Search Results (2,144)

The influence of minor components on leaching molecular iodine (I₂) through polypropylene (PP)-based packaging from a povidone iodine-based (PVP-I) formulation, simulating an ophthalmic application, was evaluated. I₂ is a cheap, broad-spectrum, and multi-target antiseptic. Nevertheless, it is volatile, and the prolonged storage of I₂-based formulations is demanding in plastic packaging because of transmission through the material. Therefore, we explored the possibility of moderating the loss of I₂ from an iodophor formulation by introducing small amounts of molecular iodine into the polymer material commonly used in eyedropper caps, i.e., PP. Thus, PP was blended via an extrusion process with a polymeric complex containing iodine (such as PVP-I) or with a second polymeric component able to complex the I₂ released from an iodophor solution. The aim of this work was to introduce I₂ into PP-based polymer matrices without using organic solvents and indirectly, i.e., through the addition of components that could generate molecular iodine or complex it in the solid phase, as I₂ is heat-sensitive. To increase the miscibility between PP and PVP-I, poly(N-vinylpyrrolidone) (PVP) or a vinyl pyrrolidone vinyl acetate copolymer 55/45 (Sokalan) were added as compatibilizers. The PP-based binary and ternary blends, in granular or sheet form, were characterized thermally (Differential Scanning Calorimetry, DSC, and Thermogravimetric analysis, TGA), mechanically (tensile tests), morphologically (scanning electron microscopy (SEM)), and chemically (attenuated total reflectance Fourier transform infrared (ATR-FTIR)). Additionally, the variation in wettability induced by the introduction of the hydrophilic minority components was determined by static contact angle measurements (static contact angle (SCA)), and tests were carried out to determine the barrier properties against oxygen (oxygen transmission rate (OTR)) and molecular iodine. The I₂ leaching of the different blends was compared with that of PP by monitoring the I₂ retention in a buffered PVP-I solution via UV-vis spectroscopy. Overall, the experimental data showed the capability of the minority components in the blends to increase thermal stability as well as act as a barrier to oxygen. Additionally, the PP blend with PVP-I induced a reduction in molecular iodine leaching in comparison with PP. Full article

The paper describes the conversion of carbon dioxide into methanol in a chemical reactor under standard operating conditions. Electro-analytical techniques, cyclic voltammetry, and chrono-amperometry characterize the process. The electrochemical redox reaction develops using various catalyzers to evaluate the performance of the carbon dioxide conversion into methanol process under variable chemical conditions. The results of the applied technique showed an incomplete redox reaction with an electronic change of z = 2.84 on average, below the ideal number, z = 6, that may be due to methanol decomposition (reverse reaction) because the system operates with a reaction constant above the equilibrium value. The methanol production may improve by draining the methanol/water solution from the chemical reactor to reduce the methanol concentration in the electrochemical cell, shifting the forward reaction towards the formation of methanol, increasing the electron change number, which approaches the ideal value, and improving the methanol production efficiency. The draining process shows a significant increase in methanol formation, which depends on the draining flow rate and the catalyzer type. A simulation process shows that if we operate in optimum conditions, with no methanol decomposition through a reverse reaction, the redox reaction fulfills the ideal condition of maximum electronic change. The experimental tests validate the simulation results, showing a relevant increase in the electron change number with values up to z = 4.2 for optimum draining flow rate conditions (0.2 L/s). The experimental results show a relative increase factor of 4.7 in methanol production, meaning we can produce more than four times more methanol compared with no draining techniques. The data analysis shows that the draining flow rate has a threshold of 0.2 L/s, beyond which the extent of the reaction reverses, reducing the methanol formation due to a chemical reaction disequilibrium. The paper concludes that using the draining method, the methanol production mass rate increases significantly from an average value of 20.9 kg/h for non-draining use, considering all catalyzer types, to a range between 91.9 kg/h and 104.3 kg/h, depending on the flow rate. Averaging all values for different flow rates and comparing with the non-draining case, we obtain an absolute methanol production mass rate of 77 kg/h, meaning an incremental percentage of 469.1%, more than four times the initial production. Although the proposed methodology looks promising, applying this procedure on an industrial scale may suffer from restrictions since the chemical reactions intervening in the methanol formation do not perform linearly. According to experimental tests, the best option among the six catalyzers used for methanol production is the plain copper, with copper oxides (Cu₂O, CuO) and copper Sulphur (CuS) as feasible alternatives. Full article

