Search Results (892)

Extracting bioactives from algae is essential for sustainable solutions aimed at enhancing human health. This study pioneers a multidimensional approach that simultaneously compares ultrasound-assisted carotenoid extraction from spray-dried (SD) and solar-dried (SolD) Spirulina platensis, evaluating both food-grade and emerging green biosolvents, validated through COSMO-SAC predictions and optimized using RSM. The SD sample showed higher carotenoid yields with most solvents, consistent with particle size data indicating less aggregation than SolD. Solvent efficacy varied depending on drying method and carotenoid type; acetone was optimal for zeaxanthin and β-carotene from SD and β-carotene from SolD, while methanol and ethanol were more effective for zeaxanthin in SolD. The green solvent 2-methyltetrahydrofuran (2-MeTHF) demonstrated excellent carotenoid affinity in COSMO-SAC predictions and ranked as the second most effective solvent in the SD sample, underscoring its potential as a sustainable alternative. RSM models using 2-MeTHF (SD) and ethanol (SolD) showed excellent prediction accuracy (R² > 98%). Optimized extraction conditions yielded ~4-fold higher total carotenoid recovery compared to non-optimized conditions. Combining computational tools and experiments offers an effective strategy to optimize sustainable extraction of health-promoting carotenoids from Spirulina. Full article

A new method is presented for the estimation of contributions to solvation free energy from dispersion, polar, and hydrogen-bonding (HB) intermolecular interactions. COSMO-type quantum chemical solvation calculations are used for the development of four new molecular descriptors of solutes for their electrostatic interactions. The new model needs one to three solvent-specific parameters for the prediction of solvation free energies. The widely used Abraham’s LSER model is used for providing the reference solvation free energy data for the determination of the solvent-specific parameters. Extensive calculations in 80 solvent systems have verified the good performance of the model. The very same molecular descriptors are used for the calculation of solvation enthalpies. The advantages of the present model over Abraham’s LSER model are discussed along with the complementary character of the two models. Enthalpy and free-energy solvation information for pure solvents is translated into partial solvation parameters (PSP) analogous to the widely used Hansen solubility parameters and enlarge significantly their range of applications. The potential and the perspectives of the new approach for further molecular thermodynamic developments are discussed. Full article

Perovskite solar cells (PSCs) have emerged as promising photovoltaic technologies owing to their high power conversion efficiency (PCE) and material versatility. Conventional optimization of PSC architectures largely depends on iterative experimental approaches, which are often labor-intensive and time-consuming. In this study, a data-driven modeling strategy is introduced to accelerate the design of efficient and mechanically robust PSCs. Seven supervised regression models were evaluated for predicting key photovoltaic parameters, including PCE, short-circuit current density (Jsc), open-circuit voltage (Voc), and fill factor (FF). Among these, a stacking ensemble framework exhibited superior predictive accuracy, achieving an R² of 0.8577 and a root mean square error of 2.084 for PCE prediction. Model interpretability was ensured through Shapley Additive exPlanations(SHAP) analysis, which identified precursor solvent composition, A-site cation ratio, and hole-transport-layer additives as the most influential parameters. Guided by these insights, ten device configurations were fabricated, achieving a maximum PCE of 24.9%, in close agreement with model forecasts. Furthermore, multiscale mechanical assessments, including bending, compression, impact resistance, peeling adhesion, and nanoindentation tests, were conducted to evaluate structural reliability. The optimized device demonstrated enhanced interfacial stability and fracture resistance, validating the proposed predictive–experimental framework. This work establishes a comprehensive approach for performance-oriented and reliability-driven PSC design, providing a foundation for scalable and durable photovoltaic technologies. Full article

