Explore the vast range of titles available.

This study focuses on predicting the solubility of paracetamol and density of solvent using temperature (T) and pressure (P) as inputs. The process for production of the drug is supercritical technique in which the focus was on theoretical investigations of drug solubility and solvent density as well. Machine learning models with a two-input, two-output structure were developed and validated using experimental data on paracetamol solubility as well as density. Ensemble models with decision trees as base models, including Extra Trees (ETR), Random Forest (RFR), Gradient Boosting (GBR), and Quantile Gradient Boosting (QGB) were adjusted to predict the two outputs. The results are useful to evaluate the feasibility of process in improving the efficacy of the drug, i.e., its enhanced bioavailability. The hyper-parameters of ensemble models as well as parameters of decision tree tuned using WOA algorithm separately for both outputs. The Quantile Gradient Boosting model showed the best performance for mole fraction (drug solubility), while the R 2 score of 0.985 was determined. For density of solvent, the Extra Trees model performed the best with an R 2 equal to 0.997.

We investigated solubility variations of a medication in supercritical carbon dioxide with an insight into preparation of nanomedicines with improved aqueous solubility. As the case study, the solubility of famotidine (FAM) medicine in sc-CO 2 (supercritical carbon dioxide) was computed as a function of temperature and pressure, with a particular focus on modeling and predicting solubility and sc-CO 2 density. Three regression models with machine learning behavior including Quadratic Polynomial Regression (QPR), Weighted Least Squares (WLS), and Orthogonal Matching Pursuit (OMP) were employed to analyze the data, and Sequential Model-Based Optimization (SMBO) was utilized for hyper-parameter tuning. Among these models, the best-performing model for predicting FAM solubility was the QPR model, with an impressive coefficient of determination (R 2 ) of 0.95858 for all sets including training and validation. Additionally, QPR exhibited low MAPE of 1.64278E + 00, RMSE of 9.6833E-02, and a maximum error of 1.49480E-01, while exhibiting a higher maximum error of 18.99 kg/m³ for density predictions, indicating areas for potential improvement. These results highlight the accuracy and precision of the QPR model in predicting FAM solubility in sc-CO 2 . For the prediction of sc-CO 2 density, QPR again proved to be the most effective model with a remarkable R 2 score of 0.99733. This model achieved a low MAPE of 1.06004E-02, RMSE of 8.4072E + 00, and a maximum error of 1.89894E + 01. The QPR model demonstrates its exceptional capability in accurately predicting sc-CO 2 density in terms of temperature and pressure.

One of the problems with new medications is their poor water solubility that is possible to be addressed by using supercritical method. This study aims to predict the solubility of raloxifene and the density of supercritical CO 2 using temperature and pressure as inputs to analyze the supercritical processing for production of drug nanoparticles. Three regression models, Extra Trees (ET), Random Forest (RF), and Gradient Boosting (GB) were proposed and optimized using Gradient-based optimization to predict density and solubility of drug. In predicting the density of supercritical CO₂, GB attained an R² value of 0.986, reflecting an excellent agreement between its estimates and the true measurements. The model exhibited an RMSE of 23.20, indicating high accuracy, with a maximum error of 33.06. Regarding the solubility of raloxifene, the ET model yielded the highest R-squared score of 0.949, indicating a good fit to the data. The model exhibited an RMSE of 0.41, with a maximum error of 0.90. Comparatively, the RF and GB models obtained slightly lower precision, for the solubility of raloxifene. The RF model exhibited an RMSE of 0.55, while the GB model had an RMSE of 0.72. The optimized models were found to be reliable in predicting solubility and density within the supercritical processing field.

R-454B is an excellent choice for refrigeration systems due to its environmentally friendly profile. In this study, the thermophysical properties of R-454B refrigerant are predicted using molecular dynamics (MD) simulations coupled with a SAFT-based equation of state (EoS). Since experimental data on the thermophysical properties of R-454B are generally scarce in technical applications, exploring these properties is essential. In this work, the COMPASS force field is employed to develop the MD simulations. The saturated density, vapor pressure, and isobaric heat capacity of R-454B were simulated. The average ARD% for the isobaric heat capacity was approximately 7.66% over the temperature range of 273.15–303.15 K. The PC-SAFT equation of state (EoS) was coupled with MD simulation to predict the thermodynamic properties of R-454B across a broad range of pressures and temperatures. In this regard, the PC-SAFT model parameters were adjusted using the simulated saturated liquid density and vapor pressure data. The obtained PC-SAFT model parameters were utilized to predict the speed of sound, specific heat capacity, and Joule–Thomson coefficient of R-454B. The results indicate that the proposed model can satisfactorily predict the vapor and liquid thermophysical properties of R-454B. This methodology can be employed to estimate second-order derivative thermodynamic properties of novel refrigerants prior to synthesis, potentially reducing the costs and time associated with experimental development.

