Search Results (350)

In this study, a series of novel alkoxylated resorcinarenes were synthesized using secondary and tertiary alcohols under mild catalytic conditions involving iminodiacetic acid. Structural characterization, including single-crystal X-ray diffraction, confirmed the successful incorporation of branched alkyl chains and highlighted the influence of substitution patterns on molecular packing. Notably, detailed mass spectrometric analysis revealed that, under specific conditions, the reaction pathway may shift toward the formation of defined oligomeric species with supramolecular characteristics—an observation that adds a new dimension to the synthetic potential of this system. To complement the chemical analysis, selected derivatives were evaluated for biological activity, focusing on bacterial growth and biofilm formation. Using four clinically relevant strains (Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Bacillus subtilis), we assessed both planktonic proliferation (OD₆₀₀) and biofilm biomass (crystal violet assay). Compound 2c (2-pentanol derivative) consistently promoted biofilm formation, particularly in S. aureus and B. subtilis, while having limited cytotoxic effects. In contrast, compound 2e and the DMSO control exhibited minimal impact on biofilm development. The results suggest that specific structural features of the alkoxy chains may modulate microbial responses, potentially via membrane stress or quorum sensing interference. This work highlights the dual relevance of alkoxylated resorcinarenes as both supramolecular building blocks and modulators of microbial behavior. Full article

Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popularity in several fields due to their ability to adapt to various battery geometries, enhanced safety features, greater thermal stability, and effectiveness in reducing dendrite growth on the anode. However, their relatively low ionic conductivity compared to liquid electrolytes has limited their application in high-performance devices. This limitation has led to recent studies revolving around the development of poly(ionic liquids) (PILs), particularly imidazolium-mediated polymer backbones as novel electrolyte materials, which can increase the conductivity with fine-tuning structural benefits, while maintaining the advantages of both solid and gel electrolytes. In this study, a curated dataset of 120 data points representing eight different polymers was used to predict ionic conductivity in imidazolium-based PILs as well as the emerging ionene substructures. For this purpose, four ML models: CatBoost, Random Forest, XGBoost, and LightGBM were employed by incorporating chemical structure and temperature as the models’ inputs. The best-performing model was further employed to estimate the conductivity of novel ionenes, offering insights into the potential of advanced polymer architectures for next-generation LIB electrolytes. This approach provides a cost-effective and intelligent pathway to accelerate the design of high-performance electrolyte materials. Full article

Peroxisome proliferator-activated receptor alpha (PPARα) is a key regulator of lipid metabolism, making its agonists valuable therapeutic targets for various diseases, including chronic peripheral neuropathy. Existing PPARα agonists face limitations such as poor selectivity, sub-optimal bioavailability, and safety concerns. We previously demonstrated that A190, a novel, potent, and selective PPARα agonist, effectively alleviates chemotherapy-induced peripheral neuropathy and CFA-induced inflammatory pain as a non-opioid therapeutic agent. However, A190 alone has solubility and permeability issues that limits its oral delivery. To overcome this challenge, in this study, four new-generation ester prodrugs of A190; A190-PD-9 (methyl ester), A190-PD-14 (ethyl ester), A190-PD-154 (isopropyl ester), and A190-PD-60 (cyclic carbonate) were synthesized and evaluated for their enzymatic bioconversion and chemical stability. The lead candidate, A190-PD-60, was further formulated as a microemulsion (A190-PD-60-ME) and optimized via Box–Behnken design. A190-PD-60-ME featured nano-sized droplets (~120 nm), low polydispersity (PDI < 0.3), and high drug loading (>90%) with significant improvement in artificial membrane permeability. Crucially, pharmacokinetic evaluation in rats demonstrated that A190-PD-60-ME reached a 16.6-fold higher Cmax (439 ng/mL) and a 5.9-fold increase in relative oral bioavailability compared with an A190-PD-60 dispersion. These findings support the combined prodrug-microemulsion approach as a promising strategy to overcome oral bioavailability challenges and advance PPARα-targeted therapies. Full article

