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Search Results (422)

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Keywords = dry electrode

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10 pages, 7575 KiB  
Article
Initial Study of Reduced Graphene Oxide Foams Modified by Mn and Bi as Capacitive Electrode Materials
by Olena Okhay, Tao Yang and Alexander Tkach
Nanoenergy Adv. 2024, 4(4), 318-327; https://doi.org/10.3390/nanoenergyadv4040019 - 29 Oct 2024
Viewed by 280
Abstract
In a view of application of porous materials in wearable electronics and self-powered systems, reduced graphene oxide (rGO) foams modified by Mn or/and Bi were produced in this study to be used as electrodes for supercapacitors. The hydrothermal method and the freeze-drying processes [...] Read more.
In a view of application of porous materials in wearable electronics and self-powered systems, reduced graphene oxide (rGO) foams modified by Mn or/and Bi were produced in this study to be used as electrodes for supercapacitors. The hydrothermal method and the freeze-drying processes were used for the preparation of the materials further morphologically, elementally and structurally analyzed. Based on the electrochemical characterization, Bi-modified rGO foam was found to be more a promising material for capacitive electrodes in comparison to the other prepared materials. Full article
(This article belongs to the Special Issue Novel Energy Materials)
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<p>SEM images of rGO (<b>a</b>), rGO with Mn (<b>b</b>), rGO with Bi (<b>c</b>), and rGO with both Mn and Bi (<b>d</b>).</p>
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<p>Energy-dispersive spectra of rGO with Bi (<b>a</b>), rGO with Mn (<b>b</b>), and rGO with both Mn and Bi (<b>c</b>) composite foams.</p>
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<p>XRD profiles of rGO (<b>a</b>), rGO with Mn (<b>b</b>), rGO with Bi (<b>c</b>), and rGO with both Mn and Bi (<b>d</b>). Symbols *, ○, x and Δ related to <span class="html-italic">α</span>-MnO<sub>2</sub>, MnO, <span class="html-italic">β</span>-Bi<sub>2</sub>O<sub>3</sub> and Bi<sub>2</sub>O<sub>2</sub>CO<sub>3</sub>, respectively.</p>
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<p>Cyclic voltammetry profiles of rGO (<b>a</b>), rGO with Mn (<b>b</b>), rGO with Bi (<b>c</b>), and rGO with both Mn and Bi (<b>d</b>), measured at scan rates of 20, 50, and 100 mV/s.</p>
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<p>Galvanostatic charge–discharge curves of of rGO (<b>a</b>), rGO with Mn (<b>b</b>), rGO with Bi (<b>c</b>), and rGO with both Mn and Bi (<b>d</b>), measured at current densities of 0.5, 1, 2, and 5 A/g.</p>
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<p>log (<span class="html-italic">i</span>) vs. log (<span class="html-italic">v</span>) plot together with the linear fits to obtain the parameter <span class="html-italic">b</span> from the slope (<b>a</b>) and the Nyquist plot (<b>b</b>) of the rGO-foam-based electrodes.</p>
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17 pages, 6541 KiB  
Article
Shotcreting with Cement–Sand Mixtures Under the Influence of an Electrostatic Field
by Valery Mishchenko, Azariy Lapidus, Dmitriy Topchiy and Dmitriy Kazakov
Buildings 2024, 14(10), 3325; https://doi.org/10.3390/buildings14103325 - 21 Oct 2024
Viewed by 449
Abstract
One of the primary and still unresolved problems of shotcreting is the high rebound rate of the material, which reaches over 20% in “dry” shotcreting. There is a practical need to improve the very principle of shotcreting and methods for optimizing the movement [...] Read more.
One of the primary and still unresolved problems of shotcreting is the high rebound rate of the material, which reaches over 20% in “dry” shotcreting. There is a practical need to improve the very principle of shotcreting and methods for optimizing the movement of torch particles. Materials and Methods: The purpose of this study was to justify the use of the electrostatic treatment of cement–sand mortar in the process of performing shotcreting works using the dry method. It was proposed that the binder and then the finished mixture be ionized step-by-step (by passing it through a non-uniform electrostatic field formed by corona electrodes). As a result, the shotcrete will be held on the fence. Results: Analysis of the modeling results shows that the presence of an electrostatic field slows down the particle and reduces the kinetic energy of the rebound. After theoretical calculations, experiments were conducted, during which, the torch size and the plant productivity were changed, and the rebound mass was weighed. After application to the surface, prototypes were formed and subjected to strength tests. It was determined that gunning in a sharply non-uniform electric field demonstrates its practical and economic efficiency due to the uniform deposition of charged particles on the treated surface and low power consumption. Conclusions: It was established that the electrostatic treatment of a cement–sand mixture during application allows concrete particles to be retained on the shotcrete surface, the rebound of the material to be reduced and the strength of concrete to be increased. Full article
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<p>A gun that allows surfaces to be painted using corona discharge.</p>
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<p>Electrophysical features of shotcrete technology using electrostatics.</p>
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<p>Scheme of movement of shotcrete particles along force fields during shotcreting.</p>
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<p>Formation of the experimental setup.</p>
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<p>Power supply unit for 170 kV, 5 kW (from industrial ozonizer).</p>
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<p>Nozzle with insulated corona electrodes.</p>
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<p>Field tests at a Voronezh construction site (private swimming pool).</p>
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<p>Minimum rebound of grade B25 shotcrete after electrostatic treatment, %.</p>
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<p>B25 shotcrete rebound.</p>
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<p>Instron 1100 HDX machine and prototypes.</p>
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<p>Graph of changes in the compressive strength limits of B25 shotcrete without treatment and with the effect of electrostatics.</p>
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<p>Graph of changes in the bending strength limits of B25 shotcrete without treatment and with the effect of electrostatics.</p>
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<p>Results of compression tests of B25 samples without treatment (28 days).</p>
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<p>Results of compression tests of B25 samples after exposure to electrostatics (28 days).</p>
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<p>Average rebound and surface strength (the torch distance and the performance of the classic setup were varied).</p>
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<p>Average rebound and surface strength (the torch distance and the performance of the installation with a nozzle for vortex application of the mixture without the use of electrostatics were changed).</p>
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<p>Strength gain of B25 concrete samples depending on the electric field strength.</p>
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16 pages, 6720 KiB  
Article
Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing
by Tianyu Wang, Shanshan Yao, Li-Hua Shao and Yong Zhu
Sensors 2024, 24(20), 6670; https://doi.org/10.3390/s24206670 - 16 Oct 2024
Viewed by 745
Abstract
Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with [...] Read more.
Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts. Full article
(This article belongs to the Section Biomedical Sensors)
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<p>(<b>a</b>) Preparation of Ag/AgCl NW dry electrode, (<b>b</b>) scanning electron micrographs of Ag NW electrodes and Ag/AgCl NW electrodes, (<b>c</b>) photographs of Ag/AgCl NW electrode before and after tensile deformation, and (<b>d</b>) impedance of each electrode.</p>
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<p>Multiple bioelectrical signal measurement methods: (<b>a</b>) electrode arrangement for different bioelectric signal measurements. Experimental setup and measurement site: (<b>b</b>) ECG measurement, (<b>c</b>) EMG measurement, and (<b>d</b>) EEG measurement.</p>
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<p>ECG signal: (<b>a</b>) part of the full-length raw signal measured by the Ag/AgCl NW electrode and commercial pre−gelled wet electrode, (<b>b</b>) the identification of the ECG signal waveform measured by the Ag/AgCl NW electrode, and (<b>c</b>) the identification of the ECG signal waveform measured by the Ag/AgCl NW electrode with a 50% tensile strain.</p>
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<p>Average FFT amplitudes of NN sequences of full-length ECG signals measured at each electrode: (<b>a</b>) Ag/AgCl NW dry electrode with commercial wet electrode; (<b>b</b>) Ag/AgCl NW dry electrode at 50% tensile strain with commercial wet electrode.</p>
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<p>HRV parameters of ECG signals of random slices measured by each electrode: (<b>a</b>) Ag/AgCl NW dry electrode with commercial wet electrode, (<b>b</b>) Ag/AgCl NW dry electrode at 50% tensile strain, and commercial wet electrode.</p>
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<p>EMG signals measured for each electrode: (<b>a</b>) Ag/AgCl NW dry electrode and commercial wet electrode, (<b>b</b>) Ag/AgCl NW dry electrode with 50% tensile strain and commercial wet electrode, (<b>c</b>) EMG signals measured for Ag/AgCl NW dry electrode with different grip strengths, and (<b>d</b>) Ag/AgCl NW dry electrode after 150% deformation by stretching with different grip strengths. Note that the mutant signal between the two red dashed lines is the EMG during grip.</p>
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<p>Raw EEG signals measured at each electrode: (<b>a</b>) Ag/AgCl NW dry electrode and (<b>b</b>) Ag/AgCl NW dry electrode with 50% tensile strain. Short-time Fourier transform (STFT, left) results of EEG signals measured at each electrode and fast Fourier transform (FFT, right) results after slicing the EEG signals after opening: (<b>c</b>,<b>d</b>) commercial wet electrode, (<b>e</b>,<b>f</b>) both Ag/AgCl NW dry electrodes, and (<b>g</b>,<b>h</b>) the Ag/AgCl NW dry electrode with 50% tensile strain. Note that the blue dashed lines in (<b>a</b>,<b>b</b>) distinguish the EEG signals after the eyes were opened.</p>
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<p>Comparison of signal-to-noise ratios of multiple bioelectric signals measured by different electrodes [<a href="#B4-sensors-24-06670" class="html-bibr">4</a>,<a href="#B12-sensors-24-06670" class="html-bibr">12</a>,<a href="#B30-sensors-24-06670" class="html-bibr">30</a>,<a href="#B31-sensors-24-06670" class="html-bibr">31</a>,<a href="#B32-sensors-24-06670" class="html-bibr">32</a>,<a href="#B33-sensors-24-06670" class="html-bibr">33</a>,<a href="#B34-sensors-24-06670" class="html-bibr">34</a>,<a href="#B35-sensors-24-06670" class="html-bibr">35</a>,<a href="#B36-sensors-24-06670" class="html-bibr">36</a>].</p>
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22 pages, 4691 KiB  
Article
Wearable EEG-Based Brain–Computer Interface for Stress Monitoring
by Brian Premchand, Liyuan Liang, Kok Soon Phua, Zhuo Zhang, Chuanchu Wang, Ling Guo, Jennifer Ang, Juliana Koh, Xueyi Yong and Kai Keng Ang
NeuroSci 2024, 5(4), 407-428; https://doi.org/10.3390/neurosci5040031 - 8 Oct 2024
Viewed by 1257
Abstract
Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain–Computer Interface (BCI) system to detect [...] Read more.
