Zengxing Zhang

Multi-scale vortex structures constitute the intrinsic skeleton of turbulent flows and govern the energy cascade process in oceanic turbulence. Elucidating their evolutionary dynamics is crucial for understanding turbulent mixing and transport. In this study, we develop an innovative Energy Cascade Multilayer Directed Network (ECMDN) framework grounded in complex network theory to directly characterize nonlinear energy coupling pathways and directional transfers among multi-scale vortices in real marine environments. By integrating multi-parameter fusion node definitions, multi-scale interaction detection, and energy transfer direction identification, the ECMDN reconstructs the nonlinear turbulent system into a topologically interpretable structure. The emergent network properties enable quantitative characterization of intermittency and inhomogeneity in the energy cascade, offering new insights into vortex interactions and cross-scale energy transfer mechanisms. Compared with conventional cascade diagnostics including spectral flux, third-order velocity structure functions, multifractal analysis and shell models that require homogeneity and local equilibrium assumptions and only output global averaged energy flux, the proposed ECMDN multilayer network retains point-wise depth coordinates of each vortex interaction, separates directed forward/inverse energy edges, and quantifies intermittency via topological metrics. Analysis of the single Shenhu thermocline shear segment demonstrates these differentiated analytical capabilities of the proposed framework. Application to shear measurements from the Shenhu Sea reveals the simultaneous occurrence of forward and inverse energy cascades, manifesting a synchronous dual-energy-cascade pattern. This indicates that vortices at a given scale can concurrently transfer energy to larger- or smaller-scale structures and receive energy from larger- or smaller-scale counterparts during the cascade process. Our findings observe a typical synchronous dual-energy-cascade pattern in the strong thermocline of the Shenhu Sea, providing a novel theoretical and methodological framework for investigating the spatiotemporal evolution of stratified ocean turbulent mixing and advancing our understanding of geophysical fluid dynamics.

A miniaturized (70 × 7.7 cm) multi-parameter synchronous observation system was developed for in situ ocean turbulence measurement, integrating micro-electromechanical system (MEMS)-based two-dimensional (2D) turbulence, pressure, temperature, conductivity, and attitude sensors. Field tests conducted at a depth of 1800 m in the northern South China Sea validated the system’s accuracy through comparisons with standard CTD (Conductivity, Temperature, and Depth) sensors, dual-probe consistency analysis, and Nasmyth spectrum fitting. The system precisely captured thermoclines, internal waves, and turbulent shear fluctuations at a depth of approximately 125 m, revealing enhanced turbulence near the thermocline due to intensified shear effects. With high spatiotemporal synchronization and reliability, the system provides an effective solution for studying multiscale ocean turbulence and associated dynamic processes.

Precise in-situ measurement of marine environmental parameters is fundamental to oceanographic research and resource development. While Micro-Electro-Mechanical Systems (MEMS) technology offers a novel pathway for sensor miniaturization, existing electrode designs remain limited. Traditional two-electrode structures suffer from severe signal drift due to electrode polarization effects, while improved four-electrode configurations struggle to completely eliminate polarization interference at the micro-scale, thereby hindering further enhancements in measurement accuracy. To address these challenges, this study presents a monolithically integrated MEMS conductivity-temperature (CT) sensor designed for ocean observation. The core innovation involves a novel seven-electrode differential structure that utilizes square-wave excitation to effectively suppress polarization effects and common-mode noise. Additionally, the monolithic integration of a thin-film platinum resistance temperature detector (RTD) and the conductivity sensor is achieved via MEMS processes, effectively eliminating spatiotemporal synchronization errors. Furthermore, a Back Propagation (BP) neural network algorithm is introduced to address the temperature dependence of conductivity measurements, significantly resolving the issue of temperature-salinity cross-sensitivity. The sensing structure was optimized via COMSOL simulations to mitigate complex multi-electrode electric field coupling. Experimental results demonstrate measurement accuracies better than ±0.004 °C for temperature and ±0.01 mS/cm for conductivity. Following BP neural network compensation, the conductivity measurement error across a wide temperature range is significantly reduced to ±0.02 mS/cm. In field trials conducted in actual sea areas, the sensor successfully captured thermocline and halocline profiles. The obtained data showed high consistency with commercial high-precision equipment, verifying the reliability and superiority of the proposed scheme.

This study introduces a dual-resonator quartz pressure sensor employing a novel stress redistribution mechanism with orthogonal force beams. Utilizing the anisotropy of AT-cut quartz, the design integrates X- and Z-resonators with force paths parallel and perpendicular to the X-axis, respectively. This configuration induces opposite pressure sensitivities, yielding a high-gain differential output. Finite element analysis guided the geometric optimization for maximum sensitivity and robustness. The fabricated device achieves a record differential sensitivity of 378.72 Hz/MPa at 25°C, increasing to 413.84 Hz/MPa at 80°C over 0–10 MPa. This positive temperature coefficient of sensitivity, combined with excellent linearity, low hysteresis (<0.18% FS), and high thermal stability, underscores its potential for demanding applications like downhole monitoring, advancing resonant sensor design through strategic mechanical engineering.

