Channel Electrode Research Articles

Adaptive Spatiotemporal Graph Convolutional Networks for Motor Imagery Classification

Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in brain computer interfaces (BCI). In view of the characteristics of non-stationarity, time-variability and individual diversity of EEG signals, a novel framework based on graph neural network is proposed for MI-EEG classification. First, an adaptive graph convolutional layer (AGCL) is constructed, by which the electrode channel information are integrated dynamically. We further propose an adaptive spatiotemporal graph convolutional network (ASTGCN), which fully exploits the characteristics of EEG signals in time domain and the channel correlations in spatial domain simultaneously. We execute the experiments using EEG signals recorded at motor imagery scenarios, where twenty-five healthy subjects performed MI movements of the right hand and feet to generate motor commands. Experimental results reveal that the proposed method outperforms state-of-the-art methods in terms of both classification quality and robustness. The advantages of ASTGCN include high accuracy, high efficiency, and robustness to cross-trial and cross-subject variations, making it an ideal candidate for long-term MI-EEG applications.

Optimizing Seizure Prediction From Reduced Scalp EEG Channels Based on Spectral Features and MAML

Epilepsy is a severe neurological disease with high prevalence and morbidity worldwide. The unpredictability of seizures prevents physicians from tailoring drugs and therapies. Recent non-invasive seizure prediction research has not improved the overall quality of life for patients. Therefore, new research studies on seizure prediction must integrate data, embedded devices, and algorithms. For a seizure prediction system to emerge as a feasible solution, we must address a reduction in EEG scalp electrode channels, along with a decrease in computational resources to train the time-series signal. In this work, we propose an optimized patient-specific channel reduction for seizure prediction using Model Agnostic Meta-Learning (MAML) applied to a Deep Neural Network (DNN). We selected and optimized the number of channels from each of the 23 subjects of the CHB-MIT Dataset. The feature vectors are extracted using Ensemble Empirical Mode Decomposition (EEMD) and Sequential Feature Selection (SFS). We implemented the MAML model to classify the small EEG data generated from the reduced number of subject-dependent electrodes. The experiment results yield an average sensitivity and specificity of 91% and 90%, respectively. Our study demonstrates that MAML is a promising approach to learn EEG patterns to predict epileptic seizures with few EEG scalp electrodes.

Robust Subject-Independent P300 Waveform Classification via Signal Pre-Processing and Deep Learning

Brain Computer Interfaces (BCIs) are capable of processing neural stimuli using electroencephalogram (EEG) measurements to aid communication capabilities. Yet, BCIs often require extensive calibration steps in order to be tuned to specific users. In this work, we develop a subject independent P300 classification framework, which eliminates the need for user-specific calibration. We begin by employing a series of pre-processing steps, where, among other steps, we consider different trial averaging methodologies and various EEG electrode configurations. We then consider three distinct deep learning architectures and two linear machine learning models as P300 signal classifiers. Through evaluation on three datasets, and in comparison to three benchmark P300 classification frameworks, we find that averaging up to seven trials while using eight specific electrode channels on a two-layered convolutional neural network (CNN) leads to robust subject independent P300 classification. In this capacity, our method achieves greater than a 0.20 gain in AUC in comparison to prior P300 classification methods. In addition, our proposed framework is computationally efficient with training time gains of greater than 3×, compared to linear machine learning models, and online evaluation time speedups of up to 2× compared to benchmark methods.

Classification of forearm EMG signals for 10 motions using optimum feature-channel combinations

Electromyography (EMG) is the study of electrical activity in the muscles. We classify EMG signals from surface electrodes (channels) using Artificial Neural Network (ANN). We evaluate classification performance of 10 different hand motions using several feature-channel combinations with wrapper method. Highest classification accuracy of 98.7% is achieved with each feature-channel combination. Compared to previous studies, we achieve the highest accuracy for 10 classes with lower number of feature-channel combination. We reduce ANN complexity without compromising the classification accuracy for deployment in low-end hardware with limited computational power along with improving the design of a low-cost hardware for EMG signal acquisition.

