Direct Current Signal Research Articles

Direct Current Ripple Component Detection Algorithm Based on Improved Variational Mode Decomposition

Background: The development of smart grids requires energy meters that enable accurate measurements. Objective: In response to the current issue where Direct Current (DC) energy meters only measure DC components and not ripple components, we propose to design a novel algorithm to achieve accurate separation of DC components and ripple components in DC signals. Methodology: This patent proposes a precise detection algorithm for ripple components. The algorithm is based on the Wavelet Decomposition combined with Flamingo Search Algorithm-Variational Mode Decomposition (WD-FSA-VMD). Firstly, we analyze the ripple characteristics in the DC signal and use the Wavelet Decomposition (WD) algorithm to separate the ripple components in the DC signal accurately. Secondly, we utilize the Flamingo Search Algorithm (FSA) to optimize the number of modal decompositions and the penalty factor in the Variational Mode Decomposition (VMD). This allows us to accurately extract the ripple components. Finally, we conducted simulation experiments comparing the improved algorithm with the original algorithm. Results: The experimental results indicate that the signal correlation coefficient obtained by the WDFSA- VMD algorithm increases by 82.64% compared to traditional VMD, and it increases by 16.72% compared to the combined Wavelet Decomposition with Whale Optimization Algorithm-Variational Mode De-composition (WD-WOA-VMD). Conclusion: The improved algorithm we proposed has good adaptability and separation performance. It is prospective for the new algorithm to provide a valuable reference to ripple detection technology.

Direct Growth of Vertical Graphene on Fiber Electrodes and Its Application in Alternating Current Line-Filtering Capacitors.

Fiber-shaped electrochemical capacitors (FSECs) have garnered substantial attention to emerging portable, flexible, and wearable electronic devices. However, achieving high electronic and ionic conductivity in fiber electrodes while maintaining a large specific surface area is still a challenge for enhancing the capacitance and rapid response of FSECs. Here, we present an electric-field-assisted cold-wall plasma-enhanced chemical vapor (EFCW-PECVD) method for direct growth of vertical graphene (VG) on fiber electrodes, which is incorporated in the FSECs. The customized reactor mainly consists of two radio frequency coils: one for plasma generation and the other for substrate heating. Precise temperature control can be achieved by adjusting the conductive plates and the applied power. With induction heating, only the substrate is heated to above 500 °C within just 5 min, maintaining a low temperature in the gas phase for the growth of VG with a high quality. Using this method, VG was easily grown on metallic fibers. The VG-coated titanium fibers for FSECs exhibit an ultrahigh rate performance and quick ion transport, enabling the conversion of an alternating current signal to a direct current signal and demonstrating outstanding filtering capabilities.

Analysis of Aluminum Alloy Double-Pulse MIG Welding Arc Signal Characteristics Based on Broadband Mode Decomposition

Welding voltage and current in arc signals are directly related to arc stability and welding quality. Process experiments with different parameters were organized according to the orthogonal experimental design method by constructing an aluminum alloy double-pulse metal inert gas (MIG) welding arc electric signal test platform. The data acquisition system of the aluminum alloy MIG welding process was established to obtain real-time arc signal information reflecting the welding process. The aluminum alloy’s collected double-pulse arc current signals are decomposed adaptively by broadband mode decomposition (BMD). The direct current (DC) signal, pulse signal, distortion signal, ripple signal, and noise signal are separated and extracted, and the composite multiscale fuzzy entropy (CMFE) is calculated for the component set of the electrical signal. The experimental results show that the current waveform obtained by the double-pulse MIG welding current signal is consistent with the corresponding weld forming diagram. Simultaneously, the composite multiscale fuzzy entropy is calculated for the arc characteristic parameters. The rationality of matching process parameters and arc stability of aluminum alloy's double-pulse MIG welding were evaluated.

