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Conductivity experiments for electrolyte formulations and their automated analysis.

ABSTRACT: Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF₆). The mass ratio of EC to PC was varied, while keeping the mass ratio of (EC + PC) and EMC at fixed values of 3:7 and 1:1. The conducting salt concentration was also varied during the study. Conductivity data was obtained from electrochemical impedance spectroscopy (EIS) measurements at various temperatures. Based on the thus obtained temperature series, the activation energy for ionic conduction was determined during the analysis. The data is presented here in a machine-readable format and includes a Python package for analyzing temperature series of electrolyte conductivity according to the Arrhenius equation and EIS data. The data may be useful e.g. for the training of machine learning models or for reference prior to experiments.

SUBMITTER: Rahmanian F

PROVIDER: S-EPMC9852460 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Conductivity experiments for electrolyte formulations and their automated analysis.

Rahmanian Fuzhan F Vogler Monika M Wölke Christian C Yan Peng P Fuchs Stefan S Winter Martin M Cekic-Laskovic Isidora I Stein Helge Sören HS

Scientific data 20230119 1

Electrolytes are considered crucial for the performance of batteries, and therefore indispensable for future energy storage research. This paper presents data that describes the effect of the electrolyte composition on the ionic conductivity. In particular, the data focuses on electrolytes composed of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and lithium hexafluorophosphate (LiPF<sub>6</sub>). The mass ratio of EC to PC was varied, while keeping the mass ra ...[more]

PMID: 36658233

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Project description:IntroductionWe define a designated data analytics workflow for the evaluation of stability experiments, which takes all data situations into account. This complements the evaluation described by the CLSI EP25 [1] guideline by including a targeted exception handling algorithm and thus allows one to automatically evaluate stability data based on linear regression analysis.DescriptionThe evaluation of stability experiments based on regression analysis requires the calculation of the confidence interval of the regression line. The stability time is estimated at the intersection of the confidence interval with the acceptance criterion. This approach results in solving a quadratic equation, with factors that depend on the estimated intercept, slope, the measurement variability and the chosen timepoints. When defining an automated data analytics workflow for this problem, the different cases for the solutions of the quadratic equation must be considered. For some data situations there might be no solution at all, other data situations result in a negative and a positive solution and finally there might be even two positive solutions. All these cases have to be considered for the choice of the right solution to become the estimated stability time. The CLSI EP25 [1] guideline on stability evaluation of in vitro diagnostic reagents addresses this problem only superficially and might even lead to incorrect results for some specific data scenarios.ResultsWe evaluate all possible data scenarios and provide examples for each. Based on the gained theoretical insights, we define a designated data analytics workflow and visualize it with a flowchart. By following this flowchart one can implement an automated analysis workflow, targeting all data scenarios with the appropriate exception handling.DiscussionWe deduce that the description for obtaining stability times according to CLSI EP25 is not fully adequate, as it addresses only best-case scenarios. However, for automated data analytics workflows all possible data situations have to be considered. With the here presented workflow one can program automated data analytics pipelines, which ensure that the right stability time is obtained, in case it exists. In addition all exceptions, where no stability times are present, are addressed in the right way and it provides hints as to the failure reason.

Project description:BackgroundThe ability of mature forms of Plasmodium falciparum infected erythrocytes to bind to a range of host receptors including those displayed on endothelial cells has been associated with the pathology of this infection. Investigations into this adhesive phenomenon have used protein and cell-based adhesion assays to quantify the ability of infected red blood cells to bind. These adhesion assays tend to have relatively high inherent variability and so require multiple experiments in order to provide good quantitation. This means that investigators doing these experiments must count many fields of adherent parasites, a task that is time-consuming and laborious. To address this issue and to facilitate cytoadherence research, developed automated protocols were developed for counting parasite adhesion.MethodsParasite adhesion assays were mainly carried out under static conditions using purified receptors, which is the simplest form of these assays and is translatable to the field. Two different software platforms were used, one commercial (Image Pro-Plus (Media Cybernetics)) and one available in the public domain (ImageSXM) based on the freely available NIH Image software. The adhesion assays were performed and parasite binding quantified using standard manual techniques. Images were also captured using video microscopy and analysed using the two automated systems. The results generated by each system were compared using the Bland and Altman method for assessing the agreement between two methods.ResultsBoth automated counting programs showed concordance compared to the 'gold standard' manual counting within the normal range of adhesion seen with these assays, although the ImageSXM technique had some systematic bias. There was some fall-off in accuracy at very high parasite densities, but this can be resolved through good design of the experiments. Cell based assays were also used as inputs to one of the automated systems (ImageSXM) and produced variable, but encouraging, results.ConclusionsThe automated counting programs are an accurate and practical way of quantifying static parasite binding assays to purified proteins. They are less accurate when applied to cell based systems, but can still provide a reasonable level of accuracy to give a semi-quantitative readout.

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Conductivity experiments for electrolyte formulations and their automated analysis.

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Conductivity experiments for electrolyte formulations and their automated analysis.

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