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The neutral and charged species emitted by pulsed-laser irradiation of polytetrafluoroethylene (PTFE) at 248 nm in vacuum have been examined. In particular, the species and properties of the emission products produced at typical fluence regimes used in the pulsed-laser deposition of PTFE thin films have been characterized. The relative intensities of the major products as well as their dependence on laser fluence are presented, and a simple model is used to fit the observed fluence dependence. Evidence that the major neutral component, the monomer (C2F4), is formed from a thermally activated unzipping reaction is presented. The ionic species are derived from the neutral decomposition products, apparently ionized by electron collisions in the weak plasma generated at the target surface.

Thin films of polytetrafluoroethylene have been deposited on amorphous substrates by the pulsed-laser deposition technique. By transmission electron microscopy, the polymer films were shown to consist of both amorphous and crystalline components. The data for the crystalline component are consistent with it being highly ordered with the long helical molecular chains aligned parallel to the film substrate interface plane. The fraction of crystalline material in the films was found to be related to the substrate temperature during deposition with the maximum amount of crystalline material occurring when the substrate temperature was close to the melting temperature of the polymer.

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Through analyzing the behavior data of MOOCs learners, a MOOCs learner's score prediction model is constructed based on clustering algorithm and neural network in this paper. By using this model, we can find out the neglected information and hidden learning rules in the MOOCs learning process. The model can provide personalized guidance for each user and improve learning efficiency. The model can provide personalized service to help learners form personalized learn-ing strategies, and it also can alert learners with low grades and risk of dropping out.