Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature an...Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.展开更多
A quantitative structure-property relationship (QSPR) study has been made for the prediction of the surface tension of nonionic surfactants in aqueous solution. The regressed model includes a topological descriptor, ...A quantitative structure-property relationship (QSPR) study has been made for the prediction of the surface tension of nonionic surfactants in aqueous solution. The regressed model includes a topological descriptor, the Kier & Hall index of zero order (KH0) of the hydrophobic segment of surfactant and a quantum chemical one, the heat of formation (fHD) of surfactant molecules. The established general QSPR between the surface tension and the descriptors produces a correlation coefficient of multiple determination, 2r=0.9877, for 30 studied nonionic surfactants.展开更多
Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alk...Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.展开更多
Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydro...Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.展开更多
Based on two-dimensional topological structures, a novel molecular electronegativity interaction vector with hybridization （MEHIV） was developed to describe atomic hybridization state in different molecular environm...Based on two-dimensional topological structures, a novel molecular electronegativity interaction vector with hybridization （MEHIV） was developed to describe atomic hybridization state in different molecular environments. Five quantitative models by MEHIV characterization and multiple linear regression modeling were successfully established to predict reduced ion mobility constants （Ko） of alkanes, aromatic hydrocarbons, fatty alcohols, fatty aldehydes and ketones and carboxylic esters. The correlation coefficients Roy by leave-one-out cross-validation are 0.792, 0.787, 0,949, 0.972 and 0.981, respectively, and the standard deviations SDcv are 0.067, 0.086, 0.064, 0.043 and 0.042, respectively. These results suggested that MEHIV is an excellent topological index descriptor with many advantages such as straightforward physicochemical meaning, high characterization competence, convenient expansibility and easy manipulation.展开更多
Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemicalbased products.The life cycles of chemical products involve...Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemicalbased products.The life cycles of chemical products involve the procedures of conceptual product designs,experimental investigations,sustainable manufactures through appropriate chemical processes and waste disposals.During these periods,one of the most important keys is the molecular property prediction models associating molecular structures with product properties.In this paper,a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions,such as activity coefficient,and so forth.The workflow of framework consists of three steps.In the first step,a database is created for collections of basic molecular information;in the second step,quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties(pseudo experimental data),which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step.The whole framework has been carried out within a molecular property prediction toolbox.Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.展开更多
Quantitative structure-property relationship(QSPR)models were developed for prediction of photolysis half-life(t_(1/2))of polychlorinated biphenyls(PCBs)in water under ultraviolet(UV)radiation.Quantum chemical descrip...Quantitative structure-property relationship(QSPR)models were developed for prediction of photolysis half-life(t_(1/2))of polychlorinated biphenyls(PCBs)in water under ultraviolet(UV)radiation.Quantum chemical descriptors computed by the PM3 Hamiltonian software were used as independent variables.The cross-validated Q^(2)_(cum)value for the optimal QSPR model is 0.966,indicating good prediction capability for lg t_(1/2)values of PCBs in water.The QSPR results show that the largest negative atomic charge on a carbon atom(Q-C)and the standard heat of formation(ΔH_(f))have a dominant effect on t_(1/2)values of PCBs.Higher Q_(C)^(-)values or lowerΔHf values of the PCBs leads to higher lg t_(1/2)values.In addition,the lg t_(1/2)values of PCBs increase with the increase in the energy of the highest occupied molecular orbital values.Increasing the largest positive atomic charge on a chlorine atom and the most positive net atomic charge on a hydrogen atom in PCBs leads to the decrease of lg t_(1/2)values.展开更多
In this paper the photolysis half-lives of the model dyes in water solutions and under ultraviolet （UV） radiation were determined by using a continuous-flow spectrophotometric method. A quantitative structure- prope...In this paper the photolysis half-lives of the model dyes in water solutions and under ultraviolet （UV） radiation were determined by using a continuous-flow spectrophotometric method. A quantitative structure- property relationship （QSPR） study was carried out using 21 descriptors based on different chemometric tools including stepwise multiple linear regression （MLR） and partial least squares （PLS） for the prediction of the photolysis half-life （t1/2） of dyes. For the selection of test set compounds, a K-means clustering technique was used to classify the entire data set, so that all clusters were properly represented in both training and test sets. The QSPR results obtained with these models show that in MLR-derived model, photolysis half-lives of dyes depended strongly on energy of the highest occupied molecular orbital （EHoMO）, largest electron density of an atom in the molecule （ED^＋） and lipophilicity （logP）. While in the model derived from PLS, besides aforementioned EHOMO and ED^＋ descriptors, the molecular surface area （Sm）, molecular weight （M-W）, electronegativity （X）, energy of the second highest occupied molecular orbital （EHoMO- 1） and dipole moment （μ） had dominant effects on logt1/2 values of dyes. These were applicable for all classes of studied dyes （including monoazo, disazo, oxazine, sulfo- nephthaleins and derivatives of fluorescein）. The results were also assessed for their consistency with findings from other similar studies.展开更多