Design of Digital Lock-in Amplifier based on FPGA

Abstract In order to achieve high-precision measurement of strong noise background signal, a digital lock-in amplifier based on field programmable gate array (FPGA) is designed. The core of the lock-in amplifier consists of a signal processing module, an acquisition and quantisation module and a digital demodulation module. The signal processing module completes the amplification and gain adjustment of the input signal and modulates the signal to be measured into a specific frequency band, which is digitally quantised by the analog to digital converter (ADC) after anti-aliasing filtering and transmitted to the FPGA, and the signal to be measured is extracted by phase-sensitive detection through the mutual-correlation digital demodulation method, thus realising the detection of the weak signal. The performance of the lock-in amplifier is evaluated with a signal generator as the input source, and the results show that the signal-to-noise improvement ratio is 35dB and the relative measurement error of voltage is less than 0.03% after the digital lock-in amplifier is processed, which accurately restores the direct current (DC) signals in the noisy background, and provides an effective technical way for the accurate measurement of weak signals.

Direct Current Triboelectric Nanogenerator Based on Contact Electrification, Air Breakdown, Electrostatic Induction, and Charge Leakage for Self‐powered Applications

AbstractDiverging from air breakdown‐based triboelectric nanogenerators (TENGs), recent TENG designs present high output power density without requiring precise control over discharge channels. However, existing researches predominantly ascribe its direct current output to electrostatic induction, disregarding the critical factor of charge leakage. This oversight hampers efforts to improve device performance, especially in material selection and optimization. Here, the generation of direct current signals ultimately stems from material charge leakage and spatial electrostatic induction is illustrated. Through theoretical analysis, visualization, and experimental measurement of four phenomena in the device, a quadruple‐effect mechanoelectrical conversion mechanism is established to refine the material selection rule. Under this guideline, the output power density is increased by 34.42% in contrast to the electrostatic induction direct current TENG. For practical applications, a power management circuit is utilized to boost the device's charging rate by up to 18 times. Furthermore, the high voltage of the device can activate discharge‐type UV tubes, demonstrating great potential in developing self‐powered wastewater treatment systems. The multiple charge behaviors proposed in this work, along with the material selection rule, lay a solid foundation for achieving high output power density in direct current TENGs.

Design and Analysis of Micro Signal Detection Circuit for Magnetic Field Detection Utilizing Coil Sensors

Eddy current inspection has been extensively employed in non-destructive testing of various conductive materials. The coil probe, as a mainstream sensor in the eddy current detection system, inevitably encounters interference from external signals while transmitting its own signal. Therefore, developing techniques to extract valuable signals from noisy ones is crucial for ensuring accurate detection. Carbon fiber composites not only possess significantly lower electrical conductivity compared to conventional metallic materials but also exhibit notable anisotropy. To address this issue, we designed an ‘8’ coil probe set where the excitation coil does not electromagnetically interfere with the detection coil. However, practical applications that require portability and miniaturization pose challenges when utilizing this coil probe set to identify carbon content or defects due to the typically weak output signal. To address this issue, this paper proposes a design that combines the ‘8’ structure of the planar coil probe with the principle of phase-locked amplification to create a dual-phase sensitive phase-locked amplification detection circuit. These specific design ideas were tested using a weak signal, which passed through the preamplifier, secondary amplifier, and band-pass filter comprising the target channel for signal amplification and noise filtering. The effective signal amplitude is proportional to the inverse phase difference between the direct current (DC) signal and inversely proportional to the amplitude of the signal. Finally, the DC signal was passed through an analog-to-digital converter (ADC). The analog-to-digital converter (A/D) is used to collect and calculate the DC signal, enabling the detection of weak electrical signals. Simulation experiments demonstrated that the signal detection circuit has an amplitude error below 0.2% and a phase error below 0.5%. The phase-locked amplification circuit designed in this paper can effectively extract the tiny impedance change signals of the planar coil sensor probe with high sensitivity and good robustness.

Ionic Rectification by Dynamic Regulation of the Electric Double Layer at the Hydrogel Interface.

