This paper focuses on a multi-stage optimization scheme for variable-leakage-flux machines (VLFMs), where the leakage flux is passively controlled by armature-reaction-induced magnetic saturation. In VLFMs, the magnetic flux is regulated by the flux weakening (FW) method and controllable leakage flux. This intrinsic coupling complicates the consideration in the flux-adjustable range during the optimization procedure, thereby limiting the full utilization of the inherent advantages of the VLF property to optimize the overall performance. Therefore, a novel design optimization scheme is developed for the VLFM in this paper, which can well decouple the armature reaction and VLF property by employing a three-stage field-circuit coupled model. The proposed model accounts for three representative operating conditions. In the first stage, key performance requirements like peak torque at low speed are considered. In the second and third stages, the minimum and maximum q-axis currents are applied, respectively, allowing direct acquisition of the PM flux linkage while eliminating the influence of the d-axis armature reaction. Consequently, the flux-adjustable range can be directly calculated and optimized in the optimization procedure. Afterwards, the proposed design optimization scheme is implemented on a recently developed partitioned magnet pole VLFM (PMP-VLFM) to obtain an optimal design. Finally, finite element (FE) simulations and experimental tests are carried out to verify the feasibility of the proposed design optimization scheme.

Existing variable flux memory machines (VFMMs) typically suffer from the unavailability of operation envelope expansion and insufficient torque at the high magnetization state (MS), which severely hinders their potential for overall highly efficient operation through MS regulation. In this article, a novel efficiency-oriented stepwise (EOS) optimization strategy for VFMM is proposed. This strategy can innovatively take the coil flux linkage directly as an optimization objective, which can flexibly regulate the operation envelopes and permanent magnet (PM) torques at different MSs. A 27-slot/4-pole VFMM with the magnetic circuit complementary (MCC) concept is taken as a design example. First, the operation principle, multiple operation conditions, and key requirements of the VFMM are theoretically investigated. Then, the MCC-VFMM and the EOS optimization strategy are introduced and implemented, respectively. Furthermore, the key electromagnetic characteristics of the initial and optimized VFMMs are comprehensively compared. Finally, an optimized prototype is fabricated and experimented with to confirm the feasibility of the proposed optimization approach.

Single-molecule nanopore profiling reveals voltage- and environment-dependent conformational dynamics, dimerization, and dissociation of SARS-CoV-2 nucleocapsid protein domain fragments, advancing nanopore-based protein domain analysis for antiviral and diagnostic applications.

Brushless dual rotor machines (BLDRMs) are emerging as a promising solution for applications requiring multiple energy flows due to their dual electric ports and dual mechanical ports. For counter-rotating propellers in underwater and aerial vehicles, achieving independent dual-speed control is essential. However, the dual-inertia characteristic of BLDRMs makes it difficult to directly apply speed controller tuning methods developed for the single-inertia machines. Besides, the inherent coupling between speed and torque in dual rotors can further degrade control performance. In this article, the concept of virtual moment of inertia (VMI) is first proposed based on the electromechanical models, enabling the BLDRMs to be equivalently transformed into conventional single-inertia machines. Building on VMI, a model compensation-based active disturbance rejection control strategy is proposed to further decouple and achieve independent speed regulation. Specifically, state equations are derived based on the outer rotor speed and the speed difference between the dual rotors, treating torque coupling as a lumped disturbance. Comprehensive details regarding the controller design and parameter selection criteria are provided to ensure the practical implementation of various types of BLDRM structures and certain other multiport machines. Finally, experiments are conducted on a BLDRM drive platform to validate the effectiveness of the proposed method.

