The Experts below are selected from a list of 195 Experts worldwide ranked by ideXlab platform
Robert A. Huggins - One of the best experts on this subject based on the ideXlab platform.
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Reference electrodes and the Gibbs Phase Rule
Solid State Ionics, 2000Co-Authors: Robert A. HugginsAbstract:Abstract Experimental work reported in the electrochemical literature often involves the use of different reference systems, and it is often difficult to translate between them. Reference electrodes used in solid state electrochemical systems are generally based upon the potentials of electrically neutral chemical species, and cell voltages can be calculated by the use of normal chemical thermodynamics. The identity and properties of the electrolyte play no role. In aqueous electrochemistry it is common to use reference electrodes that involve neutral species/ion equilibria at the electrochemical interface. The pH of the electrolyte is an important consideration in some cases, but not in others. These differences can be understood in terms of the Gibbs Phase Rule and the difference between zero-degree-of-freedom (ZDF) electrodes and those in which an additional intensive parameter, such as the electrolyte pH, must also be specified. The interrelationship between these two fundamentally different types can be readily seen using potential–pH plots, or Pourbaix diagrams.
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The relation between the Gibbs Phase Rule and reference electrodes
Ionics, 1998Co-Authors: Robert A. HugginsAbstract:Reference electrodes play an important role in the study of many aspects of electrochemical systems. Experimental work in the literature often involves the use of different reference systems, and it is sometimes difficult to translate between measurements made with one from those made using another.
Brian A Pethica - One of the best experts on this subject based on the ideXlab platform.
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the thermodynamics of protein folding a critique of widely used quasi thermodynamic interpretations and a restatement based on the gibbs duhem relation and consistent with the Phase Rule
Physical Chemistry Chemical Physics, 2010Co-Authors: Brian A PethicaAbstract:Interpretations of data in the extensive literature on the unfolding of proteins in aqueous solution follow a variety of methods involving assumptions leading to estimates of thermodynamic quantities associated with the unfolding transition. Inconsistencies and thermodynamic errors in these methods are identified. Estimates of standard molar free energies and enthalpies of unfolding using incompletely defined equilibrium constants and the van't Hoff relation are unsound, and typically contradict model-free interpretation of the data. A widely used routine for estimating the change in heat capacity associated with unfolding based on changes in the unfolding temperature and enthalpy co-induced by addition of denaturant or protective additives is thermodynamically incorrect by neglect of the Phase Rule. Many models and simulations predicting thermodynamic measures of unfolding are presently making comparisons with insecure quantities derived by incorrect thermodynamic analyses of experimental data. Analysis of unfolding via the Gibbs–Duhem equation with the correct Phase Rule constraints avoids the assumptions associated with incomplete equilibrium constants and misuse of the van't Hoff relation, and applies equally to positive, negative, sitewise or diffuse solute binding to the protein. The method gives the necessary relations between the thermodynamic parameters for thermal and isothermal unfolding and is developed for the case of two-state unfolding. The differences in binding of denaturants or stabilizers to the folded and unfolded forms of the protein are identified as major determinants of the unfolding process. The Phase Rule requires the temperature and enthalpy of unfolding to depend generally on the protein concentration. The available evidence bears out this expectation for thermal unfolding, indicating that protein–protein interactions influence folding. A parallel dependence of the denaturant concentrations for isothermal unfolding on the protein concentration is anticipated. The degree of unfolding as measured by UV, CD, fluorescence and other non-calorimetric methods may not show the same temperature and concentration ranges for unfolding among themselves or as compared to DSC or isothermal calorimetry. Such disparities indicate distinct stages in unfolding detectable by particular methods.
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The thermodynamics of protein folding: a critique of widely used quasi-thermodynamic interpretations and a restatement based on the Gibbs–Duhem relation and consistent with the Phase Rule
Physical chemistry chemical physics : PCCP, 2010Co-Authors: Brian A PethicaAbstract:Interpretations of data in the extensive literature on the unfolding of proteins in aqueous solution follow a variety of methods involving assumptions leading to estimates of thermodynamic quantities associated with the unfolding transition. Inconsistencies and thermodynamic errors in these methods are identified. Estimates of standard molar free energies and enthalpies of unfolding using incompletely defined equilibrium constants and the van't Hoff relation are unsound, and typically contradict model-free interpretation of the data. A widely used routine for estimating the change in heat capacity associated with unfolding based on changes in the unfolding temperature and enthalpy co-induced by addition of denaturant or protective additives is thermodynamically incorrect by neglect of the Phase Rule. Many models and simulations predicting thermodynamic measures of unfolding are presently making comparisons with insecure quantities derived by incorrect thermodynamic analyses of experimental data. Analysis of unfolding via the Gibbs–Duhem equation with the correct Phase Rule constraints avoids the assumptions associated with incomplete equilibrium constants and misuse of the van't Hoff relation, and applies equally to positive, negative, sitewise or diffuse solute binding to the protein. The method gives the necessary relations between the thermodynamic parameters for thermal and isothermal unfolding and is developed for the case of two-state unfolding. The differences in binding of denaturants or stabilizers to the folded and unfolded forms of the protein are identified as major determinants of the unfolding process. The Phase Rule requires the temperature and enthalpy of unfolding to depend generally on the protein concentration. The available evidence bears out this expectation for thermal unfolding, indicating that protein–protein interactions influence folding. A parallel dependence of the denaturant concentrations for isothermal unfolding on the protein concentration is anticipated. The degree of unfolding as measured by UV, CD, fluorescence and other non-calorimetric methods may not show the same temperature and concentration ranges for unfolding among themselves or as compared to DSC or isothermal calorimetry. Such disparities indicate distinct stages in unfolding detectable by particular methods.
