An ECG Signal Acquisition and Analysis System Based on Machine Learning with Model Fusion.

Recently, cardiovascular disease has become the leading cause of death worldwide. Abnormal heart rate signals are an important indicator of cardiovascular disease. At present, the ECG signal acquisition instruments on the market are not portable and manual analysis is applied in data processing, which cannot address the above problems. To solve these problems, this study proposes an ECG acquisition and analysis system based on machine learning. The ECG analysis system responsible for ECG signal classification includes two parts: data preprocessing and machine learning models. Multiple types of models were built for overall classification, and model fusion was conducted. Firstly, traditional models such as logistic regression, support vector machines, and XGBoost were employed, along with feature engineering that primarily included morphological features and wavelet coefficient features. Subsequently, deep learning models, including convolutional neural networks and long short-term memory networks, were introduced and utilized for model fusion classification. The system's classification accuracy for ECG signals reached 99.13%. Future work will focus on optimizing the model and developing a more portable instrument that can be utilized in the field.

Giant Water Uptake Enabled Ultrahigh Proton Conductivity of Graphdiyne Oxide.

Proton conductors have attracted great attention in various fields, especially in energy production. Here, we find that graphdiyne oxide (GDYO), derived from graphdiyne (GDY), features the highest proton conductivity of 0.54 S cm-1 (100 % RH, 348 K) among the oxidized carbon allotropes reported so far. The sp- and sp2 -co-hybridized carbon skeleton of GDY enables GDYO with the giant water uptake, which is 2.4 times larger than that of graphene oxide (GO), resulting in ultrahigh proton conductivity by increasing the proton concentration and proton conduction pathways. This ultrahigh proton conductivity of GDYO is further proved in a methanol fuel cell by using GDYO membrane as proton exchange membrane. The GDYO membrane enables the cell with higher open circuit voltage, larger power density and lower methanol permeability, compared with commercial Nafion 117. Moreover, the GDYO membrane bears high ion exchange capacity, good acidic stability and low swelling ratio.

Highly-Efficient Ion Gating through Self-Assembled Two-Dimensional Photothermal Metal-Organic Framework Membrane.

Biological ion channels regulate the ion flow across cell membrane via opening or closing of the pores in response to various external stimuli. Replicating the function of high ion gating effects with artificial porous materials has been challenging. Herein, we report that the self-assembled two-dimensional metal-organic framework (MOF) membrane can serve as an excellent nanofluidic platform for smart regulation of ion transport. The MOF membrane with good photothermal performance exhibits extremely high ion gating ratio (up to 104 ), which is among the highest values in MOF membrane nanochannels for light-controlled ion gating reported so far. By repeatedly turning on and off the light, the nanofluidic device shows outstanding stability and reversibility that can be applied in the remote light-switching system. This work may spark promising applications of MOF membrane with variety of stimuli responsive properties in ion sieving, biosensing, and energy conversion.

Light-Regulated Nanofluidic Ionic Diodes with Heterogeneous Channels Stemming from Asymmetric Growth of Metal-Organic Frameworks.

Nanofluidic ionic diodes have attracted much attention, because of the unique property of asymmetric ion transport and promising applications in molecular sensing and biosensing. However, it remains a challenge to fabricate diode-like nanofluidic system with molecular-size pores. Herein, we report a new and facile approach to construct nanofluidic ionic diode by in situ asymmetric growth of metal-organic frameworks (MOFs) in nanochannels. We implement microwave-assisted strategy to obtain asymmetric distribution of MOFs in porous anodic aluminum oxide with barrier layer on one side. After etching the barrier layer and modifying with positively charged molecules, the nanofluidic device possesses asymmetric geometry and surface charge, performing the ionic current rectification (ICR) behavior in different electrolyte concentrations. Moreover, the ICR ratio is readily regulated with visible light illumination mainly due to the enhancement of surface charge of MOFs, which is further confirmed by finite element simulation. This study provides a reliable way to build the nanofluidic platform for investigating the asymmetric ion transport through the molecular-size pores, which is envisaged to be important for molecular sensing based on ICR with molecular-size pores.

Double-Barreled Micropipette Enables Neuron-Compatible In Vivo Analysis.

The sensors based on ion transport in a confined nano-/microchannel (i.e., iontronic sensors) have brought new opportunities for in vivo neurochemical assay, especially for electroinactive molecules. However, the interference on spontaneous neuronal activity induced by the electric field around the sensors has not been addressed. Here, the electric field distribution with a double-barreled micropipette was shrunk and quantified by finite element simulation, which can explain and minimize the influence on spontaneous neuronal activity. The parameters affecting the electric field distribution, including the pore size, applied voltage, and angle degree, were studied to balance the sensitivity and interference on spontaneous neuronal activity. The double-barreled micropipette, as a pH sensor with high selectivity and sensitivity, has been successfully applied to real-time pH sensing in rat brain. This study offers a new way for in vivo monitoring neurochemical dynamics with neuron-compatibility.

