Free Energy Relationship Research Articles

Retention properties of acetone-water mobile phases on a biphenylsiloxane-bonded silica stationary phase in reversed-phase liquid chromatography.

The solvation parameter model system constants and retention factors were used to interpret retention properties of 39 calibration compounds on a biphenylsiloxane-bonded stationary phase (Kinetex biphenyl) for acetone-water binary mobile phase systems containing 30-70% v/v. Variation in system constants, phase ratios, and retention factors of acetone-water binary mobile phases systems were compared with more commonly used acetonitrile and methanol mobile phase systems. Retention properties of acetone mobile phases on a Kinetex biphenyl column were more similar to that of acetonitrile than methanol mobile phases except with respect to selectivity equivalency. Importantly, selectivity differences arising between acetone and acetonitrile systems (the lower hydrogen-bond basicity of acetone-water mobile phases and differences in hydrogen-bond acidity, cavity formation and dispersion interactions) could be exploited in reversed-phase liquid chromatography method development on a Kinetex biphenyl stationary phase.

Simulating and Predicting Adsorption of Organic Pollutants onto Black Phosphorus Nanomaterials.

Layered black phosphorus (BP) has exhibited exciting application prospects in diverse fields. Adsorption of organics onto BP may influence environmental behavior and toxicities of both organic pollutants and BP nanomaterials. However, contributions of various intermolecular interactions to the adsorption remain unclear, and values of adsorption parameters such as adsorption energies (Ead) and adsorption equilibrium constants (K) are lacking. Herein, molecular dynamic (MD) and density functional theory (DFT) was adopted to calculate Ead and K values. The calculated Ead and K values for organics adsorbed onto graphene were compared with experimental ones, so as to confirm the reliability of the calculation methods. Polyparameter linear free energy relationship (pp-LFER) models on Ead and logK were developed to estimate contributions of different intermolecular interactions to the adsorption. The adsorption in the gaseous phase was found to be more favorable than in the aqueous phase, as the adsorbates need to overcome cohesive energies of water molecules onto BP. The affinity of the aromatics to BP was comparable to that of graphene. The pp-LFER models performed well for predicting the Ead and K values, with external explained variance ranging from 0.90 to 0.97, and can serve as effective tools to rank adsorption capacities of organics onto BP.

Linear relationships for modeling CO2 absorption in aqueous alkanolamine solutions in a thermodynamically consistent way

AbstractA thermodynamically consistent model for the carbon dioxide (CO2) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well‐known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. The proposed relationships have been applied to the development of an electrolyte nonrandom two liquid (NRTL) activity coefficient model for CO2 absorption in aqueous 1‐amino‐2‐propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor–liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.

Novel quantitative structure activity relationship models for predicting hexadecane/air partition coefficients of organic compounds

Predicting the logarithm of hexadecane/air partition coefficient (L) for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships. Herein, two quantitative structure activity relationship (QSAR) models were developed with 1272 L values for the organic compounds by using multiple linear regression (MLR) and support vector machine (SVM) algorithms. On the basis of the OECD principles, the goodness of fit, robustness and predictive ability for the developed models were evaluated. The SVM model was first developed, and the predictive capability for the SVM model is slightly better than that for the MLR model. The applicability domain (AD) of these two models has been extended to include more kinds of emerging pollutants, i.e., oraganosilicon compounds. The developed QSAR models can be used for predicting L values of various organic compounds. The van der Waals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound. These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic compounds.

A universal free energy relationship for both hard and soft radical addition in water

