino-N-{4-[(4-aminobenzoyl)amino]phenyl}benzamide (PABA), was synthesized. Subsequently, six FPI films (FPAIs, FPEIs, and FPEsIs) with distinct structural features were prepared through homopolymerization of PABA and five other diamines (containing amide bonds, ether, and ester groups) with fluorinated dianhydride (6FDA). Systematic characterization of thermal, mechanical, optical, and dielectric properties revealed that these films exhibit excellent thermal stability (T_g: 296–388 °C), mechanical strength (σ: 152.5–248.1 MPa, E: 2.1–3.4 GPa), and optical transparency (T_{550 nm}: 82–86%). Notably, they demonstrated a low dielectric constant (D_k as low as 2.8) and dielectric loss (D_f down to 0.002) under both low- and high-frequency electric fields. Furthermore, molecular dynamics simulations and quantum chemical were employed to calculate critical physical parameters and HOMO–LUMO energy levels of the six FPIs. This computational analysis provides deeper insights into the structure–performance correlations governing dielectric behavior and optical transparency in FPIs. The findings establish valuable theoretical guidance for designing advanced PI films with tailored dielectric properties and high transparency. Full article

Recent studies focus on recovering materials from Waste Electrical and Electronic Equipment (WEEE). Printed Circuit Boards (PCBs) are promising due to their heterogeneous composition, which includes precious metals, ceramics, and polymers. This research analyzes the leaching process of computer PCB waste to recover valuable metals such as copper and gold. The study involved physical-mechanical processing of PCB samples followed by chemical composition characterization. Metal extraction was performed through a three-stage leaching process. The first two stages used 2 M and 3 M sulfuric acid with hydrogen peroxide as leaching agents, achieving about 75% copper extraction. In the third stage, parameters for gold leaching using thiosulfate were evaluated, including concentrations of ammonium hydroxide and copper sulfate, reaction times (1–4 h), and temperatures (30, 40, and 50 ${\phantom{­}}^{\circ }$C). The leaching solution comprising 0.12 M sodium thiosulfate, 0.2 M ammonium hydroxide, and 20 mM copper sulfate yielded maximum gold extractions of 14.76% for fine and 15.73% for coarse fractions at 40 ${\phantom{­}}^{\circ }$C. In conclusion, the proposed method for recovering metals from PCBs can reduce the environmental impact of improper WEEE disposal while promoting a circular economy of secondary raw materials. Full article

This study proposes an innovative framework integrating geographic information systems (GISs), water quality index (WQI) analysis, and advanced machine learning (ML) models to evaluate the prevalence and impact of organic and inorganic pollutants across the urban–industrial confluence zones (UICZ) surrounding the National Capital Territory (NCT) of India. Surface water samples (n = 118) were systematically collected from the Gautam Buddha Nagar, Ghaziabad, Faridabad, Sonipat, Gurugram, Jhajjar, and Baghpat districts to assess physical, chemical, and microbiological parameters. The application of spatial interpolation techniques, such as kriging and inverse distance weighting (IDW), enhances WQI estimation in unmonitored areas, improving regional water quality assessments and remediation planning. GIS mapping highlighted stark spatial disparities, with industrial hubs, like Faridabad and Gurugram, exhibiting WQI values exceeding 600 due to untreated industrial discharges and wastewater, while rural regions, such as Jhajjar and Baghpat, recorded values below 200, reflecting minimal anthropogenic pressures. The study employed four ML models—linear regression (LR), random forest (RF), Gaussian process regression (GPR), and support vector machines (SVM)—to predict WQI with high precision. SVM_Poly emerged as the most effective model, achieving testing CC, RMSE, and MAE values of 0.9997, 11.4158, and 5.6085, respectively, outperforming RF (0.9925, 29.8107, 21.7398) and GPR_PUK (0.9811, 68.4466, 54.0376). By leveraging machine learning models, this study enhances WQI prediction beyond conventional computation, enabling spatial extrapolation and early contamination detection in data-scarce regions. Sensitivity analysis identified total suspended solids as the most critical predictor influencing WQI, underscoring its relevance in monitoring programs. This research uniquely integrates ML algorithms with spatial analytics, providing a novel methodological contribution to water quality assessment. The findings emphasize the urgency of mitigating the fate and transport of organic and inorganic pollutants to protect Delhi’s hydrological ecosystems, presenting a robust decision-support system for policymakers and environmental managers. Full article