This study designed and fabricated a MEMS Pt thin-film temperature sensor suitable for the medium-to-high temperature range of 33-600 °C. A Pt film with a thickness of approximately 1 μm was deposited on an Al₂O₃ ceramic substrate via magnetron sputtering, and annealing experiments were conducted within the temperature range of 500-1000 °C. The microstructure, morphology, and crystal orientation of the thin film were characterized by scanning electron microscopy (SEM) and X-ray diffraction (XRD), while the resistance-temperature curve, resistance change rate, and temperature coefficient of resistance (TCR) of the sensor were tested. The results show that annealing at 800 °C for 4 hours is the optimal process: under this condition, the platinum thin film exhibits a strong (111) crystal orientation preference with uniform and dense grains. The TCR increases from 2740 ppm/°C before annealing to 3742 ppm/°C, and the resistance change rate reaches -26.14%. Within the range of 33-600 °C, the maximum temperature measurement error is less than 0.2%, the temperature drift is less than 0.08% FS, and the resistance-temperature curve shows excellent linearity. This sensor, featuring both high precision and stability, is suitable for high-precision temperature measurement scenarios in medium-to-high temperature environments, such as hot-end monitoring of aerospace engines and diagnosis of automotive exhaust systems.

Conductivity sensing plays a crucial role in biochemical and industrial process monitoring, particularly in complex liquid environments such as fermentation systems. This study developed a monolithically integrated sensor based on microelectromechanical systems (MEMS) technology, which innovatively combines a concentric four-electrode conductivity sensor with a four-wire platinum resistance temperature detector (RTD). The sensor is designed to track fermentation progress by performing in situ, real-time monitoring of conductivity and temperature variations. Electric field simulations based on rigorous geometric modeling confirmed the establishment of a uniform sensing field between the current excitation electrodes, effectively confining the measurement region. This concentric ring electrode design successfully generated a uniform current density field between the two voltagesensing electrodes, thereby providing an ideal condition for accurate solution conductivity measurement. The sensor was fabricated on a BF33 glass substrate using standard MEMS processes, including photolithography, ion beam etching (IBE), and plasma-enhanced chemical vapor deposition (PECVD) of a SiO2 insulation layer, achieving high integration and batch manufacturability. Comprehensive performance characterization verified its excellent microstructural characteristics and electrochemical performance. The sensor demonstrated outstanding overall performance: a temperature measurement accuracy of ±0.3%, a conductivity measurement accuracy of ±2%; excellent long-term stability with a monthly drift of less than 0.2% for the temperature sensor and less than 3% for the conductivity sensor. A compensation model, established using the integrated temperature sensor, effectively eliminated the impact of temperature fluctuations on conductivity measurements. Ultimately, in a practical yogurt fermentation application, the conductivitytime profile obtained by this sensor showed close agreement with the trend measured by a pH sensor, successfully revealing the dynamic changes of key fermentation stages. This study not only validates the high reliability of the integrated sensor but also provides a solid foundation for its broad application in monitoring complex biological processes.

Packaged temperature sensors exhibit significant thermal hysteresis effects, which directly impact their dynamic performance and accuracy during rapid temperature variations in marine environments. This paper focuses on platinum resistance thermometers (PRTs), which are commonly used in oceanic applications, and a dynamic testing method was proposed that employs temperature step excitation within a fully liquid environment, along with a full range dynamic error compensation approach based on the fireworks algorithm. Initially, a dynamic testing system was developed, and its testing repeatability was verified. Subsequently, a sample database for PRTs at various temperature steps was created. By optimizing the fitness function, the fireworks algorithm was utilized on sample database during the iterative process to design a dynamic error compensation filter. The resulting compensation filter demonstrated enhanced universality across various temperature step sizes within the sensor measurement range. Through the filter’s compensation, the rise time of the packaged PRT was reduced from an average of 477 ms to 121 ms within the measurement range. Furthermore, the dynamic response characteristics of the packaged PRT closely resembled those of the bare PRT. The dynamic testing method, which simulates heat transfer in real-world scenarios, in conjunction with the dynamic compensation method introduced in this paper, can also be employed to achieve dynamic compensation for sensors operating under diverse testing principles.

The gradient of the ionic conductivity is a critical factor for assessing the spatial variability of ionic distributions, as evident in the complex dynamics that are characteristic of various aqueous environments. Accurate observations of the conductivity fluctuations resulting from ion movement in solutions are essential for determining the purity of the aqueous phase and the surrounding environment. This study presents a comprehensive overview of nondestructive immersion measurements of ionic conductivity gradients. The phenomena associated with these gradients arise from intrinsic differences in the medium physical properties, the wide variety of ion characteristics, the intricacies of interfacial conditions, and the interactions between chemical mass transport phenomena in aqueous settings. A thorough review of the latest advancements in ionic conductivity gradient detection methodologies based on electrical, optical, and acoustic principles is presented, highlighting the key milestones and breakthroughs achieved to date. Furthermore, the ionic conductivity gradient formation principles and underlying mechanisms are reviewed, and different electrode measurements, inductively coupled systems, optical refractometry systems, and acoustic velocity evaluation measurements are classified. Additionally, the applications of nondestructive immersion measurements in water quality monitoring, oceanographic research, chemical production, industrial process control, and other ion-involving electrochemical reactions are summarized. The prospects and future directions for enhancing the cost effectiveness of field nondestructive testing and long-term stability of the equipment using new materials, structures, and AI technologies are also discussed. The technological innovation in nondestructive immersion measurements is expected to catalyze transformative changes in the optimization of chemical production, ushering in a new era of efficiency and control in industrial processes.