Ion transport underlies the operation of biological ion channels and governs the performance of electrochemical energy-storage devices. A long-standing puzzle is that smaller alkali metal ions, such as Li+, migrate more slowly in water than larger ions, in apparent violation of the Stokes-Einstein relation. This anomaly is conventionally attributed to dielectric friction, viewed as a collective drag force arising from electrostatic interactions between a drifting ion and the surrounding solvent. Here, by combining nanopore transport measurements over electric fields spanning several orders of magnitude with molecular dynamics simulations, we show that the time-averaged electrostatic interaction between a drifting ion and its surrounding water molecules is not a drag force but a net driving force. By comparing charged ions with neutral reference counterparts, we reveal that ionic charge introduces additional Lorentzian peaks in the frequency-dependent friction coefficient. These peaks originate predominantly from short-range Lennard-Jones (LJ) interactions within the first hydration layer and constitute additional channels for energy dissipation, strongest for Li+ and progressively weaker for Na+ and K+. Our results demonstrate that electrostatic interactions primarily act to tighten the local hydration structure, thereby amplifying short-range LJ interactions rather than directly opposing ion motion. This microscopic mechanism provides a unified physical explanation for the breakdown of the Stokes-Einstein relation in aqueous ion transport.

The increasing penetration of inverter-based resources necessitates electromagnetic transient (EMT) simulation models that achieve both high precision and efficiency for system dynamic behavior analysis. However, existing associated discrete circuit (ADC) converter models, such as L/C-ADC, often suffer from spurious harmonic spikes and virtual power loss in real-time EMT simulations. To address these issues, this article proposes a generalized half-bridge (GHB) ADC model, introducing three key innovations: 1) defining GHB modules as the basic modeling unit, enabling a unified framework of two-level (TL) converters across diverse topologies; 2) employing multistate parameter settings to represent both complementary and noncomplementary operating modes while maintaining a fixed admittance matrix; 3) introducing an initial switching error correction method of TL-GHB modules that combines an improved state judgment strategy with internal variable reinitialization to mitigate numerical errors. Simulation and experimental results validate its high accuracy in capturing high-frequency switching dynamics and noncomplementary states while maintaining high efficiency, thereby demonstrating its effectiveness for real-time EMT simulation of TL converters.

This paper proposes a novel arch-shaped-magnet variable-flux memory machine (ASM-VFMM). The proposed machine adopts a dual-layer permanent magnet (PM) rotor structure. In the first layer, an arch-shaped magnet arrangement is utilized to increase the volume of low-coercive-force (LCF) magnets, which contributes to improved magnetic flux adjustment (MFA) performance. The second layer incorporates an asymmetric PM (APM) layout to create a parallel magnetic circuit, enabling further suppression of air-gap flux density at the weakened-flux state. The topological development of the proposed machine is first described, covering the conventional series magnetic circuit (SMC) structure, the intermediary APM structure, and the proposed ASM structure. A theoretical modeling analysis is then conducted for the three machines. This confirms the superiority of the proposed design regarding its MFA capability. A comprehensive electromagnetic performance evaluation is carried out for the proposed machine, alongside comparative assessments of the other two machines. The results show that the proposed design outperforms the other two machines in terms of magnetization performance, MFA range, and on-load magnetization stabilization capability. Notably, the proposed machine exhibits excellent overall efficiency characteristics, especially under high-speed operating conditions.

This paper proposes a new heteropolar series-parallel variable flux machine (HSP-VFM), in which low coercive force (LCF) magnets play different roles. In the proposed topology, LCF magnets are placed on the q-axis, forming series magnetic circuits with adjacent high coercive force (HCF) magnets under north poles and parallel circuits under south poles. This arrangement enables a simple U-shaped magnet structure while achieving both wide flux adjusting (FA) range and strong demagnetization withstand (DW) capability. For fair evaluation, two benchmark machines with identical dimensions, a pure series VFM and a pure parallel VFM, are established for comparison. Results show that the proposed HSP-VFM uniquely combines the advantages of both benchmarks: it maintains robust DW capability while achieving significantly reduced air-gap flux density in the weakened-flux state. By selecting the optimal magnetization state for each operating point, the proposed machine extends its high-efficiency region (>92%) to 7900 r/min, outperforming both conventional topologies. With its simple structure and balanced performance, the HSP-VFM presents an attractive solution for wide speed range applications.

