38 research outputs found
Classical density-functional theory of simple electrode-electrolyte interfaces
No full textThis thesis is dedicated to the theoretical study of the electrolyte-electrode interfacewith classical density-functional theory, with a focus on how the modeling of solventimpacts the process quantities of electrolyte solutions. First, physical effects and relevantparameter spaces for the electric double layers (EDLs), that consists of counter-ions neara charged electrode, are deduced by discussing technical applications. Afterwards, theframework of classical density-functional theory and observables from liquid-state theoryare introduced.A historic overview of commonly applied EDL models is discussed and put intoa modern context, emphasizing the significance of the Stern layer for the theoreticaldescription of EDLs. The Stern layer is usually introduced by arguing that solvent particlesadsorbed on the electrode hinder ions from approaching the electrode at arbitrarily closedistances. The primitive model (PM) describes the electrolyte as charged hard-spheresand includes an intrinsic Stern layer due to the hard-core interactions. In this thesisit is examined how well the PM can reproduce experimentally measured differentialcapacitances under the premise that ion diameters should reflect ion sizes determinedfrom scattering experiments and it is demonstrated that predicted capacitances are toobig and larger ion diameters or bigger Stern layers are necessary to map theoreticalprediction onto experimental data.Based on previous observations this work proposes a new model extension for theprimitive model, where two diameters are attributed to each ion species. One determinesthe hard ion-ion interactions and the other specifies the hard ion-wall interactions.This new extension allows to consider the influence of the solvent by means of a radii-independent Stern layer and hydration-shells. Within this model extension two processquantities for EDLs are observed. First, the differential capacitance attains much closervalues to empirically determined ones, whereas the ion diameters as well as the Sterndistance are set to reasonable values that conform to sizes from scattering experiments ofions and water molecules. Second, new effects for the reversible heat production duringcharge-up of the EDL are discovered within this model extension if additionally neutralhard solvent particles are added to the system. For example situations exists where oneelectrode can be cooled whereas the other one is heated during the charging process.However, the heat contributions of both electrodes average out, such that the predictedeffects could not be confirmed with the available experimental data.Moreover, this work considers how to use more complex solvent particles by developinga framework that allows to determine the mean-field electrostatic excess free-energyfunctional of particles that are constructed from arbitrary charge distributions. Acomparison of the radial distribution function of a system of simple model particlesthat consist of a point charge embedded within a spherical homogeneous charge densityof opposite charge, obtained from theory and Monte Carlo simulations, reveals thatthe taken approach can not resolve structure in the system under bulk conditions. Itis demonstrated that the mean-field electrostatic functional actively suppresses theformation of structure in the system, even if the charge distributions are embedded intohard-spheres
Ab-initio study of abundant low-coordinated sites on the MgO(001) surface and related vicinal surfaces applying a hybrid functional of density functional theory
No full textThe focus of this theoretical work lies on magnesium oxide (MgO), which serves as a prototype for the investigation of (earth alkaline) metal oxides. The main goal is to take another important step towards the identification of MgO surface F-centers within the framework of density functional theory (DFT). It uses periodic supercells instead of embedded clusters. This approach allows to treat delocalised states and to assign the position of localised states with respect to valence and conduction band as well as surface states. Furthermore it is of interest to include additional defects like kink sites
The Seherr-Thoss palace in Szymanów (Simsdorf). An unknown Silesian creation of Schinkel?