Detecting stress is important for improving human health and potential, because moderate levels of stress may motivate people towards better performance at cognitive tasks, while chronic stress exposure causes impaired performance and health risks. We propose a Brain–Computer Interface (BCI) system to detect stress in the context of high-pressure work environments. The BCI system includes an electroencephalogram (EEG) headband with dry electrodes and an electrocardiogram (ECG) chest belt. We collected EEG and ECG data from 40 participants during two stressful cognitive tasks: the Cognitive Vigilance Task (CVT), and the Multi-Modal Integration Task (MMIT) we designed. We also recorded self-reported stress levels using the Dundee Stress State Questionnaire (DSSQ). The DSSQ results indicated that performing the MMIT led to significant increases in stress, while performing the CVT did not. Subsequently, we trained two different models to classify stress from non-stress states, one using EEG features, and the other using heart rate variability (HRV) features extracted from the ECG. Our EEG-based model achieved an overall accuracy of 81.0% for MMIT and 77.2% for CVT. However, our HRV-based model only achieved 62.1% accuracy for CVT and 56.0% for MMIT. We conclude that EEG is an effective predictor of stress in the context of stressful cognitive tasks. Our proposed BCI system shows promise in evaluating mental stress in high-pressure work environments, particularly when utilizing an EEG-based BCI. Full article
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<p>Cognitive Vigilance Task. (<b>A</b>) A critical number, 32, is shown in this trial. The number 32 is critical, as the digits differ by 1. The other numbers are not critical, as their digits do not differ by 0 or 1. Comparisons between numbers in different squares (e.g., 61 and 60) are not relevant in this task, and do not constitute a critical number. (<b>B</b>) A no-go trial. In no-go trials (which all included a critical number, in this case 89), a white star appeared at the bottom left of the screen. Participants were required to wait for trial to time-out. For both screenshots, the red annotations did not appear to the participants during the actual task.</p>
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<p>Multi-Modal Integration Task. (<b>A</b>) This screenshot shows a trial in which all properties of the suspect match the rules, meaning that the participant should respond by pressing the spacebar. (<b>B</b>) This screenshot shows a no-go trial, meaning that the participant should respond by waiting for the trial to timeout. For both screenshots, the red annotations did not appear to the participants during the actual task.</p>
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<p>Example of the experimental setup. The right-side screen enabled the operator to monitor the EEG/ECG signals and data acquisition process. The left-side screen was for displaying visual cues to the participant for the cognitive tasks. Each participant wore an EEG headband on their forehead, and an ECG chest belt.</p>
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<p>Heart rate (HR) and R-R interval plots for one participant for one short practice session (7 blocks). HR is measured in beats per minute (BPM), and the R-R interval is measured in milliseconds (ms). The R-R interval is normalized to zero mean. The red line is the mean HR and R-R interval, respectively.</p>
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<p>Diagram of our stress detection algorithm, indicating how it was trained and tested. The blue dashed box indicates the blocks that were labelled as “non-stressed”, and the orange dashed box indicates the blocks that were labelled as “stressed”.</p>
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<p>Changes in subjective stress levels before and after the CVTs and MMITs. Error bars represent the standard error of the mean, and the significance levels were calculated using paired two-tailed <span class="html-italic">t</span>-tests (α = 0.05). (<b>A</b>) Performing the CVT did not significantly increase the reported levels of distress. (<b>B</b>) Performing the MMIT significantly increased the reported levels of distress.</p>
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<p>Predicted stress levels of a participant as they performed the CVT and MMIT. The green points represent task blocks recorded during the MMIT, and the purple points represent task blocks recorded during the CVT. There was an overall trend towards higher predicted stress levels; however, the block-to-block variability was considerable.</p>
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<p>Stress detection accuracy for the CVT and MMIT, based on the model input and number of blocks used. Top: The accuracy of the stress-detection model built from CVT data. Bottom: The accuracy of the stress-detection model built from MMIT data. The x-axis represents the number of blocks from the start and end of the session used to label stress for training and testing, and the y-axis indicates classification accuracy. Reference accuracy levels of 50% (chance level) and 75% (minimum level for a useful brain-machine interface) are shown with red dotted lines.</p>
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16 pages, 3708 KiB  
Article
Combined Method Comprising Low Burden Physiological Measurements with Dry Electrodes and Machine Learning for Classification of Visually Induced Motion Sickness in Remote-Controlled Excavator
by Naohito Yoshioka, Hiroki Takeuchi, Yuzhuo Shu, Taro Okamatsu, Nobuyuki Araki, Yoshiyuki Kamakura and Mieko Ohsuga
Sensors 2024, 24(19), 6465; https://doi.org/10.3390/s24196465 - 7 Oct 2024
Viewed by 753
Abstract
The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a [...] Read more.
The construction industry is actively developing remote-controlled excavators to address labor shortages and improve work safety. However, visually induced motion sickness (VIMS) remains a concern in the remote operation of construction machinery. To predict the occurrence and severity of VIMS, we developed a prototype system that acquires multiple physiological signals with different mechanisms under a low burden and detects VIMS from the collected data. Signals during VIMS were recorded from nine healthy adult males operating excavator simulators equipped with multiple displays and a head-mounted display. Light gradient-boosting machine-based VIMS detection binary classification models were constructed using approximately 30,000 s of time-series data, comprising 23 features derived from the physiological signals. These models were validated using leave-one-out cross-validation on seven participants who experienced severe VIMS and evaluated through area under the curve (AUC) scores. The mean receiver operating characteristic curve AUC score was 0.84, and the mean precision–recall curve AUC score was 0.71. All features were incorporated into the models, with saccade frequency and skin conductance response identified as particularly important. These trends aligned with subjective assessments of VIMS severity. This study contributes to advancing the use of remote-controlled machinery by addressing a critical challenge to operator performance and safety. Full article
(This article belongs to the Section Physical Sensors)
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<p>Block diagram of the prototype system.</p>
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<p>Dry electrodes. (<b>a</b>) Film electrodes for ECG, EDA, and EOG [<a href="#B10-sensors-24-06465" class="html-bibr">10</a>]. (<b>b</b>) Flexible dry electrodes for EEGs [<a href="#B11-sensors-24-06465" class="html-bibr">11</a>].</p>
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<p>Low-burden physiological measurement system. (<b>a</b>) Levers with electrodes and sensor. (<b>b</b>) Seatbelt with an accelerometer. (<b>c</b>) Helmet and HMD with electrodes.</p>
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<p>Experimental environment. (<b>a</b>) MD system: in this photo, the helmet comprises only the inner frame. (<b>b</b>) HMD system, with a multi-display only for experimenters.</p>
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<p>Experimental scenario, wherein excavation work with an excavator is simulated [<a href="#B14-sensors-24-06465" class="html-bibr">14</a>].</p>
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<p>SSQ score pre- and post-experiment.</p>
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<p>ROC curve and PR curve of (<b>a</b>) P01, (<b>b</b>) P02, (<b>c</b>) P03, (<b>d</b>) P04, (<b>e</b>) P05, (<b>f</b>) P06, and (<b>g</b>) P07.</p>
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<p>Feature the importance of each model and the mean value of all models.</p>
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<p>Change trends in (<b>a</b>) accuracy rate, (<b>b</b>) VIMS level, and (<b>c</b>–<b>o</b>) features. The red line indicates the mean, and the light red highlight indicates the 95% confidence interval. The gray dotted line in the graph of each feature indicates the baseline. <span class="html-italic">R</span> and <span class="html-italic">R</span><sup>2</sup> represent the correlation coefficients and the coefficients of determination with the VIMS level.</p>
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20 pages, 3739 KiB  
Article
Advancements on Lumped Modelling of Membrane Water Content for Real-Time Prognostics and Control of PEMFC
by Massimo Sicilia, Davide Cervone, Pierpaolo Polverino and Cesare Pianese
Energies 2024, 17(19), 4841; https://doi.org/10.3390/en17194841 - 27 Sep 2024
Viewed by 517
Abstract
PEMFCs play a key role in the energy transition scenarios thanks to the zero emissions, versatility, and power density. PEMFC performances are improved optimizing water management to ensure proper ion transport: it is well known that a well-balanced water content avoids either electrodes [...] Read more.