Asymmetric variable flux memory machines (VFMMs) utilize the magnetic field shifting (MFS) effect to significantly improve torque output under low magnetization states (MSs). However, machine parameters exhibit strong nonlinear variations under different MSs. These parameters primarily include inductance and permanent magnet flux linkage. Such variations increase the parameter tuning difficulty for traditional PI current controllers. Furthermore, they degrade the tracking accuracy and stability of the current control. To address this issue, this article proposes a prediction-error-adaptive deadbeat predictive current control (PEA-DPCC) strategy. A predictive current model is established to incorporate parameter variations and the MFS effect. This model unifies complex parameter mismatches into equivalent input terms. Besides, an adaptive variable step-size update law is constructed to compensate for the parameter perturbations. This adaptation mechanism is directly driven by the predictive current error. Finally, experiments are conducted on an asymmetric VFMM testbench to verify the proposed control strategy. The experimental results demonstrate that the PEA-DPCC exhibits stronger robustness under different MSs.

Variable flux memory machines (VFMMs) require the injection of a d-axis current pulse for magnetization-state (MS) manipulation. However, the conventionally injected d-axis current produces no electromagnetic torque during MS manipulation, which inevitably causes severe torque drop and speed fluctuations. To address this issue, this article first reveals a unique positive-torque characteristic during demagnetization of asymmetric VFMMs. Specifically, the magnetic-field-shifting (MFS) effect in the asymmetric rotor enables the negative d-axis demagnetizing pulse to contribute a positive electromagnetic torque. By utilizing this positive-torque characteristic, a current-angle regulation (CAR) method is proposed to actively mitigate the torque shock. Furthermore, a torque-model-based feedforward compensation (TM-FFC) method is developed to reduce speed fluctuations during MS manipulation. To further achieve rapid dynamic response and enhance speed fluctuation suppression, an improved active disturbance rejection control (ADRC) with a model-compensated differential extended state observer (MCD-ESO) is proposed to accelerate disturbance estimation. Finally, experiments on an asymmetric VFMM prototype validate the effectiveness of the proposed methods.

Inspired by the brain's efficient ionic processing, fluidic memristors using ions as charge carriers have emerged as a promising platform for future neuromorphic systems. To better replicate the brain's dynamic functions, it's essential to explore additional memristive materials and switching mechanisms. In this work, we present a self-heating-induced blocking memristor (SIBM), in which the memory effect arises from thermally triggered reversible precipitation. When subjected to periodic voltage stimulation, SIBM shows threshold-type unipolar resistive switching and features a clear negative differential resistance (NDR). We demonstrate various neuromorphic functions and experimentally validate a fluidic memristor array capable of repeated memory operations. These results provide insights for the design of next-generation neuromorphic fluidic devices.

Variable flux memory machines (VFMMs) can adjust the remanence of low coercive force (LCF) magnets to flexibly regulate the magnetic fluxes, demonstrating the potential for high efficiency across a wide speed range. However, existing VFMMs suffer from overlapping high-efficiency regions across different magnetization states (MSs), limiting their ability to maintain efficiency across the entire operational range required. Thus, this paper proposes a novel multi-state subregion optimization strategy for VFMMs. The proposed strategy specifically utilizes the concept of predefined high-efficiency regions, strategically allocating different MSs to avoid overlaps in their high-efficiency regions. By integrating the high-efficiency regions of various MSs, the overall efficiency of the VFMM can be enhanced. A multiseries magnetic circuit VFMM is chosen as the optimization case. Initially, the topology of the investigated VFMM and its application requirements for electric vehicles are introduced. Subsequently, the proposed optimization strategy is elaborated, where three implementation steps are investigated to select an optimized machine. Furthermore, key electromagnetic performances are compared between the optimized and initial machines. The results indicate that the overall efficiency of the optimized VFMM has significantly improved compared to the initial one. Finally, a VFMM prototype is validated experimentally, confirming the effectiveness of the proposed strategy.