Hydrogels play a pivotal role in the realm of iontronics, contributing to the realization of futuristic human-machine interactions. The electric double layer (EDL) between the hydrogel and electrode provides an essential ionic-electronic coupling interface. While prior investigations primarily delved into elucidating the formation mechanism of the EDL, our study shifts the focus to showcasing the current generation through the mechanical modulation of the EDL at the hydrogel-metal interfaces. The dynamic EDL was constructed by the mechano-driven contact-separation process between the polyacrylamide (PAAm) hydrogel and Au. Influencing factors on the dynamic regulation of the EDL such as ion concentration, types of salt, contact-separation frequency, and deformation degree were investigated. Dehydration usually limits the practical applications of hydrogels, and it is a long-standing and difficult problem. However, it seemed to be able to slow the EDL formation process here, resulting in a sustained continuous direct current signal output. Such hydrogel iontronics could rectify the displacement electronic current of a triboelectric nanogenerator by the ionic current. The directional migration of ions could be further enhanced by using charge-collecting metals with different work functions, for example, Au and Al. It offers a paradigm to enable ionic rectification that could be seamlessly incorporated into electronic systems, ushering in a new era for efficient energy harvesting and biomimetic nervous systems.

AutoML-driven diagnostics of the feeder motor in fused filament fabrication machines from direct current signals

AbstractPart defects in additive manufacturing are more frequent compared to machining or molding. Failures can go unnoticed for hours, wasting resources and extending process cycle times. This paper describes a Machine Learning based method for automated sensing of onset failure in additive manufacturing machinery. Investigations are conducted on a Fused Filament Fabrication (FFF) 3D printer, and the same methods are then applied to a digital light processing 3D printer. The investigation focuses on signal-based analysis, specifically passive sensing of stepper motors relating DC current measurements to the torque on a stepper, as opposed to any active acoustic interrogation of the part. Passive methods are used to characterize the loading on a feeder stepper in an FFF machine, forming a model that can identify early signs of filament-based failure with 85.65% 10-fold cross-validation accuracy. Efforts show filament breakage can be detected minutes before material runout would cause a defect, allowing ample time to pause, correct, or control the print. The machine learning pipeline was not naively conceived but optimized through automated machine learning.

Classification Algorithm for DC Power Quality Disturbances Based on SABO-BP

To better address and improve the issues related to DC power quality, this paper proposes an identification method tailored for DC power quality disturbances. First, it explores the underlying mechanisms and waveform characteristics of common DC power disturbances. By integrating the results of time–frequency analysis obtained through the S-transform, five distinct features are designed and extracted to serve as classification indicators. The SABO algorithm is subsequently employed to optimize the BP neural network, assisting in determining the optimal input weights and hidden layer thresholds. This optimization technique helps prevent the network from becoming stuck in local minima, thereby enhancing its robustness and generalization capabilities. This paper presents a simulation system for AC/DC power systems to conduct experimental verification. The system simulates various DC power quality issues and monitors abnormal waveforms. According to the designated classification index, the features of simulated disturbance signals are extracted. The SABO-BP classification prediction model is then used to automatically classify and identify the samples. The experimental results demonstrate high accuracy in classification and identification using the proposed method. In comparison to the BP neural network method, the SABO-BP method demonstrates an 8.207% improvement in accurately identifying disturbance signals. It is capable of accurately identifying direct current power quality signals, thereby assisting in the evaluation and control of power quality issues.