Fast-Scanning Potential-Gated Organic Electrochemical Transistors for Highly Sensitive Sensing of Dopamine in Living Rat Brain.

Developing techniques for the highly sensitive assay of neurotransmitters is essential for understanding physiological and pathological processes. Here, we demonstrate a fast-scanning potential (FSP)-gated organic electrochemical transistor (OECT): for the highly sensitive sensing of dopamine (DA) in a living rat brain. The configuration combines the selectivity of fast-scan cyclic voltammetry (FSCV) with the high sensitivity of an OECT. The combined use of FSP as a gating mode and transconductance (gm ) as a sensing parameter further improve the sensing performance in terms of sensitivity, limit of detection, reproducibility, and stability. The FSP-OECT exhibits a sensitivity of 0.899 S M-1 and a low limit of detection down to 5 nM and was validated for in vivo monitoring of the basal level and electrically stimulated release of DA.

Galvanic Redox Potentiometry for Self-Driven in Vivo Measurement of Neurochemical Dynamics at Open-Circuit Potential.

Understanding the real-time correlation between chemical patterns and neural processes is critical for deciphering brain function. Voltammetry has enabled this task but with a number of challenges for current-based electrolysis in vivo. Herein, we report galvanic redox potentiometry (GRP) potentially as a universal strategy for in vivo monitoring of neurochemicals, with ascorbic acid (AA) as a typical example. The GRP sensor is constructed on a self-driven galvanic cell configuration, where AA is spontaneously oxidized at the indicating single-walled carbon nanotube-modified carbon fiber electrode (SWNT-CFE), while oxygen reduced at the laccase-modified reference CFE (Lac-CFE). At thermodynamic equilibrium, open-circuit potential (OCP) can be a linear indicator of the concentration of AA. The resulting sensor shows a high selectivity to AA dynamics in the presence of coexisting electroactive neurochemicals, which is mainly determined by the driving force for the cell reaction, as suggested by principal investigation. Sensing sensitivity of this OCP-based GRP method is not affected by nonspecific protein adsorption and electrode fouling. Moreover, a micropipette compartment of the reference electrode is designed to suppress mass crossover and prevent disturbance to oxygen reduction through confinement effect. The in vivo application of the GRP sensor is illustrated by measuring the basal level of cortical AA in live rat brain (230 ± 40 µM) and its dynamics during ischemia/reperfusion. The GRP concept is demonstrated as a prominent method for in vivo, real-time, quantitative analysis of brain neurochemistry.

Neuromorphic functions with a polyelectrolyte-confined fluidic memristor.

Reproducing ion channel-based neural functions with artificial fluidic systems has long been an aspirational goal for both neuromorphic computing and biomedical applications. In this study, neuromorphic functions were successfully accomplished with a polyelectrolyte-confined fluidic memristor (PFM), in which confined polyelectrolyte-ion interactions contributed to hysteretic ion transport, resulting in ion memory effects. Various electric pulse patterns were emulated by PFM with ultralow energy consumption. The fluidic property of PFM enabled the mimicking of chemical-regulated electric pulses. More importantly, chemical-electric signal transduction was implemented with a single PFM. With its structural similarity to ion channels, PFM is versatile and easily interfaces with biological systems, paving a way to building neuromorphic devices with advanced functions by introducing rich chemical designs.

Perspective on Nanofluidic Memristors: From Mechanism to Application.

Nanofluidic memristors are memory resistors based on nanoconfined fluidic systems exhibiting history-dependent ion conductivity. Toward establishing powerful computing systems beyond the Harvard architecture, these ion-based neuromorphic devices attracted enormous research attention owing to the unique characteristics of ion-based conductors. However, the design of nanofluidic memristor is still at a primary state and a systematic guidance on the rational design of nanofluidic system is desperately required for the development of nanofluidic-based neuromorphic devices. Herein, we proposed a systematic review on the history, main mechanism and potential application of nanofluidic memristors in order to give a prospective view on the design principle of memristors based on nanofluidic systems. Furthermore, based on the present status of these devices, some fundamental challenges for this promising area were further discussed to show the possible application of these ion-based devices.

Robust Adaptive Tracking Control for Hypersonic Vehicle Based on Interval Type-2 Fuzzy Logic System and Small-Gain Approach.