Accurate prediction of oil production rates through wellhead chokes is critical for optimizing crude oil production and operational efficiency in the petroleum industry. The central thrust of this investigation involves the systematic creation of machine learning (ML) paradigms for the robust prediction of choke flow performance. This endeavor is rigorously informed by comprehensive data acquired from an operational petroleum production facility in the Middle East. Within the dataset, produced gas-oil ratio (GOR), choke size, basic sediment and water (BS&W), wellhead pressure (THP), and crude oil API stand out as key parameters. Each plays a vital role in forecasting the oil production rate. To ensure reliability, robust data preprocessing was conducted using the Monte Carlo outlier detection (MCOD) method to recognize and manage data outliers. The models were trained using 198 data points, employing K-fold cross-validation (five folds) to ensure generalization. Gradient boosting machine (GBM) models were optimized using advanced algorithms like self-adaptive differential evolution (SADE), evolution strategy (ES), Bayesian probability improvement (BPI), and Batch Bayesian optimization (BBO). Among these, SADE demonstrated superior performance based on metrics such as average absolute relative error (AARE%), R 2 , and mean squared error (MSE). Furthermore, SHAP (SHapley Additive exPlanations) analysis was used to interpret the models and highlight the dominant influence of choke size and THP on the predictions. Overall, this research work presents a data-driven framework for highly accurate and interpretable predictions, significantly contributing to production optimization initiatives in the oil and gas sector.

Dimensional precision of casts is essential for the quality of fixed prosthesis therapy, with the impression technique being a crucial component affecting this precision. This in vitro study is to evaluate the dimensional precision of casts generated from four varieties of impression materials. Utilizing 20 specimens, four types of impression materials were fabricated and subsequently classified into four groups: condensation silicone impression material group (heavy and light body), condensation silicone impression material group (light body only), addition silicone impression material group (heavy and light body), and alginate impression material group. Dimensional stability was evaluated by acquiring imprints of an acrylic mold with three supports that replicate a slightly edentulous arch, which were then filled with stone. The stability was assessed by shooting photos with a Canon digital macro-lens camera, thereafter measuring the distances between the three posts using AutoCAD software (three lines were measured). This study evaluated the dimensional precision of four frequently utilized impression materials in comparison to the primary standard. Dimensional accuracy was evaluated along three measurement lines, revealing substantial deviations from the standard for all materials (   < 0.05). Among the investigated materials, addition silicone light & heavy body displayed the highest values (mean perimeter of 155.024 mm), which was closest to the control (perimeter of 163.405 mm), indicating minimal dimensional changes and exceptional dimensional stability. Conversely, Condensation silicone light & heavy body showed least values (146.06 mm) suggesting the least dimensional accuracy compared to the other three impression materials. Alginate and Condensation silicone light body showed comparable results when compared with each other (   > 0.05) and were statistically better than Condensation silicone light & heavy and lower than addition silicone light & heavy. These findings highlight the significance of material selection for attaining accurate mold dimensions in clinical applications. In summary Although the digital technique may be more dependable and less complicated way to evaluate the qualities of impression materials, addition silicone heavy and light body demonstrated superior dimensional accuracy when compared with the other three impression materials. In contrast, among the four impression materials tested, condensation silicone heavy & light body had the largest dimensional shifts, indicating the lowest degree of dimensional accuracy.

