With the rapid progress of artificial intelligence, humans are moving toward the era of the intelligent connection of all things. Therefore, the demand for sensors is drastically increasing with developing intelligent social applications. Traditional sensors must be triggered by an external power source and the energy consumption is high for equipment that is widely distributed and working intermittently, which is not conducive to developing sustainable green and healthy applications. However, self-powered sensors based on triboelectric nanogenerators (TENG) can autonomously harvest energy from the surrounding environment and convert this energy into electrical energy for storage. Sensors can also be self-powered without an external power supply, which is vital for smart cities, smart homes, smart transportation, environmental monitoring, wearable devices, and bio-medicine. This review mainly summarizes the working mechanism of TENG and the research progress of self-powered sensors based on TENG about the Internet of Things (IoT), robotics, human–computer interaction, and intelligent medical fields in recent years.

Fluid simulations of capacitively coupled plasmas (CCPs) are crucial for understanding their discharge physics, yet the high computational cost results in a major bottleneck. To overcome this limitation, we develop a deep learning-based surrogate model to replicate the output of a one-dimensional CCP fluid model with near-instantaneous inference speed. Through a systematic evaluation of three architectures, i.e. feedforward neural network (FNN), attention-enhanced long short-term memory network (ALSTM), and convolutional-transformer hybrid network (CTransformer) it is found that the sequence-structured ALSTM model can achieve the optimal balance between speed and accuracy, with an overall prediction error of only 1.73% for electron density, electric field, and electron temperature in argon discharge. This study not only achieves significant simulation acceleration but also reveals that the model can accurately extrapolate from low-pressure conditions dominated by complex non-local effects to high-pressure conditions governed by simple local behavior, whereas the reverse extrapolation fails. This finding suggests that training under low-pressure conditions enables the model to capture more comprehensive physical features. From the perspective of model weights, both low-pressure and high-pressure models assign important weights to the sheath region. However, the low-pressure model exhibits higher weight peaks in the sheath, indicating stronger ability to capture the essential physics of sheath dynamics. In contrast, the high-pressure model, because of its lower weighting in the sheath region, may fail to adequately resolve complex sheath dynamics when predicting under new operating conditions, thereby limiting its extrapolation capability with high fidelity. To ensure the reliability of this data-driven model in practical applications, we establish a trust boundary with a normalized mean absolute spatial error of 5% for model performance through systematic extrapolation experiments. When the model's extrapolation error falls below this threshold, the spatial distribution curves of predicted parameters such as electron density and electron temperature closely match the true physical distributions. However, once the error exceeds this critical point, systematic deviations such as morphological distortion and amplitude discrepancies begin to appear in the predicted spatial distributions, significantly deviating from the true physical laws. In the future, we will develop neural network models capable of processing high-dimensional spatial data and combining multi-dimensional input features such as various discharge gases, ultimately realizing a dedicated AI model for the field of capacitively coupled plasmas.

流体模型是一类广泛用于低温气体放电宏观模拟的连续介质方法，能够以较低计算成本描述射频容性耦合等离子体（RF-CCP）中的密度分布、鞘层运动、电子加热和电离过程。然而，作为在时间和空间上离散求解的数值模型，其稳定性和物理准确性依赖于空间分辨率和界面通量构造方式。本文以一维 13.56 MHz 氩气 RF-CCP 放电为对象，选取 1 Torr / 40 V 和 2 Torr / 200 V两组工况，探讨了空间网格和通量构造方式对电子通量、鞘层结构、功率吸收、电离源项、电子密度以及电流密度的影响。结果表明，电子通量在粗网格条件下首先受到界面通量构造方式的影响，并表现出不同的空间输运强度。电子通量差异会进一步改变鞘层厚度、电子吸收功率，并通过平均电子温度和电离速率系数反馈到电离源项，最终影响电子密度分布，此外，总功率密度和总电流密度的幅值也受到界面通量构造方式的影响，因此，在本文所考察的一维低气压氩气 RF-CCP流体模拟中，数值收敛并不意味着所得物理量已经准确可靠，应结合电子通量、鞘层演化、电子吸收功率、电离源项以及电流密度评估收敛解的可信性。