Structural architectures of polymer proton exchange membranes suitable for high-temperature fuel cell applications

High-temperature proton exchange membrane (HT-PEM) fuel cells offer more advantages than low-temperature PEM fuel cells. The ideal characteristics of HT-PEMs are high conductivities, low-humidity operation conditions, adequate mechanical properties, and competitive costs. Various molecular moieties, such as benzimidazole, benzo-thiazole, imide, and ether ether ketone, have been introduced to polymer chain backbones to satisfy the application requirements for HT-PEMs. The most common sulfonated polymers based on the main chain backbones have been employed to improve the rties. Side group/chain engineering, includ crosslinking, has been widely applied to HT-PEMs to further improve their proton conductivity, thermal stability, and mechanical properties. Currently, phosphoric acid-doped polybenzimidazole is the most successful polymer material for application in HT-PEMs. The compositing/blending modification methods of polymers are effective in obtaining high PA-doping levels and superior mechanical properties. In this review, the current progress of various membrane materials used for HT-PEMs is summarized. The synthesis and performance characteristics of polymers containing specific moieties in the chain backbones applied to HT-PEMs are discussed systemically. Various modification approaches and their deficiencies associated with HT-PEMs are analyzed and clarified. Prospects and future challenges are also presented

Self-learning based Fourier ptychographic microscopy.

Fourier Ptychographic Microscopy (FPM) is a newly proposed computational imaging method aimed at reconstructing a high-resolution wide-field image from a sequence of low-resolution images. These low-resolution images are captured under varied illumination angles and the FPM recovery routine then stitches them together in the Fourier domain iteratively. Although FPM has achieved success with static sample reconstructions, the long acquisition time inhibits real-time application. To address this problem, we propose here a self-learning based FPM which accelerates the acquisition and reconstruction procedure. We first capture a single image under normally incident illumination, and then use it to simulate the corresponding low-resolution images under other illumination angles. The simulation is based on the relationship between the illumination angles and the shift of the sample's spectrum. We analyze the importance of the simulated low-resolution images in order to devise a selection scheme which only collects the ones with higher importance. The measurements are then captured with the selection scheme and employed to perform the FPM reconstruction. Since only measurements of high importance are captured, the time requirements of data collection as well as image reconstruction can be greatly reduced. We validate the effectiveness of the proposed method with simulation and experimental results showing that the reduction ratio of data size requirements can reach over 70%, without sacrificing image reconstruction quality

Self-learning based Fourier ptychographic microscopy.

Fourier Ptychographic Microscopy (FPM) is a newly proposed computational imaging method aimed at reconstructing a high-resolution wide-field image from a sequence of low-resolution images. These low-resolution images are captured under varied illumination angles and the FPM recovery routine then stitches them together in the Fourier domain iteratively. Although FPM has achieved success with static sample reconstructions, the long acquisition time inhibits real-time application. To address this problem, we propose here a self-learning based FPM which accelerates the acquisition and reconstruction procedure. We first capture a single image under normally incident illumination, and then use it to simulate the corresponding low-resolution images under other illumination angles. The simulation is based on the relationship between the illumination angles and the shift of the sample's spectrum. We analyze the importance of the simulated low-resolution images in order to devise a selection scheme which only collects the ones with higher importance. The measurements are then captured with the selection scheme and employed to perform the FPM reconstruction. Since only measurements of high importance are captured, the time requirements of data collection as well as image reconstruction can be greatly reduced. We validate the effectiveness of the proposed method with simulation and experimental results showing that the reduction ratio of data size requirements can reach over 70%, without sacrificing image reconstruction quality

Mechanism of ozone adsorption and activation on B-, N-, P-, and Si-doped graphene: A DFT study