PEMFCs play a key role in the energy transition scenarios thanks to the zero emissions, versatility, and power density. PEMFC performances are improved optimizing water management to ensure proper ion transport: it is well known that a well-balanced water content avoids either electrodes flooding or membrane drying, causing gas starvation at the active sites or low proton conductivity, respectively. In this paper, an analytical formulation for water transport dynamics within the membrane, derived from membrane water balance, is proposed to overcome the limitations of PEM dynamics model largely adopted in the literature. The dynamics is simulated thanks to the introduction of a characteristic time with a closed analytical form, which is general and easily implementable for any application where both low computational time and high accuracy are required. Furthermore, the net water molar fluxes at the membrane boundaries can be easily computed as well for a cell’s simulation. The analytical formulation has a strong dependency on the operative conditions, as well as physical parameters of the membrane itself. From the proposed formulation, for a 200 µm membrane, the characteristic time can vary from 5 s up to 50 s; this example shows how control strategies must consider PEM dynamic behavior. Full article
(This article belongs to the Special Issue Current Advances in Fuel Cell and Batteries)
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<p>Schematic representation of PEM electrolyte structure and main protons transport mechanisms.</p>
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<p>First seven coefficients (<b>a</b>) and eigenvalues (<b>b</b>) for different Peclet numbers.</p>
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<p>Representation of the time evolution of the normalized solution for different Peclet numbers: <span class="html-italic">Pe</span> = 0.2 (<b>a</b>), <span class="html-italic">Pe</span> = 0.5 (<b>b</b>), <span class="html-italic">Pe</span> = 1 (<b>c</b>), and <span class="html-italic">Pe</span> = 2 (<b>d</b>).</p>
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<p>Comparison between analytical and integral solutions (<b>a</b>) and error distribution (<b>b</b>) for <span class="html-italic">Pe</span> = 3.</p>
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<p>Sensitivity analysis of average solutions’ varying Pe numbers: <span class="html-italic">Pe</span> = 0.2 (<b>a</b>), <span class="html-italic">Pe</span> = 0.5 (<b>b</b>), <span class="html-italic">Pe</span> = 1 (<b>c</b>), and <span class="html-italic">Pe</span> = 2 (<b>d</b>).</p>
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<p>Comparison between <tt>τ</tt><sub>1</sub> and <tt>τ</tt><sub>d</sub> varying <span class="html-italic">Pe</span> from 0 to 10.</p>
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<p>Representation of <span class="html-italic">t<sub>d</sub></span> variation with the membrane thickness (<b>a</b>) and molar concentration (<b>b</b>), as well as electrodes’ negative (<b>c</b>) and positive (<b>d</b>) pressure gradients.</p>
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<p>Steady-state solution (black) and its tangent lines (red for anode, blue for cathode) at boundaries with an intersection for <span class="html-italic">Pe</span> = 3, <span class="html-italic">λ<sub>a</sub></span> = 7, and <span class="html-italic">λ<sub>c</sub></span> = 14.</p>
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<p>Water content with a steady-state (dashed lines) and dynamic (solid lines) membrane.</p>
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24 pages, 10077 KiB  
Article
Emotion Recognition Using EEG Signals through the Design of a Dry Electrode Based on the Combination of Type 2 Fuzzy Sets and Deep Convolutional Graph Networks
by Shokoufeh Mounesi Rad and Sebelan Danishvar
Biomimetics 2024, 9(9), 562; https://doi.org/10.3390/biomimetics9090562 - 18 Sep 2024
Viewed by 1004
Abstract
Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain–computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). [...] Read more.
Emotion is an intricate cognitive state that, when identified, can serve as a crucial component of the brain–computer interface. This study examines the identification of two categories of positive and negative emotions through the development and implementation of a dry electrode electroencephalogram (EEG). To achieve this objective, a dry EEG electrode is created using the silver-copper sintering technique, which is assessed through Scanning Electron Microscope (SEM) and Energy Dispersive X-ray Analysis (EDXA) evaluations. Subsequently, a database is generated utilizing the designated electrode, which is based on the musical stimulus. The collected data are fed into an improved deep network for automatic feature selection/extraction and classification. The deep network architecture is structured by combining type 2 fuzzy sets (FT2) and deep convolutional graph networks. The fabricated electrode demonstrated superior performance, efficiency, and affordability compared to other electrodes (both wet and dry) in this study. Furthermore, the dry EEG electrode was examined in noisy environments and demonstrated robust resistance across a diverse range of Signal-To-Noise ratios (SNRs). Furthermore, the proposed model achieved a classification accuracy of 99% for distinguishing between positive and negative emotions, an improvement of approximately 2% over previous studies. The manufactured dry EEG electrode is very economical and cost-effective in terms of manufacturing costs when compared to recent studies. The proposed deep network, combined with the fabricated dry EEG electrode, can be used in real-time applications for long-term recordings that do not require gel. Full article
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<p>The proposed electrode design and customized deep architecture provide a general framework for classifying two types of emotions: positive and negative.</p>
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<p>Copper bars of various diameters.</p>
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<p>Electrode copper bases are machined and ready for sintering.</p>
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<p>Powdered samples inside the sintering furnace.</p>
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<p>Samples taken from the furnace with a copper base and silver top.</p>
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<p>The amplifier used in the experiment for the proposed dry electrode.</p>
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<p>Recording of EEG signals from one of the participants based on the dry electrode (Three electrodes FP1, PZ, and FZ have been used for recording according to the image).</p>
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<p>Musical stimulation scenario to evoke positive and negative emotions.</p>
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<p>Proposed deep network representation in combination with TF2 for automatic recognition of emotions.</p>
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<p>Details of each layer in the proposed pipeline.</p>
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<p>Electrode sample at the imaging point of the SEM.</p>
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<p>Illustrates the silver powder utilized in the annealing procedure, in conjunction with an EDXA instrument. (<b>a</b>) powder particles, (<b>b</b>) EDX results.</p>
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<p><a href="#biomimetics-09-00562-f013" class="html-fig">Figure 13</a> shows an EDXA image of the silver block that came into being on the copper base after the silver powder was sintered. (<b>a</b>) sintering of the silver powder, (<b>b</b>) EDX analysis.</p>
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<p>Optimization of the number and computational efficiency of the proposed DFCGN network.</p>
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<p>Considered polynomial values for the proposed DFCGN network.</p>
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<p>Comparison of error performance and accuracy of dry electrodes made with dry and wet electrodes from different brands. (The suggested dry electrode, dry electrode, and wet electrode are shown with blue, red, and yellow legends, respectively).</p>
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<p>ROC diagram for the various evaluated electrodes (from left: recommended dry electrode, wet electrode, and dry electrode).</p>
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<p>TSNE diagram for the first and last layers of the proposed DFCGN model to recognize two different classes of positive and negative emotion according to the recorded suggested dry electrode.</p>
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<p>The proposed network’s performance in comparison to other networks.</p>
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<p>The effect of environmental noise on the proposed dry electrode and dry electrode.</p>
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25 pages, 9089 KiB  
Article
Remotely Powered Two-Wire Cooperative Sensors for Bioimpedance Imaging Wearables
by Olivier Chételat, Michaël Rapin, Benjamin Bonnal, André Fivaz, Benjamin Sporrer, James Rosenthal and Josias Wacker
Sensors 2024, 24(18), 5896; https://doi.org/10.3390/s24185896 - 11 Sep 2024
Viewed by 650
Abstract
Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of [...] Read more.