Analysis of PMSM Short-Circuit Detection Systems Using Transfer Learning of Deep Convolutional Networks

Abstract Modern permanent magnet synchronous motor (PMSM) diagnostic systems are now combined with advanced artificial intelligence techniques, such as deep neural networks. However, the design of such systems is mainly focussed on a selected type of damage or motor type with a limited range of rated parameters. The application of the idea of transfer learning (TL) allows the fully automatic extraction of universal fault symptoms, which can be used for various diagnostic tasks. In the research, the possibility of using the TL idea in the implementation of PMSM stator windings fault-detection systems was considered. The method is based on the characteristic symptoms of stator defects determined for another type of motor or mathematical model in the target diagnostic application of PMSM. This paper presents a comparison of PMSM motor inter-turn short circuit fault detection systems using TL of a deep convolutional network. Due to the use of direct phase current signal analysis by the convolutional neural network (CNN), it was possible to ensure high accuracy of fault detection with simultaneously short reaction time to occurring fault. The technique used was based on the use of a weight coefficient matrix of a pre-trained structure, the adaptation of which was carried out for different sources of diagnostic information.

Assessing the Discriminatory Capabilities of iEK Devices under DC and DC-Biased AC Stimulation Potentials.

There is a rising need for rapid and reliable analytical methods for separating microorganisms in clinical and biomedical applications. Microscale-insulator-based electrokinetic (iEK) systems have proven to be robust platforms for assessing a wide variety of microorganisms. Traditionally, iEK systems are usually stimulated with direct-current (DC) potentials. This work presents a comparison between using DC potentials and using DC-biased alternating-current (AC) potentials in iEK systems for the separation of microorganisms. The present study, which includes mathematical modeling and experimentation, compares the separation of bacterial and yeast cells in two distinct modes by using DC and DC-biased AC potentials. The quality of both separations, assessed in terms of separation resolution (Rs), showed a complete separation (Rs = 1.51) with the application of a DC-biased low-frequency AC signal but an incomplete separation (Rs = 0.55) with the application of an RMS-equivalent DC signal. Good reproducibility between experimental repetitions (<10%) was obtained, and good agreement (~18% deviation) was observed between modeling and experimental retention times. The present study demonstrates the potential of extending the limits of iEK systems by employing DC-biased AC potentials to perform discriminatory separations of microorganisms that are difficult to separate with the application of DC potentials.

Gas-solid two-phase flow triboelectric generator for powder triboelectrification energy collection and road early warning

With the rapid development of modern industrial processes, electrostatic safety issues caused by powder in production and daily life have attracted people's attention. Since powder is small in size and easily agglomerates, it is difficult to observe or design devices to monitor the electrostatic process generated by powder. Herein, a continuous gas-solid two-phase flow triboelectric nanogenerator (GS-TENG) was developed to harvest triboelectricity from the collision process between the powder-air flow and the solid surface. Compared with the signals under non-continuous airflow, the short-circuit current and output voltage signals generated under continuous airflow impact exhibit an unidirectional direct current peak characteristic. Additionally, in order to analyze the relationship between motion of powder airflow and the triboelectric signals, the two-phase flow more comprehensively, a high-speed camera was used to record motion during the charge generation process. Results showed that 3 mm nylon sphere generated normal alternating current signals upon contact and separation from PTFE, while nylon power with the diameter of about 30 µm produced direct current triboelectric signals owing to the effect of point discharge. At a wind speed of 32 m/s, 5 cm distance, 90° impact angle, the short-circuit current and output voltage can reach the maximum values of 20 µA and 130 V, respectively, which can serve as a power source sufficient to power 63 commercial LEDs. Based on the gas-solid two-phase flow triboelectric nanogenerator, a self-powered road warning lights was designed, indicating application prospects in dusty weather.

An Ultra-Low-Temperature Alternating Current Filter.

Traditional alternating current filter based on aluminum electrolytic capacitors (AECs) suffer from abrupt drop of filtering capability at ultra-low temperatures (≤-30°C), which greatly hinders the reliable working of electronics at extremely cold conditions. Herein, an ultra-low-temperature alternating current (AC) filter for the first time enabled by high-frequency supercapacitor based on covalently bonded hollow carbon onion-graphene hybrid structure is reported. It is found that the covalent bonding junctions enable high electronic conductivity and efficient ion adsorption/desorption behavior in the hybrid structure. Moreover, the hybrid structure owns positive curvature and shallows pores for fast ion diffusion kinetics. Consequently, the supercapacitor exhibits a record short resistor-capacitor time constant (τRC ) of 0.098ms at 120Hz at room temperature. Combining with low-melting-point electrolyte, the supercapacitor possesses excellent filtering capability and can output stable direct current signal with low fluctuation coefficients in a temperature range of -50 to 0°C. More interestingly, the filter presents high negative phase angle, low dissipation factor, short τRC , and high capacitance retention below -30 °C, whereas AEC cannot work properly owing to its phase angle<45°. This work realizes the fabrication of an ultra-low-temperature AC filter, which presents a critical step forward for promoting the development of ultra-low-temperature electronics.