AbstractPrioritization of (eco)toxicological risks and technological endpoints among 100,000+ potential substances, conditions and mechanisms requires computationally ‘inexpensive’ and accurate tools to ‘screen’ reactivity and identify reaction products. Such prediction tools are very scarce. Left unresolved, charge‐transfer and hydration complicate predictions. Based on experimental reaction of radicals (3O2, CH3•, CO3•− etc.) with organic substrates, we hypothesized that a universal linear free energy relationship (LFER) can rationalize these reactions to accurately predict addition rates.We calculated free energies of forming charge‐separated intermediates from explicit descriptions of addition reaction products. We combined these energies, via a thermodynamic cycle, with electron transfer energies to calculate product formation energies. All energies include consideration of hydration effects by water. We ascribe feasibilities of ‘hard’ (ionic) and ‘soft’ (covalent) mechanisms to the relevance of the charge‐separated intermediate.Analysis shows that activation energies effectively relate to a combination of product formation energies and charge‐separation energies. The relative importance of these is determined by a mixing parameter, which is mostly constant for a given radical (substrate). Via the Eyring equation, our universal LFER explains up to 94% of the variance in data for rate constants. Prediction error is a factor 5 (2 SD), only slightly larger than variation in experimentation, particularly sensitive to varying reaction conditions.Minimal parametrization ensures that our new framework for calculating reactivity of radical addition is accurate, robust and with satisfactory rational. Calculation time for 100 reactions is <1 h on a standard desktop PC. In anticipation of underpinning with an even wider range of reagents, this ‘inexpensive’ calculus can more easily assess a greater domain of structures and extrapolate to new structures. This helps to better assess and select favourable non‐toxic, environmentally friendly and technologically superior chemical (sub)structures.

Photochemistry of para substituted benzanilides in solution: Preparative and mechanistic studies.

Preparative and mechanistic studies on the photochemical reaction of a series of p-substituted benzanilides in polar and nonpolar solvents have been carried out. The aim of this work is mainly focused to show whether the reaction solvent and the electronic effects of the substituents affect the product distribution, the chemical yields and the rate of formation of the 5-substituted-2-aminobenzophenone derivatives. Application of the Hammett linear free energy relationship (LFER) on the rate of formation of 2-aminobenzophenone derivatives, on the lower energy band of the UV-visible absorption spectra of the benzanilides and 5-substituted-2-aminobenzophenone derivatives allows a satisfactory quantification of the substituent effects. Furthermore, the solvent effect was also analyzed on the photoreaction by means of the Reichardt's solvent parameter (ET(30). Finally, (TD-) DFT calculations have been carried out to support the trends observed experimentally.

Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy.

We present a group contribution method (SoluteGC) and a machine learning model (SoluteML) to predict the Abraham solute parameters, as well as a machine learning model (DirectML) to predict solvation free energy and enthalpy at 298 K. The proposed group contribution method uses atom-centered functional groups with corrections for ring and polycyclic strain while the machine learning models adopt a directed message passing neural network. The solute parameters predicted from SoluteGC and SoluteML are used to calculate solvation energy and enthalpy via linear free energy relationships. Extensive data sets containing 8366 solute parameters, 20,253 solvation free energies, and 6322 solvation enthalpies are compiled in this work to train the models. The three models are each evaluated on the same test sets using both random and substructure-based solute splits for solvation energy and enthalpy predictions. The results show that the DirectML model is superior to the SoluteML and SoluteGC models for both predictions and can provide accuracy comparable to that of advanced quantum chemistry methods. Yet, even though the DirectML model performs better in general, all three models are useful for various purposes. Uncertain predicted values can be identified by comparing the three models, and when the 3 models are combined together, they can provide even more accurate predictions than any one of them individually. Finally, we present our compiled solute parameter, solvation energy, and solvation enthalpy databases (SoluteDB, dGsolvDBx, dHsolvDB) and provide public access to our final prediction models through a simple web-based tool, software packages, and source code.

Some aspects of additives effects on retention in supercritical fluid chromatography studied by linear free energy relationships method

This work is dedicated to the investigation of additives effects on retention mechanisms in supercritical fluid chromatography. Additives are compounds which are added to the mobile phase in small quantities and greatly affect retention factors, peak shape, separation selectivity and other chromatographic parameters. Linear free energy relationship (LFER) method with an expanded set of descriptors including the ones taking ionic interactions into account was used to probe the effect of four types of additive: trifluoroacetic acid, diethylamine, ammonium acetate and water – on retention on four polar stationary phase bearing different functional groups: bare silica, cyano, 2-ethylpyridine and zwitter-ionic sulfobetaine. Effects of all additives were shown to be complex, involving different intermolecular interactions and not uniform. The direction and magnitude of retention change depends not only on additive concentration, but on all other component of a chromatography system: type of a stationary phase, mobile phase composition and the nature of the solute. Various subtle effects were registered, the most peculiar among them being the fact that diethylamine and ammonium acetate provide identical changes of LFER constants. We hypothesize that this is caused by the convergence of diethylamine into a methylcarbamate via reaction with carbon dioxide and methanol, which then behaves as a salt additive.