Bioimpedance imaging aims to generate a 3D map of the resistivity and permittivity of biological tissue from multiple impedance channels measured with electrodes applied to the skin. When the electrodes are distributed around the body (for example, by delineating a cross section of the chest or a limb), bioimpedance imaging is called electrical impedance tomography (EIT) and results in functional 2D images. Conventional EIT systems rely on individually cabling each electrode to master electronics in a star configuration. This approach works well for rack-mounted equipment; however, the bulkiness of the cabling is unsuitable for a wearable system. Previously presented cooperative sensors solve this cabling problem using active (dry) electrodes connected via a two-wire parallel bus. The bus can be implemented with two unshielded wires or even two conductive textile layers, thus replacing the cumbersome wiring of the conventional star arrangement. Prior research demonstrated cooperative sensors for measuring bioimpedances, successfully realizing a measurement reference signal, sensor synchronization, and data transfer though still relying on individual batteries to power the sensors. Subsequent research using cooperative sensors for biopotential measurements proposed a method to remove batteries from the sensors and have the central unit supply power over the two-wire bus. Building from our previous research, this paper presents the application of this method to the measurement of bioimpedances. Two different approaches are discussed, one using discrete, commercially available components, and the other with an application-specific integrated circuit (ASIC). The initial experimental results reveal that both approaches are feasible, but the ASIC approach offers advantages for medical safety, as well as lower power consumption and a smaller size. Full article
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Figure 1
<p>The conventional approach to measuring a bioimpedance <math display="inline"><semantics> <mrow> <mi>Z</mi> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math>. Two current electrodes (in red) are connected with double-shielded cables to the central unit where a current source <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> injects a current through the skin. The current flows through the impedance to be measured <math display="inline"><semantics> <mrow> <mi>Z</mi> </mrow> </semantics></math> and is drained by another current electrode driven by the current source <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mo>−</mo> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>.</mo> </mrow> </semantics></math> Any practical deviation between the two current sources flows through the RL electrode (called the right leg electrode because it was originally developed for ECG and placed on the right leg). The current <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> is translated across the impedance <math display="inline"><semantics> <mrow> <mi>Z</mi> </mrow> </semantics></math> by a voltage drop <math display="inline"><semantics> <mrow> <mi>e</mi> <mo>=</mo> <mi>Z</mi> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> measured in the same way as biopotentials (difference <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>v</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>)</mo> <mo>.</mo> </mrow> </semantics></math> The controller <math display="inline"><semantics> <mrow> <mi>G</mi> </mrow> </semantics></math> driving the voltage source <math display="inline"><semantics> <mrow> <msup> <mi>u</mi> <mo>′</mo> </msup> </mrow> </semantics></math> allows the common ground potential to be set equal to the body potential, thus avoiding possible saturation of the electronics due to disturbing currents picked up in the environment and flowing through the skin/electrode impedance of the RL electrode. When the impedance is measured at a given angular frequency <math display="inline"><semantics> <mrow> <mi>ω</mi> </mrow> </semantics></math>, it can be decomposed into a real part and imaginary part: <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Z</mi> </mrow> <mrow> <mi>ω</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> <mo>=</mo> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>ω</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> <mo>+</mo> <mi>j</mi> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mi>ω</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math>. Furthermore, the current is a cosine wave <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> <mo>=</mo> <mi>I</mi> <mrow> <mrow> <mi mathvariant="normal">cos</mi> </mrow> <mo>⁡</mo> <mrow> <mi>ω</mi> <mi>t</mi> </mrow> </mrow> </mrow> </semantics></math>, and the resistance <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>ω</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math> and reactance <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mi>ω</mi> </mrow> </msub> <mfenced separators="|"> <mrow> <mi>t</mi> </mrow> </mfenced> </mrow> </semantics></math> are extracted from the voltage <math display="inline"><semantics> <mrow> <mi>e</mi> </mrow> </semantics></math> with IQ demodulation, i.e., multiplication of the voltage <math display="inline"><semantics> <mrow> <mi>e</mi> </mrow> </semantics></math> by <math display="inline"><semantics> <mrow> <mrow> <mrow> <mi mathvariant="normal">cos</mi> </mrow> <mo>⁡</mo> <mrow> <mfenced separators="|"> <mrow> <mi>ω</mi> <mi>t</mi> </mrow> </mfenced> </mrow> </mrow> <mo>/</mo> <mi>I</mi> </mrow> </semantics></math> and by <math display="inline"><semantics> <mrow> <mrow> <mrow> <mi mathvariant="normal">sin</mi> </mrow> <mo>⁡</mo> <mrow> <mfenced separators="|"> <mrow> <mi>ω</mi> <mi>t</mi> </mrow> </mfenced> </mrow> </mrow> <mo>/</mo> <mi>I</mi> </mrow> </semantics></math>, respectively, followed by low-pass filters.</p>
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<p>Cooperative sensors are active electrodes with additional circuitry that enables their connection via a parallel bus with up to two wires. The sensors communicate their measured data to a central unit, which also provides a synchronized clock. In applications not required to be defibrillator proof, the parallel bus can be made from conductive fabric. In this case, the controller <math display="inline"><semantics> <mrow> <mi>G</mi> </mrow> </semantics></math> of the central unit maintains the voltage between the lower textile and the body at nearly 0 V, removing the need for bottom-side insulation. The top conductive textile can easily be insulated with an additional layer of fabric (e.g., a regular garment) if the excess leakage currents are electronically monitored. Highly integrated cooperative sensors can be attached and connected to the fabric, making the assembly seamless while maintaining the usual properties of the fabric (flexibility, stretchability, breathability, and washability). Cooperative sensors can be current electrodes (when the current <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> is different from 0) or potential electrodes (when the current <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>i</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> is zero). Symbol legend in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>The connection of 16 sensors around a body part (e.g., chest or limb) for EIT measurements. (<b>a</b>) A device with two different types of sensors, one with a potential electrode and one with a current electrode. (<b>b</b>) A device with a single type of sensor with a potential or current electrode depending on the current/function (equal to 0 for potential electrode; different from 0 for current electrode). The symbol legend is in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>A simple bootstrap circuit is used to achieve extremely high impedance (input impedance for potential electrode and output impedance for current electrode) by leveraging the floating battery in each sensor. The parts added for the measurement of bioimpedances are shown in red—the other parts are the same as for biopotentials only [<a href="#B1-sensors-24-05896" class="html-bibr">1</a>]. The implementation of the current source (in red) can be simple thanks to the bootstrapping circuit [<a href="#B15-sensors-24-05896" class="html-bibr">15</a>] that significantly increases the open-loop impedance and has a rail-to-rail voltage range. The current return for the red current source comes from the upper wire only. As the lower wire is used for the measurement of potential, the impedance of the wires does not affect the measurement of bioimpedance. Patients are protected from leakage currents by diodes (not depicted) that prevent stored charge from leaving a sensor while simultaneously enabling the recharging of the batteries through the two-wire bus when the system is not being worn. See <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a> for a symbol legend.</p>
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<p>Remotely powered cooperative sensors for bioimpedance measurement with dry electrodes, with digital communication at 1.28 Mb/s in both directions (full duplex) and remote power supply at 500 Hz. Symbol legend in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>Remotely powered cooperative sensors for bioimpedance measurement with dry electrodes, with analog communication at 500,000 samples per second and remote supply voltage <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> at 1 MHz. Left: schematic overview of central unit circuit; middle: schematic overview of sensor circuit; right: detailed circuit diagram of sensor. Symbol legend is shown in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>A supply voltage <math display="inline"><semantics> <mrow> <mi>U</mi> </mrow> </semantics></math> consisting of a 1 MHz square wave with a sync marker (periodicity break) consisting of an HH period with a Manchester edge (in blue) every 1 s (every 1,000,000 periods of the 1 MHz square wave). The other periods contain a powering period H and a communication period L. If the period is HL, the sensors understand it as a 1, whereas LH is understood as 0. This upstream digital communication can be used to configure or control the sensors. The sensors harvest energy during subperiod H, and one of them (determined by the sensor ID and the position of the period with respect to the synch marker, shown in red in the figure) transmits an analog value during subperiod L.</p>
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<p>A possible implementation of the comb filter. The symbol legend is provided in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>WELMO vest with embedded EIT chest strap with off-the-shelf components (textile part made by Smartex in framework of EU project WELMO). Left: worn vest, middle top: front view of cooperative sensor with stainless steel dry current/potential electrode and stethoscope (center), middle bottom: back view of cooperative sensor with its two connectors to 2-wire parallel bus, right top: open vest with embedded EIT chest strap with reference and RL textile electrodes ① and cooperative sensors with dry electrode ② and stethoscope ③, right bottom: back view of EIT chest strap showing 2-wire parallel bus and attachment washers.</p>
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<p>A possible implementation of the principles shown in <a href="#sensors-24-05896-f005" class="html-fig">Figure 5</a> (as prototyped in the device shown in <a href="#sensors-24-05896-f009" class="html-fig">Figure 9</a>). Note that the safety protection circuit is not pictured for simplicity. The symbol legend is provided in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>Configurations <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mn>1</mn> </mrow> </mfenced> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mn>2</mn> </mrow> </mfenced> </mrow> </semantics></math> ((<b>top</b>) and (<b>bottom</b>)), where the EIT device (left and in black) is connected to two resistors <math display="inline"><semantics> <mrow> <mi>r</mi> </mrow> </semantics></math> (in red) to provide information corresponding to different resistance matrices <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mfenced separators="|"> <mrow> <mn>1</mn> </mrow> </mfenced> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mfenced separators="|"> <mrow> <mn>2</mn> </mrow> </mfenced> </mrow> </msup> </mrow> </semantics></math>, etc. (right), for the optimization function <math display="inline"><semantics> <mrow> <mi>f</mi> </mrow> </semantics></math>, allowing to compute by optimization the calibration function <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>I</mi> <mo>,</mo> <mi>u</mi> </mrow> </mfenced> <mo>↦</mo> <mfenced separators="|"> <mrow> <mi>i</mi> <mo>,</mo> <mi>U</mi> </mrow> </mfenced> </mrow> </semantics></math>. The symbol legend is shown in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
Full article ">Figure 11 Cont.