Carbon dioxide (CO₂) capture using alkanolamines remains the most mature technology, yet faces challenges including solvent loss, high regeneration energy and equipment corrosion. Ionic liquids (ILs) are proposed as alternatives, but their high viscosity and production costs hinder industrial use. Thus, blending ILs with amines offers a promising approach. This work presents new experimental data for aqueous blends of 1-butyl-3-methylimidazolium hydrogen sulfate, $\left[\mathrm{B}\mathrm{m}\mathrm{i}{\mathrm{m}}^{+}\right]\left[\mathrm{H}\mathrm{S}{\mathrm{O}}_4^{-}\right],$ with 2-amino-2-methyl-1-propanol (AMP) and 3-(methylamino)propylamine (MAPA) and for choline glycine, $\left[\mathrm{C}{\mathrm{h}}^{+}\right]\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$, with AMP, modeled using the modified Kent–Eisenberg approach. It was shown that substituting a portion of the amine with $\left[\mathrm{B}\mathrm{m}\mathrm{i}{\mathrm{m}}^{+}\right]\left[\mathrm{H}\mathrm{S}{\mathrm{O}}_4^{-}\right]$ reduces CO₂ uptake per mole of amine due to the lower solution’s basicity, despite the added sites for physical absorption. In contrast, the replacement of an amine portion with $\left[\mathrm{C}{\mathrm{h}}^{+}\right]\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$ enhances both physical and chemical interactions, leading to increased CO₂ solubility per mole of amine. Finally, replacing a small portion of water with $\left[\mathrm{C}{\mathrm{h}}^{+}\right]\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$ does not significantly alter the bulk CO₂ solubility (moles of CO₂ per kg of solvent) but lowers the solvent’s vapor pressure. Given the non-toxic nature of $\left[\mathrm{C}{\mathrm{h}}^{+}\right]\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$, the resulting solvent poses no added environmental risk. Model predictions agree well with experimental data (deviations of 2.0–11.6%) and indicate low unreacted amine content at CO₂ partial pressures of 1–10 kPa for carbamate-forming amines, i.e., $\left[\mathrm{G}\mathrm{l}{\mathrm{y}}^{-}\right]$, and MAPA. Consequently, at higher CO₂ partial pressures, the solubility increases due to carbamate hydrolysis and molecular CO₂ dissolution. Full article

A chemical platform for post-polymerization methods was developed, starting from the ecodesign and enzymatic synthesis of safe and sustainable bio-based polyesters containing discrete units of itaconic acid. This unsaturated bio-based monomer enables the covalent linkage of molecules that can impart desired properties such as hydrophilicity, flexibility, permeability, or affinity for biological targets. Molecular descriptor-based computational methods, which are generally used for modeling the pharmacokinetic properties of drugs (ADME), were employed to predict in silico the hydrophobicity (LogP), permeability, and flexibility of virtual terpolymers composed of different polyols (1,4-butanediol, glycerol, 1,3-propanediol, and 1,2-ethanediol) with adipic acid and itaconic acid. Itaconic acid, with its reactive vinyl group, acts as a chemical platform for various post-polymerization functionalizations. Poly(glycerol adipate itaconate) was selected because of its higher hydrophilicity and synthetized via solvent-free enzymatic polycondensation at 50 °C to prevent the isomerization or crosslinking of itaconic acid. The ecotoxicity and marine biodegradability of the resulting oligoester were assessed experimentally in order to verify its compliance with safety and sustainability criteria. Finally, the viability of the covalent linkage of biomolecules via Michael addition to the vinyl pendant of the oligoesters was verified using four molecules bearing thiol and amine nucleophilic groups: N-acetylcysteine, N-Ac-Phe-ε-Lys-OtBu, Lys-Lys-Lys, and glucosamine. Full article

Background/Objectives: Hospital compounding is essential for the delivery of patient-tailored therapies—particularly for pediatric and oncology patients and other groups requiring precise dosing. Its role is expected to grow as, for instance, the UK MHRA’s new Guidance on Decentralised Manufacturing promotes alternative manufacturing pathways that integrate hospital preparation units. However, drug substances that remain stable in commercial oral formulations may undergo rapid degradation under alternative conditions (e.g., aqueous suspension, light exposure, or in the presence of specific excipients). Despite these risks, formulation strategies in hospital compounding often rely on empirical practices and lack structured guidance regarding stability, impurity control, and reproducibility. Methods: This study proposes a risk-based scientific framework for formulation design, integrating degradation profiling with predictive toxicology. Potential degradation pathways (hydrolytic, oxidative, and photolytic) are systematically identified through forced-degradation studies combined with ab initio modeling. These risks are translated into formulation strategies using a structured decision tree encompassing solvent selection, pH adjustment, excipient compatibility, and packaging considerations, even in the absence of a pharmacopeial monograph. The toxicological relevance of degradation products is evaluated using in silico approaches aligned with ICH M7 guidelines, thereby defining critical quality attributes (cQAs) and critical process parameters (CPPs). Results: The applicability of the framework is demonstrated through hospital compounding case studies, with further extension toward advanced applications such as semi-solid extrusion (SSE) 3D printing. Conclusions: By integrating mechanistic understanding of drug degradation into formulation planning, the proposed framework enhances the safety, reproducibility, and quality of compounded preparations. This approach reinforces Good Preparation Practices (GPPs) and is consistent with international quality-by-design (QbD) principles in the context of personalized medicine. Full article