The development of novel platinum-based anticancer agents remains a critical objective in medicinal inorganic chemistry, particularly in light of resistance and toxicity limitations associated with cisplatin. In this study, the synthesis, structural characterization, quantum chemical analysis, and cytotoxic evaluation of four new acetylplatinum(II) complexes (cis-[Pt(COMe)₂(PASO₂)₂], cis-[Pt(COMe)₂(DAPTA)₂], trans-[Pt(COMe)Cl(DAPTA)₂], and trans-[Pt(COMe)Cl(PASO₂)]: 1–4, respectively) bearing cage phosphine ligands PASO₂ (2-thia-1,3,5-triaza-phosphaadamantane 2,2-dioxide) and DAPTA (3,7-diacetyl-1,3,7-triaza-5-phosphabicyclo[3.3.1]nonane) are presented. The coordination geometries and NMR spectral features of the cis/trans isomers were elucidated through multinuclear NMR and DFT calculations at the B3LYP/6-311++G(d,p)/LanL2DZ level, with strong agreement between experimental and theoretical data. Quantum Theory of Atoms in Molecules (QTAIM) analysis was applied to investigate bonding interactions and assess the covalent character of Pt–ligand bonds. Cytotoxicity was evaluated against five human cancer cell lines. The PASO₂-containing complex in cis-configuration, 1, demonstrated superior activity against thyroid (8505C) and head and neck (A253) cancer cells, with potency surpassing that of cisplatin. The DAPTA complex 2 showed enhanced activity toward ovarian (A2780) cancer cells. These findings highlight the influence of ligand structure and isomerism on biological activity, supporting the rational design of phosphine-based Pt(II) anticancer drugs. Full article

Combustion modeling using computational fluid dynamics (CFD) offers detailed insights into the flame structure and thermo-chemical processes. Furthermore, it has been extensively used in the past to optimize industrial furnaces. Despite the increasing computational power, the prediction of the reaction kinetics in flames is still related to high calculation times, which is a major drawback for large-scale combustion systems. To speed-up the simulation, artificial neural networks (ANNs) were applied in this study to calculate the chemical source terms in the flame instead of using a chemistry solver. Since one ANN may lack accuracy for the entire input feature space (temperature, species concentrations), the space is sub-divided into four regions/ANNs. The ANNs were tested for different fuel mixtures, degrees of turbulence, and air-fuel/oxy-fuel combustion. It was found that the shape of the flame and its position were well predicted in all cases with regard to the temperature and CO. However, at low temperature levels (<800 K), in some cases, the ANNs under-predicted the source terms. Additionally, in oxy-fuel combustion, the temperature was too high. Nevertheless, an overall high accuracy and a speed-up factor for all simulations of 12 was observed, which makes the approach suitable for large-scale furnaces. Full article

Background: Informal electronic waste (e-waste) recycling practices release a complex mixture of pollutants, particularly heavy metals, into the environment. Chronic exposure to these contaminants has been linked to a range of health risks, but the molecular underpinnings remain poorly understood. In this study, we investigated the alterations in metabolic profiles due to e-waste exposure and linked these metabolites to systemic biological effects. Methods: We applied untargeted high-resolution metabolomics using dual-column LC-MS/MS and a multi-step analysis workflow combining MS1 feature detection, MS2 annotation, and chemical ontology classification, to characterize urinary metabolic alterations in 91 e-waste workers and 51 community controls associated with the Agbogbloshie site (Accra, Ghana). The impacts of heavy metal exposure in e-waste workers were assessed by establishing linear regression and four-parameter logistic (4PL) models between heavy metal levels and metabolite concentrations. Results: Significant metal-associated metabolomic changes were identified. Both linear and nonlinear models revealed distinct sets of exposure-responsive compounds, highlighting diverse biological responses. Ontology-informed annotation revealed systemic effects on lipid metabolism, oxidative stress pathways, and xenobiotic biotransformation. This study demonstrates how integrating chemical ontology and nonlinear modeling facilitates exposome interpretation in complex environments and provides a scalable template for environmental biomarker discovery. Conclusions: Integrating dose–response modeling and chemical ontology analysis enables robust interpretation of exposomics datasets when direct compound identification is limited. Our findings indicate that e-waste exposure induces systemic metabolic alterations that can underlie health risks and diseases. Full article

