Soluble Model Research Articles

Solubility Determination and Model Evaluation of Triethylamine Hydrochloride in Three Binary Mixed Solvents

Solubility Determination and Model Evaluation of Triethylamine Hydrochloride in Three Binary Mixed Solvents

New solubility model to correlate solubility of anticancer drugs in supercritical carbon dioxide and evaluation with Kruskal–Wallis test

Anti-cancer drugs are the result of human endeavor in search of a treatment for a deadly disease like cancer. The advent of the ideology of sustainability and eco-friendliness has propelled the manipulation of supercritical fluid technology in numerous fields. Beyond green technology, they account for the production of nanoparticles, due to which they are highly exploited for the production of anti-cancer drugs, making the existing technologies obsolete. Production of anticancer drugs through supercritical fluid technology requires the knowledge of anticancer drug as well as an efficient model to correlate its solubility in the supercritical fluid. Thus, in this work, a new model for correlating the solubility of anticancer drugs in supercritical carbon dioxide based on thermodynamic frame work has been proposed. Over eighteen important anticancer drugs are considered for evaluation. Further, five successful six-parameter models are considered to compare the performance of the new thermodynamic model. Various statistical parameters and Kruskal-Wallis test have been used to compare and evaluate the significance of the difference between the models in order to test the efficacy of the proposed model.

Optimization of Online Soluble Solids Content Detection Models for Apple Whole Fruit with Different Mode Spectra Combined with Spectral Correction and Model Fusion.

Soluble solids content (SSC) is one of the main quality indicators of apples, and it is important to improve the precision of online SSC detection of whole apple fruit. Therefore, the spectral pre-processing method of spectral-to-spectral ratio (S/S), as well as multiple characteristic wavelength member model fusion (MCMF) and characteristic wavelength and non-characteristic wavelength member model fusion (CNCMF) methods, were proposed for improving the detection performance of apple whole fruit SSC by diffuse reflection (DR), diffuse transmission (DT) and full transmission (FT) spectra. The modeling analysis showed that the S/S- partial least squares regression models for all three mode spectra had high prediction performance. After competitive adaptive reweighted sampling characteristic wavelength screening, the prediction performance of all three model spectra was improved. The particle swarm optimization-extreme learning machine models of MCMF and CNCMF had the most significant enhancement effect and could make all three mode spectra have high prediction performance. DR, DT, and FT spectra all had some prediction ability for apple whole fruit SSC, with FT spectra having the strongest prediction ability, followed by DT spectra. This study is of great significance and value for improving the accuracy of the online detection model of apple whole fruit SSC.

On the Eigenvalues of a Soluble Model of Coupled Molecular and Nuclear Hamiltonians

In this paper, a special type of soluble model corresponding to a coupled molecular and nuclear Hamiltonians H, the so-called generalized Friedrichs model, is considered. We aim to determine and provide the most important properties of the well-known Faddeev operator corresponding to H accommodating a number of discrete eigenvalues. Furthermore, we provide a formula for counting the multiplicity of discrete eigenvalues of H.

Solubility evaluation of palm-based Mono-diacylglycerols (MDAGs) in food grade solvent (hexane, ethanol, acetone, water) using QSPR model approach

Mono-diacylglycerols (MDAGs) are emulsifiers widely used in the food industry. MDAGs based on refined bleached deodorized palm stearin (RBDPS) have not been widely developed. MDAGs have been synthesized using glicerolysis between RBDPS and glycerols. Due to their low solubility in food-grade solvents (hexane, ethanol, acetone, and water), it has been difficult to purify or fractionate MDAGs only by a simple method. This research attempted to develop a model of MDAGs solubility in food-grade solvents and understand the parameters that influence the solubility of MDAGs in food-grade solvents. The process used a combination of experimental data collection and quantitative structure–property relationship (QSPR) models. The QSPR model for MDAGs solubility in a single and binary solvent demonstrated that the descriptors of Δ dipole moment (Δµ), E HOMO, E LUMO, electronic energy, gap energy HOMO-LUMO (ΔE1, ΔE2) softness (S), electrophilicity index (ω), and electron transfer (ΔN) have a significant impact in determining the solubility of MDAGs. Both the single and the binary solvents solubility models can give satisfactory prediction results: the square of the correlation coefficient (R2pred) was 0.933 and 0.948, and the root-mean-square error (RMSE) was 0.075 and 0.031, respectively, for the whole data set. In addition, this paper provided a new and effective method for predicting the solubility of MDAGs in single and binary food-grade solvent systems.