This research introduces the preparation and analysis of a newly heterogeneous catalyst developed silica nanospheres supporting a ferrocene‐containing ionic liquid (IL) (SiO2@Benzothiazole‐Cl@Fc) for the A3 coupling reaction. The catalyst facilitates the efficient synthesis of 1,3,5‐trisubstituted pyrazoles from aromatic hydrazides, aldehydes, and aromatic alkynes. Incorporating ferrocene enhances the catalytic activity. Comprehensive characterization techniques, including NMR, Fourier transform infrared, transmission electron microscopy, energy‐dispersive X‐ray spectroscopy, and scanning electron microscopy, confirm the successful functionalization of silica nanospheres. The catalytic performance was evaluated under various reaction conditions, demonstrating high yields and selectivity for the desired pyrazole products. This work highlights the potential of ferrocene‐based ILs in green chemistry applications, providing a sustainable approach to synthesizing valuable heterocyclic compounds. This research introduces the preparation and analysis of a newly heterogeneous catalyst developed silica nanospheres supporting a ferrocene‐containing ionic liquid (SiO2@Benzothiazole‐Cl@Fc) for the A3 coupling reaction. The catalyst facilitates the efficient synthesis of 1,3,5‐trisubstituted pyrazoles from aromatic hydrazides, aldehydes, and aromatic alkynes.

Carbon fibre composites (CFCs) hold significant promise for energy storage and harvesting applications owing to their exceptional strength-to-weight ratio and structural versatility, but their electrochemical performance is constrained by inherent limitations such as low surface area and restricted ion transport pathways. This review examines how strategic integration of nanomaterials—including graphene, carbon nanotubes and MXenes—can overcome these challenges by enhancing surface reactivity, improving electrical conductivity and facilitating efficient ion diffusion, thereby enabling high-performance multifunctional composites. We discuss key advances in nanomaterial-incorporated CFCs for structural batteries and supercapacitors, where tailored interfaces and hierarchical architectures contribute to superior energy and power densities, as well as their emerging role in integrated energy harvesting systems that combine energy storage with triboelectric, piezoelectric or thermoelectric conversion capabilities. The analysis further addresses critical manufacturing challenges related to nanomaterial dispersion, interfacial bonding and scalable processing, while evaluating solutions such as advanced deposition techniques and hybrid material designs. By systematically reviewing both fundamental mechanisms and practical considerations, this work provides insights into the development of next-generation smart composites that simultaneously achieve mechanical robustness and advanced electrochemical functionality for applications ranging from wearable electronics to electric vehicles and aerospace systems.

Ferroptosis, a form of regulated cell death characterized by the accumulation of lipid peroxides, has emerged as a crucial player in cancer biology, particularly in breast cancer. This review article explores the intricate regulation of ferroptosis by non-coding RNAs (ncRNAs) within the breast cancer tumor microenvironment (TME). We delve into the mechanisms through which various classes of ncRNAs, including microRNAs, long non-coding RNAs, and circular RNAs, modulate the ferroptotic response in breast cancer cells. Furthermore, we examine the interactions between ferroptosis and the TME, specifically focusing on cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs). By highlighting the bidirectional relationships between these components, we aim to elucidate how the modulation of ferroptosis by ncRNAs can influence the behavior of CAFs and TAMs, ultimately impacting tumor progression and therapeutic response. This comprehensive overview underscores the potential of targeting ncRNA-mediated regulation of ferroptosis as a novel therapeutic strategy in breast cancer treatment, with implications for enhancing the efficacy of existing therapies and improving patient outcomes.

Characteristics of photon plasmon coupling in black phosphorus (BP) embedded between chiroferrite layer is analyzed in THz frequency spectrum. The frequency behavior and propagation characteristics of SPPs are analyzed under various physical parameters i.e., chirality parameter ( ), gyrotropy ( ), carrier density ( ), and the number of layers ( ). Numerical results indicate the significant dependency of surface plasmon frequency on these parameters along ( ) and ( ) conductivities of BP. As chirality parameter ( ) increases, the plasmonic frequency increases for both conductivities. While gyrotropy ( ) shifts the plasmonic response, with larger values of leading to decrease the plasmonic frequency. The carrier density also influences the plasmon frequency, with higher values resulting in higher frequencies for both ( ) and ( ). Furthermore, the number of BP layers (N) has notable impact, as an increase in N causes a steeper rise in frequency for both ( ) and ( ). Propagation loss or imaginary part of propagation constant for different carries density is also analyzed for both conductivities. Based on numerical results is suitable for higher frequencies compared to ​. This suggests that ​ might possess properties that facilitate its performance or responsiveness at higher frequencies in contrast to ​. However, exhibits higher effective mode index for the proposed waveguide structure. Additionally, modulating carrier density can control both the phase velocity and propagation length. The proposed waveguide structure holds promising potential for plasmonic community to fabricate nanophotonic devices due to anisotropy of chiroferrite and BP medium in THz frequency regime.