Victor R Vasquez | University of Nevada, Reno (original) (raw)

Papers by Victor R Vasquez

Environmental Progress, 2004

The effects of uncertainty in thermophysical properties on the evaluation of the environmental pe... more The effects of uncertainty in thermophysical properties on the evaluation of the environmental performance is demonstrated with a chemical process to recover toluene and ethyl acetate by absorption from a gaseous waste stream of a cellophane production plant. In this case study, the environmental performance is defined as the estimation of the volatile organic compounds (VOCs) and total emissions of the plant and of several environmental risk indexes. We found that estimations of VOCs are very sensitive to uncertainty in thermophysical properties such as infinite-dilution activity coefficients, and vapor pressures (through uncertain temperature variations). Additionally, we concluded that calculation of the total emissions can be very sensitive to fuel content factors such as those used to estimate greenhouse gases. This can have such an impact on the emission calculations that a detailed model of the given chemical process might not be required for the estimation of the total emissions. In other words, a simpler process flowsheet model can perform the same task just as well, with the results within the variations caused by uncertainty in the thermophysical properties. We demonstrate a Monte Carlo approach that allows the detection of such uncertainty characteristics in a design, providing a rational basis for prediction of the associated environmental risks. This procedure also enables the deconvolution of various sources of uncertainty, and the estimation of physical property uncertainty through a similarity approach. We concluded that our framework can be used to enhance decision making by uncovering uncertainties and sensitivities in chemical process simulation. © 2004 American Institute of Chemical Engineers Environ Prog, 2004

An inside-variance estimation method (IVEM) for regression of the kinetic parameters in kinetic m... more An inside-variance estimation method (IVEM) for regression of the kinetic parameters in kinetic models and binary interaction parameters in thermodynamic models is proposed. The new method substantially improves the model predictions when compared with traditional least squares regression methods.

Computers & Chemical Engineering, 2000

A novel approach called equal probability sampling (EPS) is used for analyzing uncertainty and se... more A novel approach called equal probability sampling (EPS) is used for analyzing uncertainty and sensitivity in thermodynamic models. Uncertainty and sensitivity analysis for simulation and design of industrial processes are becoming increasingly important. The (EPS) method produces more realistic results in uncertainty analysis than methods based on other sampling techniques such as Latin hypercube sampling (LHS) or shifted Hammersley sampling

Risk Analysis, 2005

A Monte Carlo method is presented to study the effect of systematic and random errors on computer... more A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of the model. Also, it was found that systematic or calibration errors, if present, cannot be neglected in uncertainty analysis of models dependent on experimental measurements such as chemical and physical properties. The approach can be used to facilitate decision making in fields related to safety factors selection, modeling, experimental data measurement, and experimental design.

Fluid Phase Equilibria, 2000

An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermo... more An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function. Most of the maximum likelihood approaches currently used to regress the parameters of thermodynamic models fix the variances, converting the problem into a

Fluid Phase Equilibria, 1999

Thermodynamic models and experimental data exhibit the usual systematic and random errors. The se... more Thermodynamic models and experimental data exhibit the usual systematic and random errors. The severity of their errors depends on their use, such as for process calculations in a process simulator. Similarly, the value of better thermodynamic models andror data should be measured with reference to such use. We have developed techniques for quantification of such thermodynamic-induced process uncertainties via Monte Carlo simulation, regression analyses, and analogies to optimization. The influence of experimental data sources and data types on the uncertainty of thermodynamic models is studied. Details and applications of our new sampling Ž . strategy EPS , which accounts for the high degree of correlation between thermodynamics model parameters, is given. This procedure directly uses the regression results in a way that is much more powerful and mathematically accurate than traditional covariance matrix techniques. Level sets are used for the Monte Carlo samples so that unbiased accurate sampling of the entire feasible region is obtained. Comparison with traditional Ž . Ž . Monte Carlo sampling, Latin Hypercube sampling LHS , and Shifted Hammersley sampling SHS are shown. The result is an unbiased estimate of uncertainties that reduces the over-and under-estimations common in traditional techniques. The approaches presented can be used for safety-factorrrisk analysis, guidelines for simulator use, experimental design, and model comparisons. They allow determinations of the value of obtaining additional phase-equilibrium data and the potential value of improved phase-equilibrium models. Examples and case studies of these applications are provided. q 0378-3812r99r$ -see front matter q 1999 Elsevier Science B.V. All rights reserved.

