Polyethylene hydrogenolysis over bimetallic catalyst with suppression of methane formation

Hydrogenolysis has been explored as a promising approach for plastic chemical recycling.Noble metals,such as Ru and Pt,are considered effective catalysts for plastic hydrogenolysis,however,they result in a high yield of low-value gaseous products.In this research,an efficient bimetallic catalyst was developed by separate impregnation of Ni and Ru on SiO_(2) support resulting in liquid products yield of up to 83.1 C%under mild reaction conditions,compared to the 65.5 C%yield for the sole noble metal catalyst.The carbon distribution of the liquid products from low density polyethylene hydrogenolysis with Ni-modified catalyst also shifted to a heavier fraction,compared to that with Ru catalyst.Meanwhile,the NiRu catalyst exhibited excellent performance in suppressing the cleavage of the end-chain C–C bond,leading to a methane yield of only 10.4 C%,which was 69%lower than that of the Ru/SiO_(2) catalyst.Temperature programmed reduction and desorption of hydrogen and propane were further conducted to reveal the detailed mechanism of low density polyethylene hydrogenolysis over the bimetallic catalyst.The results suggested that the Ni-Ru alloy exhibited stronger H adsorption properties indicating improved hydrogen coverage on the catalyst surface thus enhancing the desorption of reaction intermediates.The carbon number distribution was ultimately skewed toward heavier liquid products.

All-natural phyllosilicate-polysaccharide triboelectric sensor for machine learning-assisted human motion prediction

The rapid development of smart and carbon-neutral cities motivates the potential of natural materials for triboelectric electronics.However,the relatively deficient charge density makes it challenging to achieve high Maxwell’s displacement current.Here,we propose a methodology for improving the triboelectricity of marine polysaccharide by incorporating charged phyllosilicate nanosheets.As a proof-of-concept,a flexible,flame-retardant,and eco-friendly triboelectric sensor is developed based on all-natural composite paper from alginate fibers and vermiculite nanosheets.The interlaced fibers and nanosheets not only enable superior electrical output but also give rise to wear resistance and mechanical stability.The fabricated triboelectric sensor successfully monitors slight motion signals from various joints of human body.Moreover,an effective machine-learning model is developed for human motion identification and prediction with accuracy of 96.2%and 99.8%,respectively.This work offers a promising strategy for improving the triboelectricity of organo-substrates and enables implementation of self-powered and intelligent platform for emerging applications.