Coulometric measurements on salt-water-immersed nanoporous carbon electrodes reveal, at a fixed voltage, a charge decrease with increasing temperature. During far-out-of-equilibrium charging of these electrodes, calorimetry indicates the production of both irreversible Joule heat and reversible heat, the latter being associated with entropy changes during electric double layer (EDL) formation in the nanopores. These measurements grant experimental access-for the first time-to the entropic contribution of the grand potential; for our electrodes, this amounts to roughly 25% of the total grand potential energy cost of EDL formation at large applied potentials, in contrast with point-charge model calculations that predict 100%. The coulometric and calorimetric experiments show a consistent picture of the role of heat and temperature in EDL formation and provide hitherto unused information to test against EDL models. DOI: 10.1103/PhysRevLett.119.166002 Where surfaces of charged electrodes meet fluids that contain mobile ions, so-called electric double layers (EDLs) form that screen the electric surface charge by a diffuse cloud of counterionic charge. This EDL has been intensively studied for over a century and is of paramount importance to many processes in physical chemistry and soft matter physics. With the ongoing development of nanomaterials, nowadays electrodes can be made from porous carbon with internal surface areas exceeding 1000 m 2 g −1 . These porous electrodes can be immersed in a variety of electrolyte solutions or ionic liquids. A socalled electric double layer capacitor (EDLC) is then formed, whose high capacitance makes it a prime candidate for capacitive energy storage, energy conversion [1,2], and water desalination [3][4][5]. In these porous electrodes, solvated ions have a size similar to that of their confining geometry; hence, a realistic theory must at least address both the electrostatics and the packing of the ions. Simulations and in situ analytical techniques [6] have revealed a wealth of phenomena in EDLCs [7], including overscreening [8], ion desolvation [9,10], in-plane structural transitions [11], layered packings of counterionic charge at high surface potentials [12], and, relatedly, oscillations in the EDL capacitance with decreasing pore width [13][14][15]. Unfortunately, the gap between (computationally demanding) first-principles models and experimental measurements on the charging behavior of porous electrodes is far from closed, with many questions remaining regarding the precise screening mechanisms at play [16]. While our understanding of the EDL is based mainly on isothermal numerical and experimental methods, recent work has revealed an interplay between temperature, heat, and entropy in the EDL. In particular, both model calculations [17] and experiments [2] indicate that the surface potential of an electrode with a fixed high surface charge should rise by about 1 mV K −1 with increasing temperature. Conversely, EDL formation under adiabatic settings induces a thermal r...
The trial-and-roulette method is a popular method to extract experts' beliefs about a statistical parameter. However, most studies examining the validity of this method only use 'perfect' elicitation results. In practice, it is sometimes hard to obtain such neat elicitation results. In our project about predicting fraud and questionable research practices among Ph.D. candidates, we ran into issues with imperfect elicitation results. The goal of the current chapter is to provide an overview of the solutions we used for dealing with these imperfect results, so that others can benefit from our experience. We present information about the nature of our project, the reasons for the imperfect results and how we resolved these supported by annotated R-syntax.
The popularity and use of Bayesian methods have increased across many research domains. The current article demonstrates how some less familiar Bayesian methods can be used. Specifically, we applied expert elicitation, testing for prior-data conflicts, the Bayesian Truth Serum, and testing for replication effects via Bayes Factors in a series of four studies investigating the use of questionable research practices (QRPs). Scientifically fraudulent or unethical research practices have caused quite a stir in academia and beyond. Improving science starts with educating Ph.D. candidates: the scholars of tomorrow. In four studies concerning 765 Ph.D. candidates, we investigate whether Ph.D. candidates can differentiate between ethical and unethical or even fraudulent research practices. We probed the Ph.D.s’ willingness to publish research from such practices and tested whether this is influenced by (un)ethical behavior pressure from supervisors or peers. Furthermore, 36 academic leaders (deans, vice-deans, and heads of research) were interviewed and asked to predict what Ph.D.s would answer for different vignettes. Our study shows, and replicates, that some Ph.D. candidates are willing to publish results deriving from even blatant fraudulent behavior–data fabrication. Additionally, some academic leaders underestimated this behavior, which is alarming. Academic leaders have to keep in mind that Ph.D. candidates can be under more pressure than they realize and might be susceptible to using QRPs. As an inspiring example and to encourage others to make their Bayesian work reproducible, we published data, annotated scripts, and detailed output on the Open Science Framework (OSF).
The trial-and-roulette method is a popular method to extract experts’ beliefs about a statistical parameter. However, most studies examining the validity of this method only use ‘perfect’ elicitation results. In practice, it is sometimes hard to obtain such neat elicitation results. In our project about predicting fraud and questionable research practices among PhD candidates, we ran into issues with imperfect elicitation results. The goal of the current chapter is to provide an over-view of the solutions we used for dealing with these imperfect results, so that others can benefit from our experience. We present information about the nature of our project, the reasons for the imperfect results, and how we resolved these sup-ported by annotated R-syntax.
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