Brain-inspired neuromorphic computing is expected to overcome the von Neumann bottleneck by eliminating the memory wall between processing and memory units. Nevertheless, critical challenges persist in synaptic device implementation, particularly regarding nonlinear/asymmetric conductance modulation and multilevel conductance states, which substantially impede the realization of high-performance neuromorphic hardware. This study demonstrates a novel advancement in photonic–electronic modulated synaptic devices through the development of an amorphous indium–gallium–zinc oxide (a-IGZO) synaptic transistor. The device demonstrates biological synaptic functionalities, including excitatory/inhibitory post-synaptic currents (EPSCs/IPSCs) and spike-timing-dependent plasticity, while achieving excellent conductance modulation characteristics (nonlinearity of 0.0095/−0.0115 and asymmetric ratio of 0.247) and successfully implementing Pavlovian associative learning paradigms. Notably, systematic neural network simulations employing the experimental parameters reveal a 93.8% recognition accuracy on the MNIST handwritten digit dataset. The a-IGZO synaptic transistor with photonic–electronic co-modulation serves as a potential critical building block for constructing neuromorphic architectures with human-brain efficiency.

Brain-inspired neuromorphic computing has been considered as a promising novel paradigm and expected to replace traditional von Neumann architecture. Artificial synapses are recognized as the hardware foundation of neuromorphic computing, while the electrolyte-gated synapse has been proposed for its advantages in emulating biological synapses. Here, we propose a coplanar all-solid-state electrolyte-gated amorphous indium–gallium–zinc–oxide synapse that demonstrates multiple forms of both short- and long-term synaptic plasticity, including inhibitory and excitatory post-synaptic currents, paired-pulse facilitation, and relearning behavior. This artificial synapse not only efficiently executes Boolean operations but also switches between ‘AND’ and ‘OR’ logic types within a single device. Furthermore, it exhibits excellent performance in long-term potentiation and depression, with coefficients of nonlinearity of 0.046 and −0.073, achieving a notable accuracy of 93.2% in simulated handwritten digit recognition. This work emphasizes the significant potential of high-performance all-solid-state electrolyte-gated synapses in driving the development of next-generation neuromorphic computing systems.

Self-powered microfluidic systems represent a promising direction toward autonomous and portable lab-on-chip technologies, yet conventional electrowetting platforms remain constrained by bulky high-voltage supplies and intricate control circuitry. In this work, we design a triboelectric nanogenerator (TENG)-based microfluidic system that harvests mechanical energy for droplet manipulation without any external electronics. The TENG integrates two triboelectric units with a 25° phase offset, enabling periodic high-voltage generation. Finite element simulations elucidate the electric field distributions of the TENG and microfluidic chip, validating the operating principle of the integrated microfluidic system. Experimental studies further quantify the effects of electrode geometry and rotational speed on the critical drivable droplet volume, demonstrating stable transport over linear, S-shaped, and circular trajectories. Remarkably, the droplet motion direction can be instantaneously reversed by reversing the TENG rotation direction, achieving bidirectional control without auxiliary circuitry. This work establishes a voltage-optimized, structurally tunable, and fully self-powered platform, offering a new paradigm for portable digital microfluidics.

The detection and monitoring of ions are essential for a broad range of applications, including industrial process control and biomedical diagnostics. Traditional ion-sensitive field-effect transistors require bulky and expensive reference electrodes, which face several limitations, including device miniaturization, high fabrication costs, and incompatibility with semiconductor manufacturing processes. Here, we introduce a reference-less semiconductor ion sensor (RELESIS) that utilizes anodic aluminum oxide film as both the sensitive and dielectric layer. The RELESIS is composed of a metal-oxide-semiconductor field-effect transistor and an interdigital electrode, which fundamentally eliminates the need for a reference electrode, thereby enabling device miniaturization. During fabrication, the anodic oxidation process is employed in place of the expensive atomic layer deposition method, significantly reducing manufacturing costs while maintaining high surface quality. In practical measurements, the RELESIS device demonstrated an excellent pH sensitivity of 57.8 mV/pH with a low hysteresis of 7 mV. As a proof-of-concept application, the RELESIS device was employed for real-time, non-destructive monitoring of milk freshness, accurately detecting pH changes from fresh to spoiled in milk samples. The combination of reference-less structure, low-cost fabrication, and superior sensing performance positions this technology as a promising platform for next-generation portable ion sensing systems in food safety, environmental monitoring, and point-of-care diagnostics.