Different fused-core stationary phase chemistries（C18,Amide,Phenyl-hexyl and Peptide ES-C18） were used for the analysis of 21 structurally representative model peptides.In addition,the effects of the mobile phase co...Different fused-core stationary phase chemistries（C18,Amide,Phenyl-hexyl and Peptide ES-C18） were used for the analysis of 21 structurally representative model peptides.In addition,the effects of the mobile phase composition（ACN or MeOH as organic modifier;formic acid or acetic acid,as acidifying component） on the column selectivity,peak shape and overall chromatographic performance were evaluated.The RP-amide column,combined with a formic acid-acetonitrile based gradient system,performed as best.A peptide reversed-phase retention model is proposed,consisting of 5 variables：log SumAA,log Sv,clog P,log nHDon and log nHAcc.Quantitative structure-retention relationship（QSRR） models were constructed for 16 different chromatographic systems.The accuracy of this peptide retention model was demonstrated by the comparison between predicted and experimentally obtained retention times,explaining on average 86% of the variability.Moreover,using an external set of 5 validation peptides,the predictive power of the model was also demonstrated.This peptide retention model includes the novel in-silico calculated amino acid descriptor,AA,which was calculated from log P,3D-MoRSE,RDF and WHIM descriptors.展开更多
Based on six thermodynamic equilibria,a unified retention model of solute in liquid chromatography(LC)is first proposed.The unified model was tested and proved to be valid for a LC sys- tem with mobile phase consistin...Based on six thermodynamic equilibria,a unified retention model of solute in liquid chromatography(LC)is first proposed.The unified model was tested and proved to be valid for a LC sys- tem with mobile phase consisting of a complete range of multiple,ternary or binary components and each kind of LC except size exclusion chromatography.In addition,so long as making some assumptions and mathematical conversions,the expressions of various popular models in LC can be derived by using the unified model.展开更多
The separation efficiency of biopolymers with a short column in liquid chromatography has been investigated in this paper. It was found that the column length has slight effect on the resolution of biopolymers under g...The separation efficiency of biopolymers with a short column in liquid chromatography has been investigated in this paper. It was found that the column length has slight effect on the resolution of biopolymers under gradient elution. The reasons have been explained by stoichiometric displacement model for retention of solute. The column 1.0 cm long was also used in the separation and purification of recombinant human granulocyte colony-simulating factor (rhG-CSF). It only took 40 min and the purity by one step was found to be almost 100%.展开更多
Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these met...Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these methods use attention mechanisms and their variants,which have room for further improvement in the speed of solving MOCO problems.In this paper,following the idea of decomposition strategy and neural combinatorial optimization,a novel fast-solving model for MOCO based on retention is proposed.A brand new calculation of retention is proposed,causal masking and exponential decay are deprecated in retention,so that our model could better solve MOCO problems.During model training,a parallel computation of retention is applied,allowing for fast parallel training.When using the model to solve MOCO problems,a recurrent computation of retention is applied,enabling quicker problem-solving.In order to make our model more practical and flexible,a preference-based retention decoder is proposed,which allows generating approximate Pareto solutions for any trade-off preferences directly.An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO.Experimental results show that,while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.展开更多
Invert traps are used to trap sewer solids flowing into a sewer drainage system, The performance of the invert trap in an open rectangular channel was experimentally and numerically analysed using field sewer solids c...Invert traps are used to trap sewer solids flowing into a sewer drainage system, The performance of the invert trap in an open rectangular channel was experimentally and numerically analysed using field sewer solids collected from a sewer drain. Experiments showed that the free water surface rises over the central opening （slot） of the invert trap, which reduces the velocity near the slot and allows more sediment to be trapped in comparison with the case for the fixed-lid model （assuming closed conduit flow with a shear-free top wall） used by earlier investigators. This phenomenon cannot be modelled using a closed conduit model as no extra space is provided for the fluctuation of the water surface, whereas this space is provided in the volume of fluid （VOF） model in the form of air space in ANSYS Fluent 14.0 software. Additionally, the zero atmospheric pressure at the free water surface cannot be modelled in a fixed-lid model. In the present study, experimental trap efflciencies of the invert trap using field sewer solids were fairly validated using a three-dimensional computational fluid dynamics model （VOF model） coupled with a stochastic discrete phase model. The flow field （i.e., velocities） predicted by the VOF model were compared with experimental velocities obtained employing particle image velocimetry. The water surface profile above the invert trap predicted by the VOF model was found to be in good agreement with the experimentally measured profile. The present study thus showed that the VOF model can be used with the stochastic discrete phase model to well predict the performance of invert traps.展开更多
