Space exploration is significant for scientific innovation, resource utilization, and planetary security. Space exploration involves several systems including satellites, space suits, communication systems, and robotics, which have to function under harsh space conditions such as extreme temperatures (− 270 to 1650 °C), microgravity (10^-6 g), unhealthy humidity (< 20% RH or > 60% RH), high atmospheric pressure (~ 1450 psi), and radiation (4000–5000 mSv). Conventional energy-harvesting technologies (solar cells, fuel cells, and nuclear energy), that are normally used to power these space systems have certain limitations (e.g., sunlight dependence, weight, degradation, big size, high cost, low capacity, radioactivity, complexity, and low efficiency). The constraints in conventional energy resources have made it imperative to look for non-conventional yet efficient alternatives. A great potential for enhancing efficiency, sustainability, and mission duration in space exploration can be offered by integrating triboelectric nanogenerators (TENGs) with existing energy sources. Recently, the potential of TENG including energy harvesting (from vibrations/movements in satellites and spacecraft), self-powered sensing, and microgravity, for multiple applications in different space missions has been discussed. This review comprehensively covers the use of TENGs for various space applications, such as planetary exploration missions (Mars environment monitoring), manned space equipment, In-orbit robotic operations /collision monitoring, spacecraft's design and structural health monitoring, Aeronautical systems, and conventional energy harvesting (solar and nuclear). This review also discusses the use of self-powered TENG sensors for deep space object perception. At the same time, this review compares TENGs with conventional energy harvesting technologies for space systems. Lastly, this review talks about energy harvesting in satellites, TENG-based satellite communication systems, and future practical implementation challenges (with possible solutions).

Women who receive continuous care from community-based midwives have a significantly reduced risk of preterm birth in comparison to those who receive standard care. This care model also significantly reduced risks of preterm births in women who are at greatest social risk of adverse outcomes.

Researchers at the Nara Institute of Science and Technology and Osaka University have developed a computational model of how human emotions are formed. This system integrates body signals, sensory input, and language, forming emotional concepts that match the self-reported human emotional judgment with 75% accuracy. The findings highlight new ways of building emotionally aware artificial intelligence, with potential applications in mental health care, interactive robots, and assistive technologies.