Applied Thermal Engineering, 2001

We have used Monte Carlo methods to study the sensitivity and uncertainty of heat exchanger desig... more We have used Monte Carlo methods to study the sensitivity and uncertainty of heat exchanger designs to physical properties estimation. The determination of appropriate con®dence intervals for the overall heattransfer coecient and total required heat-exchange area plays a very important role in heat-exchanger thermal designs. The physical property models used exhibit systematic and random errors, which can change depending on how the models are used. Of particular interest in this work were the errors in properties estimation at high temperatures. Experimental physical properties of hot-gas mixtures at high temperatures (i.e., combustion gases) are very limited, and heat-exchanger simulation and design relies heavily on predictive models for physical properties. In this work, case studies of heat-exchanger performance and design under the in¯uence of random and systematic errors on property estimation are presented. The results show that the performance and design can be very sensitive to errors in physical properties. The analysis method proposed can be used to identify when and which, properties play a sig-ni®cant role in the error propagation for this type of equipment. Further, the methodology proposed can be used to study the uncertainty in heat-exchanger performance and cost introduced by these types of errors. Ó

Fluid Phase Equilibria, 1998

A method is presented using Monte Carlo Simulation coupled with the Latin Hypercube Sampling tech... more A method is presented using Monte Carlo Simulation coupled with the Latin Hypercube Sampling technique and parameter correlations to determine the effect of different sets of experimental data on the uncertainties in model parameters and, thus, the uncertainties in predicted process performance. Specifically, the estimation of binary interaction parameters for the NRTL equation was studied for three liquid-liquid ternary systems: diisopropyl ether q acetic acid q water, 1,1,2-trichloroethaneq acetoneq water, and chloroformq acetoneq water. From the experimental data available for these systems, regressions were done and the obtained parameters were used to perform simulations on a liquid-liquid extractor. The results show that the differences between the binary parameters from different sets of experimental data lead to significant differences in the uncertainty of predicted extractions in process units. q 1998 Elsevier Science B.V.

Industrial & Engineering Chemistry Research, 2004

In this work, we study the robustness of nonlinear regression methods under uncertainty for param... more In this work, we study the robustness of nonlinear regression methods under uncertainty for parameter estimation for chemical kinetics models. We used Monte Carlo simulation to study the influence of two main types of uncertainty, namely, random errors and incomplete experimental data sets. The regression methods analyzed were least-squares minimization (LSM), maximum likelihood (ML), and a method that automatically reweights the objective function during the course of the optimization called IVEM (inside-variance estimation method). Although this work represents a preliminary attempt toward understanding the effects of uncertainty in nonlinear regression, the results from the analysis of the case studies indicate that the performance of the regression procedures can be highly sensitive to uncertainty due to random errors and incomplete data sets. The results also suggest that traditional methods of assigning weights a priori to regression functions can affect the performance of the regression unless these weights correspond to a careful characterization of the residual statistics of the regression problem. In the case in which there is no prior knowledge of these weights (particularly in maximum likelihood regression), we suggest that they be characterized, in a preliminary way, by performing a least-squares minimization regression first or by using a method that automatically estimates these weights during the course of the regression (IVEM). We believe that the performance of regression techniques under uncertainty requires attention before a regression method is chosen or the parameters obtained are deemed valid.

Industrial & Engineering Chemistry Research, 1998

ABSTRACT Parameters for the UNIQUAC (universal quasi-chemical) model were regressed from VLE and ... more ABSTRACT Parameters for the UNIQUAC (universal quasi-chemical) model were regressed from VLE and LLE data for the systems: diisopropyl ether + acetic acid + water; 1,1,2-trichloroethane + acetone + water; and chloroform + acetone + water. The results show a very significant effect of the data type used in the regressions (binary versus ternary) on the uncertainty of the predicted performance of sample liquid−liquid extraction units.