Granular activated carbon (GAC) serves as a cost-efficient electrocatalyst cathode in electrochemical water treatment. This study investigates the impact of current intensity and cathode mesh size on the electrocatalytic generation of reactive oxygen species (ROS), i.e., hydrogen peroxide (H₂O₂) and hydroxyl radicals (•OH), for removing p-nitrophenol (PNP) as a representative contaminant. The findings suggest that these parameters exert a factorial effect on PNP removal, which is statistically endorsed via the analysis of variance. The −20 + 40 mesh GAC exhibited superior electrocatalytic performance due to its optimal balance of porosity and active surface area. Additionally, the reactor configuration was also studied. Employing two reactors in series configuration resulted in a 23% increase in H₂O₂ generation and a 32% enhancement in overall PNP removal compared with the single reactor configuration. This enhancement is attributed to (i) the enhanced electroactive area, (ii) the greater retention time of PNP over the electrocatalyst surface, and (iii) the increased dissolved oxygen and H₂O₂ content in the second reactor, promoting the overall H₂O₂ generation. Numerical simulations were conducted to compute H₂O₂ concentration profiles, providing a detailed representation of the physical, chemical, and electrochemical processes. The model exhibited a high degree of accuracy compared with the experimental measurements, with R² values ranging from ~0.76 to 0.99 and MAE values between ~0.04 and 0.23 mg/L. The simulation results highlight a strong interplay between H₂O₂ generation, its reaction kinetics during PNP removal, and electrode utilization efficiency. These findings emphasize the importance of optimizing the applied current magnitude and reactor operation duration to maximize electrode efficiency and H₂O₂ generation and utilization, while minimizing electrochemical bubble blockage. Overall, this study provides fundamental insights to optimize the electroactive area for enhanced ROS generation toward efficient contaminant removal, supporting sustainable groundwater remediation technologies in the face of emerging pollutants. Full article

Topological indices are numerical invariants derived from graph structures that are essential tools used in computational chemistry and biology for encoding molecular information. By exploiting the inherent symmetries of molecular graphs, we develop efficient algorithms to compute these indices, particularly for large and complex molecules. These indices are rooted in vertex degrees, edge degrees, and other graph parameters, have been extensively studied, and are crucial for understanding the relationship between molecular structure and properties. Recent research has focused on the entire Zagreb indices, which integrate both vertex and edge degrees considering adjacency and incidence relationships. This paper introduces a novel variant, namely, the modified forgotten entire Zagreb index. The efficacy of this new index is underscored by its robust correlation with the physical and chemical properties of octane isomers and lower benzenoid hydrocarbons. Additionally, we derive explicit formulas for this index for several significant graph families. Full article

The spectra of internal friction and temperature dependencies of the frequency of a free-damped oscillation process excited in the specimens of an amorphous–crystalline copolymer of polyoxymethylene with the co-monomer trioxane (POM-C) with a degree of crystallinity ~60% in the temperature range from −150 °C to +170 °C has been studied. It has been established that the spectra of internal friction show five local dissipative processes of varying intensity, manifested in different temperature ranges of the spectrum. An anomalous decrease in the frequency of the oscillatory process was detected in the temperature ranges where the most intense dissipative losses appear on the spectrum of internal friction. Based on phenomenological model representations of a standard linear solid, the physical–mechanical (shear modulus defect, temperature position of local regions of inelasticity) and physical–chemical (activation energy, discrete relaxation time, intensities of detected dissipative processes) characteristics of each local dissipative process were calculated. It was found that the intensities of dissipative processes remain virtually unchanged for both annealed and non-annealed samples. The maximum variation in the shear modulus defect is 0.06%. Additionally, according to computational data, small changes are also characteristic of the following parameters: the activation energy varies from 0.5 to 1.4 kJ/mol and the relaxation time changes from 0.002 to 0.007 s, depending on the presence or absence of annealing. As a result of annealing, there is a significant increase in the relaxation microinheterogenity of the polymer system across the entire temperature range (250% for the low-temperature region and 115% for the high-temperature region). Full article