In order to predict the critical micelle concentration (cmc) of nonionic surfactants in aqueous solution,a quantitative structure-property relationship (QSPR) was found for 77 nonionic surfactants belonging to eight s...In order to predict the critical micelle concentration (cmc) of nonionic surfactants in aqueous solution,a quantitative structure-property relationship (QSPR) was found for 77 nonionic surfactants belonging to eight series. The best-regressed model contained four quantum-chemical descriptors,the heat of formation (ΔH),the molecular dipole moment (D),the energy of the lowest unoccupied molecular orbital (E_ LUMO ) and the energy of the highest occupied molecular orbital (E_ HOMO ) of the surfactant molecule; two constitutional descriptors,the molecular weight of surfactant (M) and the number of oxygen and nitrogen atoms (n_ ON ) of the hydrophilic fragment of surfactant molecule; and one topological descriptor,the Kier & Hall index of zero order (KH0) of the hydrophobic fragment of the surfactant. The established general QSPR between lg(cmc) and the descriptors produced a relevant coefficient of multiple determination:R 2=0.986. When cross terms were considered,the corresponding best model contained five descriptors E_ LUMO ,D,KH0,M and a cross term n_ ON ·KH0,which also produced the same coefficient as the seven-parameter model.展开更多
Development of energy-efficient lubricants is a way to reduce energy consumption for transportation,with the tendency to design molecules that are beneficial in reducing the viscosity of synthetic oils.Oligoether este...Development of energy-efficient lubricants is a way to reduce energy consumption for transportation,with the tendency to design molecules that are beneficial in reducing the viscosity of synthetic oils.Oligoether esters(OEEs),as a low-viscosity ester base oil,have characteristics such as simple synthesis and excellent lubrication effect;however,the application of OEEs in tribology field has rarely been investigated.The objective of the present study is to investigate the effect of structure on the lubricating performance of OEEs and to develop a predictive model for OEEs based on quantitative structure‒property relationship(QSPR)through a combination of experiment and statistical modeling.Results showed that glycol chains contribute positively to lubrication with the ether functional groups increasing the sites of adsorption.Compared to branched-chain OEEs,straight-chain OEEs exhibited reduced wear,which was mainly due to the thicker adsorption film formed by the straight-chain structure.Furthermore,carbon films were detected on lightly worn surfaces,indicating that OEEs underwent oxidation during the friction process.Based on the results of principal component analysis(PCA)and partial least squares(PLS),it could be found that the predictive models of viscosity‒temperature performance,thermal stability performance,coefficient of friction(COF),and wear volume(WV)performed well and robustly.Among them,COF and WV can be best predicted with an R^(2) of about 0.90.展开更多
Extractive distillation is an effective method for separating azeotropic or close boiling point mixtures by adding a third component.Various technologies for performing the extractive distillation process have been ex...Extractive distillation is an effective method for separating azeotropic or close boiling point mixtures by adding a third component.Various technologies for performing the extractive distillation process have been explored to protect the environment and save resources.This paper focuses on the improvement of these advanced technologies in recent years.Extractive distillation is retrieved and analyzed from the view of phase equilibrium,selection of solvent in extractive distillation,process design,energy conservation,and dynamic control.The quantitative structure–property relationship used in extractive distillation is discussed,and the future development of extractive distillation is proposed to determine how the solvent affects the relative volatility of the separated mixture.In the steady state design,the relationship between the curvature of the residue curve and parameters of the optimal steady state is also highlighted as another field worthy of further study to simplify the distillation process.展开更多
基金the financial support from the National Key Research and Development Program of China(2022YFB4101302-01)the National Natural Science Foundation of China(22178243)the science and technology innovation project of China Shenhua Coal to Liquid and Chemical Company Limited(MZYHG-22-02).
文摘Direct coal liquefaction(DCL)is an important and effective method of converting coal into high-valueadded chemicals and fuel oil.In DCL,heating the direct coal liquefaction solvent(DCLS)from low to high temperature and pre-hydrogenation of the DCLS are critical steps.Therefore,studying the dissolution of hydrogen in DCLS under liquefaction conditions gains importance.However,it is difficult to precisely determine hydrogen solubility only by experiments,especially under the actual DCL conditions.To address this issue,we developed a prediction model of hydrogen solubility in a single solvent based on the machine-learning quantitative structure–property relationship(ML-QSPR)methods.The results showed that the squared correlation coefficient R^(2)=0.92 and root mean square error RMSE=0.095,indicating the model’s good statistical performance.The external validation of the model also reveals excellent accuracy and predictive ability.Molecular polarization(a)is the main factor affecting the dissolution of hydrogen in DCLS.The hydrogen solubility in acyclic alkanes increases with increasing carbon number.Whereas in polycyclic aromatics,it decreases with increasing ring number,and in hydrogenated aromatics,it increases with hydrogenation degree.This work provides a new reference for the selection and proportioning of DCLS,i.e.,a solvent with higher hydrogen solubility can be added to provide active hydrogen for the reaction and thus reduce the hydrogen pressure.Besides,it brings important insight into the theoretical significance and practical value of the DCL.