Solubility Measurement, Modeling, and Solvent Effect of 4,4′-Dimethoxybenzophenone in Ten Monosolvents and in Three Binary Solvent Mixtures from 293.15 to 333.15 K

Solubility Measurement, Modeling, and Solvent Effect of 4,4′-Dimethoxybenzophenone in Ten Monosolvents and in Three Binary Solvent Mixtures from 293.15 to 333.15 K

Will we ever be able to accurately predict solubility?

Accurate prediction of thermodynamic solubility by machine learning remains a challenge. Recent models often display good performances, but their reliability may be deceiving when used prospectively. This study investigates the origins of these discrepancies, following three directions: a historical perspective, an analysis of the aqueous solubility dataverse and data quality. We investigated over 20 years of published solubility datasets and models, highlighting overlooked datasets and the overlaps between popular sets. We benchmarked recently published models on a novel curated solubility dataset and report poor performances. We also propose a workflow to cure aqueous solubility data aiming at producing useful models for bench chemist. Our results demonstrate that some state-of-the-art models are not ready for public usage because they lack a well-defined applicability domain and overlook historical data sources. We report the impact of factors influencing the utility of the models: interlaboratory standard deviation, ionic state of the solute and data sources. The herein obtained models, and quality-assessed datasets are publicly available.

A novel model to predict phase equilibrium state of hydrates from the relationship of gas solubility

The study of hydrate phase equilibrium is crucial for ensuring the safety of natural gas pipeline transportation and the process of hydrate recovery. While scientists typically focus on the chemical potential of hydrates, the role of gas solubility in hydrate phase equilibrium remains unclear, and this study fills this gap. This work investigated the solubility of gas at the equilibrium point of the hydrate phase through model calculations. Additionally, a new model of hydrate phase equilibrium is established based on the relationship between solubility. Firstly, a solubility model based on gas-liquid equilibrium theory showed higher prediction accuracy in comparison to the PR equation and Duan model and was then used to calculate gas solubility under hydrate phase equilibrium conditions. Afterwards, a novel model was developed to predict hydrate equilibrium state based on the relationship between gas solubility and hydrate phase equilibrium temperature, and it was further compared with the Chen–Guo model and CSMGem in terms of prediction accuracy under pure water and brine settings. The results showed: (a) The calculation deviation of the solubility model was 0.7–8.7% in pure water settings and 2.6–11.7% in brine settings; (b) A strong linear correlation between the phase equilibrium temperature of hydrates and gas solubility was also found; (c) This proposed model achieved over 10 times the accuracy of the Chen–Guo model and the CSMGem in predicting the phase equilibrium state of N2 and CO2 hydrates, and 3–10 times higher accuracy than that of the Chen–Guo model and CSMGem in brine. This work suggests that the gas solubility equilibrium theory can provide a more accurate prediction of hydrate states.

Thermodynamic Analysis of the Absorption of Common Refrigerants in Fluorinated Deep Eutectic Solvents

The potential to decrease the negative environmental impact of greenhouse gases in terms of global warming potential (GWP) is a current challenge. Apart from CO2, fluorinated greenhouse gases (F-gases), which are human-made chemicals used in refrigeration and other industrial applications, have a huge environmental impact due to their high GWPs. In this regard, the design of units to capture and recover these gases would contribute to their reuse, avoiding the negative impact of their final emission or incineration. Fluorinated Deep Eutectic Solvents (FDESs) have been considered as promising solvents for the absorption and the selective separation of F-gases. However, the complex and expensive experimental labour to fully characterize FDESs delays its study and development. In this work, a computational approach is applied to develop accurate thermodynamic models of the gas solubility of several F-refrigerants in DESs. The molecular-based equation of state (EoS) soft-SAFT is used to assess the absorption of three F-gases (1,1,1,2-tetrafluoroethane (R-134a), difluoromethane (R-32), and pentafluoroethane (R-125)) at different temperatures in five DESs derived from fluorinated salts and perfluorinated acids. New molecular models are developed through the soft-SAFT approach for FDESs in good agreement with experimental data, and the solubility of F-gases is also calculated at varying temperatures with high accuracy. Then, an in-depth analysis of the characteristics of the refrigerants, and of the FDESs affecting the F-gases absorption is performed, and the enthalpy and entropy of absorption and the selectivity are calculated. It has been encountered that the best FDESs for each particular F-gas separation can be obtained by modifying the composition of DES and the operating temperature. Finally, an assessment based on the selectivity obtained from the soft-SAFT model is carried out to choose the most adequate solvent to separate the studied F-gases.