Microfluidics integrated n-type organic electrochemical transistor for metabolite sensing

The organic electrochemical transistor (OECT) can translate biochemical binding events between a recognition unit and its analyte into an electrical signal. We present an OECT comprising an n-type (electron transporting) conjugated polymer-based channel and lateral gate electrode functionalized with the enzyme, glucose oxidase. The device is integrated with a microfluidic system for real-time glucose monitoring in a flow-through manner. The n-type polymer has direct electrical communication with glucose oxidase, allowing glucose detection while surpassing hydrogen peroxide production. The microfluidic-integrated OECT shows superior features compared to its microfluidic-free counterpart, including higher current and transconductance values as well as improved signal-to-noise (SNR) ratios, which enhances the sensor sensitivity and its detection limit. Thanks to the low noise endowed by the integrated microfluidics, the gate current changes upon metabolite recognition could be resolved, revealing that while the relative changes in gate and drain currents are similar, the drain current output has a higher SNR. This is the first demonstration of the integration of a microfluidic system with an n-type accumulation mode OECT for real-time enzymatic metabolite detection. The microfluidic-integrated design provides new insights into the mechanisms leading to high sensor sensitivities, crucial for the development of portable and autonomous lab-on-a-chip technologies.

FML-Based Reinforcement Learning Agent with Fuzzy Ontology for Human-Robot Cooperative Edutainment

The currently observed developments in Artificial Intelligence (AI) and its influence on different types of industries mean that human-robot cooperation is of special importance. Various types of robots have been applied to the so-called field of Edutainment, i.e., the field that combines education with entertainment. This paper introduces a novel fuzzy-based system for a human-robot cooperative Edutainment. This co-learning system includes a brain-computer interface (BCI) ontology model and a Fuzzy Markup Language (FML)-based Reinforcement Learning Agent (FRL-Agent). The proposed FRL-Agent is composed of (1) a human learning agent, (2) a robotic teaching agent, (3) a Bayesian estimation agent, (4) a robotic BCI agent, (5) a fuzzy machine learning agent, and (6) a fuzzy BCI ontology. In order to verify the effectiveness of the proposed system, the FRL-Agent is used as a robot teacher in a number of elementary schools, junior high schools, and at a university to allow robot teachers and students to learn together in the classroom. The participated students use handheld devices to indirectly or directly interact with the robot teachers to learn English. Additionally, a number of university students wear a commercial EEG device with eight electrode channels to learn English and listen to music. In the experiments, the robotic BCI agent analyzes the collected signals from the EEG device and transforms them into five physiological indices when the students are learning or listening. The Bayesian estimation agent and fuzzy machine learning agent optimize the parameters of the FRL agent and store them in the fuzzy BCI ontology. The experimental results show that the robot teachers motivate students to learn and stimulate their progress. The fuzzy machine learning agent is able to predict the five physiological indices based on the eight-channel EEG data and the trained model. In addition, we also train the model to predict the other students’ feelings based on the analyzed physiological indices and labeled feelings. The FRL agent is able to provide personalized learning content based on the developed human and robot cooperative edutainment approaches. To our knowledge, the FRL agent has not applied to the teaching fields such as elementary schools before and it opens up a promising new line of research in human and robot co-learning. In the future, we hope the FRL agent will solve such an existing problem in the classroom that the high-performing students feel the learning contents are too simple to motivate their learning or the low-performing students are unable to keep up with the learning progress to choose to give up learning.