In a continued search for novel plant-based therapeutics with multi-target pharmacological potential, the medicinal plant Dischidia bengalensis (Apocynaceae) was investigated for the first time for its anti-inflammatory, analgesic, and thrombolytic properties, addressing critical therapeutic areas such as rheumatoid arthritis, acute pain, and thrombosis. The methanolic extract and solvent fractions (dichloromethane, n-hexane, and ethyl acetate) were evaluated through integrated in vivo, in vitro, and in silico approaches. Phytochemical screening and GC–MS profiling revealed a diverse array of bioactive constituents, including fatty acids, terpenoids, and phenolic derivatives, many of which are reported to exhibit pharmacological activities. In vivo assays demonstrated that the methanolic extract (400 mg/kg) markedly suppressed carrageenan-induced paw edema (92.31% inhibition) from the 2nd to 4th hour (p  <  0.05, p  <  0.01), while the n-hexane fraction produced the most pronounced analgesic response in both writhing and tail-immersion models (p  <  0.001). Furthermore, the methanolic extract displayed significant thrombolytic activity (33.38  ±  4.27% at 20 mg/mL, p < 0.001) in human blood clot lysis, suggesting potential application in cardiovascular disorders. The scientific novelty of this study was further underscored by in silico molecular docking, ADME/T, and PASS prediction studies. Key bioactive compounds, identified by GC-MS, showed strong binding affinities and promising drug-like properties against pivotal human targets such as TNF-α (PDB: 2AZ5), COX-2 (PDB: 6COX), and tissue plasminogen activator. These findings conclusively establish D. Bengalensis as a promising and novel source of lead compounds for the development of novel therapeutics against inflammatory, pain-related, and cardiovascular disorders. Full article

Deep eutectic solvents (DESs) have emerged as sustainable and tunable alternatives to conventional solvents for the extraction of polysaccharides. This review presents a structure-informed framework linking DES composition to polysaccharide solubility, emphasizing the differential responsiveness of amorphous, interfacial, and crystalline domains. Amorphous polysaccharides are efficiently extracted under mild DES conditions, while crystalline polymers often require stronger hydrogen bond acceptors or thermal/mechanical activation. Beyond dissolution, DESs modulate key properties of the extracted polysaccharides, including molecular weight, monomer composition, and bioactivity. Comparative analysis highlights how acidic, basic, or metal-coordinating DESs selectively target distinct polymer classes. Emerging innovations, such as in situ DES formation, mechanochemical systems, and switchable solvents, enhance efficiency and reduce downstream processing demands. Furthermore, the integration of machine learning and COSMO-RS modeling enables predictive solvent design, reducing reliance on empirical screening. By combining mechanistic insight, compositional tailoring, and computational tools, this review provides a scientifically grounded perspective for advancing DES-mediated extraction processes and enabling structure-preserving, application-oriented recovery of polysaccharides in food, pharmaceutical, and biorefinery domains. Full article

Levonorgestrel (LNG), a synthetic progestin widely used in pharmaceuticals, is increasingly recognized as an emerging aquatic contaminant capable of exerting adverse biological effects beyond endocrine disruption. Acting in a xenobiotic-like manner, LNG may perturb redox homeostasis and induce oxidative stress in non-target species. To elucidate these mechanisms, this study integrates experimental toxicology with supervised machine learning to characterize tissue-specific and dose–time related oxidative responses in adult Zebrafish (Danio rerio). Fish were exposed to two environmentally relevant concentrations of LNG (0.312 µg/L; LNG-L and 6.24 µg/L; LNG-H) and a solvent control (LNG-C) for 24, 48, and 96 h in triplicate static bioassays. Redox biomarkers—superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and malondialdehyde (MDA)—were quantified in liver and muscle tissues. LNG-H exposure elicited a time-dependent increase in SOD activity, variable CAT responses, and a marked elevation in hepatic GPx, with sustained MDA levels indicating persistent lipid peroxidation. Five classification algorithms (Logistic Regression, Multilayer Perceptron, Gradient-Boosted Trees, Decision Tree and Random Forest) were trained to discriminate exposure outcomes based on biomarker profiles; GBT yielded the highest performance (96.17% accuracy), identifying hepatic GPx as the most informative feature (AUC = 0.922). Regression modeling via Extreme Gradient Boosting (XGBoost) further corroborated the dose- and time-dependent predictability of GPx responses (R² = 0.922, MAE = 0.019). These findings underscore hepatic GPx as a sentinel biomarker of LNG-induced oxidative stress and demonstrate the predictive utility of machinelearning-enhanced toxicological frameworks in detecting and modeling sublethal contaminant effects with high temporal resolution in aquatic systems. Full article