Background/Objectives: Human Herpes Simplex Virus 1 (HSV-1) is a common pathogen that establishes lifelong latent infections. The emergence of drug resistance necessitates novel therapeutic strategies. This study introduces a novel antiviral approach: a bivalent degrader designed to induce the degradation of an essential protein. Methods: A structural model of ICP0, generated via the Chai-1 AI platform, was analyzed with fpocket, P2Rank, and KVFinder to identify a superior allosteric target site. An iterative de novo design workflow with CReM-dock then yielded a lead scaffold based on its predicted affinity and drug-like properties. This selected “warhead” was used to rationally design the final bivalent degrader, ICP0-deg-01, for the ICP0 dimer model. Results: The generative process yielded a lead chemical scaffold that was selected based on its predicted binding affinity and favorable drug-like properties. This scaffold was used to rationally design a single candidate bivalent degrader, ICP0-deg-01. Our structural model predicts that ICP0-deg-01 can successfully bridge two ICP0 protomers, forming an energetically favorable ternary complex. Conclusions: This work provides a computational proof-of-concept for a novel class of anti-herpetic agents and identifies a lead candidate for future molecular dynamics simulations and experimental validation. Full article

In this study, we optimize the kitchen waste fermentation process by adjusting the fermentation time and temperature to prepare high-efficiency carbon sources to enhance nitrogen and phosphorus removal during sewage treatment. Simulated kitchen waste fermentation experiments were performed, and the impact on the pollutant removal efficiencies was analyzed using a sequence batch reactor (SBR). The results showed that the volatile fatty acid (VFA) concentration peak occurred on the first day of fermentation, the maximum increment was 543.19 mg/L, and the maximum soluble chemical oxygen demand/total nitrogen (COD/TN) ratio was 40.49. However, the highest total nitrogen (TN) removal efficiency was 70.42% on the second day of fermentation. An increase in temperature promoted organic matter release, with the highest soluble COD concentration of 22.69 g/L observed at 45 °C. Further, the maximum VFAs production (935.08–985.13 mg/L) occurred from 25 to 35 °C. In addition, the fermentation products in this temperature range also showed the optimal removal efficiencies for total phosphorus (TP) and TN at 91.50% and 79.63%, respectively. Although 15 °C and 45 °C were beneficial for COD reduction, they were not conducive to nitrogen and phosphorus removal. The energy consumption and the synergistic pollutant removal showed that the optimal fermentation conditions were 2 days at 35 °C. Under these conditions, the kitchen waste-derived carbon source achieved efficient TN and TP removal, as well as COD reduction. Therefore, these conditions provide a feasible solution for the “reduction and sustainability” of kitchen waste. Full article

Recent concerns regarding artificial intelligent (AI) technologies have spurred studies into improving wastewater treatment efficiency and identifying low-carbon processes. Treating one cubic meter of wastewater necessarily consumes a certain amount of chemicals and energy. Approximately 20% of the total chemical consumption is attributed to phosphorus and nitrogen removal, with the exact proportion varying based on treatment quality and facility size. To promote sustainability in wastewater treatment plants (WWTPs), there has been a shift from traditional control systems to AI-based strategies. Research in this area has demonstrated notable improvements in wastewater treatment efficiency. This review provides an extensive overview of the literature published over the past decades, aiming to advance the ongoing discourse on enhancing both the efficiency and sustainability of chemical dosing systems in WWTPs. It focuses on AI-based approaches utilizing algorithms such as neural networks and fuzzy logic. The review encompasses AI-based wastewater treatment processes: parameter analysis/forecasting, model development, and process optimization. Moreover, it summarizes six promising areas of AI-based chemical dosing, including acid–base regents, coagulants/flocculants, disinfectants/disinfection by-products (DBPs) management, external carbon sources, phosphorus removal regents, and adsorbents. Finally, the study concludes that significant challenges remain in deploying AI models beyond simulated environments to real-world applications. Full article