Computers & Chemical Engineering, 2000

An inside-variance estimation method (IVEM) for regression of the kinetic parameters in kinetic m... more An inside-variance estimation method (IVEM) for regression of the kinetic parameters in kinetic models and binary interaction parameters in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function. Once the objective function is selected, most regression strategies consist of weighting the objective function

Chemical Engineering Communications, 2004

An exponential and heavy tail analysis method is presented to study the effect of systematic and ... more An exponential and heavy tail analysis method is presented to study the effect of systematic and random errors present in thermodynamic data on chemical process design and simulation. The true distribution tail characteristics (important for high levels of quality assurance) can be far from the estimates obtained with typical Gaussian distribution analysis. Pareto (heavy) or exponential distributions may represent the

Bioresource Technology, 2011

The equilibrium moisture content (EMC) of raw lignocellulosic biomass, along with four samples su... more The equilibrium moisture content (EMC) of raw lignocellulosic biomass, along with four samples subjected to thermal pretreatment, was measured at relative humidities ranging from 11% to 97% at a constant temperature of 30°C. Three samples were prepared by treatment in hot compressed water by a process known as wet torrefaction, at temperatures of 200, 230, and 260°C. An additional sample was prepared by dry torrefaction at 300°C. Pretreated biomass shows EMC below that of raw biomass. This indicates that pretreated biomass, both dry and wet torrefied, is more hydrophobic than raw biomass. The EMC results were correlated with a recent model that takes into account additional non-adsorption interactions of water, such as mixing and swelling. The model offers physical insight into the water activity in lignocellulosic biomass.

Energy & Fuels, 2010

... Wei Yan, Jason T. Hastings, Tapas C. Acharjee, Charles J. Coronella* and Victor R. V squez. .... more ... Wei Yan, Jason T. Hastings, Tapas C. Acharjee, Charles J. Coronella* and Victor R. V squez. ... Ind. Eng. Chem. Res. 1996, 35, 2709. 9. Funke, A.; Ziegler, F. Proceedings of the 17th European Biomass Conference, Hamburg, Germany, 2009. 10. ...

Bioresource Technology, 2011

As a renewable non-food resource, lignocellulosic biomass has great potential as an energy source... more As a renewable non-food resource, lignocellulosic biomass has great potential as an energy source or feedstock for further conversion. However, challenges exist with supply logistics of this geographically scattered and perishable resource. Hydrothermal carbonization treats any kind of biomass in 200 to 260°C compressed water under an inert atmosphere to produce a hydrophobic solid of reduced mass and increased fuel value. A maximum in higher heating value (HHV) was found when 0.4 g of acetic acid was added per g of biomass. If 1 g of LiCl and 0.4 g of acetic acid were added per g of biomass to the initial reaction solution, a 30% increase in HHV was found compared to the pretreatment with no additives, along with greater mass reduction. LiCl addition also reduces reaction pressure. Addition of acetic acid and/or LiCl to hydrothermal carbonization each contribute to increased HHV and reduced mass yield of the solid product.

Physical Review Letters, 2006

We present Monte Carlo simulation results for square-well homopolymers at a series of bond length... more We present Monte Carlo simulation results for square-well homopolymers at a series of bond lengths. Although the model contains only isotropic pairwise interactions, under appropriate conditions this system shows spontaneous chiral symmetry breaking, where the chain exists in either a left- or a right-handed helical structure. We investigate how this behavior depends upon the ratio between bond length and monomer radius.

Physical Review Letters, 2006

We present Monte Carlo simulation results for square-well homopolymers at a series of bond length... more We present Monte Carlo simulation results for square-well homopolymers at a series of bond lengths. Although the model contains only isotropic pairwise interactions, under appropriate conditions this system shows spontaneous chiral symmetry breaking, where the chain exists in either a left- or a right-handed helical structure. We investigate how this behavior depends upon the ratio between bond length and monomer radius.