(Invited) Ion Tunable Electronic Materials Systems for Neuromorphic Computing

Tuning electronic conductance through solid state electrochemical ion insertion has emerged as a promising technology to enable next-generation, ultralow energy computing architectures1-3. Unlike two-terminal non-volatile memory elements, the three-terminal redox transistor decouples the ‘write’ and ‘read’ operations using a ‘gate’ electrode to tune the conductance state through charge transfer reactions involving ion injection into the channel electrode through a solid-state electrolyte. The insertion of ions into the bulk of the channel acts to dope the material through a gradual composition modulation that leads up to thousands of finely spaced conductance levels (synaptic weights) with near-ideal analog behavior. These properties enable low-energy operation without compromising analog performance and non-volatility. However, the strong coupling of ionic and electronic processes sharply challenges our current understanding of solid-state electrochemical systems, particularly at decreasing dimensions and timescales relevant to computing technology. In my talk I will discuss the rich portfolio of challenging, exciting fundamental science questions about ion tunable electronic materials systems and how we can harness these to realize a new paradigm for low power neuromorphic computing. Fuller, E. J.; El Gabaly, F.; Leonard, F.; Agarwal, S.; Plimpton, S. J.; Jacobs-Gedrim, R. B.; James, C. D.; Marinella, M. J.; Talin, A. A., Li-Ion Synaptic Transistor for Low Power Analog Computing. Advanced Materials 2017, 29.Fuller, E. J.; Keene, S. T.; Melianas, A.; Wang, Z. R.; Agarwal, S.; Li, Y. Y.; Tuchman, Y.; James, C. D.; Marinella, M. J.; Yang, J. J.; Salleo, A.; Talin, A. A., Parallel programming of an ionic floating-gate memory array for scalable neuromorphic computing. Science 2019, 364 (6440), 570.Li, Y. Y.; Fuller, E. J.; Asapu, S.; Agarwal, S.; Kurita, T.; Yang, J. J.; Talin, A. A., Low-Voltage, CMOS-Free Synaptic Memory Based on LixTiO2 Redox Transistors. Acs Applied Materials & Interfaces 2019, 11 , 38982.

Microfluidic Devices Using Electrochemical Micropumps and Microvalves for Autonomous Solution Processing

In realizing sophisticated micro analytical devices, controlled solution transport in microfluidic channels is indispensable. The procedures have predominantly been conducted using external pumps (or pressure source) and valves. However, because of this, the entire setups become very bulky, although the chips may be small. If the control of solution transport is realized using integrated microfluidic components, it will accelerate automation and realization of user-friendly devices. Apart from chemical analysis, realization of artificial lives that move by direct conversion of chemical energy to mechanical energy is becoming a hot topic. Independent autonomous microfluidic systems that can control chemical reactions in a coordinated manner can also be critical components in these devices. In this study, we attempted to realize such autonomous microfluidic devices by integrating simple switchable hydrophobic microvalves that employ a conducting polymer and pressure changes by electrochemical production and shrinkage of hydrogen bubbles.Doped polypyrrole (PPy) was used for the valve. A platinum electrode was formed on a glass substrate, and PPy was grown on the electrode. For this purpose, the electrode was immersed in a solution containing 0.2 M pyrrole and 0.2 M sodium dodecylbenzenesulfonate (NaDBS), and electropolymerization was carried out at a constant current of 10 μA. In as-deposited PPy, the alkyl chains of NaDBS stand upward and the surface is hydrophobic. When PPy is reduced, polar groups come to the surface and becomes hydrophilic.First, a two-channel test device with a control flow channel and a main flow channel was fabricated (Figure 1). The valve was formed in the main flow channel and a zinc electrode was formed in the control flow channel. The electrodes were connected each other. Also, solutions in the solution reservoirs of the two flow channels were connected with an iridium wire with oxides on both ends as an alternative for a liquid junction. When a solution was injected into the main flow channel, it stopped at the valve. When another solution was injected into the control channel and wetted the zinc electrode, PPy on the valve was reduced. As a result, and the valve opened.As the next step, a three-channel device was fabricated (Figure 2). Interdigitated platinum electrodes were formed in flow channel 2 in the figure. One of them was connected to a zinc electrode in flow channel 1 and the other was connected to a platinum electrode in flow channel 3. Solutions in the reservoirs of the flow channels were connected with Ag/AgCl wires. As a preliminary experiment, a pH indicator (thymol blue) was first injected into flow channel 2. When a KCl solution was injected into flow channel 1, the color of the area around the interdigitated electrodes of flow channel 2 changed to blue by the electrolysis of water. When a 0.1 M AgNO3 solution was injected into flow channel 3, the color changed to yellow again because of the oxidization of hydrogen bubbles.Another device with the same basic structure was used to demonstrate autonomous bidirectional transport of a solution (Figure 3). In addition to the structures shown in Figure 2, the PPy valve used in the device shown in Figure 1 was formed in flow channel 3 to control the injection of the solution in the reservoir. The valve was connected to a zinc electrode formed at the end of flow channel 2. Furthermore, solution reservoirs were closed with a polyimide tape. Following the same procedure for the previous device, hydrogen bubbles were produced on one of the interdigitate electrodes in flow channel 2 by the reduction of protons. When the solution in flow channel 2 reached the zinc electrode at the end, the valve in flow channel 3 opened and a AgNO3 solution flowed through the platinum electrode. Following this, Ag+ ions were reduced to silver there and the hydrogen bubbles in flow channel 2 were oxidized on one of the interdigitate electrodes. As a result, the bubbles shrank and the solution in the flow channel moved backward.For the device shown in Figure 3, the injection of the solution in flow channel 1 can be carried out by forming an additional valve at the solution reservoir and open it using another zinc electrode in another control flow channel. Many units like the one shown in Figure 3 can be connected to the same control flow channel and operated sequentially. The technique will enable autonomous multiplexed and/or sequential processing of solutions within microfabricated analytical devices and autonomous actuation of soft robots. Figure 1