Measurement and Thermodynamic Modeling of Oxaprozin Solubility in Polymers and Mixed Solutions

Measurement and Thermodynamic Modeling of Oxaprozin Solubility in Polymers and Mixed Solutions

Thermodynamic Modeling of Solubility of Some Antibiotics in Supercritical Carbon Dioxide Using Simplified Equation of State Approach

Antibiotics are playing crucial role in the treatment of humans since the last few centuries. Their usage has several benefits along with side effects. The efficacy of antibiotics for the treatment of ailments may be retained by controlling the drug dosage. This may be achieved with supercritical fluid technology (SFT). The antibiotic drug solubility in supercritical carbon dioxide (scCO2) is available only at specific temperature and pressure conditions, for effective utilization of SFT, solubility at various conditions are required. Equation of state (EoS) method is used for solubility data modeling and it requires critical properties of the solute, molar volume of the solute and sublimation pressure of the solute along with fugacity coefficient, pressure and temperature. These properties are estimated using group contribution methods. For antibiotics solute critical properties, molar volume and sublimation pressure are unavailable and existing group contribution methods are also not applicable due to the lack of functional group contributions in their techniques. Thus, there is a need to address EoS methodology without using solute properties. Hence, a new EoS methodology for solubility modeling is, proposed without using critical properties of the solute, molar volume of the solute and vapour pressure of the solute. Thus, this study focuses on the development of new solubility model that correlates antibiotics using equation of state (EoS). Importantly, the proposed solubility model does not use the critical properties of the antibiotics. Correlating ability of the proposed model is indicated in terms of regression coefficient and arithmetic average relative deviation percentage (AARD %).

Research on the upgrade and maintenance method of apple soluble solids content models

AbstractIn this paper, 3 years (2020, 2021, and 2022) of Bingtangxin apples were used to study the upgrade and maintenance method of apple soluble solids content (SSC) model using near infrared (NIR) spectroscopy. Modeling by partial least squares (PLS) and one‐dimensional convolutional neural network (1D‐CNN) algorithm. The “base model” is built with apples in 2020 and then upgrade and maintain it. The upgraded model is used to predict the sample in 2021 and 2022. Three methods were used to upgrade and maintenance the model, which are based on updated data, direct correction‐based method, and similar band‐based method. Among the three upgrade methods, the model maintenance method using updated data had the greatest improvement in prediction correlation coefficients (Rp) in both years, and both obtained good predictions. The use of direct correction resulted in the greatest reduction in the root mean square error of prediction (RMSEP) in both years. However, the Rp of the model is not improved much by using the direct correction method. The model maintenance method using similar bands combined with different modeling methods also gives good prediction results. Finally, after comparison, the model maintenance method of selecting similar bands is preferred. In the case of limited improvement in the effect of using the similar band method, the model is maintained using the method of adding updated data. The investigation of model upgrade and maintenance methods can reduce human and material consumption and make apple SSC models more versatile.Practical applicationsNIR technology is the first choice for online fruit quality inspection, but its inspection process requires constant upgrading and maintenance with models. The quality of apples varies from year to year, and the model built with samples from a single year does not work well in predicting samples from other years, resulting in the need to upgrade and maintain the fruit quality detection model. However, sometimes, although a large amount of experimental material is lost, it still results in a generic calibration model that is too cumbersome and leads to poor prediction results. At this time, finding an effective model upgrade and maintenance method can both reduce the sample consumption and make the model prediction accuracy improve, and also reduce the model complexity and make the maintained model more robust.