The growing demand to reduce emissions of pollutants and CO₂ from internal combustion engines has led to a critical need for the development of ultra-lean burn engines that can maintain combustion stability while mitigating the risk of knock. One of the most effective techniques is the pre-chamber spark-ignition (PCSI) system, where the primary combustion within the cylinder is initiated by high-energy reactive gas jets generated by pilot combustion in the pre-chamber. Due to the complex physical and chemical processes involved in PCSI systems, performing 3D CFD simulations is crucial for in-depth analysis and achieving optimal design parameters. Moreover, combining a detailed CFDs model with a calibrated 0D/1D model is expected to provide a wealth of new insights that are difficult to gather through experimental methods alone, making it an indispensable tool for improving the understanding and optimization of these advanced engine systems. In this context, numerous previous studies have utilized CFD models to optimize key design parameters, including the geometric configuration of the pre-chamber, and to study combustion characteristics under various operating conditions in PCSI engines. Recent studies indicate that several advanced models designed for conventional spark-ignition (SI) engines may not accurately predict performance under the demanding conditions of Turbulent Jet Ignition (TJI) systems, particularly when operating in lean mixtures and environments with strong turbulence–chemistry interactions. This review highlights the pivotal role of Computational Fluid Dynamics (CFDs) in optimizing the design of pre-chamber spark-ignition (PCSI) engines. It explores key case studies and examines both the advantages and challenges of utilizing CFDs, not only as a predictive tool but also as a critical component in the design process for improving PCSI engine performance. Full article

Evaluating the efficiency of wastewater treatment plants (WWTPs) and their impact on receiving surface water bodies is a complex and highly significant task due to its regulatory implications for both environmental and public health. The monitoring of many water quality parameters related to the compliance of treated wastewater with environmental standards has led to the development of a unitless metric, the Wastewater Quality Index (WWQI), which serves as a practical tool for regulatory authorities. The aim of this research is to propose an appropriate WWQI methodology, incorporating a set of water quality indicators and a weighting approach, to evaluate wastewater effluents under operational monitoring. In this study, WWQI was successfully applied to access the operation of 21 WWTPs’ effluents within a single monitoring campaign, outside the mandatory monitoring schemes. The WWQI was computed for physical-chemical parameters including chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), total suspended solids (TSS), electrical conductivity (EC) and pH, priority substances (Cd, Ni and Pb) and a specific contaminant (Cr) using the weighted approach in the WWQI calculation, based on equal weighting, expert judgement and PCA weighing using factor loadings. The three approaches give similar results for the calculated WWQI. The expert judgment approach is more suitable for evaluating WWTP performance during a single monitoring campaign due to its simplicity compared to the PCA-based approach and its ability to prioritize specific water quality parameters over an equal weightage method. Full article

The presented work concerns computational investigations of the physical properties of composite materials based on polymer matrix and nonlinear optical (NLO) active chromophores. The structural, electronic, and optical properties of selected tetrathiafulvalene (TTF)-based chromophores have been calculated using quantum chemical methods. The polymer matrix changes the physical properties of the inserted chromophores influencing their optical parameters. To explain the mechanism of the NLO signal occurrence from the composites based on poly(methyl methacrylate) (PMMA) matrix and TTF chromophores, their structures are modeled using the classical molecular dynamics. In consequence, the structural properties of the composites are discussed according to the NLO requirements. By developing the theoretical model based on a discrete multipole local field approach, the impact of polymer matrix on the optical properties of chromophores is explained. Full article

Nowadays, almost all countries in the world are intensifying their search for locally available energy sources to become independent of external supplies. The production of alternative fuels from biomass and waste by thermal treatment or direct use in the combustion process is still the simplest method for fast and cheap heat production. However, the different characteristics of these fuels can cause problems in the operation of the plants, resulting in increased air pollution. Therefore, the analysis of the thermal treatment of solid fuels is still an important issue from a practical point of view. This work aimed to study biomass combustion in a small-scale reactor using the in-house Extended DEM (XDEM) method based on mixed Lagrangian–Eulerian approaches. This was provided by a novel, independently developed coupling computational interface. This interface allows for a seamless integration between CFD and DEM, improving computational efficiency and accuracy. In addition, significant advances have been made in the underlying physical models. Within the DEM framework, each particle undergoes the thermochemical processes, allowing for the prediction of its shape and structural changes during heating. Together, these changes contribute to a more robust and reliable simulation tool capable of providing detailed insights into complex multi-phase flows and granular material behavior. Numerical results were obtained for a non-typical geometry to check the influence of the walls on the distribution of the parameters in the reactor. The results show that XDEM is a very good tool for predicting the phenomena during the thermal treatment of solid fuels. In particular, it provides information about all the moving particles undergoing chemical reactions, which is very difficult to obtain from measurements. Full article