Thermodynamic controls on rates of iron oxide reduction by extracellular electron shuttles.

Anaerobic microbial respiration in suboxic and anoxic environments often involves particulate ferric iron (oxyhydr-)oxides as terminal electron acceptors. To ensure efficient respiration, a widespread strategy among iron-reducing microorganisms is the use of extracellular electron shuttles (EES) that transfer two electrons from the microbial cell to the iron oxide surface. Yet, a fundamental understanding of how EES-oxide redox thermodynamics affect rates of iron oxide reduction remains elusive. Attempts to rationalize these rates for different EES, solution pH, and iron oxides on the basis of the underlying reaction free energy of the two-electron transfer were unsuccessful. Here, we demonstrate that broadly varying reduction rates determined in this work for different iron oxides and EES at varying solution chemistry as well as previously published data can be reconciled when these rates are instead related to the free energy of the less exergonic (or even endergonic) first of the two electron transfers from the fully, two-electron reduced EES to ferric iron oxide. We show how free energy relationships aid in identifying controls on microbial iron oxide reduction by EES, thereby advancing a more fundamental understanding of anaerobic respiration using iron oxides.

Application of machine learning in predicting the adsorption capacity of organic compounds onto biochar and resin

Detailed prediction of the adsorption amounts of organic pollutants in water is essential to the clean development and management of water resources. In this study, Kriging and polyparameter linear free energy relationship model are coupled to predict adsorption capacity of organic pollutants by biochar and resin. It's based on 1750 adsorption experimental data sets which contains 73 organic compounds on 50 biochars and 30 polymer resins. The Kriging-LFER model shows better accuracy and predictive performance for adsorption (R2 are 0.940 and 0.976) than the published NN-LFER model (R2 are 0.870 and 0.880). Local sensitivity analysis method is adopted to evaluate the influence of each variable on the adsorption coefficient of resin and find out that top sensitive parameters are V and log Ce, to guide parameter optimization. Data's uncertainty analysis is presented by Monte Carlo method. It predicts that the adsorption coefficient will range from 0.062 to 0.189 under the 95% confidence interval. The Kriging-LFER model provides great significance for understanding the importance of various parameters, reducing the number of experiments, adjusting the direction of experimental improvement, and evaluating the fate of organic pollutants in the environment.

Investigation of partition coefficients and fingerprints of atmospheric gas- and particle-phase intermediate volatility and semi-volatile organic compounds using pixel-based approaches

Ambient gas- and particle-phase intermediate volatility and semi-volatile organic compounds (I/SVOCs) of Beijing were analyzed by a thermal desorption comprehensive two-dimensional gas chromatography quadrupole mass spectrometry (TD-GC × GC-qMS). A pixel-based scheme combing the integration-based approach was applied for partition coefficients estimation and fingerprints identification. Blob-by-blob recognition was firstly utilized to characterize I/SVOCs from the molecular level. 412 blobs in gas-phase and 460 blobs in particle-phase were resolved, covering a total response of 47.5% and 43.5%. A large pool of I/SVOCs was found with a large diversity of chemical classes in both gas- and particle-phase. Acids (8.5%), b-alkanes (5.8%), n-alkanes (C8-C25, 5.3%), and aromatics (4.4%) were dominant in gas-phase while esters (7.0%, including volatile chemical product compounds, VCPs), n-alkanes (C9-C34, 5.7%), acids (4.6%), and siloxanes (3.6%) were abundant in particle-phase. Air pollutants were then evaluated by a two-parameter linear free energy relationship (LFER) model, which could be further implemented in the two-dimensional volatility basis set (2D-VBS) model. Multiway principal component analysis (MPCA) and partial least squares-discriminant analysis (PLS-DA) implied that naphthalenes, phenol, propyl-benzene isomers, and oxygenated volatile organic compounds (OVOCs) were key components in the gas-phase under different pollution levels. This work gives more insight into property estimation and fingerprints identification for complex ambient samples.

Predicting Solute Descriptors for Organic Chemicals by a Deep Neural Network (DNN) Using Basic Chemical Structures and a Surrogate Metric.

