Jian He

Stimulated Brillouin scattering (SBS) has emerged as a fundamental nonlinear process in integrated photonics, enabling advanced coherent signal processing, narrow-linewidth lasers, and ultra-high-resolution sensing. However, harnessing SBS at the chip scale presents inherent challenges, primarily originating from weak optomechanical interactions and short phonon lifetimes. This review provides a comprehensive and systematic overview of the latest advances in enhancing on-chip SBS. For the first time, we categorize the enhancement approaches into three interrelated pathways: waveguide geometry engineering (exploiting radiation pressure and phonon confinement), material-core optimization (ranging from chalcogenides to piezoelectric materials), and hybrid dynamic control schemes (such as phonon injection and electro-optic tuning). We further explore the frontier applications of engineered on-chip SBS in microwave photonics, quantum information processing, and optomechanical sensing. Finally, we offer perspectives on future directions and remaining challenges toward realizing scalable and multifunctional Brillouin photonic integrated circuits. This work serves as a roadmap for unlocking the full potential of Brillouin interactions in next-generation integrated systems.

Limb movement analysis provides a convenient and effective approach for physiological safety monitoring and motion standardization assessment of personnel working in humid, wading, or underwater environments. However, in wet and wading environments, the existing wearable flexible sensors generally suffer from several limitations such as insufficient sensitivity, rapid performance degradation, and poor environmental stability. In this paper, we propose a corrosion-resistant tribo-piezoelectric hybrid sensor (CRTPHS) primarily composed of an intrinsically corrosion-resistant fluorinated graphene oxide film with a micro-truncated pyramid array structure (MFGOF) and gold-coated polydimethylsiloxane-barium zirconate titanate (PDMS-BZT). The CRTPHS exhibited a sensitivity of up to 1.86 V/N, a response time of 106 ms, a peak power density of 1.98 mW/m2, and stable output performance after 50,000 cycles. In corrosion resistance tests, the CRTPHS retained a sensitivity of 1.27 V/N and excellent underwater sensing capability even after 3 days of seawater immersion. By integrating a lightweight random forest (RF) algorithm, the CRTPHS enabled recognition of five simulated armed-swimming motion states, achieving an average cross-subject accuracy of 99.25% ± 0.07% under leave-one-subject-out cross-validation (LOSO-CV). This study not only provides a novel fabrication method for developing corrosion-resistant self-powered sensors but also offers a new strategy for intelligent posture recognition in corrosive seawater environments.

A decorrelation preprocessing method based on linear predictive coding (LPC) is proposed for enhancing entropy-source signals from a Brillouin random fiber laser (BRFL). Unlike structure-optimization-based approaches, the proposed algorithm directly suppresses predictable components in raw BRFL entropy-source signals by extracting prediction residuals from historical-sample-based linear prediction. Experimental results show that LPC preprocessing significantly reduces the autocorrelation sidelobe ratio of the BRFL sampling sequence from −0.044 to −28.917dB, increases the spectral flatness from 0.005 to 0.952, and improves the amplitude min-entropy from 5.454 to 6.190 bit/sample. After random-bit postprocessing, the generated bitstream exhibits good 0/1 balance and low residual correlation and successfully passes the NIST randomness tests. These results demonstrate the effectiveness of LPC-based decorrelation preprocessing in enhancing BRFL entropy-source signals, indicating its potential for fiber-laser-based physical random number generation and secure information processing.

Objective: The development of brain-computer interface (BCI) technology has enabled animals to execute movements in accordance with human intent. The rat robot represents a novel robotic system based on BCI technology. However, due to limitations in electrode fabrication techniques and the use of simplistic control strategies, current rat robots are restricted to limited movement patterns, which hinder their applicability in real-world scenarios. To address these challenges, we have developed a portable wireless neural stimulator and a novel 3D integrated stimulating electrode. By refining the locomotion control strategy, we aim to achieve complex, high-degree-of-freedom movement in rat robot systems. Methods: 3D integrated electrodes were implanted into the rats' head, with no reward-based training required. By utilizing a wearable wireless stimulation backpack to connect the electrodes and deliver electrical stimulation to multiple brain regions, thereby enabling the rat to perform forward movement, turning, and stopping behaviors. Results: The experimental results demonstrate that under optimized stimulation parameters, the forward speed of the rat robot can be controlled to achieve 31.06 ± 1.21 m/min, the turning angle can reach up to 150 ± 1.22°, and the stopping duration can be flexibly adjusted. Furthermore, we presented a practical scenario in which the rat robot successfully executed a predefined navigation task in a real-world environment, thereby validating its high degree of movement flexibility and control precision. Conclusion: This study achieved high-degree-of-freedom motion control of rat robots without the need for reward-based training, which was previously unattainable. Significance: This research establishes a crucial foundation and provides valuable technical references for the application of animal robots in fields such as information reconnaissance and wreckage search and rescue operations.

