High performance floating self-excited sliding triboelectric nanogenerator for micro mechanical energy harvesting

Non-contact triboelectric nanogenerator (TENG) enabled for both high conversion efficiency and durability is appropriate to harvest random micro energy owing to the advantage of low driving force. However, the low output (<10 μC m−2) of non-contact TENG caused by the drastic charge decay limits its application. Here, we propose a floating self-excited sliding TENG (FSS-TENG) by a self-excited amplification between rotator and stator to achieve self-increased charge density, and the air breakdown model of non-contact TENG is given for a maximum charge density. The charge density up to 71.53 μC m−2 is achieved, 5.46 times as that of the traditional floating TENG. Besides, the high output enables it to continuously power small electronics at 3 m s−1 weak wind. This work provides an effective strategy to address the low output of floating sliding TENG, and can be easily adapted to capture the varied micro mechanical energies anywhere.

AI and social theory

In this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.