基金funded by a Ph.D.grant of "Institute for the Promotion of Innovation through Science and Technology in Flanders(IWT-Vlaanderen)"(No.091241 for MD and 073402 for SVD)the Special Research Fund of the Ghent University (Grant no.BOF 01J22510 for EW and BOF 01D38811 for SS)
文摘Different fused-core stationary phase chemistries（C18,Amide,Phenyl-hexyl and Peptide ES-C18） were used for the analysis of 21 structurally representative model peptides.In addition,the effects of the mobile phase composition（ACN or MeOH as organic modifier;formic acid or acetic acid,as acidifying component） on the column selectivity,peak shape and overall chromatographic performance were evaluated.The RP-amide column,combined with a formic acid-acetonitrile based gradient system,performed as best.A peptide reversed-phase retention model is proposed,consisting of 5 variables：log SumAA,log Sv,clog P,log nHDon and log nHAcc.Quantitative structure-retention relationship（QSRR） models were constructed for 16 different chromatographic systems.The accuracy of this peptide retention model was demonstrated by the comparison between predicted and experimentally obtained retention times,explaining on average 86% of the variability.Moreover,using an external set of 5 validation peptides,the predictive power of the model was also demonstrated.This peptide retention model includes the novel in-silico calculated amino acid descriptor,AA,which was calculated from log P,3D-MoRSE,RDF and WHIM descriptors.
基金This work was supported by the Excellent Young Faculty Foundation of the State Education Committee of China
文摘Based on six thermodynamic equilibria,a unified retention model of solute in liquid chromatography(LC)is first proposed.The unified model was tested and proved to be valid for a LC sys- tem with mobile phase consisting of a complete range of multiple,ternary or binary components and each kind of LC except size exclusion chromatography.In addition,so long as making some assumptions and mathematical conversions,the expressions of various popular models in LC can be derived by using the unified model.
文摘The separation efficiency of biopolymers with a short column in liquid chromatography has been investigated in this paper. It was found that the column length has slight effect on the resolution of biopolymers under gradient elution. The reasons have been explained by stoichiometric displacement model for retention of solute. The column 1.0 cm long was also used in the separation and purification of recombinant human granulocyte colony-simulating factor (rhG-CSF). It only took 40 min and the purity by one step was found to be almost 100%.
基金supported by the National Natural Science Foundation of China(No.62102002).
文摘Multiobjective combinatorial optimization(MOCO)problems have a wide range of applications in the real world.Recently,learning-based methods have achieved good results in solving MOCO problems.However,most of these methods use attention mechanisms and their variants,which have room for further improvement in the speed of solving MOCO problems.In this paper,following the idea of decomposition strategy and neural combinatorial optimization,a novel fast-solving model for MOCO based on retention is proposed.A brand new calculation of retention is proposed,causal masking and exponential decay are deprecated in retention,so that our model could better solve MOCO problems.During model training,a parallel computation of retention is applied,allowing for fast parallel training.When using the model to solve MOCO problems,a recurrent computation of retention is applied,enabling quicker problem-solving.In order to make our model more practical and flexible,a preference-based retention decoder is proposed,which allows generating approximate Pareto solutions for any trade-off preferences directly.An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO.Experimental results show that,while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.
文摘Invert traps are used to trap sewer solids flowing into a sewer drainage system, The performance of the invert trap in an open rectangular channel was experimentally and numerically analysed using field sewer solids collected from a sewer drain. Experiments showed that the free water surface rises over the central opening （slot） of the invert trap, which reduces the velocity near the slot and allows more sediment to be trapped in comparison with the case for the fixed-lid model （assuming closed conduit flow with a shear-free top wall） used by earlier investigators. This phenomenon cannot be modelled using a closed conduit model as no extra space is provided for the fluctuation of the water surface, whereas this space is provided in the volume of fluid （VOF） model in the form of air space in ANSYS Fluent 14.0 software. Additionally, the zero atmospheric pressure at the free water surface cannot be modelled in a fixed-lid model. In the present study, experimental trap efflciencies of the invert trap using field sewer solids were fairly validated using a three-dimensional computational fluid dynamics model （VOF model） coupled with a stochastic discrete phase model. The flow field （i.e., velocities） predicted by the VOF model were compared with experimental velocities obtained employing particle image velocimetry. The water surface profile above the invert trap predicted by the VOF model was found to be in good agreement with the experimentally measured profile. The present study thus showed that the VOF model can be used with the stochastic discrete phase model to well predict the performance of invert traps.