Progress in Scaling up and Streamlining a Nanoconfined, Enzyme-Catalyzed Electrochemical Nicotinamide Recycling System for Biocatalytic Synthesis.

An electrochemically driven nicotinamide recycling system, referred to as the ‘electrochemical leaf’ has unique attributes that may suit it to the small‐scale industrial synthesis of high‐value chemicals. A complete enzyme cascade can be immobilized within the channels of a nanoporous electrode, allowing complex reactions to be energized, controlled and monitored continuously in real time. The electrode is easily prepared by depositing commercially available indium tin oxide (ITO) nanoparticles on a Ti support, resulting in a network of nanopores into which enzymes enter and bind. One of the enzymes is the photosynthetic flavoenzyme, ferredoxin NADP+ reductase (FNR), which catalyzes the quasi‐reversible electrochemical recycling of NADP(H) and serves as the transducer. The second enzyme is any NADP(H)‐dependent dehydrogenase of choice, and further enzymes can be added to build elaborate cascades that are driven in either oxidation or reduction directions through the rapid recycling of NADP(H) within the pores. In this Article, we describe the measurement of key enzyme/cofactor parameters and an essentially linear scale‐up from an analytical scale 4 mL reactor with a 14 cm2 electrode to a 500 mL reactor with a 500 cm2 electrode. We discuss the advantages (energization, continuous monitoring that can be linked to a computer, natural enzyme immobilization, low costs of electrodes and low cofactor requirements) and challenges to be addressed (optimizing minimal use of enzyme applied to the electrode).

EEG analysis of mathematical cognitive function and startle response using single channel electrode

Electroencephalographic (EEG) signals are non-invasive means of measuring brain functions. EEG has been used in areas ranging from analysis of neurological disorders, emotional states and sleep pattern to Brain Machine Interface. Here we study cognitive functions from a limited set of daily activities of normal human subjects. For this purpose, we utilize EEG signal obtained from prefrontal cortex, specifically Brodmann Area 10L, which is known to play an important role in these functions. In this work, we study characteristics of a task requiring focussed attention (Mathematical Cognition (T3)), an involuntary response to external stimuli (Startle Response (T2)) and the state of rest (Relax (T1)). The single channel EEG is preprocessed and statistical features are extracted from (i) preprocessed data, (ii) wavelet decomposition, and (iii) cepstral analysis. These features are then separately input to various classifiers. Results are reported for 2 class (T1 vs T2, T1 vs T3, T2 vs T3) and 3 class (T1 vs T2 vs T3) classification framework. Experimental results reveal that cepstral analysis is most effective for classification across both frameworks. In 3-class classification, cepstral analysis results in a mean accuracy of 96.61% across classifiers. This framework is shown to be effective in classifying the chosen set of cognitive states using EEG and can be extended to broader classes for more conclusive inferences. In addition, gender differences peak at 81.7%, 77.09% and 77.04% for T1, T2 and T3 respectively, indicating that there are significant differences between the genders, although performing the same cognitive task.

Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG.

Deep learning has achieved great success in areas such as computer vision and natural language processing. In the past, some work used convolutional networks to process EEG signals and reached or exceeded traditional machine learning methods. We propose a novel network structure and call it QNet. It contains a newly designed attention module: 3D-AM, which is used to learn the attention weights of EEG channels, time points, and feature maps. It provides a way to automatically learn the electrode and time selection. QNet uses a dual branch structure to fuse bilinear vectors for classification. It performs four, three, and two classes on the EEG Motor Movement/Imagery Dataset. The average cross-validation accuracy of 65.82%, 74.75%, and 82.88% was obtained, which are 7.24%, 4.93%, and 2.45% outperforms than the state-of-the-art, respectively. The article also visualizes the attention weights learned by QNet and shows its possible application for electrode channel selection.