Rapid Preparation of All-In-One Compressible Capacitors with Flame Carbonized Melamine Foam for AC Line Filtering

Melamine foam (MF) is a good candidate for electrochemical filter capacitors due to its 3D porous structure, flexibility, and nitrogen-containing property. However, traditional thermal carbonization would cause a severe loss of nitrogen and need several hours to perform the transition from melamine to carbon. Moreover, to construct all-in-one electrochemical capacitors, the nonconductive MF must be deposited a conductive layer on the surface of MFs. Herein, we developed a flame carbonization method to carbonize the MFs to an all-in-one structure in less than one minute. The carbonized MF exhibits 2.6 times higher nitrogen content than that of the traditional carbonized. MF-based all-in-one compressible electrochemical capacitors deliver excellent alternating current (AC) line filtering performance, such as a low phase angle of −83.1° at 120 Hz, a low resistance capacitance time constant of 157 μs, and a short relaxation time constant of 46 μs. The AC signal of 120 Hz, even a higher frequency of 2000 Hz, can be successfully converted into a stable direct current (DC) signal. Besides, the influence of electrolyte, compressive strain, and the thickness/carbonization time of MFs on the electrochemical performance has been studied. This work provides an ingenious design and effective preparation strategy for MFs-based all-in-one electrochemical capacitors.

Experimental Demonstration of In-Memory Computing in a Ferrofluid System.

Magnetic fluids are excellent candidates for several important research fields including energy harvesting, biomedical applications, soft robotics, and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that have been demonstrated, there is no evidence for their computing capability, including the emulation of synaptic functions, which requires complex non-linear dynamics. Here, it is experimentally demonstrated that a Fe3 O4 water-based ferrofluid (FF) can perform electrical analogue computing and be programmed using quasi direct current (DC) signals and read at radio frequency (RF) mode. Features have been observed in all respects attributable to a memristive behavior, featuring both short and long-term information storage capacity and plasticity. The colloid is capable of classifying digits of a 8×8 pixel dataset using a custom in-memory signal processing scheme, and through physical reservoir computing by training a readout layer. These findings demonstrate the feasibility of in-memory computing using an amorphous FF system in a liquid aggregation state. This work poses the basis for the exploitation of a FF colloid as both an in-memory computing device and as a full-electric liquid computer thanks to its fluidity and the reported complex dynamics, via probing read-out and programmingports.

Optical Phase‐Sensitive Reflectometry Based on Orthogonal Frequency‐Division Multiplexed LFM Signal in Fractional Fourier Domain

AbstractLinear frequency modulated (LFM) phase‐sensitive optical time‐domain reflectometry (φ‐OTDR) relieves the trade‐off between sensing range and spatial resolution; however, it is still limited by low sidelobe suppression ratio and signal fading. Fractional Fourier transform (FrFT) is applied to LFM φ‐OTDR for signal generation and compression. An LFM pulse is generated using the p‐order FrFT of a direct current signal and then compressed using FrFT with order 1−p to achieve a high sidelobe suppression ratio. Orthogonal frequency‐division multiplexing in the fractional Fourier domain is utilized to suppress signal fading. In experiments, fading‐free distributed phase measurement is realized using a multiplexed LFM probe pulse generated using a 0.4‐order FrFT. Dynamic sensing is demonstrated via single‐frequency or sweep‐frequency vibration, thereby validating high sensing performance. This work will not only open up a route for signal processing in LFM pulse based high‐performance φ‐OTDR, but also has significant potential for applications in other sensing systems using LFM signals.