Environmental Progress, 2004

The effects of uncertainty in thermophysical properties on the evaluation of the environmental pe... more The effects of uncertainty in thermophysical properties on the evaluation of the environmental performance is demonstrated with a chemical process to recover toluene and ethyl acetate by absorption from a gaseous waste stream of a cellophane production plant. In this case study, the environmental performance is defined as the estimation of the volatile organic compounds (VOCs) and total emissions of the plant and of several environmental risk indexes. We found that estimations of VOCs are very sensitive to uncertainty in thermophysical properties such as infinite-dilution activity coefficients, and vapor pressures (through uncertain temperature variations). Additionally, we concluded that calculation of the total emissions can be very sensitive to fuel content factors such as those used to estimate greenhouse gases. This can have such an impact on the emission calculations that a detailed model of the given chemical process might not be required for the estimation of the total emissions. In other words, a simpler process flowsheet model can perform the same task just as well, with the results within the variations caused by uncertainty in the thermophysical properties. We demonstrate a Monte Carlo approach that allows the detection of such uncertainty characteristics in a design, providing a rational basis for prediction of the associated environmental risks. This procedure also enables the deconvolution of various sources of uncertainty, and the estimation of physical property uncertainty through a similarity approach. We concluded that our framework can be used to enhance decision making by uncovering uncertainties and sensitivities in chemical process simulation. © 2004 American Institute of Chemical Engineers Environ Prog, 2004

An inside-variance estimation method (IVEM) for regression of the kinetic parameters in kinetic m... more An inside-variance estimation method (IVEM) for regression of the kinetic parameters in kinetic models and binary interaction parameters in thermodynamic models is proposed. The new method substantially improves the model predictions when compared with traditional least squares regression methods.

Computers & Chemical Engineering, 2000

A novel approach called equal probability sampling (EPS) is used for analyzing uncertainty and se... more A novel approach called equal probability sampling (EPS) is used for analyzing uncertainty and sensitivity in thermodynamic models. Uncertainty and sensitivity analysis for simulation and design of industrial processes are becoming increasingly important. The (EPS) method produces more realistic results in uncertainty analysis than methods based on other sampling techniques such as Latin hypercube sampling (LHS) or shifted Hammersley sampling

Risk Analysis, 2005

A Monte Carlo method is presented to study the effect of systematic and random errors on computer... more A Monte Carlo method is presented to study the effect of systematic and random errors on computer models mainly dealing with experimental data. It is a common assumption in this type of models (linear and nonlinear regression, and nonregression computer models) involving experimental measurements that the error sources are mainly random and independent with no constant background errors (systematic errors). However, from comparisons of different experimental data sources evidence is often found of significant bias or calibration errors. The uncertainty analysis approach presented in this work is based on the analysis of cumulative probability distributions for output variables of the models involved taking into account the effect of both types of errors. The probability distributions are obtained by performing Monte Carlo simulation coupled with appropriate definitions for the random and systematic errors. The main objectives are to detect the error source with stochastic dominance on the uncertainty propagation and the combined effect on output variables of the models. The results from the case studies analyzed show that the approach is able to distinguish which error type has a more significant effect on the performance of the model. Also, it was found that systematic or calibration errors, if present, cannot be neglected in uncertainty analysis of models dependent on experimental measurements such as chemical and physical properties. The approach can be used to facilitate decision making in fields related to safety factors selection, modeling, experimental data measurement, and experimental design.

Fluid Phase Equilibria, 2000

An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermo... more An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function. Most of the maximum likelihood approaches currently used to regress the parameters of thermodynamic models fix the variances, converting the problem into a

Fluid Phase Equilibria, 1999

Thermodynamic models and experimental data exhibit the usual systematic and random errors. The se... more Thermodynamic models and experimental data exhibit the usual systematic and random errors. The severity of their errors depends on their use, such as for process calculations in a process simulator. Similarly, the value of better thermodynamic models andror data should be measured with reference to such use. We have developed techniques for quantification of such thermodynamic-induced process uncertainties via Monte Carlo simulation, regression analyses, and analogies to optimization. The influence of experimental data sources and data types on the uncertainty of thermodynamic models is studied. Details and applications of our new sampling Ž . strategy EPS , which accounts for the high degree of correlation between thermodynamics model parameters, is given. This procedure directly uses the regression results in a way that is much more powerful and mathematically accurate than traditional covariance matrix techniques. Level sets are used for the Monte Carlo samples so that unbiased accurate sampling of the entire feasible region is obtained. Comparison with traditional Ž . Ž . Monte Carlo sampling, Latin Hypercube sampling LHS , and Shifted Hammersley sampling SHS are shown. The result is an unbiased estimate of uncertainties that reduces the over-and under-estimations common in traditional techniques. The approaches presented can be used for safety-factorrrisk analysis, guidelines for simulator use, experimental design, and model comparisons. They allow determinations of the value of obtaining additional phase-equilibrium data and the potential value of improved phase-equilibrium models. Examples and case studies of these applications are provided. q 0378-3812r99r$ -see front matter q 1999 Elsevier Science B.V. All rights reserved.