To meet the massive increase in energy demand, extensive research has been conducted over the past few decades on developing clean and sustainable energy storage methods. Hydrogen is considered as one of the most promising future energy carriers due to its high energy density and renewability, but it requires storage. Storing hydrogen using metal hydride offers several advantages, including stability, safety compactness and reversibility of the hydrogen absorption/desorption process. Thermal management during hydrogen storage using metal hydride is critically important since the reaction between the metal and hydrogen is highly exothermic. We are aiming to develop thermal storage systems based on composite phase change materials (CPCMs) that absorb the heat generated during hydrogen absorption and release it during desorption, in an effort to improve energy storage efficiency. Lightweight, shape-stable CPCMs are prepared by loading the selected organic phase change materials into expanded graphite and hydrophobic monolithic silica aerogel. The chemical structure, microstructure, thermal properties and leakage of CPCMs are investigated. These samples were subjected to variable power electrical heating to simulate the heat generated during hydrogen reaction, forming lanthanum hydride, according to its published reaction kinetics. Full article

Lubricating oils are utilised in equipment and machinery to reduce friction and enhance material utilisation. The utilisation of oil leads to an increase in its thickness and density over time. Current methods for assessing oil life are slow, expensive, and complex, and often only applicable in laboratory settings and unsuitable for real-time or field use. This leads to unexpected equipment failures, unnecessary oil changes, and economic and environmental losses. A comprehensive review of the extant literature revealed no studies and no national or international patents on neural network algorithm-based oil life modelling and classification using green sensors. In order to address this research gap, this study, for the first time in the literature, provides a green conductivity sensor with high-accuracy prediction of oil life by integrating real-time field measurements and artificial neural networks. This design is based on analysing resistance change using a relatively low-cost, three-dimensional, eco-friendly sensor. The sensor is characterised by its simplicity, speed, precision, instantaneous measurement capability, and user-friendliness. The MLP and LVQ algorithms took as input the resistance values measured in two different oil types (diesel, bench oil) after 5–30 h of use. Depending on their degradation levels, they classified the oils as ‘diesel’ or ‘bench oil’ with 99.77% and 100% accuracy. This study encompasses a sensing system with a sensitivity of 50 µS/cm, demonstrating the proposed methodologies’ efficacy. A next-generation decision support system that will perform oil life determination in real time and with excellent efficiency has been introduced into the literature. The components of the sensor structure under scrutiny in this study are conducive to the creation of zero waste, in addition to being environmentally friendly and biocompatible. The developed three-dimensional green sensor simultaneously detects physical (resistance change) and chemical (oxidation-induced polar group formation) degradation by measuring oil conductivity and resistance changes. Measurements were conducted on simulated contaminated samples in a laboratory environment and on real diesel, gasoline, and industrial oil samples. Thanks to its simplicity, rapid applicability, and low cost, the proposed method enables real-time data collection and decision-making in industrial maintenance processes, contributing to the development of predictive maintenance strategies. It also supports environmental sustainability by preventing unnecessary oil changes and reducing waste. Full article