Kendali Arah pada Brain Computer Interface Berbasis Steady State Visual Evoked Potentials

Various studies regarding to Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) system with Electroencephalogram (EEG) signal has developed as BCI implementation on directional control, however lackness found on those studies which are long time on classification duration, to many electrode channels used and the electrode channels located on special area. This study we developed the SSVEP based BCI system with one second classification duration, four active channels used and electrode channels located based on The International 10-20 System. Stimulus used are red colored object with 11 Hz frequency rate represents as left directional control class, blue colored object with 13 Hz frequency rate represents as right directional control class and white colored background represents as relax class. Filter bank with eight frequency range (11 Hz, 22 Hz, 33 Hz, 13 Hz, 26 Hz, 39 Hz, 12-29 Hz dan 30-50 Hz) followed by Root Mean Square (RMS) used as feature extraction for every second of data. Artificial Neural Network (ANN) classification and 5-Fold Cross Validation are used to knowing the performance of the developed system. Developed BCI system resulted accuracy 78,20% with True Positive Rate (TPR) 86,00% and False Discovery Rate (FDR) 23,21%.

Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN

Epilepsy patients who do not have their seizures controlled with medication or surgery live in constant fear. The psychological burden of uncertainty surrounding the occurrence of random seizures is one of the most stressful and debilitating aspects of the disease. Despite the research progress in this field, there is a need for a non-invasive prediction system that helps disrupt the seizure epileptiform. Electroencephalogram (EEG) signals are non-stationary, nonlinear and vary with each patient and every recording. Full use of the non-invasive electrode channels is impractical for real-time use. We propose two frontal-temporal electrode channels based on ensemble empirical mode decomposition (EEMD) and Relief methods to address these challenges. The EEMD decomposes the segmented data frame in the ictal state into its intrinsic mode functions, and then we apply Relief to select the most relevant oscillatory components. A deep neural network (DNN) model learns these features to perform seizure prediction and early detection of patient-specific EEG recordings. The model yields an average sensitivity and specificity of 86.7% and 89.5%, respectively. The two-channel model shows the ability to capture patterns from brain locations for non-fontal-temporal seizures.

X-ray Imaging of Water Distribution in Cathode Electrode of PEFC and Control of Liquid Water Transport by Electrode Perforation

Water flooding in cathode electrodes is a critical issue for achieving high efficiency and high durability of polymer electrolyte fuel cells (PEFCs). To alleviate flooding, it’s necessary to design the optimum electrode/channel structure for positively removing liquid water from catalyst layers (CLs) and gas diffusion layers (GDLs) toward flow channels. In the previous works, Gerteisen et al. [1] presented a concept of perforated GDL structure to secure water passages through the porous electrode. Turhan et al. [2] revealed that the hydrophilization of cathode channel wall effectively enhances the liquid suction from the under-land locations into gas channels. Nishida et al. [3] have proposed the novel electrode/channel structure combining the electrode perforation and channel hydrophilization, and have investigated its effect on the improvements of water discharge and cell performance. In this study, the liquid water distribution in the cathode electrode of an operating PEFC was directly visualized using X-ray imaging, and the effects of introduction of penetration groove on the water transport inside the GDL were examined. Furthermore, the impact of combinational structure with electrode perforation and channel hydrophilization on the performance characteristics of the cell was demonstrated during the constant-current operation test.Figs. 1(a) and 1(b) show the outline assembly drawing of the small-sized visualization cell for X-ray radiography, and the measurement region in the cross-sectional view. The membrane electrode assembly (MEA) is sandwiched between two carbon separators with a single-serpentine flow field (number of channels: 7, channel width: 1.0 mm, depth: 1.0 mm, length: 88.5 mm). The effective electrode area is 2.88cm2. The X-ray beam is emitted to the cathode GDL in the in-plane direction, and the through-plane water distributions in the GDL are observed under the 4th channel and under the land between the 3rd and 4th channel. As shown in Fig. 1(b), the penetration groove (width: 100 or 200 µm, length: 11 mm) installed into the porous media by using a thin-edged blade is positioned along the sidewall of the 4th channel. Liquid water accumulated in the CL is firstly discharged into the largely opened groove. Subsequently, water droplets gathered in the groove grow up gradually and come into contact with the channel sidewall. When the droplets are attached to the hydrophilized sidewall, they are spread out on the wall surface and liquid films are moved upward. This water suction through the penetration groove and along the hydrophilic channel wall effectively reduces water flooding near the cathode CL.Fig. 2 presents the through-plane distributions of water saturation in the cathode GDL under the channel and land. Fig. 2(a) denotes the result for the non-modified GDL/channel structure (non-perforated GDL and non-hydrophilized channel); Figs. 2(b) and 2(c) denote for the customized structures with penetration grooves (width: 100 and 200 µm) and channel hydrophilization. The current density is increased stepwise from 0.17 to 1.39 A/cm2 for 40 min. All visualization images are obtained after 40 min of operation. In the case with the penetration groove, the water saturation in the GDL is clearly decreased around the groove because liquid water accumulated in the porous media is discharged into the large groove. Furthermore, the reduction of water saturation under the channel due to the electrode perforation is more remarkable as compared to that under the land. The region of water saturation reduction in the GDL is enlarged with increasing the groove width from 100 to 200 µm.