No full textCelem artykułu jest próba interpretacji architektury pałacu Seherr-Thossów w Szymanowie (powiat świdnicki). O wyjątkowości tego dzieła przesądziło połączenie w jego koncepcji architektonicznej trzech różnych elementów stylowo-kompozycyjnych, wywodzących się z tak różnych źródeł jak koncepcje A. Palladia (neopalladianizmu), J.-N.-L. Duranda („nowa architektura”) oraz K.F. Schinkla (jego architektura willowa). Niewielka liczba zachowanych archiwaliów uniemożliwia zarówno ścisłe datowanie powstania pałacu, jak i ustalenie twórcy jego projektu. Na podstawie przeprowadzonych badań nad pałacem można wskazać na połowę lat 20. XIX w. jako bardzo prawdopodobny czas jego realizacji oraz na architekta Ludwika Persiusa – ucznia Schinkla – jako domniemanego autora projektu.The aim of the article is to interpret the architecture of the Seherr-Thoss palace in Szymanów (Świdnica powiat – a Polish administrative unit). The uniqueness of the building is caused by the combination of three stylistic and composition elements originating from diverse sources, such as A. Palladio (neo-Palladian style), J.-N.-L. Durand (“new architecture”) and K.F. Schinkel (his villa architecture). A small number of archives makes it impossible to properly date the palace or to determine its designer. The conducted research indicates the 1820s as probable time of its erection and the architect Ludwig Persius – Schinkel’s student – as the probable author of the project
EZ: An Easy Way to Conduct a More Fine-Grained Analysis of Faked and Nonfaked Implicit Association Test (IAT) Data
No full textAlthough faking on the Implicit Association Test (IAT) is a relevant problem, it has not yet been considered for the traditional IAT effect ( measure). Research has suggested that diffusion-model-based IAT effects may be useful as is related to the construct-related variance and and have both been assumed to provide indications of faking. Recent research used fast-dm to reanalyze nonfaked and faked IAT data under various faking conditions (faking low vs. faking high scores in a naïve vs. informed manner). The results showed that faking affected . However, there was an impact on when people knew how to fake and had to fake low scores. Thus, diffusion model analyses deliver additional information, but they are also very complex to perform. The diffusion tool EZ is easy to handle and very powerful, but researchers do not yet know whether , , and deliver similar information about the components in IAT results when they are obtained with EZ. Thus, we used EZ to reanalyze the data set described above. The results from fast-dm and EZ were comparable, but EZ had somewhat higher statistical power. was impacted by faking, thus replicating the finding that diffusion model analyses cannot yet be used to completely separate construct- and faking-specific variance from each other. However, replicating and extending the findings that were obtained with fast-dm, informed faking had an impact on and , which might both serve as indicators of faking. Thus, our results indicate that EZ as well as fast-dm is a powerful tool that can help researchers to interpret IAT results
A tutorial on how to compute traditional IAT effects with R
No full textIn this tutorial, we explain the background of the 10 traditional IAT effects and their mathematical details. We also present R code as well as example data so that readers can easily compute all of the traditional IAT effects. Last but not least, we present example outputs to provide examples of what the results might look like
EZ: An easy way to conduct a more fine-grained analysis of faked and nonfaked Implicit Association Test (IAT) data. The Quantitative Methods for Psychology
No full textAlthough faking on the Implicit Association Test (IAT) is a relevant problem, it has not yet been considered for the traditional IAT effect (D measure). Research has suggested that diffusion-model-based IAT effects may be useful as IATv is related to the construct-related variance and IATa and IATt0 have both been assumed to provide indications of faking. Recent research used fast-dm to reanalyze nonfaked and faked IAT data under various faking conditions (faking low vs. faking high scores in a naïve vs. informed manner). The results showed that faking affected IATv. However, there was an impact on IATa when people knew how to fake and had to fake low scores. Thus, diffusion model analyses deliver additional information, but they are also very complex to perform. The diffusion tool EZ is easy to handle and very powerful, but researchers do not yet know whether IATv, IATa, and IATt0 deliver similar information about the components in IAT results when they are obtained with EZ. Thus, we used EZ to reanalyze the data set described above. The results from fast-dm and EZ were comparable, but EZ had somewhat higher statistical power. IATv was impacted by faking, thus replicating the finding that diffusion model analyses cannot yet be used to completely separate construct- and faking-specific variance from each other. However, replicating and extending the findings that were obtained with fast-dm, informed faking had an impact on IATa and IATt0, which might both serve as indicators of faking. Thus, our results indicate that EZ as well as fast-dm is a powerful tool that can help researchers to interpret IAT results
EZ : An Easy Way to Conduct a More Fine-Grained Analysis of Faked and Nonfaked Implicit Association Test (IAT) Data
