Stimulus-responsive hydrogels have broad applications in the biomedical, sensing, and actuation fields. However, conventional fabrication methods are often limited to 2D structures, hindering the creation of complex, personalized 3D hydrogel architectures. Furthermore, hydrogels responding to only a single stimulus and delays in fabrication techniques restrict their practical utility in biomedicine. In this study, we developed two novel multi-stimuli-responsive hydrogels (based on Gelatin/Sodium Alginate and Tannic Acid/EDTA-FeNa complexes) specifically designed for direct ink writing (DIW) 3D printing. We systematically characterized the printed properties and optimized component ratio, revealing sufficient mechanical strength (e.g., tensile modulus: Gel/SA-TA ≥ 0.22854 ± 0.021 MPa and Gel/SA-TA@Fe³⁺ ≥ 0.35881 ± 0.021 MPa), high water content (e.g., water absorption rate Gel/SA-TA ≥ 70.21% ± 1.5% and Gel/SA-TA@Fe³⁺ ≥ 64.86% ± 1.28%), excellent biocompatibility (e.g., cell viability: Gel/SA-TA and Gel/SA-TA@Fe³⁺ ≥ 90%) and good shape memory performance (e.g., the highest shape recovery rate of Gel/SA-TA reaches 74.85% ± 4.776%). Furthermore, we explored electrical characteristics, showing that the impedance value of Gel/SA-TA@Fe³⁺ hydrogel changes significantly under finger bending and NIR irradiation. This investigation demonstrates the potential of these 3D-printed multi-stimuli hydrogels for applications such as wearable flexible strain sensors.

This paper presents a modular embedded edge-AI teaching platform built around the STM32N6 microcontroller, designed to meet demand for low-power, real-time, deployable edge intelligence in engineering education. The platform uses a heterogeneous architecture combining an ARM Cortex-M55 core with a dedicated Neural-ART NPU, enabling efficient on-device inference for both classroom projects and vision-based competition tasks. To improve stability across multi-peripheral setups, a multi-power-domain supply architecture combines switched-mode power supplies with low-noise LDO regulators, plus dual-input power switching, reverse-current and reverse-polarity protection, overcurrent limiting, and soft-start control. High-speed modular peripherals are integrated on board—MIPI-CSI camera input, RGB display output, high-speed NOR Flash, and SPI, I2C, UART, and TIMER expansion interfaces—with an 80-pin board-to-board connector for flexible extension. A four-layer PCB layout improves signal integrity for reliable high-speed operation. Deploying a custom lightweight vision algorithm (PEPoseNet), the prototype achieves an inference-only latency of 18.4 ms and 28.31 FPS/Watt energy efficiency within a sub-3 W power budget. These results confirm the platform’s reliability as an educational training platform for embedded edge-AI computing.