Deducing solute differential heat capacity from experimental solubilities. An exemplified treatment of ascorbic acid to improve solubility prediction

ContextSolubility prediction based on the general solubility equation (GSE) rests on reliable values for the isobaric heat capacity difference ΔCp,1 of the solid solute. Usually, this value is estimated with either zero or the melting entropy ΔS1(Tm,1) or, in few cases, is extrapolated from data of thermally stable melts of the solute. This causes uncertainties in the prediction. ObjectiveTo improve prediction accuracy a simple regression method is proposed that determines ΔCp,1 from measured solubilities. Materials and methodsPublished experimental solubilities in neat organic solvents at 298 K of a model compound (L-(+)-ascorbic acid (LAA)) have been regressed using the GSE together with the Hansen parameter model for the activity coefficient. Results and discussionRegression yielded ΔCp,1 = 238 J∙mol−1∙K−1 which agrees well with cross-validation results and is consistent with estimates from various group contribution methods. It was found that prediction accuracy improved in the order of increasing ΔCp,1, that is, from 0, via 91 (=ΔS1(Tm,1)) to 238 J∙mol−1∙K−1. It could be shown that mole fraction solubility of LAA can be forecast this way with an accuracy within current inter-laboratory variation. ConclusionThe proposed method shows a general way to improve prediction accuracy of activity coefficient based solubility models by determining ΔCp,1 without resorting to common assumptions. The method is universally applicable and easy to implement.

An accurate model for the solubilities of anhydrite in H2O–NaCl solutions at temperatures up to 1073 K, pressures up to 14,000 bar, and mole fractions of NaCl up to 0.3

An accurate density-based solubility model is established for anhydrite in H2O–NaCl solutions, which is valid for temperatures up to 1073 K, pressures up to 14,000 bar, and mole fractions of NaCl (xNaCl) up to 0.3. The isobaric linear dependence of the mineral solubility logarithm on the solvent density logarithm should be a common phenomenon similar to isothermal linear dependence, particularly at relatively high pressures. The terms about P in the equations of the density-based model are rational and necessary to elevate the accuracy of mineral solubility models. This model reproduces the complicated changing trends of anhydrite solubility with temperature, pressure, and xNaCl. Compared to numerous experimental data, the average relative deviations of calculated anhydrite solubilities in water and the aqueous NaCl solution are 3.52% and 9.64%, respectively, which are within experimental uncertainties. According to this model, the anhydrite deposition would result from any salinity variation departing from the specified xNaCl in skarn- or porphyrite-type iron ore-forming hydrothermal fluids.

Solubility of digitoxin in supercritical CO2: Experimental study and modeling

In this communication, the solubility of digitoxin drug in supercritical CO2 was studied at different operating conditions (311 < T (K) < 343, 120 < P (bar) < 300). The results revealed digitoxin drug solubility (in mole fraction) was between 0.095 × 10−5 to 1.12 × 10−5. In the case of thermodynamic solubility modeling, cubic and non-cubic equation of states i.e. SAFT (statistical associating fluid theory), SRK (Soave-Redlich-Kwong) and sPC-SAFT (simplified perturbed chain SAFT) EoSs and six density-based correlations (Chrastil, Kumar-Johnston (KJ), Mendez-Santiago-Teja (MST), Garlapati and Madras (GM), Bartle et al. and Sung-Shim models) were considered. All used equations indicated reasonable behavior with appropriate accuracy for the solubility of the digitoxin drug. Meanwhile, sPC-SAFT EoS and Kumar-Johnston correlation with AARD% set to 8.96 % and 6.25 %, respectively exhibited greater accuracy in fitting the solubility data. Moreover, total, solvation and vaporization enthalpies of the digitoxin/supercritical carbon dioxide binary mixture were calculated based on KJ, Chrastil and Bartle et al. models.