Climate change and global decarbonization targets drive the search for more efficient and cost-effective combustion technologies. Chemical looping combustion (CLC) using solid oxygen carriers with chemical looping with oxygen uncoupling (CLOU) functionality has attracted growing interest due to its inherent potential for CO₂ capture without requiring additional separation processes. This study introduces a conceptual proof-of-concept design of a novel fuel reactor design for a dual-fluidized bed CLC system operating with solid fuels. The new configuration incorporates a perforated conveyor for circulating CLOU-type oxygen carriers, thereby avoiding direct contact between the carriers and the fuel–ash mixture. This approach prevents the slip of unburned fuel and ash into the air reactor, minimizes the loss of oxygen carriers, and improves combustion efficiency. The proposed reactor concept enables the generation of flue gas with a high CO₂ concentration, which facilitates its subsequent capture and reduces the energy penalty associated with traditional CCS techniques. The improved phase separation and better control of oxygen carrier residence time contribute to enhanced system performance and reduced operating costs. Preliminary process simulations conducted in the CeSFaMB environment, using boundary and initial conditions from a CLC test rig, were included to illustrate the potential of the design. Experimental validation is outside the scope of this study and will be presented in future work once the dedicated test facility is operational. This contribution should therefore be regarded as a conceptual proof-of-concept study, and experimental validation together with techno-economic benchmarking will be reported in follow-up publications once the dedicated test facility is operational. Full article

This study analyzes the effects of washing conditions on polycyclic aromatic hydrocarbon (PAH) content in firefighter protective clothing. The analysis involved specially prepared textile packages made of materials used in such clothing: an outer shell, a moisture barrier membrane, and a thermal insulation lining. Package samples were subjected to simulated exposure to a selected group of PAH compounds. Ultra-high-performance liquid chromatography with fluorescence detection (UHPLC/FL) was applied to determine PAH content. The study showed that washing conditions (water temperature and the number of rinses) influenced the effectiveness of removal of chemical contaminants. The most favorable results were obtained for the washing process conducted at 60 °C with three rinse cycles, which resulted in the lowest concentration of total PAHs in the two examined types of textile packages (0.40 µg·g⁻¹ and 0.60 µg·g⁻¹ in the outer shell, 3.9 µg·g⁻¹ and 6.2 µg·g⁻¹ in the membrane, and 0.40 µg·g⁻¹ and 0.41 µg·g⁻¹ in the thermal lining of packages A and B, respectively). The higher washing temperature (60 °C) had a more favorable effect on average washing effectiveness as compared with the lower temperature (40 °C) in both the two- and three-rinse variants. The average washing effectiveness also varied according to the type of material and amounted to 70% and 54% for textile package types A and B, respectively. Full article

This work aims to present tes-thermo, a Python library developed to solve thermodynamic equilibrium problems using the Gibbs energy minimization approach. The library is a variant of TeS v.3, a standalone executable developed for the same purpose. The tool formulates the chemical equilibrium problem of combined phases as a nonlinear programming problem, implemented using Pyomo (Python Optimization Modeling Objects) and solved with IPOPT (Interior Point OPTimizer). To validate the tool and demonstrate its robustness, the supercritical water gasification (SCWG) of methanol and ethanol was investigated. The Peng–Robinson equation of state was employed to account for non-idealities in the gas phase. Experimental and simulated data from the literature were used for validation, and, in both cases, the results were satisfactory, with root mean square errors consistently below 0.23. The SCWG processes studied revealed that hydrogen production is favored by increasing temperature and decreasing pressure. For both methanol and ethanol, increasing the carbonaceous substrate fraction in the feed promotes hydrogen formation; however, it also leads to reduced hydrogen relative yield due to the enhanced formation of methane and carbon monoxide under these conditions. Consequently, although hydrogen production increases, the hydrogen molar fraction in the dry gas stream tends to decrease with the higher substrate content. As expected, the SCWG of methanol produces more hydrogen and less carbon monoxide compared to ethanol under similar conditions. This behavior is consistent with the higher carbon content in ethanol, which favors reactions leading to carbon oxides. In summary, tes-thermo proves to be a robust and reliable tool for conducting research and studies on topics related to thermodynamic equilibrium. Full article

