Density functional theory calculations are reported for prediction of the trends in CS bond dissociation energies and atomic spin densities for radicals using S,S′‐bis(α,α′‐dimethyl‐α‐acetic acid) trithiocarbonate (TTCA) and bis(2‐oxo‐2‐phenylethyl) trithiocarbonate (TTCB) as reversible addition fragmentation chain transfer (RAFT) reagents. The calculations predict that the value of the CS bond length (1.865 Å) of TTCA is longer than that (1.826 Å) of TTCB, and TTCA is more effective for the polymerization of styrene (St) compared to TTCB as predicted by density functional theory. In photopolymerizations, pseudo‐first‐order kinetics were confirmed for TTCB‐mediated photopolymerization of St due to the linear increase of ln([M]0/[M]) up to about 28% conversion, suggesting the living characteristics behavior of the photopolymerization of St in the presence of TTCB. For both TTCA and TTCB the polydispersities change with increasing conversion in the range 1.10–1.45, typical for RAFT‐prepared (co)polymers and well below the theoretical lower limit of 1.50 for a normal free radical polymerization. In addition, the triblock copolymer polystyrene‐block‐poly(butyl acrylate)‐block‐polystyrene (PSPBAPS) was successfully prepared, with very good control over molecular weight and narrow polydispersity (Mw/Mn = 1.45), using PSSC(S)SPS as macro‐photoinitiator under UV irradiation at room temperature. This indicated that this reversible and valid strategy led to a better controlled block copolymer with defined structures. Copyright © 2007 Society of Chemical Industry

With the rapid development of the internet, social media has become an essential tool for getting information, and attracted a large number of people join the social media platforms because of its low cost, accessibility and amazing content. It greatly enriches our life. However, its rapid development and widespread also have provided an excellent convenience for the range of fake news, people are constantly exposed to fake news and suffer from it all the time. Fake news usually uses hyperbole to catch people’s eyes with dishonest intention. More importantly, it often misleads the reader and causes people to have wrong perceptions of society. It has the potential for negative impacts on society and individuals. Therefore, it is significative research on detecting fake news. In the paper, we built a model named SMHA-CNN (Self Multi-Head Attention-based Convolutional Neural Networks) that can judge the authenticity of news with high accuracy based only on content by using convolutional neural networks and self multi-head attention mechanism. In order to prove its validity, we conducted experiments on a public dataset and achieved a precision rate of 95.5% with a recall rate of 95.6% under the 5-fold cross-validation. Our experimental result indicates that the model is more effective at detecting fake news.

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