This paper proposes a hybrid design methodology combining sensitivity analysis and multi-objective optimization. The method solves multi-parameter optimization challenges in a new consequent-pole hybrid excited machine with segmented stator (CHE-SS) machine for electric vehicles (EVs). The initial parameters are determined through electromagnetic field analysis and engineering experience, then the parameter response surface models are constructed via finite element (FE) method. Taguchi method, leave-one-out cross-validation (LOOCV) method and analysis of variance (ANOVA) method are adopted to quantify the sensitivity coefficients of the design parameters. Therefore, the critical influencing factors in the sensitivity analysis can be determined. A multi-objective optimization model including output torque, torque ripple, cogging torque, and torque regulating factor is subsequently developed. The optimization employs genetic algorithms (GA) for global exploration. The sequential quadratic programming (SQP) is adopted for the local refinement. Then, Pareto-optimal solutions can be obtained. The FE results validate the effectiveness of the proposed optimization method in this paper, which can offer a new method for the design of the multi-phase machine.

Recent advances in fluidic memristors have revolutionized the field of iontronics, enabling unprecedented control over ionic transport and presenting new opportunities for breakthroughs in neuromorphic computing and artificial intelligence. Unlike traditional long-channel-based fluidic memristors, we introduce an innovative droplet memristor that leverages an ionic liquid-electrolyte interface within a custom pore-microwell architecture. We developed a robust physical model to simulate and predict the memristor's conductance states, which guided the fabrication of a specialized memristor device. In this model, the dynamic expansion and contraction of the charged droplet under an electric field leads to variable conductance, thereby inducing memory effects. Our experimental results demonstrate the successful implementation of neuromorphic functions and responses using the droplet memristor, including pulse signal processing and mechanical stimulus-response. Both theoretical and experimental findings demonstrate the remarkable neuromorphic behaviors of droplet memristors, paving the way for the development of advanced neuromorphic devices.

Real-time simulation (RTS) of large-scale power electronic converter systems is often hindered by the heavy computational burden associated with high-dimensional, time-varying matrices. Digital twin (DT) technology, as an extension of RTS, enables continuous system monitoring and optimization throughout the lifecycle, offering a more accurate representation of real-world dynamics. In this article, an RTS model for power electronic converters that guarantees high stability is proposed. Besides, a DT platform is developed to validate the RTS model, integrating a physical controller, a three-phase dc-ac power electronic converter prototype, and high-speed I/O interfaces on RT-LAB. The proposed model ensures high stability, featuring without interface time delay, constant admittance matrices, and low-dimensional nodes, offering significant advantages over existing models. Embedded within the DT platform for application, the model enables efficient and reliable digital replication of the physical power electronic converter. Comprehensive RTS and DT experiments across multiple scenarios validate its superior dynamic performance and accuracy.

Because of the flexible magnetization state adjustment capability, hybrid-magnetic-circuit variable flux memory machines (HMC-VFMMs) can achieve a wide operating range and overall high efficiency, offering potential candidates for electric vehicles. Nevertheless, the existing HMC-VFMMs still suffer the narrow flux adjustment (FA) ranges, which seriously hinder the potential for efficiency improvements in the high-speed region. This article proposes a novel magnetic circuit complementary VFMM (MCC-VFMM) to enhance the FA range and on-load demagnetization resistance (ODR) capabilities simultaneously. Two different parallel magnetic circuit structures are employed in the proposed MCC-VFMM to significantly enhance its FA range, while the ingeniously complementary layout of the dual-layer PM structure can form series magnetic circuits with fine ODR capability simultaneously. First, the structural evolution and corresponding operating principle of the proposed MCC-VFMM are introduced individually. Moreover, the simplified equivalent magnetic circuit models of the proposed machine and the existing HMC-VFMM are developed to determine theoretically the enhanced FA capability of the proposed MCC-VFMM. Furthermore, a comprehensive comparison of the electromagnetic performance of the HMC- and MCC-VFMMs is investigated utilizing the finite element method. Finally, an MCC-VFMM prototype is tested, thereby confirming the feasibility of the above analysis.