No full textZV so möglich (06.11.24, Deu)Although faking on the Implicit Association Test (IAT) is a relevant problem, it has not yet been considered for the traditional IAT effect (D measure). Research has suggested that diffusion-model-based IAT effects may be useful as IATv is related to the construct-related variance and IATa and IATt0 have both been assumed to provide indications of faking. Recent research used fast-dm to reanalyze nonfaked and faked IAT data under various faking conditions (faking low vs. faking high scores in a naıve vs. informed manner). The results showed that faking affected IATv. However, there was an impact on IATa when people knew how to fake and had to fake low scores. Thus, diffusion model analyses deliver additional information, but they are also very complex to perform. The diffusion tool EZ is easy to handle and very powerful, but researchers do not yet know whether IATv, IATa, and IATt0 deliver similar information about the components in IAT results when they are obtained with EZ. Thus, we used EZ to reanalyze the data set described above. The results from fast-dm and EZ were comparable, but EZ had somewhat higher statistical power. IATv was im- pacted by faking, thus replicating the finding that diffusion model analyses cannot yet be used to completely separate construct- and faking-specific variance from each other. However, replicating and extending the findings that were obtained with fast-dm, informed faking had an impact on IATa and IATt0, which might both serve as indicators of faking. Thus, our results indicate that EZ as well as fast-dm is a powerful tool that can help researchers to interpret IAT results
A tutorial on how to compute traditional IAT effects with {R}
No full textThe Implicit Association Test (IAT) is the most frequently used and the most popular measure for assessing implicit associations across a large variety of psychological constructs. Altogether, 10 algorithms have been suggested by the founders of the IAT to compute what can be called the traditional IAT effects (i.e., the six D measures: D1, D2, D3, D4, D5, D6, and the four conventional measures [C measures]: C1, C2, C3, C4). Researchers can decide which IAT effect they want to use, whereby the use of D measures is recommended on the basis of their properties. In this tutorial, we explain the background of the 10 traditional IAT effects and their mathematical details. We also present R code as well as example data so that readers can easily compute all of the traditional IAT effects. Last but not least, we present example outputs to illustrate what the results might look like
A tutorial on how to compute traditional IAT effects with {R}
No full textThe Implicit Association Test (IAT) is the most frequently used and the most popular measure for assessing implicit associations across a large variety of psychological constructs. Altogether, 10 algorithms have been suggested by the founders of the IAT to compute what can be called the traditional IAT effects (i.e., the six D measures: D1, D2, D3, D4, D5, D6, and the four conventional measures [C measures]: C1, C2, C3, C4). Researchers can decide which IAT effect they want to use, whereby the use of D measures is recommended on the basis of their properties. In this tutorial, we explain the background of the 10 traditional IAT effects and their mathematical details. We also present R code as well as example data so that readers can easily compute all of the traditional IAT effects. Last but not least, we present example outputs to illustrate what the results might look like
Lying on the Dissection Table: Anatomizing Faked Responses
No full textResearch has shown that even experts cannot detect faking above chance, but recent studies have suggested that machine learning may help in this endeavor. However, faking differs between faking conditions, previous efforts have not taken these differences into account, and faking indices have yet to be integrated into such approaches. We reanalyzed seven data sets (N = 1,039) with various faking conditions (high and low scores, different constructs, naïve and informed faking, faking with and without practice, different measures [self-reports vs. implicit association tests; IATs]). We investigated the extent to which and how machine learning classifiers could detect faking under these conditions and compared different input data (response patterns, scores, faking indices) and different classifiers (logistic regression, random forest, XGBoost). We also explored the features that classifiers used for detection. Our results show that machine learning has the potential to detect faking, but detection success varies between conditions from chance levels to 100%. There were differences in detection (e.g., detecting low-score faking was better than detecting high-score faking). For self-reports, response patterns and scores were comparable with regard to faking detection, whereas for IATs, faking indices and response patterns were superior to scores. Logistic regression and random forest worked about equally well and outperformed XGBoost. In most cases, classifiers used more than one feature (faking occurred over different pathways), and the features varied in their relevance. Our research supports the assumption of different faking processes and explains why detecting faking is a complex endeavor
