The dielectric elastomer generator (DEG) has attracted attention in converting mechanical energy into electrical energy, due to its high energy density, fast response, and light weight, which together make DEG a promising technology for electromechanical conversion. In this article, recent research papers on DEG are reviewed. First, we present the working principles, parameters, materials, and deformation modes of DEG. Then, we introduce DEG prototypes in the field of collecting mechanical energy, including small-scale applications for wind energy and human motion energy, and large-scale applications for wave energy. At the end of the review, we discuss the challenges and perspectives of DEG. We believe that DEG will play an important role in mechanical energy harvesting in the future.

Soaring developments in wearable electronics raise an urgent need for stretchable electrets. However, achieving soft electrets simultaneously possessing excellent stretchability, longevity, and high charge density is still challenging. Herein, a facile approach is proposed to prepare an all-polymer hybrid composite electret based on the coupling of elastomer and ePTFE membrane. The composite electrets are fabricated via a facile casting and thermal curing process. The obtained soft composite electrets exhibit constantly high surface potential (−0.38 kV) over a long time (30 days), large strain (450%), low hysteresis, and excellent durability (15,000 cycles). To demonstrate the applications, the stretchable electret is utilized to assemble a self-powered flexible sensor based on the electrostatic induction effect for the monitoring of human activities. Additionally, output signals in the pressure mode almost two orders of magnitude larger than those in the strain mode are observed and the sensing mechanism in each mode is investigated.

With the rise of 6G communication technology, the issue of communication coverage in mountainous areas has become increasingly prominent. These regions are characterized by complex terrain, sparse user distribution, and small-scale clustering, making it difficult for traditional ground-based base stations, constrained by fixed locations and terrain obstructions, to achieve comprehensive signal coverage in mountainous areas. To address this challenge, this paper conducts an in-depth analysis of mountainous terrain and the differentiated needs of users, utilizing UAV-assisted base station signal coverage and designing an adaptive multi-scale bidirectional twin delayed deep deterministic policy gradient (AMB-TD3) algorithm to optimize base station layout and plan UAV routes. The algorithm significantly enhances performance by introducing a dynamic weight adaptation mechanism, multi-timescale coupling, and bidirectional information interaction strategies. In experiments, the best signal coverage rate of AMB-TD3 reached 98.094%, verifying its practicality in solving base station signal coverage issues in complex mountainous scenarios.