Lecture

Introduction to Part II: pKa values

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Description

This lecture introduces the concept of pKa values in solution chemistry, explaining the deprotonation steps and constants involved in determining the total concentration of protons and ligands. The instructor demonstrates how to formulate and simplify the equation to obtain the measured constants, emphasizing the linear regression approach for obtaining accurate values.

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Instructors (2)

Florian Frédéric Vincent Breider

Florian Breider obtained his PhD in the field of the stable isotope biogeochemistry from the University of Neuchatel in 2013. This was followed by seven months of postdoc at EPFL in the Atmospheric Particles Research Laboratory and two years as research associate at Tokyo Institute of Technology (Japan) where he conducted studies on nitrous oxide biogeochemistry in oceans. From 2015 to 2018, he was research scientist in the Laboratory for Water Quality and Treatment at EPFL where he conducted research on disinfection by-products and antibiotic resistant bacteria. Since May 2018, he is director of the Central Environmental Laboratory at the Institute of Environmental Engineering of EPFL.

Christian Ludwig

2005 - today: Adjunct Professor at EPFL in the field of Solid Waste Treatment and head of the Chemical Processes and Materials research group (CPM) at Paul Scherrer Institute (PSI). Joint EPFL-PSI Professorship on Solid Waste Treatment. 2000 - today: Head, Group of Chemical Processes and Materials (CPM) at Paul Scherrer Institut (PSI). In 2009 the LEM unit was closed and the CPM group is now affiliated to the Bioenergy and Catalysis Laboratory (LBK) of the Energy and Environment Research Division (ENE). Since June 2002 permanent position ("tenure"). 1997 - 1999: Senior Scientist. Paul Scherrer Institut (PSI), General Energy Research Department, Element Cycles Section. 1995 - 1997: Research Fellow. Swiss Federal Institute for Environmental Science and Technology (EAWAG), Department of Resource and Waste Management. 1993 - 1995: Post-doc Fellow. University of California Davis, Department of Land, Air, and Water Resources (LAWR). 1990 - 1993: PhD Student. University of Berne, Department of Inorganic, Analytical, and Physical Chemistry. 1989 - 1990: Master Student. University of Berne, Department of Inorganic, Analytical, and Physical Chemistry.

Official source

Ontological neighbourhood

Statistics

Data analysis: Regression analysis

Related concepts (20)

Linear regression

In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

Deprotonation

Deprotonation (or dehydronation) is the removal (transfer) of a proton (or hydron, or hydrogen cation), (H+) from a Brønsted–Lowry acid in an acid–base reaction. The species formed is the conjugate base of that acid. The complementary process, when a proton is added (transferred) to a Brønsted–Lowry base, is protonation (or hydronation). The species formed is the conjugate acid of that base. A species that can either accept or donate a proton is referred to as amphiprotic.

Nonlinear regression

In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. In nonlinear regression, a statistical model of the form, relates a vector of independent variables, , and its associated observed dependent variables, . The function is nonlinear in the components of the vector of parameters , but otherwise arbitrary.

Bayesian linear regression

Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often labelled ) conditional on observed values of the regressors (usually ).

Segmented regression

Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions.

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