Nonlinear Hall effects in two-dimensional moiré superlattices

The Hall effect refers to the generation of a voltage in a direction perpendicular to the applied current. Since its discovery in 1879, the Hall effect family has become a huge group, and its in-depth study is an important topic in the field of condensed matter physics. The newly discovered nonlinear Hall effect is a new member of Hall effects. Unlike most of previous Hall effects, the nonlinear Hall effect does not need to break the time-reversal symmetry of the system but requires the spatial inversion asymmetry. Since 2015, the nonlinear Hall effect has been predicted and observed in several kinds of materials with a nonuniform distribution of the Berry curvature of energy bands. Experimentally, when a longitudinal alternating current (AC) electric field is applied, a transverse Hall voltage will be generated, with its amplitude proportional to the square of the driving current. Such a nonlinear Hall signal contains two components: one is an AC transverse voltage oscillating at twice the frequency of the driving current, and the other is a direct current (DC) signal converted from the injected current. Although the history of the nonlinear Hall effect is only a few years, its broad application prospects in fields of wireless communication, energy harvesting, and infrared detectors have been widely recognized. The main reason is that the frequency doubling and rectification of electrical signals via some nonlinear Hall effects are achieved by an inherent quantum property of the material - the Berry curvature dipole moment, and therefore do not have the thermal voltage thresholds and/or the transition time characteristic of semiconductor junctions/diodes. Unfortunately, the existence of the Berry curvature dipole moment has more stringent requirements for the lattice symmetry breaking of the system apart from the spatial inversion breaking, and the materials available are largely limited. This greatly reduces the chance to optimize the signal of the nonlinear Hall effect and limits the application and development of the nonlinear Hall effect. The rapid development of van der Waals stacking technology in recent years provides a brand new way to design, tailor and control the symmetry of lattice, and to prepare artificial moiré crystals with certain physical properties. Recently, both theoretical results and experimental studies on graphene superlattices and transition metal dichalcogenide superlattices have shown that artificial moiré superlattice materials can have larger Berry curvature dipole moments than those in natural non-moiré crystals, which has obvious advantages in generating and manipulating the nonlinear Hall effect. On the other hand, abundant strong correlation effects have been observed in two-dimensional superlattices. The study of the nonlinear Hall effect in two-dimensional moiré superlattices can not only give people a new understanding of the momentum space distribution of Berry curvatures, contributing to the realization of more stable topological transport, correlation insulating states and superfluidity states, but also expand the functional space of moiré superlattice materials which are promising for the design of new electronic and optoelectronic devices. This review paper firstly introduces the birth and development of the nonlinear Hall effect and discusses two mechanisms of the nonlinear Hall effect: the Berry curvature dipole moment and the disorder. Subsequently, this paper summaries some properties of two-dimensional moiré superlattices which are essential in realizing the nonlinear Hall effect: considerable Berry curvatures, symmetry breaking effects, strong correlation effects and tunable band structures. Next, this paper reviews theoretical and experimental progress of nonlinear Hall effects in graphene and transition metal dichalcogenides superlattices. Finally, the future research directions and potential applications of the nonlinear Hall effect based on moiré superlattice materials are prospected.

A Hardware System for Synchronous Processing of Multiple Marine Dynamics MEMS Sensors.