基金the National Natural Science Foundation of China(to grant No.29903006 and 29973023)the Visiting Scholar Foundation of Key Laboratory in University of China for their financial support
文摘A quantitative structure-property relationship (QSPR) study has been made for the prediction of the surface tension of nonionic surfactants in aqueous solution. The regressed model includes a topological descriptor, the Kier & Hall index of zero order (KH0) of the hydrophobic segment of surfactant and a quantum chemical one, the heat of formation (fHD) of surfactant molecules. The established general QSPR between the surface tension and the descriptors produces a correlation coefficient of multiple determination, 2r=0.9877, for 30 studied nonionic surfactants.
基金This work was financially supported by the National Basic Research Program of China (2003CB415002), the China Postdoctoral Science Foundation (No. 2003033486) and the Natural Science Research Fund of University in Jiangsu (04KJB150149)
文摘Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.
基金supported by the Natural Science Foundation of Fujian Province (D0710019)the Natural Science Foundation of Overseas Chinese Affairs Office of the State Council (06QZR09)
文摘Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.
基金the State Key Laboratory of Chemo/Biosensing and Chemometrics Foundation(No.05-12-1)
文摘Based on two-dimensional topological structures, a novel molecular electronegativity interaction vector with hybridization （MEHIV） was developed to describe atomic hybridization state in different molecular environments. Five quantitative models by MEHIV characterization and multiple linear regression modeling were successfully established to predict reduced ion mobility constants （Ko） of alkanes, aromatic hydrocarbons, fatty alcohols, fatty aldehydes and ketones and carboxylic esters. The correlation coefficients Roy by leave-one-out cross-validation are 0.792, 0.787, 0,949, 0.972 and 0.981, respectively, and the standard deviations SDcv are 0.067, 0.086, 0.064, 0.043 and 0.042, respectively. These results suggested that MEHIV is an excellent topological index descriptor with many advantages such as straightforward physicochemical meaning, high characterization competence, convenient expansibility and easy manipulation.
基金The authors are grateful for the financial supports of the National Natural Science Foundation of China(Grant Nos.22078041 and 21808025)the Fundamental Research Funds for the Central Universities(Grant No.DUT20JC41).
文摘Chemical industry is always seeking opportunities to efficiently and economically convert raw materials to commodity chemicals and higher value-added chemicalbased products.The life cycles of chemical products involve the procedures of conceptual product designs,experimental investigations,sustainable manufactures through appropriate chemical processes and waste disposals.During these periods,one of the most important keys is the molecular property prediction models associating molecular structures with product properties.In this paper,a framework combining quantum mechanics and quantitative structure-property relationship is established for fast molecular property predictions,such as activity coefficient,and so forth.The workflow of framework consists of three steps.In the first step,a database is created for collections of basic molecular information;in the second step,quantum mechanics-based calculations are performed to predict quantum mechanics-based/derived molecular properties(pseudo experimental data),which are stored in a database and further provided for the developments of quantitative structure-property relationship methods for fast predictions of properties in the third step.The whole framework has been carried out within a molecular property prediction toolbox.Two case studies highlighting different aspects of the toolbox involving the predictions of heats of reaction and solid-liquid phase equilibriums are presented.
基金The research was supported by the Open Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering(No.2009490511)the special fund of State Key Joint Laboratory of Environment Simulation and Pollution Control(No.10Y08ESPCN)the National High Technology Research and Development Program of China(No.2009AA05Z306).
文摘Quantitative structure-property relationship(QSPR)models were developed for prediction of photolysis half-life(t_(1/2))of polychlorinated biphenyls(PCBs)in water under ultraviolet(UV)radiation.Quantum chemical descriptors computed by the PM3 Hamiltonian software were used as independent variables.The cross-validated Q^(2)_(cum)value for the optimal QSPR model is 0.966,indicating good prediction capability for lg t_(1/2)values of PCBs in water.The QSPR results show that the largest negative atomic charge on a carbon atom(Q-C)and the standard heat of formation(ΔH_(f))have a dominant effect on t_(1/2)values of PCBs.Higher Q_(C)^(-)values or lowerΔHf values of the PCBs leads to higher lg t_(1/2)values.In addition,the lg t_(1/2)values of PCBs increase with the increase in the energy of the highest occupied molecular orbital values.Increasing the largest positive atomic charge on a chlorine atom and the most positive net atomic charge on a hydrogen atom in PCBs leads to the decrease of lg t_(1/2)values.
