Shaoxi Wang

Electrocardiographic imaging (ECGI) is a noninvasive technique for reconstructing cardiac electrical activity by recording body-surface potentials and geometric information of the heart and torso. The ECGI forward problem is the cornerstone of ECGI. Depending on the forward model, ECGI can reconstruct epicardial/endocardial surface potentials, activation/recovery sequences, transmembrane voltages, and other electrophysiological quantities of interest. This article reviews the modeling process and research progress of forward modeling. This review systematically summarizes the mathematical methods used in the ECGI forward problem by classifying them into three representative models: the cardiac surface potential (CSP), equivalent double-layer (EDL), and transmembrane voltage (TMV) models, with detailed derivations and guidance on their practical applications.

The low noise amplifier (LNA) is a crucial component in RF front-end circuits, responsible for amplifying weak signals. However, it is susceptible to damage under high-power microwave (HPM) exposure, which can lead to performance degradation and eventual failure of the RF circuit. This article presents a cross-scale investigation of the failure behaviors and mechanisms of a commercial GaAs MMIC LNA chip subjected to HPM injection, integrating both experimental and simulation-based analyses. Initially, we developed an HPM injection test platform and monitored critical parameters, such as output gain and operating current, in real-time during the injection process. By analyzing the gain variations, we determined the power thresholds for chip degradation and damage. At the macroscopic level, analysis of the damaged circuits indicated that the GaAs pHEMT device in the first amplification stage of the LNA is the most vulnerable and fails first under HPM exposure. At the microscopic level, failure mode analysis using focused ion beam (FIB), Transmission electron microscope (TEM), and energy dispersive X-Ray spectroscopy (EDX) techniques revealed that the failure mode is primarily thermal failure, with melting occurring in the aluminum gate and the gate–source region. Simulation-based analysis of the failure mechanisms showed that under HPM exposure, the electric field concentrates in the region near the source beneath the gate, resulting in avalanche breakdown. This initiates a thermal-electrical positive feedback loop, progressively increasing the internal temperature of the device, which leads to the melting of the gate and gate–source region, ultimately causing thermal failure. The findings of this study offer critical insights into the failure mechanisms of LNAs in high electromagnetic environments, providing a foundation for future work on enhancing the reliability of RF circuits under HPM conditions.

Precise, contactless manipulation of micro- and nanoscale biological entities is pivotal for biomedical research, diagnostics, and therapeutic applications. However, most existing approaches suffer from limited programmability, low selectivity, or require physical contact and labeling, which restrict their applicability in complex biomedical environments. Here, we present a bipolar electrode-associated micromotor propulsion (BAMP) platform based on a rotating alternating current-flow field effect transistor (ROT-FFET), enabling the programmable control of cell enrichment, trajectory steering, and separation via electric field modulation. In a specific range of frequencies and voltages, particles or cells behave as active, interacting micromotors, mimicking the dynamics of living systems. By dynamically reconfiguring induced-charge electroosmotic (ICEO) flow and dielectrophoretic (DEP) forces, this system achieves real-time manipulation of synthetic particles and live cells (e.g., yeast, 293T, and red blood cells) with velocities up to 3.5 μm s^-1. Notably, contactless and label-free sorting of cells is finally demonstrated by exploiting their dielectric properties. In the future, the precise controllability of this approach can be combined with directed motion to develop modular building blocks for bottom-up fabrication with broad applicability in additive manufacturing, hybrid microrobotics, and biomedical microdevices.

Introduction: Electrocardiographic Imaging (ECGI) offers a non-invasive approach to reconstruct cardiac electrical activity. However, the inverse problem of ECGI is highly ill-conditioned, making it sensitive to errors. In practice, rigid displacements of the heart during beating introduce geometric errors into the ECGI problem. This study aims to investigate the impact of cardiac rigid motion on the accuracy of ECGI.Methods: We employed the Boundary Element Method (BEM) to solve the forward problem and the Tikhonov method to address the inverse problem. We utilized a dataset from the CRVTI/SCI Institute, which involves Langendorff-perfused dog hearts suspended in a torso-shaped tank. Based on clinical experience, six different types of cardiac movement patterns, including translations and rotations, were designed to assess the impact of various displacements on the accuracy of the ECGI solution.Results: Our study found that among the translational and rotational movements of the heart, rotational motion should be prioritized for attention, as it caused significantly stronger changes in ECGI correlation coefficient (CC) and relative error (RE) than translational motion. Among the translations along the coordinate axes, movement along the y-axis (anterior-posterior movement within the chest cavity) had the least impact. For rotational movements, rolling had the least impact, yaw had moderate impact, and pitch had the greatest impact.Conclusion: The inverse solution of ECGI demonstrates a certain robustness to changes in heart position, with CC changes of less than 2% for 10 mm displacements and less than 5% for 10° rotations. This suggests that ECGI changes due to cardiac geometric motion can be disregarded within a certain range.