Surface ozone pollution poses a significant threat to human health and ecosystems. However, its highly variable spatiotemporal distribution, especially at hourly scales across China, complicates effective risk management. This variability presents substantial challenges for accurate estimation and forecasting, underscoring the importance of evaluating current hourly surface ozone estimation methods. Therefore, this study collaboratively evaluated the performance of chemical transport model simulations and satellite-based estimates of hourly surface ozone concentrations over mainland China in 2019. Using data from 3185 ground monitoring stations operated by the Ministry of Ecology and Environment, as well as six independent observation sites in Hong Kong, Xianghe, Nam Co, Akedala, Longfengshan, and Waliguan, this study found that both datasets exhibited systematic biases and lacked spatiotemporal consistency. The Community Atmosphere Model with Chemistry simulation results exhibited an average relative bias of 23.17%, generally overestimated ozone concentrations in high-altitude regions, but outperformed the satellite-based estimates at the independent sites, while consistently underestimating ozone concentrations in densely populated urban areas. In contrast, the satellite-based estimates performed better in regions with dense monitoring sites, with mean biases typically within 10% of observations, but their accuracy was limited in remote areas due to sparse ground-based calibration. It is particularly noteworthy that both datasets showed deficiencies in capturing extremely high-value events, nighttime ozone variations, and dynamic transport processes, underscoring challenges in the representation of photochemical processes in the model and in the design of satellite estimation algorithms. The results highlight the importance of optimizing model parameterization schemes, improving satellite estimation algorithms, and integrating multi-source data to enhance the accuracy and stability of hourly ozone estimates. This study provides multi-scale quantitative insights into the relative strengths and limitations of different ozone estimation methods, laying a solid scientific foundation for future data integration, regional air quality management, and policy development. Full article

A methane-air premixed gas explosion is one of the most destructive disasters in the process of coal mining, and the dynamic coupling between the shock wave triggered by the explosion and the surrounding rock of the roadway can lead to the destabilization of the surrounding rock structure, the destruction of equipment, and casualties. The aim of this study is to systematically reveal the propagation characteristics of the blast wave, the spatial and temporal evolution of the wall load, and the damage mechanism of the surrounding rock by establishing a two-way fluid-solid coupling numerical model. Based on the Ansys Fluent fluid solver and Transient Structure module, a framework for the co-simulation of the fluid and solid domains has been constructed by adopting the standard $k-\epsilon$ turbulence model, finite-rate/eddy-dissipation (FR/ED) reaction model, and nonlinear finite-element theory, and by introducing a dynamic damage threshold criterion based on the Drucker–Prager and Mohr–Coulomb criteria. It is shown that methane concentration significantly affects the kinetic behavior of explosive shock wave propagation. Under chemical equivalence ratio conditions (9.5% methane), an ideal Chapman–Jouguet blast wave structure was formed, exhibiting the highest energy release efficiency. In contrast, lean ignition (7%) and rich ignition (12%) conditions resulted in lower efficiencies due to incomplete combustion or complex combustion patterns. In addition, the pressure time-history evolution of the tunnel enclosure wall after ignition triggering exhibits significant nonlinear dynamics, which can be divided into three phases: the initiation and turbulence development phase, the quasi-steady propagation phase, and the expansion and dissipation phase. Further analysis reveals that the closed end produces significant stress aggregation due to the interference of multiple reflected waves, while the open end increases the stress fluctuation due to turbulence effects. The spatial and temporal evolution of the strain field also follows a three-stage dynamic pattern: an initial strain-induced stage, a strain accumulation propagation stage, and a residual strain stabilization stage and the displacement is characterized by an initial phase of concentration followed by gradual expansion. This study not only deepens the understanding of methane-air premixed gas explosion and its interaction with the roadway’s surrounding rock, but also provides an important scientific basis and technical support for coal mine safety production. Full article