<p>Configurations <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mn>1</mn> </mrow> </mfenced> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mn>2</mn> </mrow> </mfenced> </mrow> </semantics></math> ((<b>top</b>) and (<b>bottom</b>)), where the EIT device (left and in black) is connected to two resistors <math display="inline"><semantics> <mrow> <mi>r</mi> </mrow> </semantics></math> (in red) to provide information corresponding to different resistance matrices <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mfenced separators="|"> <mrow> <mn>1</mn> </mrow> </mfenced> </mrow> </msup> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msup> <mrow> <mi>R</mi> </mrow> <mrow> <mfenced separators="|"> <mrow> <mn>2</mn> </mrow> </mfenced> </mrow> </msup> </mrow> </semantics></math>, etc. (right), for the optimization function <math display="inline"><semantics> <mrow> <mi>f</mi> </mrow> </semantics></math>, allowing to compute by optimization the calibration function <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mi>I</mi> <mo>,</mo> <mi>u</mi> </mrow> </mfenced> <mo>↦</mo> <mfenced separators="|"> <mrow> <mi>i</mi> <mo>,</mo> <mi>U</mi> </mrow> </mfenced> </mrow> </semantics></math>. The symbol legend is shown in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>Errors <math display="inline"><semantics> <mrow> <mi>u</mi> <mo>−</mo> <mi>R</mi> <mi>I</mi> </mrow> </semantics></math> (<b>top</b>) and <math display="inline"><semantics> <mrow> <mi>U</mi> <mo>−</mo> <mi>R</mi> <mi>i</mi> </mrow> </semantics></math>, i.e., after calibration (<b>bottom</b>) for configuration <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mn>1</mn> </mrow> </mfenced> </mrow> </semantics></math>. Comparable results are obtained for other configurations <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mn>2</mn> </mrow> </mfenced> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mfenced separators="|"> <mrow> <mn>16</mn> </mrow> </mfenced> </mrow> </semantics></math>.</p>
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<p>A block diagram of the ASIC implementation of cooperative sensors for bioimpedance measurements. The circuit blocks that interface with the 2-wire sensor bus are marked in red, and the signal processing circuits are in green. The symbol legend is shown in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>A diagram of the central unit based on the approach shown in <a href="#sensors-24-05896-f006" class="html-fig">Figure 6</a>. The symbol legend is shown in <a href="#app1-sensors-24-05896" class="html-app">Appendix A</a>.</p>
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<p>A cut view of the integration of a sensor realized with an ASIC. ① A PCB (green) with a mounted ASIC (black) and finger spring contacts (yellow). ② The bottom part of the housing with an over-molded stainless steel skin electrode ③, with the connection between the PCB and the electrode being obtained with a spring contact (in yellow). ④ The top part of the housing with over-molded wire contacts ⑤ (only one is shown). ⑥ An electrically conductive track on a slightly compressible textile ⑦. ⑧ A clamp pressing the sensor onto the textile. ⑨ A reinforcement ring on belt textile ⑩. The height of the ASIC sensor (without a clamp and without textile) is 4.7 mm. The size of the sensor can be reduced if only the bioimpedance is considered (in our development, we had sensors that also included a stethoscope, not shown in this figure).</p>
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<p>(<b>a</b>) Real setup and (<b>b</b>) functional diagram of setup for first verifications of concept including four sensors, i.e., ASIC (left), central unit (right), and resistance to measure (center).</p>
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<p>The setup used to measure the input impedance of the ASIC frontend amplifier.</p>
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<p>(<b>Top row</b>): the ASIC sensor harness worn (<b>left</b>) and open (<b>right</b>), exposing the sensors and the electrodes on the sensors and two textile electrodes. (<b>Middle row</b>): the ASIC sensor clamped to a 3D knit on two electrically conductive tracks, realized as conductive tapes (black). (<b>Bottom row</b>): the belt textile with the reinforcement ring (seen as a slight bump in the photo) added on top of the conductive tapes.</p>
Full article ">Figure A1
<p>Symbol legends used in figures. Electronic symbols in black, functional diagram symbols in blue. A1: resistance, A2: impedance, A3: voltage source, A4: controlled voltage source, A5: voltage (between two conductors), A6: summator, A7: multiplicator, B1: inductance, B2: shielded cable (e.g., coaxial cable), B3: current source, B4: controlled current source, B5: current (in a conductor), B6: transfer function, B7: low-pass filter, C1: capacitance, C2: diode, C3: power supply block, C4: LDO (low-dropout regulator), C5: switch, C6: electrode, C7: pass-through (combination of RL electrode with controller <span class="html-italic">G</span> resulting virtually in a 0 Ω connection with body core), D1: operational amplifier, D2: instrumentation amplifier, D3: power supply including a battery, D4: battery, D5: connection to positive power supply rail, D6: clock recovery and sync block, D7: down sampling by 2, E1: follower, E2: Schmitt trigger, E3: power supply block harvesting energy with controlled current, E5: common ground, E6: modulator, E7: demodulator.</p>
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13 pages, 4393 KiB  
Article
A Cost-Effective and Easy-to-Fabricate Conductive Velcro Dry Electrode for Durable and High-Performance Biopotential Acquisition
by Jun Guo, Xuanqi Wang, Ruiyu Bai, Zimo Zhang, Huazhen Chen, Kai Xue, Chuang Ma, Dawei Zang, Erwei Yin, Kunpeng Gao and Bowen Ji
Biosensors 2024, 14(9), 432; https://doi.org/10.3390/bios14090432 - 6 Sep 2024
Viewed by 910
Abstract
Compared with the traditional gel electrode, the dry electrode is being taken more seriously in bioelectrical recording because of its easy preparation, long-lasting ability, and reusability. However, the commonly used dry AgCl electrodes and silver cloth electrodes are generally hard to record through [...] Read more.
Compared with the traditional gel electrode, the dry electrode is being taken more seriously in bioelectrical recording because of its easy preparation, long-lasting ability, and reusability. However, the commonly used dry AgCl electrodes and silver cloth electrodes are generally hard to record through hair due to their flat contact surface. Claw electrodes can contact skin through hair on the head and body, but the internal claw structure is relatively hard and causes discomfort after being worn for a few hours. Here, we report a conductive Velcro electrode (CVE) with an elastic hook hair structure, which can collect biopotential through body hair. The elastic hooks greatly reduce discomfort after long-time wearing and can even be worn all day. The CVE electrode is fabricated by one-step immersion in conductive silver paste based on the cost-effective commercial Velcro, forming a uniform and durable conductive coating on a cluster of hook microstructures. The electrode shows excellent properties, including low impedance (15.88 kΩ @ 10 Hz), high signal-to-noise ratio (16.0 dB), strong water resistance, and mechanical resistance. After washing in laundry detergent, the impedance of CVE is still 16% lower than the commercial AgCl electrodes. To verify the mechanical strength and recovery capability, we conducted cyclic compression experiments. The results show that the displacement change of the electrode hook hair after 50 compression cycles was still less than 1%. This electrode provides a universal acquisition scheme, including effective acquisition of different parts of the body with or without hair. Finally, the gesture recognition from electromyography (EMG) by the CVE electrode was applied with accuracy above 90%. The CVE proposed in this study has great potential and promise in various human–machine interface (HMI) applications that employ surface biopotential signals on the body or head with hair. Full article
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Figure 1

Figure 1
<p>Fabrication and wearing method of the conductive Velcro as an electrode. (<b>a</b>) The internal structure of CVE; (<b>b</b>) the manufacturing process of CVE; (<b>c</b>) the microstructure of CVE; (<b>d</b>) the wearing method of CVE.</p>
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<p>Microstructure of the CVE. (<b>a</b>) Scanning electron microscopy (SEM) of the electrode surface at 43 times magnification; (<b>b</b>) SEM of the conductive silver on the hook hair surface at 5000 times; (<b>c</b>) ultra-depth microscope picture of the hook microstructure on the CVE surface.</p>
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<p>Material selection and performance of CVE with some typical dry electrodes. (<b>a</b>) Schematic diagram of CVE inside the elastic strap; (<b>b</b>) schematic diagram of arm wearing position when collecting electromyographic signals with CVE; (<b>c</b>) materials of silver paste, copper paste, and graphite paste; (<b>d</b>) electrochemical impedance curves of electrodes made of silver paste, copper paste, and graphite paste; (<b>e</b>) impedance comparison at typical frequencies of 10 Hz and 100 Hz for electrodes made of three pastes; (<b>f</b>) pictures of CVE, claw electrode, AgCl electrode, and silver cloth electrode; (<b>g</b>) impedance curves of the four electrodes above; (<b>h</b>) impedance comparison at typical frequencies of 10 Hz and 100 Hz for these four electrodes.</p>
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<p>Biopotential acquisition using CVE. (<b>a</b>) Distribution of electrode positions for EMG, EOG, and ECG signal acquisition; analysis of SNR of EMG signals collected using CVE, AgCl electrodes, claw electrodes, and silver cloth electrodes under a grip strength of (<b>b</b>) 5 N and (<b>c</b>) 10 N; EOG signals collected using (<b>d</b>) CVE and (<b>e</b>) AgCl electrodes, respectively; ECG signals collected using (<b>f</b>) CVE and (<b>g</b>) AgCl electrodes, respectively.</p>
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<p>Water resistance and compression resistance tests. (<b>a</b>) Magnetic stirrer with stirring function; (<b>b</b>) impedance comparison of conductive Velcro before and after washing; (<b>c</b>) comparison of impedance before and after washing conductive Velcro (10 Hz and 100 Hz); (<b>d</b>) when the load is 0.5 N, the schematic diagram of compression Velcro; (<b>e</b>) when the load is 10 N, the schematic diagram of compression Velcro; (<b>f</b>) Velcro photo after cyclic compression ends; (<b>g</b>) after 50 cycles of compression, the corresponding relationship between displacement and load; (<b>h</b>) the lowest displacement change diagram during cyclic compression; (<b>i</b>) the highest displacement change diagram during cyclic compression.</p>
Full article ">Figure 6
<p>CNN convolutional neural network for gesture recognition by EMG signals recorded from CVE. (<b>a</b>) Four hand gestures to be classified: palm open, palm closed, wrist adduction, wrist extension; (<b>b</b>) confusion plots for all the gesture recognition results; (<b>c</b>) internal structure diagram of convolutional neural network; (<b>d</b>) robot moves forward, turns left, turns right, and stops in the virtual environment controlled by the EMG signals.</p>
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24 pages, 13050 KiB  
Article
Features of Increasing the Wear Resistance of 90CrSi Tool Steel Surface under Various Electrophysical Parameters of Plasma Electrolytic Treatment
by Sergey N. Grigoriev, Ivan V. Tambovskiy, Tatiana L. Mukhacheva, Irina A. Kusmanova, Pavel A. Podrabinnik, Nikolay O. Khmelevsky, Igor V. Suminov and Sergei A. Kusmanov
Metals 2024, 14(9), 994; https://doi.org/10.3390/met14090994 - 31 Aug 2024
Viewed by 607
Abstract
The paper investigates the feasibility of plasma electrolytic treatment (PET) of 90CrSi tool steel to enhance hardness and wear resistance. The influence of electrophysical parameters of PET (polarity of the active electrode, chemical-thermal treatment, and polishing modes) on the composition, structure, morphology, and [...] Read more.