文摘In this paper the photolysis half-lives of the model dyes in water solutions and under ultraviolet （UV） radiation were determined by using a continuous-flow spectrophotometric method. A quantitative structure- property relationship （QSPR） study was carried out using 21 descriptors based on different chemometric tools including stepwise multiple linear regression （MLR） and partial least squares （PLS） for the prediction of the photolysis half-life （t1/2） of dyes. For the selection of test set compounds, a K-means clustering technique was used to classify the entire data set, so that all clusters were properly represented in both training and test sets. The QSPR results obtained with these models show that in MLR-derived model, photolysis half-lives of dyes depended strongly on energy of the highest occupied molecular orbital （EHoMO）, largest electron density of an atom in the molecule （ED^＋） and lipophilicity （logP）. While in the model derived from PLS, besides aforementioned EHOMO and ED^＋ descriptors, the molecular surface area （Sm）, molecular weight （M-W）, electronegativity （X）, energy of the second highest occupied molecular orbital （EHoMO- 1） and dipole moment （μ） had dominant effects on logt1/2 values of dyes. These were applicable for all classes of studied dyes （including monoazo, disazo, oxazine, sulfo- nephthaleins and derivatives of fluorescein）. The results were also assessed for their consistency with findings from other similar studies.
文摘In order to predict the critical micelle concentration (cmc) of nonionic surfactants in aqueous solution,a quantitative structure-property relationship (QSPR) was found for 77 nonionic surfactants belonging to eight series. The best-regressed model contained four quantum-chemical descriptors,the heat of formation (ΔH),the molecular dipole moment (D),the energy of the lowest unoccupied molecular orbital (E_ LUMO ) and the energy of the highest occupied molecular orbital (E_ HOMO ) of the surfactant molecule; two constitutional descriptors,the molecular weight of surfactant (M) and the number of oxygen and nitrogen atoms (n_ ON ) of the hydrophilic fragment of surfactant molecule; and one topological descriptor,the Kier & Hall index of zero order (KH0) of the hydrophobic fragment of the surfactant. The established general QSPR between lg(cmc) and the descriptors produced a relevant coefficient of multiple determination:R 2=0.986. When cross terms were considered,the corresponding best model contained five descriptors E_ LUMO ,D,KH0,M and a cross term n_ ON ·KH0,which also produced the same coefficient as the seven-parameter model.
基金the National Natural Science Foundation of China(No.52175156)the Key Research and Development Projects of Shaanxi Province(No.2021GY-157)+1 种基金the Young Talent fund of University Association for Science and Technology in Shaanxi(No.20220615)the Special Fund for Basic Scientific Research of Central Colleges(Chang an University)with Nos.300102221512,300102221510,and 300102222502.
文摘Development of energy-efficient lubricants is a way to reduce energy consumption for transportation,with the tendency to design molecules that are beneficial in reducing the viscosity of synthetic oils.Oligoether esters(OEEs),as a low-viscosity ester base oil,have characteristics such as simple synthesis and excellent lubrication effect;however,the application of OEEs in tribology field has rarely been investigated.The objective of the present study is to investigate the effect of structure on the lubricating performance of OEEs and to develop a predictive model for OEEs based on quantitative structure‒property relationship(QSPR)through a combination of experiment and statistical modeling.Results showed that glycol chains contribute positively to lubrication with the ether functional groups increasing the sites of adsorption.Compared to branched-chain OEEs,straight-chain OEEs exhibited reduced wear,which was mainly due to the thicker adsorption film formed by the straight-chain structure.Furthermore,carbon films were detected on lightly worn surfaces,indicating that OEEs underwent oxidation during the friction process.Based on the results of principal component analysis(PCA)and partial least squares(PLS),it could be found that the predictive models of viscosity‒temperature performance,thermal stability performance,coefficient of friction(COF),and wear volume(WV)performed well and robustly.Among them,COF and WV can be best predicted with an R^(2) of about 0.90.
基金Supported by the National Natural Science Foundation of China(21676152)
文摘Extractive distillation is an effective method for separating azeotropic or close boiling point mixtures by adding a third component.Various technologies for performing the extractive distillation process have been explored to protect the environment and save resources.This paper focuses on the improvement of these advanced technologies in recent years.Extractive distillation is retrieved and analyzed from the view of phase equilibrium,selection of solvent in extractive distillation,process design,energy conservation,and dynamic control.The quantitative structure–property relationship used in extractive distillation is discussed,and the future development of extractive distillation is proposed to determine how the solvent affects the relative volatility of the separated mixture.In the steady state design,the relationship between the curvature of the residue curve and parameters of the optimal steady state is also highlighted as another field worthy of further study to simplify the distillation process.