This study combines theoretical modeling and experimental validation to explore anaerobic digestion comprehensively. Developing a computational model is crucial for accurately simulating a digester’s performance, considering various feedstocks and operational parameters. The main objective was to adapt the anaerobic digestion model 1 (ADM1) simulation code to align with the laboratory-scale anaerobic digestion reactor’s specifications, especially regarding the liquid–gas transfer process. Within this computational framework, users may define model parameters and elucidate processes occurring in compartments reflecting the physical design. The model accurately predicts total concentrations of chemical oxygen demand (COD) as well as the produced biogas, with an average difference of less than 10% between experimental and simulated data. This consistency underscores the reliability and effectiveness of the adapted model in capturing anaerobic digestion nuances under specified conditions. Full article

This study offers a comprehensive examination, both theoretically and experimentally, of the potential of methanol (M) as a sustainable aviation fuel (SAF) assessed in combination with kerosene (Ke—Jet-A aviation fuel + 5% Aeroshell oil). Different blends of methanol and kerosene (10%, 20%, and 30% vol. of (M) was added to Ke) were tested in an aviation micro turbo-engine under various operating regimes, such as idle, cruise, and maximum. Key engine parameters, including combustion temperature, fuel consumption, and thrust, were closely monitored during these trials. Essential performance indicators such as combustion efficiency, thermal efficiency, and specific consumption for all fuel blends under maximum operating conditions are also presented. Physical and chemical characteristics, such as viscosity, density, calorific value and flash point, were determined for each blend. Moreover, elemental analysis and FTIR spectroscopy were utilized to evaluate the chemical composition of the fuels. This study further investigated the air requirements for stoichiometric combustion and computed the resulting CO₂ and H₂O emissions. Experimental tests were conducted on the Jet Cat P80^® micro turbo-engine, covering assessments of starting procedures, acceleration, deceleration, and pollutant emissions (CO and SO₂) during various engine operating conditions. The results suggest that the examined fuel blends demonstrate stable engine performance at concentrations of 10% and 20% methanol. However, observations indicate that with an increase in methanol concentration, particularly at 30%, the stability of the engine at idle and, notably, at maximum speed decreases significantly. Specifically, at a 30% methanol concentration, the engine no longer operates stably, exhibiting significant rpm fluctuations, leading to the decision not to explore higher concentrations. Full article

The present review aims to summarize the more recent scientific literature and updated state of the art on the research effort spent in adapting hardware–software tools to understand the true needs of honeybee colonies as a prerequisite for any sustainable management practice. A SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis was also performed with the aim of identifying the key factors that could support or impair the diffusion of precision beekeeping (PB) systems. Honeybee husbandry, or beekeeping, is starting to approach precision livestock farming (PLF), as has already happened in other animal husbandry sectors. A transition from the current paradigm of rational beekeeping to that of precision beekeeping (PB) is thus expected. However, due to the peculiarities of this species and the related farming practices, the PB technological systems (PB systems) are still undergoing a development process that, to some extent, limits their large-scale practical application. Several physical–chemical (weight, temperature, humidity, sound, gases) and behavioral traits (flight activity, swarming) of the hive are reviewed in light of the evolution of sensors, communication systems, and data management approaches. These advanced sensors are equipped with a microprocessor that records data and sends it to a remote server for processing. In this way, through a Wireless Sensor Network (WSN) system, the beekeeper, using specific applications on a personal computer, tablet, or smartphone, can have all the above-mentioned parameters under remote control. In general, weight, temperature, and humidity are the main hive traits monitored by commercial sensors. Surprisingly, flight activity sensors are rarely available as an option in modular PB systems marketed via the web. The SWOT analysis highlights that PB systems have promising strength points and represent great opportunities for the development of beekeeping; however, they have some weaknesses, represented especially by the high purchasing costs and the low preparedness of the addressed operators, and imply some possible threats for beekeeping in terms of unrealistic perception of the apiary status if they applied to some hives only and a possible adverse impact on the honeybees’ colony itself. Even if more research is expected to take place in the next few years, indubitably, the success of commercial PB systems will be measured in terms of return on investment, conditioned especially by the benefits (higher yields, better colonies’ health) that the beekeeper will appraise as a consequence of their use. Full article