This paper presents a novel robust adaptive tracking control method for a hypersonic vehicle in a cruise flight stage based on interval type-2 fuzzy-logic system (IT2-FLS) and small-gain approach. After the input-output linearization, the vehicle model can be decomposed into two uncertain subsystems by considering matching disturbances and parametric uncertainties. For each subsystem, an interval type-2 Takagi-Sugeno-Kang fuzzy logic system (IT2-TSK-FLS) is then employed to approximate the unavailable model information. Following the idea of a small-gain approach, a composite feedback form for each subsystem is constructed, based on which the final robust adaptive tracking control law is developed. Rigorous stability analysis shows that all signals in the derived closed-loop system are kept uniformly ultimately bounded (UUB). The main contribution of this paper is that the proposed control law for the hypersonic vehicle is with only two adaptive parameters in total which can greatly alleviate the computation and storage burden in practice; meanwhile its superiority over the conventional minimal-learning-parameter (MLP)-based one is specifically illustrated. Comparative numerical simulations of three cases demonstrate the effectiveness of our proposed control method with respect to complicated uncertainties.

Synaptic Iontronic Devices for Brain-Mimicking Functions: Fundamentals and Applications.

Inspired by the information transmission mechanism in the central nervous systems of life, synapse-mimicking devices have been designed and fabricated for the purpose of breaking the bottleneck of von Neumann architecture and realizing the construction of effective hardware-based artificial intelligence. In this case, synaptic iontronic devices, dealing with current information with ions instead of electrons, have attracted enormous scientific interests owing to their unique characteristics provided by ions, such as the designability of charge carriers and the diversity of chemical regulation. Herein, the basic conception, working mechanism, performance metrics, and advanced applications of synaptic iontronic devices based on three-terminal transistors and two-terminal memristors are systematically reviewed and comprehensively discussed. This Review provides a prospect on how to realize artificial synaptic functions based on the regulation of ions and raises a series of further challenges unsolved in this area.

Dynamic Behavior of Charged Particles at the Nanopipette Orifice.

Understanding the dynamic behavior of charged particles driven by flow and electric field in nanochannels/pores is highly important for both fundamental study and practical applications. While a great breakthrough has been made in understanding the translocation dynamics of charged particles within the nanochannels/pores, studies on the dynamics of particles at the orifice of nanochannels/pores are scarcely reported. Here, we study particle motion at a smaller-sized orifice of a nanopipette by combining experimentally observed current transients with simulated force conditions. The theoretical force analysis reveals that dielectrophoretic force plays an equally important role as electrophoretic force and electroosmotic force, although it has often been neglected in understanding the particle translocation dynamics within the nanopipette. Under the combined action of these forces, it thus becomes difficult for particles to physically collide with the orifice of the nanopipette, resulting in a relatively low decrease in the current transients, which coincides with experimental results. We then regulate the dynamic behavior by altering experimental conditions (i.e., bias potential, nanopipette surface charge, and particle size), and the results further validate the presence and influence of forces being considered. This study improves the understanding of the relationship between particle properties and observed current transients, providing more possibilities for accurate single-particle analysis and single-entity regulation.

Micrometer-scale transient ion transport for real-time pH assay in living rat brains.

Ion transport has been widely used for various applications such as sensing, desalination and energy conversion; however, nearly all applications are based on steady-state ion transport. Herein, we for the first time demonstrate the capability of transient ion transport for in vivo sensing with both high spatial (∼µm) and temporal (∼ms) resolution by using pH as the model target. Transient ion transport behavior (i.e., time-dependent ion current change) was observed by applying high-frequency pulse potential. Importantly, we proposed the ion distribution transient model for this time-dependent ion transport behavior. With this model, the temporal resolution of the as-developed pH microsensor based on ion current was improved to the ms level, thus satisfying the requirement of neurochemical recording. Moreover, our microsensor features good reproducibility, selectivity, and reversibility, and can thus real-time monitor the pH change in living rat brains. This study demonstrates the first example of in vivo sensing based on ion transport, opening a new way to neurochemical monitoring with ultrahigh spatiotemporal resolution. This study is also helpful to understand the transient process of asymmetric ion transport.

Improved electrocatalytic activity for ethanol oxidation by Pd@N-doped carbon from biomass.

Pd@N-doped carbon (Pd@CN) exhibited four and two times higher peak current density toward ethanol electrooxidation than Pd@active carbon and Pd@non-nitrogen carbon. Controlled experimental results indicated that the incorporation of nitrogen into the carbon matrix improved the percentage of Pd(0) and increased the binding energy of Pd in Pd@CN, and accordingly enhanced the catalytic activity.