Soft piezoelectric electronic skins are expected to work reliably under repeated joint bending, where both stretchability and stress transfer efficiency are important. In this work, a BaTiO₃/PDMS piezoelectric electronic skin, denoted as BPPE-skin, was prepared by a blade-coating method. Rather than only increasing the amount of piezoelectric filler, this study focuses on how the PDMS matrix composition affects the mechanical deformation and electrical response of the composite. The PDMS base-to-curing-agent ratio was adjusted from 5:1 to 30:1, and the BaTiO₃ loading was varied from 10 to 90 wt%. Tensile tests show that increasing the base-to-curing-agent ratio reduces the elastic modulus and improves stretchability, while excessive softening weakens effective stress transfer. As a result, the voltage output reaches its highest value at a 10:1 ratio. Increasing the BaTiO₃ content further enhances the output, mainly because more active piezoelectric particles participate in electromechanical conversion. The optimized film, containing 90 wt% BaTiO₃ with a 10:1 PDMS ratio, maintains good flexibility and can be attached conformally to different body joints. Tests on the knee, elbow, wrist, and fingers produce distinct voltage patterns during bending motions, indicating the potential of BPPE-skin for wearable, self-powered human motion monitoring.

Traditional biometric technologies, including fingerprint, iris, and facial recognition, have been widely deployed for identity verification but remain constrained by privacy risks and environmental sensitivities. In contrast, gait recognition has emerged as a promising behavioral biometric due to its non-intrusive nature and difficulty of being disguised. Herein, we proposed a gait recognition method based on multi-sensor fusion and multi-scale feature extraction. Plantar pressure distribution and limb acceleration data were synchronously acquired via a custom-developed smart pressure insole integrated with an inertial measurement unit (IMU), enabling an end-to-end recognition pipeline. A lightweight parallel dual-branch (LPDB) attention module was designed to reduce computational overhead, while a multi-scale feature fusion (MSFF) method effectively integrated heterogeneous sensor data, capturing structural invariants within gait cycles and enhancing cross-cycle consistency. The system achieved a recognition accuracy of 98.5%. Furthermore, the piezoresistive sensor fabricated for the smart insole incorporated disordered nanofibers to form a hierarchical micro-protrusion conductive network, yielding a high sensitivity of 45.4 kPa−1 in low-pressure regimes, significantly surpassing conventional porous material-based sensors. In addition, the as-prepared sensor exhibited superior anti-counterfeiting properties to facial features or fingerprints. This work offered a robust, privacy-preserving identification solution with potential applications in medical rehabilitation, sports science, robotic control, and the artificial intelligence of things (AIoT).

Graphene possesses exceptional mechanical strength, high electrical conductivity, and a stable lattice structure, making it an ideal material for sensors in advanced manufacturing. However, these sensors face stability challenges due to complex electromagnetic interference (EMI) environments generated by electrical equipment. Therefore, investigating the influence of EMI on sensor performance is of significant importance. In this study, simulations were performed to analyze electrical parameter perturbations of intrinsic graphene films under EMI conditions. The Magnetic Fields, Solid Mechanics, and Electrostatics modules in COMSOL Multiphysics were employed to construct a coupled model of a three-phase power transformer and a graphene-based pressure sensor. The results indicate that EMI can induce baseline drift on the order of ~5% full scale (FS) in the graphene current density, accompanied by degradation in signal-to-noise ratio (SNR) exceeding ~15 dB under typical simulation conditions. Graphene in direct contact with metal electrodes shows enhanced sensitivity to EMI, with more pronounced noise amplification due to interfacial coupling effects. In contrast, cavity-suspended graphene configurations exhibit relatively improved robustness, suggesting that suspended membrane architectures can mitigate EMI by reducing parasitic coupling and enhancing mechanical isolation. Compared with previous studies, this work highlights the role of multiphysics coupling and membrane suspension in influencing EMI-induced perturbations, providing theoretical guidance for the design of graphene-based sensors in power system and industrial Internet of Things (IoT) applications.