Vipin Kumar - One of the best experts on this subject based on the ideXlab platform.
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PKDD - Predicting Rare Classes: Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting
Principles of Data Mining and Knowledge Discovery, 2002Co-Authors: Mahesh V Joshi, Ramesh C Agarwal, Vipin KumarAbstract:Learning good classifier models of rare events is a challenging task. On such problems, the recently proposed two-Phase Rule induction algorithm, PNRule, outperforms other non-meta methods of Rule induction. Boosting is a strong meta-classifier approach, and has been shown to be adaptable to skewed class distributions. PNRule's key feature is to identify the relevant false positives and to collectively remove them. In this paper, we qualitatively argue that this ability is not guaranteed by the boosting methodology. We simulate learning scenarios of varying difficulty to demonstrate that this fundamental qualitative difference in the two mechanisms results in existence of many scenarios in which PNRule achieves comparable or significantly better performance than AdaCost, a strong cost-sensitive boosting algorithm. Even a comparable performance by PNRule is desirable because it yields a more easily interpretable model over an ensemble of models generated by boosting. We also show similar supporting results on real-world and benchmark datasets.
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predicting rare classes comparing two Phase Rule induction to cost sensitive boosting
European Conference on Principles of Data Mining and Knowledge Discovery, 2002Co-Authors: Mahesh V Joshi, Ramesh C Agarwal, Vipin KumarAbstract:Learning good classifier models of rare events is a challenging task. On such problems, the recently proposed two-Phase Rule induction algorithm, PNRule, outperforms other non-meta methods of Rule induction. Boosting is a strong meta-classifier approach, and has been shown to be adaptable to skewed class distributions. PNRule's key feature is to identify the relevant false positives and to collectively remove them. In this paper, we qualitatively argue that this ability is not guaranteed by the boosting methodology. We simulate learning scenarios of varying difficulty to demonstrate that this fundamental qualitative difference in the two mechanisms results in existence of many scenarios in which PNRule achieves comparable or significantly better performance than AdaCost, a strong cost-sensitive boosting algorithm. Even a comparable performance by PNRule is desirable because it yields a more easily interpretable model over an ensemble of models generated by boosting. We also show similar supporting results on real-world and benchmark datasets.
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mining needle in a haystack classifying rare classes via two Phase Rule induction
International Conference on Management of Data, 2001Co-Authors: Mahesh Joshi, Ramesh C Agarwal, Vipin KumarAbstract:Learning models to classify rarely occurring target classes is an important problem with applications in network intrusion detection, fraud detection, or deviation detection in general. In this paper, we analyze our previously proposed two-Phase Rule induction method in the context of learning complete and precise signatures of rare classes. The key feature of our method is that it separately conquers the objectives of achieving high recall and high precision for the given target class. The first Phase of the method aims for high recall by inducing Rules with high support and a reasonable level of accuracy. The second Phase then tries to improve the precision by learning Rules to remove false positives in the collection of the records covered by the first Phase Rules. Existing sequential covering techniques try to achieve high precision for each individual disjunct learned. In this paper, we claim that such approach is inadequate for rare classes, because of two problems: splintered false positives and error-prone small disjuncts. Motivated by the strengths of our two-Phase design, we design various synthetic data models to identify and analyze the situations in which two state-of-the-art methods, RIPPER and C4.5 Rules, either fail to learn a model or learn a very poor model. In all these situations, our two-Phase approach learns a model with significantly better recall and precision levels. We also present a comparison of the three methods on a challenging real-life network intrusion detection dataset. Our method is significantly better or comparable to the best competitor in terms of achieving better balance between recall and precision.
