Déséquilibre électrolytique

Résumé

Un déséquilibre électrolytique est une anomalie de la concentration des électrolytes dans le corps. Les électrolytes jouent un rôle vital dans le maintien de l'homéostasie. Ils aident à réguler la fonction cardiaque et neurologique, l'équilibre hydrique, l'apport d'oxygène, l'équilibre acido-basique et bien plus encore. Les déséquilibres électrolytiques peuvent se développer en consommant ou en excrétant trop peu ou trop d'électrolyte. Des exemples d'électrolytes comprennent le calcium, le chlorure, le magnésium, le phosphate, le potassium et le sodium. Les perturbations électrolytiques sont impliquées dans de nombreux processus pathologiques et constituent une partie importante de la prise en charge des patients en médecine. Les causes, la gravité, le traitement et les conséquences de ces troubles peuvent différer considérablement en fonction de l'électrolyte impliqué. Les perturbations électrolytiques les plus graves impliquent des anomalies des taux de sodium, de potassium ou de

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https://fr.wikipedia.org/wiki/D%C3%A9s%C3%A9quilibre_%C3%A9lectrolytique

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The linear scaling relationship of the binding energies of different intermediates limits the catalyst performance in CO 2 electroreduction. Here we demonstrate a cation concentration gradient strategy to promote the activity and tune the selectivity of CO 2 electroreduction, thereby breaking the scaling relationship. In optimal concentrations of the potassium acetate (KAc) electrolyte, Cu, Ag and In catalysts deliver current densities that are 7.1, 3.2, 2.7 times higher than those obtained in 0.5 M KAc for C 2 H 4 , CO, and formate production, respectively. Increasing the concentration of KAc also changes the selectivity from CO to formate on Ag, and from CO to C 2 products on Cu. In situ surface-enhanced Raman spectroscopy and computational simulations reveal that the binding energies of intermediates are changed at different electrolyte concentrations, which is due to a local electrostatic interaction modulated by potassium cations at the electrode surface.

2022

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Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Amin Kaboli

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data preprocessing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial with Gradient Penalty Networks (WGAN-GP) to generate synthetic samples to populate the rare fault class and enrich the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is also proposed. To verify the effectiveness of the developed framework, different bearing datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM significantly outperforms the other state-of-the-art FDD frameworks.

MDPI2022

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Lithium-ion batteries are widely implemented as energy storage devices due to their high energy density and low cost. They enabled modern portable electronics and electric ve-hicles, and are key to manage the integration of intermittent renewable electricity sources, such as solar and wind, into the power grid and decentralized microgrids. The use of flam-mable organic electrolytes however raises safety concerns, particularly for large-scale sta-tionary batteries. Furthermore, supply chain risks and the use of critical raw materials are a major challenge for the ongoing battery revolution. Aqueous electrolytes offer promising properties, as they are inherently non-flammable and potentially more cost effective. Combined with electrode materials based on abundant raw materials, aqueous batteries are among the most promising candidates to replace cur-rent battery chemistries relying on critical cobalt and flammable organic electrolytes. How-ever, due to the narrow electrochemical stability window of water (practically ca. 1.5 V), aqueous batteries have not yet met the energy density to be serious competitors to current lithium-ion battery technology. The goal of this thesis was to develop aqueous electrolytes with enhanced electrochem-ical stability, thus enabling new high-voltage cell chemistries. Exploring aqueous electrolytes with high salt concentrations, so-called water-in-salt electrolytes, we significantly increased the electrochemical stability and developed highly stable 2 V class aqueous sodium-ion bat-teries. Operating near the solubility limit of the electrolyte salt, crystallization turned out to be a major challenge for water-in-salt electrolytes. In this thesis, we demonstrated that add-ing highly asymmetric anions into the solution can effectively suppress crystallization, allow-ing stable operation of saturated electrolytes at temperatures as low as -10 °C. We further examined the impact of anion asymmetry on the local solution structure and dynamics using molecular dynamics simulations, relating the enhanced supercooling to sustained rotational motion of the asymmetric anion. Further, we demonstrate that the anion has a crucial impact on solution structure and cell performance. We establish that high salt concentration alone does not guarantee high-voltage stability and that due to the limited solubility of most sodium salts new approaches are needed. We provide guidelines for future work and the evaluation of experimental data in order to spur progress in the field of aqueous batteries.

EPFL2020

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Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Amin Kaboli

MDPI2022

EPFL2020