Dissolved gas analysis remains the most widely utilized non-intrusive diagnostic method for detecting incipient faults in insulating liquid-immersed transformers. Despite their prevalence, conventional ratio-based methods often suffer from ambiguity and limited potential for automation applicrations. To address these limitations, this study proposes a unified multiclass classification model that integrates traditional gas ratio features with supervised machine learning algorithms to enhance fault diagnosis accuracy. The performance of six machine learning classifiers was systematically evaluated using training and testing data generated through four widely recognized gas ratio schemes. Grid search optimization was employed to fine-tune the hyperparameters of each model, while model evaluation was conducted using 10-fold cross-validation and six performance metrics. Across all the diagnostic approaches, ensemble models, namely random forest, XGBoost, and LightGBM, consistently outperformed non-ensemble models. Notably, random forest and LightGBM classifiers demonstrated the most robust and superior performance across all schemes, achieving accuracy, precision, recall, and F1 scores between 0.99 and 1, along with Matthew correlation coefficient values exceeding 0.98 in all cases. This robustness suggests that ensemble models are effective at capturing complex decision boundaries and relationships among gas ratio features. Furthermore, beyond numerical classification, the integration of physicochemical and dielectric properties in this study revealed degradation signatures that strongly correlate with thermal fault indicators. Particularly, the CIGRÉ-based classification using a random forest classifier demonstrated high sensitivity in detecting thermally stressed units, corroborating trends observed in chemical deterioration parameters such as interfacial tension and CO₂/CO ratios. Access to over 80 years of operational data provides a rare and invaluable perspective on the long-term performance and degradation of power equipment. This extended dataset enables a more accurate assessment of ageing trends, enhances the reliability of predictive maintenance models, and supports informed decision-making for asset management in legacy power systems. Full article

Infrasound signal classification remains a critical challenge in geophysical monitoring systems, where classification performance is fundamentally constrained by feature extraction efficacy. Existing two-dimensional feature extraction methods suffer from inadequate representation of spatiotemporal signal dynamics, leading to performance degradation in long-distance detection scenarios. To overcome these limitations, we present a novel classification framework that effectively captures spatiotemporal infrasound characteristics through Gramian Angular Field (GAF) transformation. The proposed method introduces an innovative encoding scheme that transforms one-dimensional infrasonic waveforms into two-dimensional GAF images while preserving crucial temporal dependencies. Building upon this representation, we develop an advanced hybrid deep learning architecture that integrates ConvLSTM networks to simultaneously extract and correlate spatial and spectral features. Extensive experimental validation on both chemical explosion and seismic infrasound datasets shows our approach achieves 92.4% classification accuracy, demonstrating consistent superiority over four state-of-the-art benchmark methods. These findings demonstrate the effectiveness of the proposed method. Full article

The chlorophyll index (CHI) is a crucial indicator for assessing the photosynthetic capacity and nutritional status of crops. However, traditional methods for measuring CHI, such as chemical extraction and handheld instruments, fall short in meeting the requirements for efficient, non-destructive, and continuous monitoring at the canopy level. This study aimed to explore the feasibility of predicting rice canopy CHI using nighttime multi-source spectral data combined with machine learning models. In this study, ground truth CHI values were obtained using a SPAD-502 chlorophyll meter. Canopy spectral data were acquired under nighttime conditions using a high-throughput phenotyping platform (HTTP) equipped with active light sources in a greenhouse environment. Three types of sensors—multispectral (MS), visible light (RGB), and chlorophyll fluorescence (ChlF)—were employed to collect data across different growth stages of rice, ranging from tillering to maturity. PCA and LASSO regression were applied for dimensionality reduction and feature selection of multi-source spectral variables. Subsequently, CHI prediction models were developed using four machine learning algorithms: support vector regression (SVR), random forest (RF), back-propagation neural network (BPNN), and k-nearest neighbors (KNNs). The predictive performance of individual sensors (MS, RGB, and ChlF) and sensor fusion strategies was evaluated across multiple growth stages. The results demonstrated that sensor fusion models consistently outperformed single-sensor approaches. Notably, during tillering (TI), maturity (MT), and the full growth period (GP), fused models achieved high accuracy (R² > 0.90, RMSE < 2.0). The fusion strategy also showed substantial advantages over single-sensor models during the jointing–heading (JH) and grain-filling (GF) stages. Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. Overall, the highest prediction accuracy was achieved during the full growth period (GP) using fused spectral data, with an R² of 0.96 and an RMSE of 1.99. This study provides a valuable reference for developing CHI prediction models based on nighttime multi-source spectral data. Full article