X-ray Imaging of Water Distribution in Cathode Electrode of PEFC and Control of Liquid Water Transport by Electrode Perforation

To alleviate water flooding in cathode electrodes of polymer electrolyte fuel cells (PEFCs), the modified electrode/channel structure with electrode perforation and channel hydrophilization has been proposed. This study investigated the liquid water distribution in the modified electrode with penetration grooves of an operating PEFC using X-ray imaging, and evaluated the effect of groove size on the water removal inside the electrode. The influence of the combinational electrode/channel structure on the cell performance was also demonstrated by the constant-current operation test. It was noted that the electrode perforation effectively reduces the water accumulation in the cathode GDL. An increase in the groove width enhances the in-plane water discharge towards the large groove. The combinational structure with electrode perforation and channel hydrophilization improves the limiting current density due to the positive through-plane water removal from the porous media to the flow channel.

Semi-transparent graphite films growth on Ni and their double-sided polymer-free transfer

Nanorange thickness graphite films (NGFs) are robust nanomaterials that can be produced via catalytic chemical vapour deposition but questions remain regarding their facile transfer and how surface topography may affect their application in next-generation devices. Here, we report the growth of NGFs (with an area of 55 cm2 and thickness of ~ 100 nm) on both sides of a polycrystalline Ni foil and their polymer-free transfer (front- and back-side, in areas up to 6 cm2). Due to the catalyst foil topography, the two carbon films differed in physical properties and other characteristics such as surface roughness. We demonstrate that the coarser back-side NGF is well-suited for NO2 sensing, whereas the smoother and more electrically conductive front-side NGF (2000 S/cm, sheet resistance − 50 Ω/sq) could be a viable conducting channel or counter electrode in solar cells (as it transmits 62% of visible light). Overall, the growth and transfer processes described could help realizing NGFs as an alternative carbon material for those technological applications where graphene and micrometer-thick graphite films are not an option.

Effect of localized helium ion irradiation on the performance of synthetic monolayer MoS2 field-effect transistors.

Helium ion irradiation is a known method of tuning the electrical conductivity and charge carrier mobility of novel two-dimensional semiconductors. Here, we report a systematic study of the electrical performance of chemically synthesized monolayer molybdenum disulfide (MoS2) field-effect transistors irradiated with a focused helium ion beam as a function of increasing areal irradiation coverage. We determine an optimal coverage range of approx. 10%, which allows for the improvement of both the carrier mobility in the transistor channel and the electrical conductance of the MoS2, due to doping with ion beam-created sulfur vacancies. Larger areal irradiations introduce a higher concentration of scattering centers, hampering the electrical performance of the device. In addition, we find that irradiating the electrode–channel interface has a deleterious impact on charge transport when contrasted with irradiations confined only to the transistor channel.