The paper investigates the feasibility of plasma electrolytic treatment (PET) of 90CrSi tool steel to enhance hardness and wear resistance. The influence of electrophysical parameters of PET (polarity of the active electrode, chemical-thermal treatment, and polishing modes) on the composition, structure, morphology, and tribological properties of the surface was studied. Tribological tests were carried out under dry friction conditions according to the shaft-bushing scheme with fixation of the friction coefficient and temperature in the friction contact zone, measurements of surface microgeometry parameters, morphological analysis of friction tracks, and weight wear. The formation of a surface hardened to 1110–1120 HV due to the formation of quenched martensite is shown. Features of nitrogen diffusion during anodic PET and cathodic PET were revealed, and diffusion coefficients were calculated. The wear resistance of the surface of 90CrSi steel increased by 5–9 times after anodic PET followed by polishing, by 16 times after cathodic PET, and up to 32 times after subsequent polishing. It is shown that in all cases, the violation of frictional bonds occurs through the plastic displacement of the material, and the wear mechanism is fatigue wear during dry friction and plastic contact. Full article
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<p>PET installation scheme: 1—linear drive; 2—workpiece-counter electrode (anode or cathode); 3—cylindrical electrolytic cell-electrode (cathode or anode depending on the processing option); 4—flow meter; 5—valve with electric drive; 6—heat exchanger; 7—pump; 8—container with fluoroplastic electric heater. The arrows indicate the direction of movement of cooling water into and out of the heat exchanger.</p>
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<p>Depiction of anodic and cathodic PET and PEP processes.</p>
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<p>Friction scheme.</p>
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<p>Scheme of a rough surface.</p>
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<p>X-ray diffraction pattern of the 90CrSi steel surface after APEN (<b>a</b>), as well as subsequent PEP in a solution of ammonium chloride with the addition of glycerol for 1 min (<b>b</b>) and 2 min (<b>c</b>) and in ammonium sulfate solution for 1 min (<b>d</b>) and 2 min (<b>e</b>).</p>
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<p>X-ray diffraction pattern of the 90CrSi steel surface after CPEN (<b>a</b>), as well as subsequent PEP in a solution of ammonium chloride with the addition of glycerol for 1 min (<b>b</b>) and 2 min (<b>c</b>) and in ammonium sulfate solution for 1 min (<b>d</b>) and 2 min (<b>e</b>).</p>
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<p>SEM image of the cross-section of the surface layer of 90CrSi steel after APEN followed by PEP in solutions of ammonium chloride with the addition of glycerol (ChG) and ammonium sulfate (S) for 1 and 2 min: 1—oxide layer, 2—modified layer. The SEM image shows scanning areas during EDX analysis in the form of horizontal stripes, which indicate the concentrations of detected elements (wt.%).</p>
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<p>SEM image of the cross-section of the surface layer of 90CrSi steel after CPEN followed by PEP in solutions of ammonium chloride with the addition of glycerol (ChG) and ammonium sulfate (S) for 1 and 2 min: 1—oxide layer, 2—modified layer. The SEM image shows scanning areas during EDX analysis in the form of horizontal stripes, which indicate the concentrations of detected elements (wt.%).</p>
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<p>Microstructure of the cross-section of the surface layer of 90CrSi steel after APEN (<b>a</b>) and CPEN (<b>b</b>): 1—oxide layer, 2—nitride layer, 3—outer hardened layer, 4—inner hardened layer.</p>
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<p>Microhardness distribution in the surface layer of 90CrSi steel after APEN (<b>a</b>) and CPEN (<b>b</b>) followed by PEP in solutions of ammonium chloride with the addition of glycerol (ChG) and ammonium sulfate (S) for 1 and 2 min.</p>
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<p>Morphology of the 9CrSi steel surface before (<b>a</b>) and after (<b>b</b>) APEN, as well as subsequent PEP in an ammonium chloride solution with the addition of glycerin for 1 min (<b>c</b>) and 2 min (<b>d</b>) and in ammonium sulfate solution for 1 min (<b>e</b>) and 2 min (<b>f</b>).</p>
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<p>Morphology of the 9CrSi steel surface before (<b>a</b>) and after (<b>b</b>) CPEN, as well as subsequent PEP in an ammonium chloride solution with the addition of glycerin for 1 min (<b>c</b>) and 2 min (<b>d</b>) and in ammonium sulfate solution for 1 min (<b>e</b>) and 2 min (<b>f</b>).</p>
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<p>Morphology of the 9CrSi steel surface before (<b>a</b>) and after (<b>b</b>) CPEN, as well as subsequent PEP in an ammonium chloride solution with the addition of glycerin for 1 min (<b>c</b>) and 2 min (<b>d</b>) and in ammonium sulfate solution for 1 min (<b>e</b>) and 2 min (<b>f</b>).</p>
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<p>Dependence of the friction coefficient of samples made of 90CrSi steel after APEN (<b>a</b>) and CPEN (<b>b</b>) followed by PEP in solutions of ammonium chloride with the addition of glycerol (ChG) and ammonium sulfate (S) for 1 and 2 min from the sliding distance.</p>
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<p>Morphology of friction tracks on the 90CrSi steel surface before (<b>a</b>) and after (<b>b</b>) APEN, as well as subsequent PEP in an ammonium chloride solution with the addition of glycerol for 1 min (<b>c</b>) and 2 min (<b>d</b>) and in a solution of ammonium sulfate C for 1 min (<b>e</b>) and 2 min (<b>f</b>).</p>
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<p>Morphology of friction tracks on the 90CrSi steel surface before (<b>a</b>) and after (<b>b</b>) CPEN, as well as subsequent PEP in an ammonium chloride solution with the addition of glycerol for 1 min (<b>c</b>) and 2 min (<b>d</b>) and in a solution of ammonium sulfate C for 1 min (<b>e</b>) and 2 min (<b>f</b>).</p>
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22 pages, 18451 KiB  
Article
Discharge Electrode Degradation in Dry Electrostatic Precipitator Cleaning of Exhaust Gases from Industrial Solid Waste Incinerators
by Tadeusz Czech, Artur Marchewicz, Arkadiusz Tomasz Sobczyk, Andrzej Krupa, Maria Gazda and Anatol Jaworek
Appl. Sci. 2024, 14(17), 7616; https://doi.org/10.3390/app14177616 - 28 Aug 2024
Viewed by 585
Abstract
The electrodes of industrial electrostatic precipitators degrade as a result of two phenomena: corrosion and erosion. The first is chemical degradation by highly reactive compounds formed during combustion, in particular, during the incineration of municipal or industrial wastes or high-sulfur coal. The degradation [...] Read more.
The electrodes of industrial electrostatic precipitators degrade as a result of two phenomena: corrosion and erosion. The first is chemical degradation by highly reactive compounds formed during combustion, in particular, during the incineration of municipal or industrial wastes or high-sulfur coal. The degradation intensity of electrostatic precipitator electrodes depends on the chemical composition of the exhaust gasses. High concentrations of chlorides, fluorides, or sulfur in the exhaust gasses cause strong corrosion of the electrostatic precipitator elements. The second mechanism is the erosion caused by solid particles conveyed by the exhaust gas stream due to their collision with the electrodes. In this study, the analysis of the degradation of electrodes of an electrostatic precipitator downstream of an industrial waste incinerator was carried out. The industrial wastes of unknown sources were subjected to thermal degradation in a rotary kiln. The aim of this study was to provide fundamental knowledge about the mechanisms of electrode degradation located on the surface of discharge electrodes of electrostatic precipitators during the combustion of industrial wastes. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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<p>Sketches of the discharge electrode of an electrostatic precipitator (<b>a</b>), and a single pair of discharge blades degraded as a result of the action of dust, acid gasses, and electric discharges (<b>b</b>). Electrode material: austenitic chromium–nickel steel (cf. <a href="#applsci-14-07616-t001" class="html-table">Table 1</a>).</p>
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<p>A fragment of a sharp electrode with its tip deformed probably as a result of DC back-corona discharge. (Photo taken with Optical Digital Microscope Keyence VHX7000N, courtesy of the company Keyence (Osaka, Japan)).</p>
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<p>SEM micrograph of thermally deformed tip of blade electrode of electrostatic precipitator.</p>
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<p>SEM images of fly ash particles collected from discharge electrode: magnification ×25,000 (<b>a</b>) and magnification ×50,000 (<b>b</b>), and number and cumulative size distribution of fly ash particles (<b>c</b>). Resolution of particles size: 0.1 µm.</p>
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<p>EDS spectrum of fly ash collected from discharge electrode in electrostatic precipitator after combustion of industrial solid waste. Inset: investigated sample of ash.</p>
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<p>EDS spectrum of clean surface of discharge electrode material after cutting (inset: image of cross-section of test sample).</p>
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<p>SEM image of microdeformation of discharge electrode blade.</p>
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<p>SEM of electrode surface showing the initial process of corrosion as cracks. A crack is marked with a white circle.</p>
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<p>EDS spectrum of cracked discharge electrode material (inset: SEM image of analyzed sample).</p>
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<p>(<b>a</b>) Image of various forms of pitting on the surface of a degraded metal electrode; (<b>b</b>) 3D profile (photos taken under a 3D profilometer Keyence VR-6200; courtesy of Keyence (Japan)).</p>
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<p>SEM micrographs of cross-section of ESP discharge electrode (<b>left</b>) and EDS spectrum of selected places (<b>right</b>): (<b>a</b>) electrode fragment, cross-section of ESP discharge electrode (measurement area marked with circle 1); (<b>b</b>) close-up view of inner surface of pitting (scanning area marked with rectangle), inner surface of pitting in ESP discharge electrode.</p>
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<p>Fragment of discharge electrode (blade) with solid particles and damages (fissures and pitting) and peeling with detached element.</p>
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<p>SEM micrographs of open pits as a result of the degradation of electrode material due to corrosion processes (<b>a</b>), and scale at the edge of the pits (<b>b</b>).</p>
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<p>Scale mixed with the dust on the metal surface as a product of corrosion (<b>left</b>), and EDS spectrum of the scale on the surface of the discharge electrode (<b>right</b>).</p>
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<p>XRD diffractogram of scale and deposit of blade electrode.</p>
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<p>Variations in sulfur, iron, chromium, nickel, carbon, and oxygen content for the inlet dust and different electrode areas: ESP-deposited particulate fly ash on the discharge electrode, clean metal electrode surface, and degraded electrode material (scale) determined from the EDS spectrum.</p>
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<p>Variations in sulfur, iron, chromium, nickel, carbon, and oxygen content for the inlet dust and different electrode areas: ESP-deposited particulate fly ash on the discharge electrode, clean metal electrode surface, and degraded electrode material (scale) determined from the EDS spectrum.</p>
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11 pages, 4073 KiB  
Article
Rational Construction of Honeycomb-like Carbon Network-Encapsulated MoSe2 Nanocrystals as Bifunctional Catalysts for Highly Efficient Water Splitting
by Changjie Ou, Zhongkai Huang, Xiaoyu Yan, Xiangzhong Kong, Xi Chen, Shi Li, Lihua Wang and Zhongmin Wan
Molecules 2024, 29(16), 3877; https://doi.org/10.3390/molecules29163877 - 16 Aug 2024
Viewed by 698
Abstract
The scalable fabrication of cost-efficient bifunctional catalysts with enhanced hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) performance plays a significant role in overall water splitting in hydrogen production fields. MoSe2 is considered to be one of the most promising candidates [...] Read more.