The detailed evolution mechanism of O-3 into Reactive oxygen species (ROS) is of paramount importance but remains elusive in catalytic ozonation. Herein, we report a density functional theory study to comprehensively reveal the specific evolution processes of O-3 into ROS on the B-, N-, P-, and Si-doped graphene, including the adsorption, decomposition and ROS generation. In contrast to some previous reports that O-3 would directly decompose into effective ROS on catalysts, our results indicate that after O-3 adsorption, the decomposition products are ground state O-2 and the adsorbed oxygen species (O-ads). The O-ads is more likely to act as a crucial intermediate for generating other ROS instead of directly attacking the organics. The type of the ROS and generation efficiency vary with the doped heteroatoms, and the heteroatoms of B, P and Si, or the neighboring C of N, would serve as active sites for O-3 adsorption and decomposition. The N-and P-doped graphene are predicted to have the superior performance in ROS generation and catalytic stability. Finally, twenty representative descriptors were adopted to build the quantitative structure-activity relationship (QSAR) with the activation energy barrier of O-3 decomposition. The result indicates that condensed dual descriptor (CDD) could be useful for preliminarily selecting the modified graphene catalysts, since it shows a very good linear relation with the activation energy barrier. This contribution provides an alternative way to gain fundamental insights into the mechanism of catalytic ozonation at the molecular level, and could be helpful for designing more-efficient catalysts in environmental remediation

Insights into the Mechanism of Ozone Activation and Singlet Oxygen Generation on N-Doped Defective Nanocarbons: A DFT and Machine Learning Study

N-doped defective nanocarbon (N-DNC) catalysts have been widely studied due to their exceptional catalytic activity in many applications, but the O-3 activation mechanism in catalytic ozonation of N-DNCs has yet to be established. In this study, we systematically mapped out the detailed reaction pathways of O-3 activation on 10 potential active sites of 8 representative configurations of N-DNCs, including the pyridinic N, pyrrolic N, N on edge, and porphyrinic N, based on the results of density functional theory (DFT) calculations. The DFT results indicate that O-3 decomposes into an adsorbed atomic oxygen species (O-ads) and an O-3(2) on the active sites. The atomic charge and spin population on the O-ads species indicate that it may not only act as an initiator for generating reactive oxygen species (ROS) but also directly attack the organics on the pyrrolic N. On the N site and C site of the N4V2 system (quadri-pyridinic N with two vacancies) and the pyridinic N site at edge, O-3 could be activated into O-1(2) in addition to O-3(2). The N4V2 system was predicted to have the best activity among the N-DNCs studied. Based on the DFT results, machine learning models were utilized to correlate the O-3 activation activity with the local and global properties of the catalyst surfaces. Among the models, XGBoost performed the best, with the condensed dual descriptor being the most important feature

Insights into the Mechanism of Ozone Activation and Singlet Oxygen Generation on N-Doped Defective Nanocarbons: A DFT and Machine Learning Study

N-doped defective nanocarbon (N-DNC) catalysts have been widely studied due to their exceptional catalytic activity in many applications, but the O-3 activation mechanism in catalytic ozonation of N-DNCs has yet to be established. In this study, we systematically mapped out the detailed reaction pathways of O-3 activation on 10 potential active sites of 8 representative configurations of N-DNCs, including the pyridinic N, pyrrolic N, N on edge, and porphyrinic N, based on the results of density functional theory (DFT) calculations. The DFT results indicate that O-3 decomposes into an adsorbed atomic oxygen species (O-ads) and an O-3(2) on the active sites. The atomic charge and spin population on the O-ads species indicate that it may not only act as an initiator for generating reactive oxygen species (ROS) but also directly attack the organics on the pyrrolic N. On the N site and C site of the N4V2 system (quadri-pyridinic N with two vacancies) and the pyridinic N site at edge, O-3 could be activated into O-1(2) in addition to O-3(2). The N4V2 system was predicted to have the best activity among the N-DNCs studied. Based on the DFT results, machine learning models were utilized to correlate the O-3 activation activity with the local and global properties of the catalyst surfaces. Among the models, XGBoost performed the best, with the condensed dual descriptor being the most important feature