Applied Thermal Engineering, 2001

We have used Monte Carlo methods to study the sensitivity and uncertainty of heat exchanger desig... more We have used Monte Carlo methods to study the sensitivity and uncertainty of heat exchanger designs to physical properties estimation. The determination of appropriate con®dence intervals for the overall heattransfer coecient and total required heat-exchange area plays a very important role in heat-exchanger thermal designs. The physical property models used exhibit systematic and random errors, which can change depending on how the models are used. Of particular interest in this work were the errors in properties estimation at high temperatures. Experimental physical properties of hot-gas mixtures at high temperatures (i.e., combustion gases) are very limited, and heat-exchanger simulation and design relies heavily on predictive models for physical properties. In this work, case studies of heat-exchanger performance and design under the in¯uence of random and systematic errors on property estimation are presented. The results show that the performance and design can be very sensitive to errors in physical properties. The analysis method proposed can be used to identify when and which, properties play a sig-ni®cant role in the error propagation for this type of equipment. Further, the methodology proposed can be used to study the uncertainty in heat-exchanger performance and cost introduced by these types of errors. Ó

Fluid Phase Equilibria, 1998

A method is presented using Monte Carlo Simulation coupled with the Latin Hypercube Sampling tech... more A method is presented using Monte Carlo Simulation coupled with the Latin Hypercube Sampling technique and parameter correlations to determine the effect of different sets of experimental data on the uncertainties in model parameters and, thus, the uncertainties in predicted process performance. Specifically, the estimation of binary interaction parameters for the NRTL equation was studied for three liquid-liquid ternary systems: diisopropyl ether q acetic acid q water, 1,1,2-trichloroethaneq acetoneq water, and chloroformq acetoneq water. From the experimental data available for these systems, regressions were done and the obtained parameters were used to perform simulations on a liquid-liquid extractor. The results show that the differences between the binary parameters from different sets of experimental data lead to significant differences in the uncertainty of predicted extractions in process units. q 1998 Elsevier Science B.V.

Industrial & Engineering Chemistry Research, 2004

In this work, we study the robustness of nonlinear regression methods under uncertainty for param... more In this work, we study the robustness of nonlinear regression methods under uncertainty for parameter estimation for chemical kinetics models. We used Monte Carlo simulation to study the influence of two main types of uncertainty, namely, random errors and incomplete experimental data sets. The regression methods analyzed were least-squares minimization (LSM), maximum likelihood (ML), and a method that automatically reweights the objective function during the course of the optimization called IVEM (inside-variance estimation method). Although this work represents a preliminary attempt toward understanding the effects of uncertainty in nonlinear regression, the results from the analysis of the case studies indicate that the performance of the regression procedures can be highly sensitive to uncertainty due to random errors and incomplete data sets. The results also suggest that traditional methods of assigning weights a priori to regression functions can affect the performance of the regression unless these weights correspond to a careful characterization of the residual statistics of the regression problem. In the case in which there is no prior knowledge of these weights (particularly in maximum likelihood regression), we suggest that they be characterized, in a preliminary way, by performing a least-squares minimization regression first or by using a method that automatically estimates these weights during the course of the regression (IVEM). We believe that the performance of regression techniques under uncertainty requires attention before a regression method is chosen or the parameters obtained are deemed valid.

Industrial & Engineering Chemistry Research, 1998

ABSTRACT Parameters for the UNIQUAC (universal quasi-chemical) model were regressed from VLE and ... more ABSTRACT Parameters for the UNIQUAC (universal quasi-chemical) model were regressed from VLE and LLE data for the systems: diisopropyl ether + acetic acid + water; 1,1,2-trichloroethane + acetone + water; and chloroform + acetone + water. The results show a very significant effect of the data type used in the regressions (binary versus ternary) on the uncertainty of the predicted performance of sample liquid−liquid extraction units.