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SIGMOD Conference - Mining needle in a haystack: classifying rare classes via two-Phase Rule induction
Proceedings of the 2001 ACM SIGMOD international conference on Management of data - SIGMOD '01, 2001Co-Authors: Mahesh Joshi, Ramesh C Agarwal, Vipin KumarAbstract:Learning models to classify rarely occurring target classes is an important problem with applications in network intrusion detection, fraud detection, or deviation detection in general. In this paper, we analyze our previously proposed two-Phase Rule induction method in the context of learning complete and precise signatures of rare classes. The key feature of our method is that it separately conquers the objectives of achieving high recall and high precision for the given target class. The first Phase of the method aims for high recall by inducing Rules with high support and a reasonable level of accuracy. The second Phase then tries to improve the precision by learning Rules to remove false positives in the collection of the records covered by the first Phase Rules. Existing sequential covering techniques try to achieve high precision for each individual disjunct learned. In this paper, we claim that such approach is inadequate for rare classes, because of two problems: splintered false positives and error-prone small disjuncts. Motivated by the strengths of our two-Phase design, we design various synthetic data models to identify and analyze the situations in which two state-of-the-art methods, RIPPER and C4.5 Rules, either fail to learn a model or learn a very poor model. In all these situations, our two-Phase approach learns a model with significantly better recall and precision levels. We also present a comparison of the three methods on a challenging real-life network intrusion detection dataset. Our method is significantly better or comparable to the best competitor in terms of achieving better balance between recall and precision.
R. Ravi - One of the best experts on this subject based on the ideXlab platform.
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Phase Rule and the azeotrope — A critique and a new interpretation
International Communications in Heat and Mass Transfer, 2013Co-Authors: R. RaviAbstract:Abstract The differences between the modern version of the Phase Rule and the one originally proposed by Gibbs are pointed out. The local analysis implied in Gibbs's approach to the Phase Rule is carried forward to its logical conclusion using the implicit function theorem. The results of the analysis are used to resolve the apparent contradictions in the interpretation of the Phase Rule, using the Gibbs–Duhem equations, for a system exhibiting an azeotrope. Specifically, the pitfalls in treating the differentials in the Gibbs–Duhem equations as variations are demonstrated. The critical role played by the rank of a submatrix in the coefficient matrix of the Gibbs–Duhem equations is highlighted. A hierarchy in the application of the Phase Rule is pointed out and the need for a unified framework for interpreting the Phase Rule is indicated.
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Toward a Unified Framework for Interpreting the Phase Rule
Industrial & Engineering Chemistry Research, 2012Co-Authors: R. RaviAbstract:The apparent differences in the statements of the Phase Rule for the two sets of variables commonly used, namely, temperature, pressure, and mole fractions on the one hand and temperature, pressure, and chemical potentials on the other are resolved by developing a framework in which the Phase Rule may be stated and analyzed in terms of a whole class of variable sets. The framework is restricted to the case where all the components are present in all the Phases. Central to the framework is the notion, due to Gibbs, of a fundamental relation for homogeneous states and the associated general equation cast in terms of intensive and specific variables. The significance of the Gibbs–Duhem equations within the context of the Phase Rule is brought out. The lead provided by Gibbs in his approach to the Phase Rule is utilized to carry out a local analysis of the equations that result from the criteria of equilibrium for the two sets of variables referred to above. The central role played by the general equations is...
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Phase Rule and the Degree of Freedom Analysis of Processes
Industrial & Engineering Chemistry Research, 2005Co-Authors: R. Ravi, D. P. RaoAbstract:The importance of specifying the state of the inlet streams in determining the degrees of freedom (DOF) of a process is highlighted in the context of a single-stage separation unit. It is shown that, for the separation of a C-component mixture, the commonly accepted value of 2C + 6 as the DOF of an equilibrium single stage holds only if neither of the inlet streams lie on the Phase envelope. The implications of this feature for the DOF of multistage processes is explored. The importance of a precise interpretation of the Phase Rule in arriving at the above results is emphasized.
Nobuyuki Matubayasi - One of the best experts on this subject based on the ideXlab platform.
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Unifying hydrotropy under Gibbs Phase Rule
Physical chemistry chemical physics : PCCP, 2017Co-Authors: Seishi Shimizu, Nobuyuki MatubayasiAbstract:The task of elucidating the mechanism of solubility enhancement using hydrotropes has been hampered by the wide variety of Phase behaviour that hydrotropes can exhibit, encompassing near-ideal aqueous solution, self-association, micelle formation, and micro-emulsions. Instead of taking a field guide or encyclopedic approach to classify hydrotropes into different molecular classes, we take a rational approach aiming at constructing a unified theory of hydrotropy based upon the first principles of statistical thermodynamics. Achieving this aim can be facilitated by the two key concepts: (1) the Gibbs Phase Rule as the basis of classifying the hydrotropes in terms of the degrees of freedom and the number of variables to modulate the solvation free energy; (2) the Kirkwood-Buff integrals to quantify the interactions between the species and their relative contributions to the process of solubilization. We demonstrate that the application of the two key concepts can in principle be used to distinguish the different molecular scenarios at work under apparently similar solubility curves observed from experiments. In addition, a generalization of our previous approach to solutes beyond dilution reveals the unified mechanism of hydrotropy, driven by a strong solute-hydrotrope interaction which overcomes the apparent per-hydrotrope inefficiency due to hydrotrope self-clustering.