The scalable fabrication of cost-efficient bifunctional catalysts with enhanced hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) performance plays a significant role in overall water splitting in hydrogen production fields. MoSe2 is considered to be one of the most promising candidates because of its low cost and high catalytic activity. Herein, hierarchical nitrogen-doped carbon networks were constructed to enhance the catalytic activity of the MoSe2-based materials by scalable free-drying combined with an in situ selenization strategy. The rationally designed carbonaceous network-encapsulated MoSe2 composite (MoSe2/NC) endows a continuous honeycomb-like structure. When utilized as a bifunctional electrocatalyst for both HER and OER, the MoSe2/NC electrode exhibits excellent electrochemical performance. Significantly, the MoSe2/NC‖MoSe2/NC cells require a mere 1.5 V to reach a current density of 10 mA cm−2 for overall water splitting in 1 M KOH. Ex situ characterizations and electrochemical kinetic analysis reveal that the superior catalytic performance of the MoSe2/NC composite is mainly attributed to fast electron and ion transportation and good structural stability, which is derived from the abundant active sites and excellent structural flexibility of the honeycomb-like carbon network. This work offers a promising pathway to the scalable fabrication of advanced non-noble bifunctional electrodes for highly efficient hydrogen evolution. Full article
(This article belongs to the Section Electrochemistry)
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<p>(<b>a</b>) Schematic illustration of the fabrication process of the honeycomb-like MoSe<sub>2</sub>/NC composite; (<b>b</b>) The advantages of MoSe<sub>2</sub>/NC.</p>
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<p>The morphologies and interior structures of the obtained samples. (<b>a</b>,<b>b</b>) SEM images. (<b>c</b>) TEM images. (<b>d</b>,<b>e</b>) HRTEM images. (<b>f</b>) The selected-area electron diffraction (SAED) patterns. (<b>g</b>–<b>k</b>) High-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) and elemental mapping images of the MoSe<sub>2</sub>/NC composite.</p>
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<p>(<b>a</b>) XRD patterns. (<b>b</b>) Raman spectra. (<b>c</b>) Adsorption and desorption isotherm curves. (<b>d</b>) Pore size distribution curves of the MoSe<sub>2</sub>/NC, the MoSe<sub>2</sub>/C, and the commercial MoSe<sub>2</sub> composites.</p>
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<p>Electrochemical properties of the MoSe<sub>2</sub>/NC, the MoSe<sub>2</sub>/C, the commercial MoSe<sub>2</sub> and the pure nickel foam for HER. (<b>a</b>) LSV curves. (<b>b</b>) Corresponding Tafel plots. (<b>c</b>) Comparison diagram of LSV and Tafel. (<b>d</b>) Nyquist plots. (<b>e</b>) Calculated Cdl of the obtained samples in 1 M KOH aqueous solution. (<b>f</b>) Electrochemical stability of the MoSe<sub>2</sub>/NC electrode at different current densities for 30 h (Inset: LSV curves of the MoSe<sub>2</sub>/NC before and after stability measurement).</p>
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<p>Electrochemical properties of the MoSe<sub>2</sub>/NC, the MoSe<sub>2</sub>/C and the MoSe<sub>2</sub> for OER. (<b>a</b>) LSV curves. (<b>b</b>) Corresponding Tafel plots. (<b>c</b>) Comparison diagram of LSV and Tafel. (<b>d</b>) Nyquist plots of the obtained three samples. (<b>e</b>) Electrochemical stability of the MoSe<sub>2</sub>/NC after 10 h test at constant point. (Inset: LSV curves of the MoSe<sub>2</sub>/NC before and after stability tests.) (<b>f</b>) Comparison of the overpotentials between our work and previous studies [<a href="#B36-molecules-29-03877" class="html-bibr">36</a>,<a href="#B37-molecules-29-03877" class="html-bibr">37</a>,<a href="#B38-molecules-29-03877" class="html-bibr">38</a>,<a href="#B39-molecules-29-03877" class="html-bibr">39</a>,<a href="#B40-molecules-29-03877" class="html-bibr">40</a>].</p>
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<p>The electrochemical performance of the MoSe<sub>2</sub>/NC‖MoSe<sub>2</sub>/NC cell during overall water splitting. (<b>a</b>) Schematic diagram of the MoSe<sub>2</sub>/NC‖MoSe<sub>2</sub>/NC electrolyzer. (<b>b</b>) LSV curves of the MoSe<sub>2</sub>/NC‖MoSe<sub>2</sub>/NC cell. (Inset: a camera picture of the electrode during water splitting.) (<b>c</b>) Stability test of the MoSe<sub>2</sub>/NC‖MoSe<sub>2</sub>/NC cell. (<b>d</b>) The comparison of the cell voltage for our electrolyzer with previous reports [<a href="#B43-molecules-29-03877" class="html-bibr">43</a>,<a href="#B44-molecules-29-03877" class="html-bibr">44</a>,<a href="#B45-molecules-29-03877" class="html-bibr">45</a>,<a href="#B46-molecules-29-03877" class="html-bibr">46</a>,<a href="#B47-molecules-29-03877" class="html-bibr">47</a>,<a href="#B48-molecules-29-03877" class="html-bibr">48</a>,<a href="#B49-molecules-29-03877" class="html-bibr">49</a>].</p>
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12 pages, 2994 KiB  
Article
On the Thermal Stability of Selected Electrode Materials and Electrolytes for Na-Ion Batteries
by Ruslan R. Samigullin, Zoya V. Bobyleva, Maxim V. Zakharkin, Emiliya V. Zharikova, Marina G. Rozova, Oleg A. Drozhzhin and Evgeny V. Antipov
Energies 2024, 17(16), 3970; https://doi.org/10.3390/en17163970 - 10 Aug 2024
Viewed by 1046
Abstract
Sodium-ion batteries are a technology rapidly approaching widespread adoption, so studying the thermal stability and safety of their components is a pressing issue. In this work, we employed differential scanning calorimetry (DSC) and ex situ powder X-ray diffraction to study the thermal stability [...] Read more.