Solute descriptors have been widely used to model chemical transfer processes through poly-parameter linear free energy relationships (pp-LFERs); however, there are still substantial difficulties in obtaining these descriptors accurately and quickly for new organic chemicals. In this research, models (PaDEL-DNN) that require only SMILES of chemicals were built to satisfactorily estimate pp-LFER descriptors using deep neural networks (DNN) and the PaDEL chemical representation. The PaDEL-DNN-estimated pp-LFER descriptors demonstrated good performance in modeling storage-lipid/water partitioning coefficient (log Kstorage-lipid/water), bioconcentration factor (BCF), aqueous solubility (ESOL), and hydration free energy (freesolve). Then, assuming that the accuracy in the estimated values of widely available properties, e.g., logP (octanol-water partition coefficient), can calibrate estimates for less available but related properties, we proposed logP as a surrogate metric for evaluating the overall accuracy of the estimated pp-LFER descriptors. When using the pp-LFER descriptors to model log Kstorage-lipid/water, BCF, ESOL, and freesolve, we achieved around 0.1 log unit lower errors for chemicals whose estimated pp-LFER descriptors were deemed "accurate" by the surrogate metric. The interpretation of the PaDEL-DNN models revealed that, for a given test chemical, having several (around 5) "similar" chemicals in the training data set was crucial for accurate estimation while the remaining less similar training chemicals provided reasonable baseline estimates. Lastly, pp-LFER descriptors for over 2800 persistent, bioaccumulative, and toxic chemicals were reasonably estimated by combining PaDEL-DNN with the surrogate metric. Overall, the PaDEL-DNN/surrogate metric and newly estimated descriptors will greatly benefit chemical transfer modeling.

Is Hammett Correlation of Dipole Moments of <i>mono</i>-Substituted Benzenes Helpful to Distinguish <i>o,p</i>-Directing Groups from <i>m</i>-Directing Groups in an Aromatic Electrophilic Substitution? A Chemical Education Perspective

In chemistry literature it is so far achieved to distinguish o,p-directing groups from m-directing groups purely on the basis of only relative yields of ortho, para and meta electrophilic substituted products in the benzene ring and it is also based on their ability to disturb the pi-electron density at the carbons. It is for the first time in chemistry literature using dipole moment data a linear free energy relationship (LFER), Hammett equation is used to distinguish o,p-directing groups from m-directing groups. This could be achieved by constructing Hammett plots with dipole moments versus Hammett para substituent constants for electron donating groups and Hammett meta substituent constants for electron withdrawing groups. Good straight lines are obtained with correlation coefficients close to 0.9 with certainly an unmistakable trend.

Taft linear free-energy relationships in the biocatalytic hydrolysis of sterically hindered nitrophenyl ester substrates

Linear free-energy relationships (LFERs) have been commonly used to uncover reaction mechanisms in organic chemistry by correlating trends in reactivity to reactant properties. However, the applications of LFERs have largely been limited to traditional organic synthesis and have much less frequently been applied toward enzyme-catalyzed reactions. In this study, we used the Taft LFER, which correlates reaction rates with steric properties of reactants, to study kinetic trends in the enzymatic hydrolysis of sterically hindered substrates. We synthesized 4-nitrophenyl ester compounds with substituents of varying degrees of steric hindrance, and then subjected these compounds to hydrolysis by the enzymes lipase, trypsin, and nattokinase. Kinetic data was obtained by using a spectrophotometer to monitor the formation of 4-nitrophenol, a bright yellow product of the ester hydrolysis with an optical readout at 413 nm. Contrary to initial hypotheses, Taft plots did not exhibit linear relationships and further analysis yielded mechanistic insight into the nature of the Taft steric parameter, the relative sensitivity of each enzyme to steric effects, and potential enzyme-substrate binding interactions. This analysis was paralleled with computational calculations to determine local charge density of the reaction center, which supported that the unexpected trends were largely a function of the aforementioned factors rather than electronic effects. Ultimately, we demonstrate the unconventional application of the Taft LFER toward biocatalytic transformations and open avenues toward the broader use of biocatalysts in synthetic organic chemistry.

Reactivities of hydrated electrons with organic compounds in aqueous-phase advanced reduction processes

Novel linear free energy relationships were determined between experimentally measured rate constants of solvated electrons and theoretically calculated one electron reduction potential for the elucidation of reductive reaction mechanisms.