Four new minor monosulfated triterpene penta- and hexaosides, cladolosides S (1), S₁ (2), T (3), and T₁ (4), were isolated from the Vietnamese sea cucumber Cladolabes schmeltzii (Sclerodactylidae, Dendrochirotida). The structures of the compounds were established based on extensive analysis of 1D and 2D NMR spectra as well as HR-ESI-MS data. Cladodosides S (1), S₁ (2) and T (3), T₁ (4) are two pairs of dehydrogenated/hydrogenated compounds that share identical carbohydrate chains. The oligosaccharide chain of cladolosides of the group S is new for the sea cucumber glycosides due to the presence of xylose residue attached to C-4 Xyl1 in combination with a sulfate group at C-6 MeGlc4. The oligosaccharide moiety of cladolosides of the group T is unique because of the position of the sulfate group at C-3 of the terminal sugar residue instead of the 3-O-Me group. This suggests that the enzymatic processes of sulfation and O-methylation that occur during the biosynthesis of glycosides can compete with each other. This can presumably occur due to the high level of expression or activity of the enzymes that biosynthesize glycosides. The mosaicism of glycoside biosynthesis (time shifting or dropping out of some biosynthetic stages) may indicate a lack of compartmentalization inside the cells of organism producers, leading to a certain degree of randomness in enzymatic reactions; however, this also offers the advantage of providing chemical diversity of the glycosides. Analysis of the hemolytic activity of a series of 26 glycosides from C. schmeltzii revealed some patterns of structure–activity relationships: the presence or absence of 3-O-methyl groups has no significant impact, hexaosides, which are the final products of biosynthesis and predominant compounds of the glycosidic fraction of C. schmeltzii, are more active than their precursors, pentaosides, and the minor tetraosides, cladolosides of the group A, are weak membranolytics and therefore are not synthesized in large quantities. Two glycosides from C. schmeltzii, cladolosides D (18) and H₁ (26), display selectivity of cytotoxic action toward triple-negative breast cancer cells MDA-MB-231, while remaining non-toxic in relation to normal mammary cells MCF-10A. Quantitative structure–activity relationships (QSAR) were calculated based on the correlational analysis of the physicochemical properties and structural features of the glycosides and their hemolytic and cytotoxic activities against healthy MCF-10A cells and cancer MCF-7 and MDA-MB-231 cell lines. QSAR highlighted the complexity of the relationships as the cumulative effect of many minor contributions from individual descriptors can have a significant impact. Furthermore, many structural elements were found to have different effects on the activity of the glycosides against different cell lines. The opposing effects were especially pronounced in relation to hormone-dependent breast cancer cells MCF-7 and triple-negative MDA-MB-231 cells. Full article

The accurate prediction of wastewater quality parameters is pivotal for evaluating the treatment stability of processes and for ensuring regulatory compliance in wastewater treatment plants. A singular machine learning model often faces challenges in fully capturing and extracting the complex nonlinear relationships inherent in multivariate time series data. To overcome this limitation, this study proposes a dual hybrid modeling framework that effectively integrates LSTM and XGBoost models, leveraging their complementary strengths. The first hybrid model refines the residues to utilize the information, whereas the second hybrid model enhances the input features by extracting temporal dependencies. A comparative analysis against three standalone models reveals that the proposed hybrid framework consistently outperforms them in both predictive accuracy and generalization ability across four key effluent indicators—chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus. These results demonstrate that the proposed hybrid machine learning framework has great potential to be used to evaluate process stability in wastewater treatment plants, paving a way for smarter, more resilient, and more sustainable wastewater management, which will improve ecological integrity and regulatory compliance. Full article

Pine nuts hold significant economic value due to their rich plant protein and healthy fats, yet precise variety classification has long been hindered by limitations of traditional techniques such as chemical analysis and machine vision. This study proposes a novel near-infrared (NIR) spectral feature selection algorithm, termed the improved binary equilibrium optimizer with selection probability (IBiEO-SP), which incorporates a dynamic probability adjustment mechanism to achieve efficient feature dimensionality reduction. Experimental validation on a dataset comprising seven pine nut varieties demonstrated that, compared to particle swarm optimization (PSO) and the genetic algorithm (GA), the IBiEO-SP algorithm improved average classification accuracy by 5.7% (p < 0.01, Student’s t-test) under four spectral preprocessing methods (MSC, SNV, SG1, and SG2). Remarkably, only 2–3 features were required to achieve optimal performance (MSC + random forest: 99.05% accuracy, 100% F1/precision; SNV + KNN: 97.14% accuracy, 100% F1/precision). Furthermore, a multimodal data synergy strategy integrating NIR spectroscopy with morphological features was proposed, and a classification model was constructed using a soft voting ensemble. The final classification accuracy reached 99.95%, representing a 2.9% improvement over single-spectral-mode analysis. The results indicate that the IBiEO-SP algorithm effectively balances feature discriminative power and model generalization needs, overcoming the contradiction between high-dimensional data redundancy and low-dimensional information loss. This work provides a high-precision, low-complexity solution for rapid quality detection of pine nuts, with broad implications for agricultural product inspection and food safety. Full article