Electrosorption mechanism and nonequilibrium thermodynamics model of room-temperature ionic liquids within graphene nanochannels

<p indent=0mm>Supercapacitor, an advanced energy-storage technique based on ion electrostatic adsorption at the solid–liquid interface, exhibits fast charge–discharge rates, high power density, high rate ability, and long cycle life. Two-dimensional nanomaterials (e.g., graphene) and room-temperature ionic liquids (RTILs) have been considered potential electrode and electrolyte candidates, respectively, for supercapacitors. However, their high concentration, nanosized confined space, and complex molecular structure/intermolecular interactions make the ion packing structure and electrosorption process substantially different from that in conventional dilute electrolytes (e.g., aqueous solution and organic electrolyte) near the nonconfined space, which cannot be interpreted by the Gouy–Chapman–Stern model and Debye–Huckel approximation based on the continuous medium hypothesis. In this study, molecular dynamics simulations are employed to comprehensively explore the in-depth mechanisms of ion distribution at equilibrium and ion transport at nonequilibrium within the multilayer graphene nanochannel/RTILs system. The ion layer structure and its number density inside the graphene nanochannels are significantly enhanced compared with the nonconfined graphene plane. Moreover, the position of counter-ion within the graphene nanochannel is much closer to the charged wall, indicating better charge-storage capability. These phenomena are mainly attributed to the more prominent interactions between graphene and ions inside the confined space. In addition, the ion transport behaviors inside the graphene nanochannel are strongly dependent on the electrode potential and channel widths. In particular, an anion adsorption-dominated charging mechanism is recognized on the graphene plane, whereas confined graphene nanochannels exhibit more complex ion transport behaviors, i.e., the anion adsorption-dominated and cation desorption-dominated behaviors alternate with increasing potential. The ion transport mechanism is highly related to the confinement, potential, ion–ion interaction, and ion–electrode interaction. Finally, an interfacial ion transport model based on nonequilibrium thermodynamics theory is proposed to analyze the real-time flux of anions and cations within the electrodes in the nonequilibrium state. It is found that the decreased charge storage at high charge–discharge rates is mainly attributed to the significant hindrance of co-ion transport, which is quite different from the traditional view (i.e., the hindrance/hysteresis of counter-ion adsorption). Such a phenomenon can be interpreted as follows. First, the transient formation of crowded counter-ions and dilute co-ions inside the graphene nanochannel leads to slower transport of the co-ions. Second, the decreasing number density of co-ions inside the graphene nanochannel also hinders the flux of co-ions in and out of the electrode. These insights reveal the energy and mass transfer mechanisms during the electrostatic adsorption process within complex graphene nanochannel/RTILs systems, which can potentially guide the design and fabrication of electrode materials and electrolytes for high-performance supercapacitors.

Accuracy of Еlectrochemical Micromachining

The accuracy attainable in electrochemical machining of depressions (depth 18 µm) with tolerance 4.5 µm in the electrode channel is investigated. The primary dimensional errors are determined. The permissible absolute and relative error in the machining parameters is established.

Fused Group Lasso: A New EEG Classification Model With Spatial Smooth Constraint for Motor Imagery-Based Brain–Computer Interface

The traditional group sparse optimization method can simultaneously achieve the channel selection and classification for the motor imagery electroencephalogram (EEG) signals, but it doesn’t consider the spatial structure information between the electrode channels. Combining the group sparsity and spatial smoothness of EEG signals, a new EEG classification model is proposed, which is an improvement of group least absolute shrinkage and selection operator (LASSO). We call it fused group LASSO. First, group LASSO is used to model the group sparsity of EEG signals, the features of the same channel are assigned the same weights. Then, based on group LASSO, channel weights are regularized by total variation norm (TV-norm), which constrains the weights of adjacent channels to the same or similar, thereby the spatial smoothness modeling of EEG signals can be achieved. Using the primal-dual theory, an optimization algorithm for the new model is given. In order to verify the effectiveness of the new model, experiments were performed on two public brain-computer interface (BCI) competition data sets and one self-collected data set. Compared with the existing sparse optimization methods, the proposed method has achieved the highest average classification accuracy of 79.24%, 86.64% and 81.09%, respectively, and with better physiological interpretability. Compared with spatial filtering methods with smooth constraints, the proposed method realized global spatial smooth in a data-driver manner, and achieved the highest average classification accuracy of 84.96% in two competition data sets. All the experimental results showed that the proposed method can significantly improve the performance of BCI systems.