Electrical sensing with nanopores has become a widely used bioanalytical tool. However, it remains unclear if and how the extremely strong electric field generated inside the pores influences biomolecular interactions. Here we show that the field disrupts the strongest known protein-ligand interaction in biology, namely biotin-avidin bonds. Remarkably, the lifetime of the interaction is decreased by at least 4 orders of magnitude. At hundreds of mV, avidin (from egg-white) starts dissociating from biotin-functionalized nanopores over a time scale of minutes even at the maximum bond valency of four. Streptavidin-coated nanoparticles, which form many more bonds, remain bound but exhibit surface mobility due to the field. These results show that nanopore sensors can give very inaccurate results when used for affinity-based detection or biomolecular interaction analysis and that the pore environment should be regarded as potentially invasive for the molecules inside.

Shaftless rim-driven thrusters (RDT) are widely used in underwater propulsion systems owing to their compact structure and high transmission efficiency. However, conventional surface-mounted permanent magnet synchronous machines (PMSMs) are typically utilized in RDTs, which exhibit limitations such as a narrow high-speed efficiency range. Therefore, a novel variable magnetic circuit memory machine (VMCMM) is proposed in this paper, in which the low coercive force (LCF) permanent magnet (PM) materials and the specialized magnet arrangements are employed. Moreover, a twelve-phase winding set with π/12 phase shift is adopted to reduce the torque ripple of the machine. Firstly, the topology evolution process and structure of the VMCMM are introduced. Secondly, the magnetic equivalent circuit (MEC) model of the VMCMM is established to theoretically verify the feasibility of flux changeable (FC) capability. Thirdly, the torque performance of the proposed machine is investigated, which theoretically confirms the validity of the twelve-phase winding in reducing torque ripple and improving torque output capability. Finally, the comprehensive electromagnetic performance of the proposed twelve-phase VMCMM is compared with a three-phase VMCMM, which confirms that the proposed machine can improve the high-speed efficiency range and torque performance.

Pulsar observation is a critical tool for exploring the universe, necessitating advanced digital backends to efficiently capture and process faint pulsar signals. This paper introduces a 4-channel wideband digital backend for pulsar observation, with each channel featuring dual polarization and a maximum bandwidth of 2 GHz. The system is capable of simultaneously processing multiple signal bands, with a total bandwidth up to 8 GHz, and employs Bartlett’s method for power spectrum estimation. The design divides 8 high-speed data streams from ADCs into 32 low-speed data streams to reduce power, while a specialized combined Fast Fourier Transform (FFT) unit with bit-reversed order is utilized to minimize resource consumption. This approach yields a compact and power-efficient solution integrated into a single Radio Frequency System on Chip (RFSoC). The backend fully utilizes the hardware resources and supports multiple observation modes, offering flexible bandwidth and frequency resolution options. The system’s performance has been validated through on-site telescope observations, confirming its capability to reliably acquire and process pulsar signals.

High-shock accelerometers are essential for automotive airbag systems, where sensors must detect extreme shock events while maintaining structural integrity. However, conventional MEMS structures often suffer from stress concentration and mechanical failure under ultra-high-g conditions. To address this, a novel capacitive MEMS accelerometer with a symmetrical four-quadrant, four-anchor spring-frame suspension is proposed. A genetic algorithm was used to optimize the connecting and support beam geometries, reducing their respective regional maximum Von Mises stresses by 39% and 21.7%. Finite element simulations show that the optimized structure withstands a 10,000 g shock with maximum Von Mises stress in the sensitive structure remaining below 1.2 x 10(8) N/m(2). The final design offers a +/- 480 g measurement range within a 700 mu m x 700 mu m footprint, demonstrating strong potential for high-reliability deployment in automotive airbag systems.