Temperature, depth, conductivity, and turbulence are fundamental parameters of marine dynamics in the field of ocean science. These closely correlated parameters require time-synchronized observations to provide feedback on marine environmental problems, which requires using sensors with synchronized power supply, multi-path data solving, recording, and storage performances. To address this challenge, this work proposes a hardware system capable of synchronously processing temperature, depth, conductivity, and turbulence data on marine dynamics collected by sensors. The proposed system uses constant voltage sources to excite temperature and turbulence sensors, a constant current source to drive a depth sensor, and an alternating current (AC) constant voltage source to drive a conductivity sensor. In addition, the proposed system uses a high-precision analog-digital converter to acquire the direct current (DC) signals from temperature, depth, and turbulence sensors, as well as the AC signals from conductivity sensors. Since the sampling frequency of turbulence sensors is different from that of the other sensors, the proposed system stores the generated data at different storage rates as multiple-files. Further, the proposed hardware system manages these files through a file system (file allocation tab) to reduce the data parsing difficulty. The proposed sensing and hardware logic system is verified and compared with the standard conductivity-temperature-depth measurement system in the National Center of Ocean Standards and Metrology. The results indicate that the proposed system achieved National Verification Level II Standard. In addition, the proposed system has a temperature indication error smaller than 0.02 °C, a conductivity error less than 0.073 mS/cm, and a pressure error lower than 0.8‱ FS. The turbulence sensor shows good response and consistency. Therefore, for observation methods based on a single point, single line, and single profile, it is necessary to study multi-parameter data synchronous acquisition and processing in the time and spatial domains to collect fundamental physical quantities of temperature, salt, depth, and turbulence. The four basic physical parameters collected by the proposed system are beneficial to the in-depth research on physical ocean motion, heat transfer, energy transfer, mass transfer, and heat-energy-mass coupling and can help to realize accurate simulation, inversion, and prediction of ocean phenomena.

A Self-Powered Dielectrophoretic Microparticle Manipulation Platform Based on a Triboelectric Nanogenerator.

Lab-on-a-chip systems aim to integrate laboratory operations on a miniaturized device with broad application prospects in the field of point-of-care testing. However, bulky peripheral power resources, such as high-voltage supplies, function generators, and amplifiers, hamper the commercialization of the system. In this work, a portable, self-powered microparticle manipulation platform based on triboelectrically driven dielectrophoresis (DEP) is reported. A rotary freestanding triboelectric nanogenerator (RF-TENG) and rectifier/filter circuit supply a high-voltage direct-current signal to form a non-uniform electric field within the microchannel, realizing controllable actuation of the microparticles through DEP. The operating mechanism of this platform and the control performance of the moving particles are systematically studied and analyzed. Randomly distributed particles converge in a row after passing through the serpentine channel and various particles are separated owing to the different DEP forces. Ultimately, the high-efficiency separation of live and dead yeast cells is achieved using this platform. RF-TENG as the power source for lab-on-a-chip exhibits better safety and portability than traditional high-voltage power sources. This study presents a promising solution for the commercialization of lab-on-a-chip.

AC/DC dual-type pressure and movement sensor based on the nanoresistance network

Flexible pressure and movement sensors with dual-type signals have broad application potential in smart electronics and robotics. However, most devices usually require the assembly of various functional materials through a multistep and tedious process to realize the alternating current (AC) and direct current (DC) dual-mode output, resulting in difficult-to-control sensor volumes, complicated structures, and flexibility limitations. Here, we develop a dual-type signal pressure and movement sensor via fabricating the electrospun [polyvinyl pyrrolidone/polyaniline]//[polyacrylonitrile/barium titanate] ([PVP/PANI]//[PAN/BTO]) bifunctional Janus nanofiber films (BJNFF), which is able to generate AC or DC signals in different working modes. In working mode I, the BJNFF can produce an AC voltage signal (2.33 V N−1) for detecting vertical pressure. In working mode II, by moving Al over the BJNFF, the BJNFF can follow the dynamic Schottky contact to generate electron migration, thereby producing a DC output voltage signal for slip sensing. As a result, BJNFF utilizes the AC and DC conduction characteristics of the nanoresistance network to achieve AC/DC output compatibility in one integrated sensing material by one-step construction. Moreover, the BJNFF exhibits high sensitivity and maintains stability after reciprocating motion. These findings provide an efficient way for developing multifunctional sensors and achieving flexible smart electronics.