Reliable Prediction of Sensory Irritation Threshold Values of Neutral Organic Compounds in Pure and Complex Mixtures Using New Models Based on Linear Free Energy Relationships and Gc×Gc Retention Parameters

Reliable Prediction of Sensory Irritation Threshold Values of Neutral Organic Compounds in Pure and Complex Mixtures Using New Models Based on Linear Free Energy Relationships and Gc×Gc Retention Parameters

Molecular electrostatic potential as a general and versatile indicator for electronic substituent effects: statistical analysis and applications.

It is necessary to quantitatively determine substituent effects to accurately elucidate reaction mechanisms in the field of organic chemistry. This paper reports that the molecular electrostatic potential (MESP) can be used as a general and versatile measure for the substituent effects in various chemical reactions by performing extensive density functional theory (DFT) calculations for more than 400 molecules, followed by statistical analyses. We observed a robust and linear correlation between the electrostatic potential and the substituent parameters for various cases of reactive systems, regardless of the DFT functionals, basis sets, and solvation models used. In addition, we statistically analysed the normality of the residuals from the linear regression to demonstrate that strong linear relationships hold universally, which indicates that the electrostatic potential can serve as a physically meaningful quantity for the predictive estimation of substituent effects. In contrast, conventionally used methods based on the charge deviation in the aromatic carbons, as computed using various charge analysis methods, (e.g., Hirshfeld charge analysis) do not demonstrate the statistical normality. Furthermore, we illustrate that MESP can be extensively adopted to strengthen the validity of the linear free energy relationships (LFERs) under various chemical conditions. The results revealed that the MESP shift derived by a functional group on a mono-substituted benzene ring is a strong predictor for the substituent effects on the electronic behaviours in chemical reactions; thus, it can serve as an alternative to other empirical parameters such as the Hammett or Swain-Lupton parameters, or the charge shift.

Study of System Properties in Reversed-Phase Liquid Chromatography for Binary and Ternary Solvent Mobile Phase Compositions Using the Solvation Parameter Model

Study of System Properties in Reversed-Phase Liquid Chromatography for Binary and Ternary Solvent Mobile Phase Compositions Using the Solvation Parameter Model

Cavity catalysis: modifying linear free-energy relationship under cooperative vibrational strong coupling.

Here, we used an unconventional idea of cooperative vibrational strong coupling of solute and solvent molecules to enhance the rate of an esterification reaction. Different derivatives of p-nitrophenyl benzoate (solute) and isopropyl acetate (solvent) are cooperatively coupled to an infrared Fabry–Perot cavity mode. The apparent rates are increased by more than six times at the ON resonance condition, and the rate enhancement follows the lineshape of the vibrational envelope. Very interestingly, a strongly coupled system doesn't obey the Hammett relations. Thermodynamics suggests that the reaction mechanism remains intact for cavity and non-cavity conditions. Temperature-dependent experiments show an entropy-driven process for the coupled molecules. Vacuum field coupling decreases the free energy of activation by 2–5 kJ mol−1, supporting a catalysis process. The non-linear rate enhancement can be due to the reshuffling of the energy distribution between the substituents and the reaction center across the aromatic ring. These findings underline the non-equilibrium behavior of cavity catalysis.

Oxygenation of Substituted (Phenylthio)acetic Acids by Acidic Bromate: A Kinetic and Mechanistic Study and Validity of Linear Free-Energy Relationships

Kinetics and mechanistic investigations on the oxygenation of (phenylthio)acetic acid and its substituted compounds using bromate in acid medium have been carried out. The reaction exhibited first-order in [bromate], 1.6 order in [H2SO4], less than one order in [substrate] and displayed solvent isotopic effect of 1.75 (kD2O/kH2O). The reaction rate is not affected by ionic strength variation, however, enhanced by lowering dielectric constant of the medium. Structural modifications in the aryl moiety of the substrate resulted in a change of reactivity, where electron-seeking substituents decreased the reaction rate. The order of reactivity among the studied substrates is paramethoxy > para-methyl > -H > para-chloro ≈ para-bromo > para-nitro(phenylthio)acetic acid and showed an excellent correlation between rate constant and the Hammett substituent constant (σ value). The reaction constant (ρ) value is negative (-1.37 at 303 K) and decreased by increase in the reaction temperature. Proposed mechanism involves decomposition of the complex formed between acid bromate and the substrate. Based on the proposed mechanism, an appropriate rate law has been derived and tested for its validity. Activation parameters and isokinetic temperature have been evaluated and discussed. The validity of linear free-energy relationships have been discussed thoroughly.