Sodium-ion batteries are a technology rapidly approaching widespread adoption, so studying the thermal stability and safety of their components is a pressing issue. In this work, we employed differential scanning calorimetry (DSC) and ex situ powder X-ray diffraction to study the thermal stability of several types of sodium-ion electrolytes (NaClO4 and NaPF6 solutions in PC, EC, DEC, and their mixtures) and various cathode and anode materials (Na3V2(PO4)3, Na3(VO)2(PO4)2F, β-NaVP2O7, and hard carbon) in combination with electrolytes. The obtained results indicate, first, the satisfactory thermal stability of liquid Na-ion electrolytes, which start to decompose only at 270~300 °C. Second, we observed that charged vanadium-based polyanionic cathodes, which appear to be very stable in the “dry” state, demonstrate an increase in decomposition enthalpy and a shift of the DSC peaks to lower temperatures when in contact with 1 M NaPF6 in the EC:DEC solution. However, the greatest thermal effect from the “electrode–electrolyte” interaction is demonstrated by the anode material: the heat of decomposition of the soaked electrode in the charged state is almost 40% higher than the sum of the decomposition enthalpies of the electrolyte and dry electrode separately. Full article
(This article belongs to the Topic Electrochemical Energy Storage Materials)
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<p>DSC curves for different electrolyte solutions: (<b>a</b>) 1M NaPF<sub>6</sub> in EC:DEC, EC:PC, and PC; (<b>b</b>) 1M, 2M, and 3M NaPF<sub>6</sub> in EC:DEC; and (<b>c</b>) 1M NaPF<sub>6</sub> and NaClO<sub>4</sub> in PC.</p>
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<p>SEM images and charge–discharge curves for the electrode samples: Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>3</sub> (<b>a</b>), Na<sub>3</sub>(VO)<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F (<b>b</b>), <span class="html-italic">β</span>–NaVP<sub>2</sub>O<sub>7</sub> (<b>c</b>), and hard carbon (<b>d</b>). Points on the curve where the electrodes were tested are marked with an asterisk.</p>
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<p>DSC curves (red line) for the charged electrode materials soaked in electrolyte: Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>3</sub> (<b>a</b>), Na<sub>3</sub>(VO)<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F (<b>b</b>), <span class="html-italic">β</span>–NaVP<sub>2</sub>O<sub>7</sub> (<b>c</b>), and hard carbon (<b>d</b>). For comparison, DSC curves for the dried electrodes (dot line) and 1M NaPF<sub>6</sub> in EC:DEC electrolyte (blue line) are also plotted.</p>
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<p>Schematic representation of the data on the enthalpy of dried [<a href="#B20-energies-17-03970" class="html-bibr">20</a>] and soaked electrodes.</p>
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<p>Results of ex situ PXRD for the charged electrodes soaked in 1M NaPF<sub>6</sub> in EC:DEC electrolyte before and after DSC experiments: Na<sub>3</sub>V<sub>2</sub>(PO<sub>4</sub>)<sub>3</sub> (<b>a</b>), Na<sub>3</sub>(VO)<sub>2</sub>(PO<sub>4</sub>)<sub>2</sub>F (<b>b</b>), <span class="html-italic">β</span>–NaVP<sub>2</sub>O<sub>7</sub> (<b>c</b>), and hard carbon (<b>d</b>).</p>
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<p>DSC curves (red line) for the charged Li-ion electrode materials soaked in electrolyte: LiFePO<sub>4</sub> (<b>a</b>) and graphite (<b>b</b>). For comparison, DSC curves for the dried electrodes (dot line) and 1M LiPF<sub>6</sub> in EC:DEC:DMC electrolyte (blue line) are also plotted.</p>
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19 pages, 2186 KiB  
Review
Recent Advances in Carbon Nanotube Technology: Bridging the Gap from Fundamental Science to Wide Applications
by Zhizhi Tao, Yuqiong Zhao, Ying Wang and Guojie Zhang
C 2024, 10(3), 69; https://doi.org/10.3390/c10030069 - 6 Aug 2024
Cited by 1 | Viewed by 3050
Abstract
Carbon nanotubes, as carbon allotropes distinguished by their intricate structures and exceptional physicochemical properties, have demonstrated substantial progress in recent years across diverse domains, including energy production, chemical synthesis, and environmental preservation. They exhibit notable attributes such as high thermal stability, superior adsorption [...] Read more.
Carbon nanotubes, as carbon allotropes distinguished by their intricate structures and exceptional physicochemical properties, have demonstrated substantial progress in recent years across diverse domains, including energy production, chemical synthesis, and environmental preservation. They exhibit notable attributes such as high thermal stability, superior adsorption capacity, and a substantial specific surface area, rendering them superb catalyst supports. Particularly in electrochemical energy storage, CNTs are extensively employed in supercapacitor electrodes owing to their elevated electrical conductivity, mechanical robustness, and electrocatalytic prowess, which facilitate significant energy storage capabilities. Their intricate pore architecture and reactive sites make functionalized carbon nanotubes well suited for synthesizing composite materials with diverse components, which are ideal for sequestering carbon dioxide from both atmospheric and indoor environments. This review presents a comprehensive examination of carbon nanotube synthesis methodologies, encompassing chemical vapor deposition, arc discharge, and laser ablation, and evaluates their impacts on the structural and functional properties of carbon nanotubes. Furthermore, this article underscores the applications of carbon nanotubes in fields such as fuel cells, photocatalysis, ammonia synthesis, dry methane reforming, Fischer–Tropsch synthesis, and supercapacitors. Despite the considerable potential of carbon nanotubes, their manufacturing processes remain intricate and costly, impeding large-scale industrial production. This review concludes by addressing the challenges in fabricating carbon nanotube composites and outlining future development prospects. Full article
(This article belongs to the Collection Novel Applications of Carbon Nanotube-Based Materials)
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<p>Schematic diagram of ion sputtering-assisted chemical vapor deposition method [<a href="#B26-carbon-10-00069" class="html-bibr">26</a>].</p>
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<p>Schematic diagram of FeNi-NCNT/DrGO synthesis [<a href="#B43-carbon-10-00069" class="html-bibr">43</a>].</p>
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<p>A schematic diagram of the preparation of an artificial vesicular structure photocatalyst [<a href="#B47-carbon-10-00069" class="html-bibr">47</a>].</p>
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<p>Schematic diagram of Mo/BCN-catalyzed ammonia synthesis mechanism [<a href="#B51-carbon-10-00069" class="html-bibr">51</a>].</p>
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<p>A schematic diagram of the modified light-Fenton method [<a href="#B59-carbon-10-00069" class="html-bibr">59</a>].</p>
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<p>FTIR spectra of MWCNTs, f-MWCNTs, CS, and CS-grafted MWCNTs [<a href="#B82-carbon-10-00069" class="html-bibr">82</a>]. Notes: Cs: chitosan.</p>
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16 pages, 5278 KiB  
Article
High-Performance Supercapacitors Using Compact Carbon Hydrogels Derived from Polybenzoxazine
by Shakila Parveen Asrafali, Thirukumaran Periyasamy and Jaewoong Lee
Gels 2024, 10(8), 509; https://doi.org/10.3390/gels10080509 - 2 Aug 2024
Viewed by 660
Abstract
Polybenzoxazine (PBz) aerogels hold immense potential, but their conventional production methods raise environmental and safety concerns. This research addresses this gap by proposing an eco-friendly approach for synthesizing high-performance carbon derived from polybenzoxazine. The key innovation lies in using eugenol, ethylene diamine, and [...] Read more.
Polybenzoxazine (PBz) aerogels hold immense potential, but their conventional production methods raise environmental and safety concerns. This research addresses this gap by proposing an eco-friendly approach for synthesizing high-performance carbon derived from polybenzoxazine. The key innovation lies in using eugenol, ethylene diamine, and formaldehyde to create a polybenzoxazine precursor. This eliminates hazardous solvents by employing the safer dimethyl sulfoxide. An acidic catalyst plays a crucial role, not only in influencing the microstructure but also in strengthening the material’s backbone by promoting inter-chain connections. Notably, this method allows for ambient pressure drying, further enhancing its sustainability. The polybenzoxazine acts as a precursor to produce two different carbon materials. The carbon material produced from the calcination of PBz is denoted as PBZC, and the carbon material produced from the gelation and calcination of PBz is denoted as PBZGC. The structural characterization of these carbon materials was analyzed through different techniques, such as XRD, Raman, XPS, and BET analyses. BET analysis showed increased surface of 843 m2 g−1 for the carbon derived from the gelation method (PBZGC). The electrochemical studies of PBZC and PBZGC imply that a well-defined morphology, along with suitable porosity, paves the way for increased conductivity of the materials when used as electrodes for supercapacitors. This research paves the way for utilizing heteroatom-doped, polybenzoxazine aerogel-derived carbon as a sustainable and high-performing alternative to traditional carbon materials in energy storage devices. Full article
(This article belongs to the Special Issue Gel Materials in Advanced Energy Systems)
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<p>(<b>a</b>) FT-IR; (<b>b</b>) <sup>1</sup>H-NMR; (<b>c</b>) <sup>13</sup>C-NMR; and (<b>d</b>) DSC thermogram of EEd-Bzo monomer.</p>
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<p>Structural characterization of the carbon materials (<b>a</b>) Raman; (<b>b</b>) XRD; (<b>c</b>) BET; and (<b>d</b>) pore-size distribution.</p>
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<p>XPS survey spectra of PBZC and PBZGC.</p>
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<p>De-convoluted XPS spectra of PBZC and PBZGC (<b>a</b>) C 1s; (<b>b</b>) N 1s; and (<b>c</b>) O 1s.</p>
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<p>FE-SEM images of (<b>a</b>–<b>c</b>) PBZC; and (<b>d</b>–<b>f</b>) PBZGC at different magnifications.</p>
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<p>FE-TEM images of (<b>a</b>–<b>c</b>) PBZGC at different magnifications; and (<b>d</b>) SAED pattern.</p>
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<p>Electrochemical characterizations of PBZC: (<b>a</b>) CV graphs; (<b>b</b>) GCD curves; (<b>c</b>) specific capacitance; and (<b>d</b>) EIS spectrum.</p>
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<p>Electrochemical characterizations of PBZGC: (<b>a</b>) CV graphs; (<b>b</b>) GCD curves; (<b>c</b>) specific capacitance; and (<b>d</b>) EIS spectrum.</p>
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<p>Comparison of electrochemical results of PBZC and PBZGC: (<b>a</b>) CV graphs; (<b>b</b>) GCD curves; (<b>c</b>) specific capacitance; and (<b>d</b>) EIS spectrum.</p>
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<p>Cyclic stability of PBZGC showing capacitance retention and coulombic efficiency for 5000 cycles.</p>
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<p>Schematic illustration showing the preparation process of EEd-Bzo monomer (Step i) and PBz-based carbon aerogel (Step ii).</p>
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