Empirical correlations of drug-and plant-based bioactive compound solubility in supercritical CO2: A comparative evaluation study

Over the last few decades, supercritical fluid extraction process has greatly assisted the search for more medicinal and therapeutic properties of bioactive compounds from an extensive range of botanical matrices. The yield of supercritical fluid extraction is often limited by the solubility of a targeted solute in the solvent and thus knowledge on solubility has become one of the fundamental variables of interest in the extraction process. However, the studies to evaluate the performance of density-based empirical models in estimating the solubility of different drug- and plant-based bioactive compounds were scarce. Therefore, this study aimed to gather the solubility data of 33 drugs and 40 plant-based bioactive compounds at varying temperatures and pressures and correlated them with 26 most widely used density-based models. The solubility data were critically analysed and reported from two perspectives: well-studied solubility-influencing factors such as pressure, temperature, and additional influencing factors what o such as molecular structure and weight. Then, a thorough evaluation of the existing empirical solubility models involving 26 predictive models for 73 bioactive compounds was performed. From the results and findings, it was evident that the 8-parameter Belghait et al. model showed the overall highest accuracies indicated by the lowest absolute average relative deviation of 7.42 %, 10.16 %, and 6.57 %, respectively for all bioactive compounds, drug-based bioactive compounds, and plant-based bioactive compounds. Belghait et al. model is also superior since its predictive performances are the best for 54 % of the bioactive compounds studied. Meanwhile, Kumar and Johnston's, Sung and Shim's, and Sodeifian et al's models demonstrated the best solubility predictions for 3-, 4-, and 6-parameter empirical models, respectively, for both plant and drug-based compounds. As the effects of pressure and temperature on solubility have widely acknowledged, this study also highlights the influence of molecular structure complexity on solubility. The main findings from this comparative evaluation proved that density-based models are efficacious in predicting solubility of both plant and drug-based bioactive compounds.

Solubility Measurement and Model Calculation of Fluorene in Six Binary Solvents from 288.15 to 328.15 K

Solubility Measurement and Model Calculation of Fluorene in Six Binary Solvents from 288.15 to 328.15 K

The roles of elevated temperature and carriers in the dyeing of polyester fibres using disperse dyes: Part 3 model of dye adsorption based on dye solubility

AbstractThis review concerns the application of disperse dyes to poly(ethylene terephthalate) fibres using aqueous immersion dyeing processes and the roles of both elevated dyeing temperatures and carriers in the disperse dye/polyester fibre and disperse dye/carrier/polyester fibre dyeing systems, respectively. In this part of the paper, a mechanistic model of the disperse dye adsorption process is presented that is based on the role of dye solubility in disperse dye‐PET fibre substantivity. It is shown that the dye solubility model of disperse dye adsorption, which can be articulated using a simple mathematical approach, is able to account for the reasons why elevated dyeing temperatures are utilised in HT dyeing processes, and also why dyeing accelerants enable dyeing to be achieved using lower temperatures of ~100°C.

A Soluble Model for the Conflict between Lying and Truth-Telling

A Soluble Model for the Conflict between Lying and Truth-Telling

Fluorescent polymer as a biosensing tool for the diagnosis of microbial pathogens

Diseases and diagnoses are predominant in the human population. Early diagnosis of etiological agents plays a vital role in the treatment of bacterial infections. Existing standard diagnostic platforms are laborious, time-consuming, and require trained personnel and cost-effective procedure, though they are producing promising results. These shortcomings have led to a thirst for rapid diagnostic procedures. Fluorescence-based diagnosis is one of the efficient rapid diagnostic methods that rely on specific and sensitive bacterial detection. Emerging bio-sensing studies on conducting polymers (CPs) are gaining popularity in medical diagnostics due to their promising properties of high fluorescence efficiency, good light stability, and low cytotoxicity. Poly[2-methoxy-5-(2′-ethylhexyloxy)-1,4-phenylenevinylene] (MEH-PPV), is the first identified soluble polymer and model material for understanding the fundamental photophysics of conventional CPs. In this present study, MEH-PPV is used as a fluorescent dye for direct pathogen detection applications by interacting with the microbial cell surface. An optimized concentration of MEH-PPV solution used to confirm the presence of selective bacterial structures. The present study endeavours towards bacterial detection based on the emission from bacteria due to interfacial interaction between polymer and bacterial surface.