The catalytic conversion of N to N(SiMe) by homogeneous transition metal compounds is a rapidly developing field, yet few mechanistic details have been experimentally elucidated for 3 d element catalysts. Herein we show that Fe(PP)(N) (PP = RPCHCHPR; R = Me, 1; R = Et, 1) are highly effective for the catalytic production of N(SiMe) from N (using KC/MeSiCl), with the yields being the highest reported to date for Fe-based catalysts. We propose that N fixation proceeds via electrophilic N silylation and 1e reduction to form unstable Fe(NN-SiMe) intermediates, which disproportionate to 1 and hydrazido Fe[N-N(SiMe)] species (3); the latter act as resting states on the catalytic cycle. Subsequent 2e reduction of 3 leads to N-N scission and formation of [N(SiMe)] and putative anionic Fe imido products. These mechanistic results are supported by both experiment and DFT calculations.

Intelligent human-machine interfaces (HMIs) integrated wearable electronics are essential to promote the Internet of Things (IoT). Herein, a curcumin-assisted electroless deposition technology is developed for the first time to achieve stretchable strain sensing yarns (SSSYs) with high conductivity (0.2 Ω cm −1) and ultralight weight (1.5 mg cm −1). The isotropically deposited structural yarns can bear high uniaxial elongation (>>1100%) and still retain low resistivity after 5000 continuous stretching-releasing cycles under 50% strain. Apart from the high flexibility enabled by helical loaded structure, a precise strain sensing function can be facilitated under external forces with metal-coated conductive layers. Based on the mechanics analysis, the strain sensing responses are scaled with the dependences on structural variables and show good agreements with the experimental results. The application of interfacial enhanced yarns as wearable logic HMIs to remotely control the robotic hand and manipulate the color switching of light on the basis of gesture recognition is demonstrated. It is hoped that the SSSYs strategy can shed an extra light in future HMIs development and incoming IoT and artificial intelligence technologies.

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