Computers & Chemical Engineering, 2000

An inside-variance estimation method (IVEM) for regression of the kinetic parameters in kinetic m... more An inside-variance estimation method (IVEM) for regression of the kinetic parameters in kinetic models and binary interaction parameters in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function. Once the objective function is selected, most regression strategies consist of weighting the objective function

Chemical Engineering Communications, 2004

An exponential and heavy tail analysis method is presented to study the effect of systematic and ... more An exponential and heavy tail analysis method is presented to study the effect of systematic and random errors present in thermodynamic data on chemical process design and simulation. The true distribution tail characteristics (important for high levels of quality assurance) can be far from the estimates obtained with typical Gaussian distribution analysis. Pareto (heavy) or exponential distributions may represent the

Bioresource Technology, 2011

The equilibrium moisture content (EMC) of raw lignocellulosic biomass, along with four samples su... more The equilibrium moisture content (EMC) of raw lignocellulosic biomass, along with four samples subjected to thermal pretreatment, was measured at relative humidities ranging from 11% to 97% at a constant temperature of 30°C. Three samples were prepared by treatment in hot compressed water by a process known as wet torrefaction, at temperatures of 200, 230, and 260°C. An additional sample was prepared by dry torrefaction at 300°C. Pretreated biomass shows EMC below that of raw biomass. This indicates that pretreated biomass, both dry and wet torrefied, is more hydrophobic than raw biomass. The EMC results were correlated with a recent model that takes into account additional non-adsorption interactions of water, such as mixing and swelling. The model offers physical insight into the water activity in lignocellulosic biomass.

Energy & Fuels, 2010

... Wei Yan, Jason T. Hastings, Tapas C. Acharjee, Charles J. Coronella* and Victor R. V squez. .... more ... Wei Yan, Jason T. Hastings, Tapas C. Acharjee, Charles J. Coronella* and Victor R. V squez. ... Ind. Eng. Chem. Res. 1996, 35, 2709. 9. Funke, A.; Ziegler, F. Proceedings of the 17th European Biomass Conference, Hamburg, Germany, 2009. 10. ...

Bioresource Technology, 2011

As a renewable non-food resource, lignocellulosic biomass has great potential as an energy source... more As a renewable non-food resource, lignocellulosic biomass has great potential as an energy source or feedstock for further conversion. However, challenges exist with supply logistics of this geographically scattered and perishable resource. Hydrothermal carbonization treats any kind of biomass in 200 to 260°C compressed water under an inert atmosphere to produce a hydrophobic solid of reduced mass and increased fuel value. A maximum in higher heating value (HHV) was found when 0.4 g of acetic acid was added per g of biomass. If 1 g of LiCl and 0.4 g of acetic acid were added per g of biomass to the initial reaction solution, a 30% increase in HHV was found compared to the pretreatment with no additives, along with greater mass reduction. LiCl addition also reduces reaction pressure. Addition of acetic acid and/or LiCl to hydrothermal carbonization each contribute to increased HHV and reduced mass yield of the solid product.

Physical Review Letters, 2006

We present Monte Carlo simulation results for square-well homopolymers at a series of bond length... more We present Monte Carlo simulation results for square-well homopolymers at a series of bond lengths. Although the model contains only isotropic pairwise interactions, under appropriate conditions this system shows spontaneous chiral symmetry breaking, where the chain exists in either a left- or a right-handed helical structure. We investigate how this behavior depends upon the ratio between bond length and monomer radius.

Physical Review Letters, 2006

We present Monte Carlo simulation results for square-well homopolymers at a series of bond length... more We present Monte Carlo simulation results for square-well homopolymers at a series of bond lengths. Although the model contains only isotropic pairwise interactions, under appropriate conditions this system shows spontaneous chiral symmetry breaking, where the chain exists in either a left- or a right-handed helical structure. We investigate how this behavior depends upon the ratio between bond length and monomer radius.