Background: Hydroxycarboxylic acid receptor 3 (HCAR3) is a receptor that is mainly expressed in human adipose tissue. It can inhibit lipolysis through the inhibition of adenylyl cyclase; thus, it is closely related to the regulation of lipids in the human body. This makes HCAR3 a compelling target for developing drugs against dyslipidemia. Notably, the reported active compounds for HCAR3 are all carboxylic acids. This observation is in line with the fact that ARG111 has been reported as the key residue to anchor the active compound in a closely related homologous protein—HCAR2. Methods: In this study, we aim to discover new chemicals, through virtual screening, that may bind with HCAR3. As there are several choices for the receptor conformation, cross-docking was conducted and the root-mean-square deviation of the docking pose from the conformation of the crystal ligand was employed to determine the best receptor conformation for screening. Ligands from the ZINC20 database were screened through molecular docking, and 30 candidates were subjected to 100 ns MD simulations. Six stable complexes were further assessed by umbrella sampling to estimate binding affinity. Results: The homology model (HCAR3_homology) was selected as the receptor. Following the protocol determined by the retrospective docking process, prospective docking was conducted to screen the ligands from the ZINC20 database. Subsequently, the top 30 compounds with a good docking score and a good interaction with ARG111 were subjected to 100 ns molecular dynamics (MD) simulations, and their binding stability was analyzed based on the resulting trajectories. Finally, six compounds were chosen for binding free energy calculation using umbrella sampling; all showed negative binding affinities. Conclusions: All six compounds selected for umbrella sampling showed negative binding affinities, suggesting their potential as novel HCAR3 ligands for the development of drugs against dyslipidemia. Full article

Heat shock proteins (HSPs), a family of proteins including HSP27, HSP40, HSP60, HSP70, and HSP90, play critical roles in cellular processes and are often dysregulated in cancer. Heat Shock Factor 1 (HSF1) protein, the master regulator of HSP expression, is also a promising target for cancer therapy due to its involvement in tumorigenesis. This study is the first to investigate the potential of two novel curcumin analogs, MS13 (1,2-bis(4-hydroxy-3-methoxyphenyl)-1,4-pentadiene-3-one) and MS17 (1,5-bis(2-hydroxyphenyl)-1,4-pentadiene-3-one), as modulators of these key targets. Employing molecular docking and molecular dynamics (MD) simulations, we investigated the interactions of MS13 and MS17 with HSF1 and the panel of HSPs. Both compounds demonstrated strong binding affinity for all the proteins, particularly for HSP70, exhibiting greater affinity compared to curcumin. Molecular docking revealed specific binding sites for both compounds on each target protein, which were further investigated using MD simulations. MS17 generally formed more stable complexes with HSP27, HSP40, HSP60, and HSP70, suggesting it might be a more potent modulator of these specific proteins. In contrast, MS13 displayed greater stability when bound to HSF1 and HSP90. These different variations could be attributed to variations in the chemical structures of MS13 and MS17, leading to distinct interactions with each protein’s binding site. MS13 and MS17 exhibit more advantageous ADMET profiles compared to curcumin, particularly in their predicted Blood–Brain Barrier (BBB) permeability and MS17’s superior passive membrane permeability and absorption. These findings highlight the potential of both MS13 and MS17 as promising leads for developing HSP modulators for cancer treatment. Full article

This paper aims to assess the water quality of the Ismailia Canal, Egypt, in accordance with Article 49 of Law 92/2013. QUAL2K and Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) are utilized to simulate the water quality parameters of dissolved oxygen (DO), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), nitrate nitrogen (NO₃-N), and ammonium (NH₃-N) in winter and summer 2023. The parameters of the QUAL2K and CNN-LSTM models were calibrated and validated in both winter and summer through trial and error, until the simulated results agreed well with the observed data. Additionally, the model’s performance was measured using different statistical criteria such as mean absolute error (MAE), root mean square (RMS), and relative error (RE). The results showed that the simulated values were in good agreement with the observed values. The results show that all parameter concentrations follow and did not exceed the limit of Article 49 of Law 92/2013 in winter and summer, except for dissolved oxygen concentration (8.73–4.53 mg/L) in winter and summer, respectively, which exceeds the limit of 6 mg/L, and in June, biological oxygen demand exceeds the limit of 6 mg/L due to increased organic matter. It is imperative to compare QUAL2K and CNN-LSTM models because QUAL2K provides a physics-based simulation of water quality processes, whereas CNN-LSTM employs deep learning in modeling complex temporal patterns. The two models enhance prediction accuracy and credibility towards enabling enhanced decision-making for Ismailia Canal water management. This research can be part of a decision support system regarding maximizing the benefits of the Ismailia Canal. Full article