Rational electrolyte design stands as a frontier in the research and development of Li-ion batteries. Nevertheless, detailed investigations about the influence of the dielectric continuum solvation model on molecular interactions are still limited. Herein, we systematically study the impacts of the dielectric constant (ε) on isolated molecules (i.e., ions and solvent molecules), isolated ion pairs, and solvation complexes via density functional theory calculations. The energy shift due to solvation cavity creation is the largest, and charged species always have larger energy shifts than neutral species. For charged species, the energy shifts gradually decrease with a decreasing proportion of Li ions and an increasing proportion of anions, while for neutral species, larger dipole moments lead to higher energy shifts. As predicted by the relative method, the energetic order of ion pairs and solvation complexes in vacuum can be dramatically changed in various dielectric continuums. Furthermore, electrochemical stability windows of charged species change dramatically with ε, while those of neutral species stay almost constant. By clarifying the impacts of dielectric continuum solvation on molecular interactions, we hope to set a benchmark for the molecular interaction calculation, which is critical for the rational design of electrolytes in Li-ion batteries. Full article

A neural model was developed to predict the distribution of ZnO nanoparticles obtained by electrochemical synthesis. It is a three-layer multilayer perceptron (MLP) artificial neural network (ANN) with five neurons in the input layer, eight neurons in the hidden layer, and one neuron in the output layer. This network has a hyperbolic tangent activation function for the neurons in the hidden layer and an exponential activation function for the neuron in the output layer. The input (independent) variables are particle size (nm), solvent type, and temperature (°C), and the output (dependent) variable is fraction share (%). The best neural model (ann08) has a root mean square error (RMSE) 0.84% for the training subset, 0.98% for the testing subset, and 1.27% for the validation subset. The RMSE values are therefore small, which enables practical use of the ANN model. Full article

The nutritional and commercial value of lettuce (Lactuca sativa L.) is determined by its foliar pigment and phenolic composition, which varies among cultivars. This study aimed to assess the capacity of hyperspectral and applied multispectral imaging, combined with machine learning algorithms, to predict and map key biochemical traits, such as chloroplastidic pigments (chlorophylls and carotenoids) and extrachloroplastidic pigments (anthocyanins, flavonoids, and phenolic compounds). Eleven cultivars exhibiting contrasting pigmentation profiles were grown under controlled greenhouse conditions, and their chlorophyll a and b, carotenoid, anthocyanin, flavonoid, and total phenolic contents were evaluated. Spectral reflectance data were acquired via a Headwall hyperspectral sensor and a MicaSense multispectral sensor, and the pigment contents were quantified via solvent extraction and a UV microplate reader. We developed predictive models via seven machine learning approaches, with partial least squares regression (PLSR) and random forest (RF) emerging as the most robust algorithms for pigment estimation. Chlorophyll a and b are highly and positively correlated (r > 0.9), which is consistent with their hyperspectral reflectance imaging results. The hyperspectral data consistently outperformed the multispectral data in terms of predictive accuracy (e.g., R² = 0.91 and 0.76 for anthocyanins and flavonoids via RF) and phenolic compounds with R² = 0.79, capturing subtle spectral features linked to biochemical variation. Spatial maps revealed strong genotype-dependent heterogeneity in pigment and phenolic distributions, supporting the potential of this approach for cultivar discrimination and pigment phenotyping. These findings demonstrate that hyperspectral imaging integrated with data-driven modelling offers a powerful, nondestructive framework for the biochemical monitoring of leafy vegetables, supporting breeding, precision agriculture, and food quality assessment. Full article