The seasonal and interannual irregularity of temperature and precipitation is a feature of the Mediterranean climate that is intensified by climate change and constitutes a relevant driver of water and soil degradation. This study was developed during three years in a hydro-agricultural area of the Alqueva irrigation system (Portugal) with Mediterranean climate conditions. The sampling campaigns included collecting water samples from eight irrigation hydrants, analyzed four times yearly. The analysis incorporated meteorological data and indices (precipitation, temperature, and drought conditions) alongside chemical parameters, using multivariate statistics (factor analysis and cluster analysis) to identify key water quality drivers. Additionally, machine learning models (Random Forest regression and Gradient Boosting machine) were employed to predict electrical conductivity (ECw), sodium adsorption ratio (SAR), and pH based on chemical and climatic variables. Water quality evaluation showed a prevalence of a slight to moderate soil sodification risk. The factor analysis outcome was a three-factor model related to salinity, sodicity, and climate. The cluster analysis revealed a grouping pattern led by year and followed by stage, pointing to the influence of inter-annual climate irregularity. Variations in water quality from the reservoirs to the distribution network were not substantial. The Random Forest algorithm showed superior predictive accuracy, particularly for ECw and SAR, confirming its potential for the reliable forecasting of irrigation water quality. This research emphasizes the importance of integrating time-sensitive monitoring with data-driven predictions of water quality to support sustainable water resources management in agriculture. This integrated approach offers a promising framework for early warning and informed decision-making in the context of increasing drought vulnerability across Mediterranean agro-environments. Full article

Silver nanoparticles, with various uses in pharmacy, cosmetics, sanitation, textiles, optoelectronics, photovoltaics, etc., that are provided by worldwide industrial production, estimated to hundreds of tons annually, are finally released in the environment impacting randomly the biosphere. An alternative synthesis approach could be implemented by replacing chemical reductants of silver with natural antioxidants ensuring production and utilization sustainability with focus on environmental pollution diminishing. We synthesized silver nanoparticles by using plant extracts, aiming to offer antimicrobial products with reduced impact on the environment through sustainable green-chemistry. Fresh extracts of lemon pulp, blueberry and blackberry fruits as well as of green tea dry leaves were the sources of the natural antioxidants able to ensure ionic silver reduction and silver nanoparticle formation in the form of colloidal suspensions. The four samples were characterized by UV–Vis spectrophotometry, scanning electron microscopy, dark field optical microscopy, X-ray diffractometry, dynamic light scattering, which evidenced specific fine granularity, plasmonic features, standard crystallinity, and good stability in water suspension. Antimicrobial activity was assayed using the agar diffusion method and the bacteria kill-time technique against Staphylococcus aureus and Escherichia coli. In both cases, all silver nanoparticles revealed their adequacy for the aimed purposes, the sample synthesized with green tea showing the best efficiency, which is in concordance with its highest contents of polyphenols, flavones and best total antioxidant activity. Various applications could be safely designed based on such silver nanoparticles for sustainable chemistry development. Full article

The content of modified materials in multicomponent gel phase change materials directly affects their performance characteristics. To investigate the influence of different contents of modified materials on the performance features of Na₂HPO₄·12H₂O-based multicomponent Gel Phase Change Materials, four single factors (Na₂SiO₃·9H₂O, C₃₅H₄₉O₂₉, KCl, and nano-α-Fe₂O₃) and their interactions were selected as influencing factors. Using the Taguchi method with an L₂₇(3¹³) orthogonal array, multi-step melt–blending experiments were conducted to prepare a novel multi-component phase change material. The characteristics of the new multi-component phase change material, including supercooling degree (ΔT), phase change temperature (T_m), latent heat of phase change (ΔH_m), and cooling time (CT), were obtained. In addition, characterization techniques such as DSC, SEM, FT-IR, and XRD were employed to analyze its thermal properties, microscopic morphology, chemical stability, and crystal structure. Based on the experimental results, the signal-to-noise ratio (S/N) was used to rank the influence of each factor on the quality characteristics, and the p-value from analysis of variance (ANOVA) was employed to evaluate the significance of each factor on the performance characteristics. Then, the effects of each significant factor on the characteristics of the multiple gel phase change materials were analyzed in detail, and the optimal mixing ratio of the new multiple gel phase change materials was selected. The results showed that Na₂SiO₃·9H₂O, KCl, and α-Fe₂O₃ were the most critical process parameters. This research work enriches the selection of composite gel phase change materials for solar greenhouses and provides guidance for the selection of different modified material contents using Na₂HPO₄·12H₂O as the starting material. Full article