Designing efficient nanoparticle-enhanced CO₂ capture systems is challenging due to the diversity of nanoparticles, solvent formulations, reactor configurations, and operating conditions. This study presents the first ANN-based meta-analysis framework developed to predict CO₂ absorption enhancement across multiple reactor systems, including batch reactors, packed columns, and membrane contactors. A curated dataset of 312 experimental data points was compiled from literature, and an artificial neural network (ANN) model was trained using six input variables: nanoparticle type, concentration, system configuration, base fluid, pressure, and temperature. The proposed model achieved high predictive accuracy (R² > 0.92; RMSE: 4.2%; MAE: 3.1%) and successfully captured complex nonlinear interactions. Feature importance analysis revealed nanoparticle concentration (28.3%) and system configuration (22.1%) as the most influential factors, with functionalized nanoparticles such as Fe₃O₄@SiO₂-NH₂ showing superior performance. The model further predicted up to 130% enhancement for ZnO in optimized membrane contactors. This AI-driven tool provides quantitative insights and a scalable decision-support framework for designing advanced nanoparticle–solvent systems, reducing experimental workload, and accelerating the development of sustainable CO₂ capture technologies. Full article

In the polyurethane industry, catalytically generated carbodiimides can modify the properties of isocyanate and, thus, the resulting foams. In this work, a kinetic reaction study was carried out to investigate the formation of a simple, bifunctional carbodiimide from a widely used polyurethane raw material: 4,4′-methylenediphenyl diisocyanate (MDI). The experimental section outlines a catalytic process, using a 3-methyl-1-phenyl-2-phospholene-1-oxide (MPPO) catalyst in ortho-dichlorobenzene (ODCB) solvent, to model industrial circumstances. The reaction produces carbon dioxide, which was observed using gas volumetry at between 50 and 80 °C to obtain kinetic data. A detailed regression analysis with linear and novel nonlinear fits showed that the initial stage of the reaction is second-order, and the temperature dependence of the rate constant is $k(T)=(3.4\pm 3.8)\mathrm{‧}{10}^6\cdot e^{{\displaystyle \frac{-7192\pm 389}{T}}}$. However, the other isocyanate group of MDI reacts with new isocyanate groups and the reaction deviates from the second-order due to oligomer (polycarbodiimide) formation and other side reactions. A linearized Arrhenius equation was used to determine the activation energy of the reaction, which was E_a = 60.4 ± 3.0 kJ mol⁻¹ at the applied temperature range, differing by only 4.6 kJ mol⁻¹ from a monoisocyanate-based carbodiimide. In addition to experimental results, computationally derived thermochemical data (from simplified DFT and IRC calculations) were applied in transition state theory (TST) for a comprehensive prediction of rate constants and Arrhenius parameters. As a result, it was found that the activation energy of the carbodiimide bond formation reaction from theoretical and experimental results was independent of the number and position of isocyanate groups, which is consistent with the principle of equal reactivity of functional groups. Full article

Xanthones from mangosteen pericarp (MP) are bioactive compounds with promising pharmaceutical and nutraceutical applications. However, their efficient and selective extraction using environmentally friendly solvents remains a challenge. This study aimed to evaluate tricaprylin (C8) and tricaprin (C10) as novel green co-extractants in supercritical carbon dioxide (scCO₂) extraction for the recovery of xanthones from MP, using a mass ratio of C8:C10 = 0.64:0.36, hereafter referred to as C8/C10, and to model extraction kinetics for process design and scale-up. Extraction performance was investigated using different C8/C10–MP mass ratios and scCO₂ conditions at temperatures of 60 °C and 70 °C and pressures of 250 bar, 350 bar, and 450 bar. A pseudo-first-order kinetic model was applied to describe the extraction profile, and the kinetic parameters were generalized using second-order polynomial functions of temperature and pressure. The highest xanthone yield (39.93 ± 0.37%) and total xanthone content (51.44 ± 2.22 mg/g) were obtained at a 40% C8/C10–MP ratio under 70 °C and 350 bar, where the C8/C10 mixture outperformed other tested co-extractants in both efficiency and selectivity, particularly for α-mangostin. The extraction profiles were well described by the pseudo-first-order kinetic model, and the generalized model predicted the extraction yield with an uncertainty of 2.3%. C8/C10 is a highly effective and scalable co-extractant for scCO₂ extraction of xanthones, offering a foundation for industrial applications in food, nutraceutical, and pharmaceutical sectors. Full article