氨(NH_(3))作为重要的化学品和能源储存介质,需求量与日俱增.本文旨在通过电化学硝酸根还原反应(NO_(3)^(−)RR),将NO_(3)^(−)转化为NH3,不仅解决了NO_(3)^(−)引起的环境污染问题,又可以满足对NH_(3)的迫切需求.然而,NO_(3)^(−)RR涉及多...氨(NH_(3))作为重要的化学品和能源储存介质,需求量与日俱增.本文旨在通过电化学硝酸根还原反应(NO_(3)^(−)RR),将NO_(3)^(−)转化为NH3,不仅解决了NO_(3)^(−)引起的环境污染问题,又可以满足对NH_(3)的迫切需求.然而,NO_(3)^(−)RR涉及多个电子和质子转移过程,其中,NO_(2)^(−)是NO_(3)^(−)活化转化和深度还原合成NH_(3)的重要中间体.酞菁铜(CuPc)能够高效地活化转化NO_(3)^(−)为NO_(2)^(−),但在低过电位时无法有效地将NO2−还原为NH3,难以获得较高的氨法拉第效率(FENH3)和分电流密度.而氮配位的铁单原子催化剂(FeNC)则有较好的NO_(2)^(−)吸附活化特性.因此,利用双组分催化剂之间的协同作用以实现高效NO_(3)^(−)RR的活性和选择性是本文的主要研究思路.本文设计了CuPc/FeNC串联催化剂,利用CuPc和FeNC对NO_(3)^(−)和NO_(2)^(−)的吸附活化能力的差异,实现了高效的协同催化转化.X射线衍射、高角环形暗场扫描透射电镜、X射线光电子能谱及X射线吸收谱结果表明,FeNC催化剂中Fe原子均匀分布于ZIF-8热解后的基底.通过将FeNC和CuPc负载于气体扩散电极,在流动电解池中完成NO_(3)^(−)RR.CuPc/FeNC催化剂在较低电势区间中能够实现接近100%的NH3法拉第效率,同时在−0.57 V vs.RHE时达到273 mA cm–2的NH3分电流密度,并且在整个电势范围内有效地抑制了NO_(2)^(–)聚集.与单组分催化剂CuPc和FeNC对比结果表明,在−0.53 V vs.RHE时,CuPc/FeNC催化剂表现出较高的FE(NH_(3))/FE(NO_(2)^(−))比值,是CuPc催化剂的50倍;同时CuPc/FeNC催化剂上NH3分电流密度是FeNC催化剂的1.5倍.进一步研究了NO_(3)^(–)RR中的串联反应机制,其中FeNC催化剂表现出较高的NO_(2)^(–)RR活性,并且有效抑制了析氢反应.此外,CuPc/FeNC催化剂和FeNC催化剂在NO_(2)^(−)RR中表现出类似的NH3分电流密度,这表明在NO_(3)^(−)RR中,CuPc/FeNC催化剂性能的提高来源于FeNC位点能够进一步还原CuPc位点产生的NO_(2)^(–).理论计算结果表明,FeNC比CuPc表现出更强的NO_(2)^(–)吸附活化能力,说明NO_(2)^(−)在FeNC上更容易进行加氢还原.NO_(3)^(−)RR反应全路径分析结果表明,对于^(*)NO_(3)还原到*NO2过程,CuPc相对于FeNC位点具有明显降低的反应自由能,说明CuPc有利于NO_(2)^(−)的生成;而FeNC位点在后续的^(*)NO_(2)还原合成^(*)NH_(3)过程中具有更低的反应自由能,这与实验结果一致.一系列非原位和原位表征证明了CuPc催化剂在高电位下存在少量金属颗粒析出,与CuPc催化剂在高电位下NH_(3)分电流密度快速增加结果一致.综上,本工作中CuPc和FeNC催化剂之间的协同作用弥补了各自的不足,通过串联反应机制,在低过电位下有效增加了NH_(3)的法拉第效率和电流密度,实现了高效的协同催化转化,为设计和合成高效催化剂提供了新思路.展开更多
This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used a...This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used as experimental materials. Positive words versus neutral words and negative words versus neutral words are classified, respectively, using the induced EEG signals. The method of temporally regularized common spatial patterns （TRCSP） is chosen to extract features from the EEG trials, and then single-trial EEG classification is achieved by linear discriminant analysis. Classification accuracies are between 55% and 65%. The statistical significance of the classification accuracies is confirmed by permutation tests, which shows the successful identification of emotional words and neutral ones, and also the ability to identify emotional words. In addition, 10 out of 15 subjects obtain significant classification accuracy for negative words versus neutral words while only 4 are significant for positive words versus neutral words, which demonstrate that negative emotions are more easily identified.展开更多
文摘氨(NH_(3))作为重要的化学品和能源储存介质,需求量与日俱增.本文旨在通过电化学硝酸根还原反应(NO_(3)^(−)RR),将NO_(3)^(−)转化为NH3,不仅解决了NO_(3)^(−)引起的环境污染问题,又可以满足对NH_(3)的迫切需求.然而,NO_(3)^(−)RR涉及多个电子和质子转移过程,其中,NO_(2)^(−)是NO_(3)^(−)活化转化和深度还原合成NH_(3)的重要中间体.酞菁铜(CuPc)能够高效地活化转化NO_(3)^(−)为NO_(2)^(−),但在低过电位时无法有效地将NO2−还原为NH3,难以获得较高的氨法拉第效率(FENH3)和分电流密度.而氮配位的铁单原子催化剂(FeNC)则有较好的NO_(2)^(−)吸附活化特性.因此,利用双组分催化剂之间的协同作用以实现高效NO_(3)^(−)RR的活性和选择性是本文的主要研究思路.本文设计了CuPc/FeNC串联催化剂,利用CuPc和FeNC对NO_(3)^(−)和NO_(2)^(−)的吸附活化能力的差异,实现了高效的协同催化转化.X射线衍射、高角环形暗场扫描透射电镜、X射线光电子能谱及X射线吸收谱结果表明,FeNC催化剂中Fe原子均匀分布于ZIF-8热解后的基底.通过将FeNC和CuPc负载于气体扩散电极,在流动电解池中完成NO_(3)^(−)RR.CuPc/FeNC催化剂在较低电势区间中能够实现接近100%的NH3法拉第效率,同时在−0.57 V vs.RHE时达到273 mA cm–2的NH3分电流密度,并且在整个电势范围内有效地抑制了NO_(2)^(–)聚集.与单组分催化剂CuPc和FeNC对比结果表明,在−0.53 V vs.RHE时,CuPc/FeNC催化剂表现出较高的FE(NH_(3))/FE(NO_(2)^(−))比值,是CuPc催化剂的50倍;同时CuPc/FeNC催化剂上NH3分电流密度是FeNC催化剂的1.5倍.进一步研究了NO_(3)^(–)RR中的串联反应机制,其中FeNC催化剂表现出较高的NO_(2)^(–)RR活性,并且有效抑制了析氢反应.此外,CuPc/FeNC催化剂和FeNC催化剂在NO_(2)^(−)RR中表现出类似的NH3分电流密度,这表明在NO_(3)^(−)RR中,CuPc/FeNC催化剂性能的提高来源于FeNC位点能够进一步还原CuPc位点产生的NO_(2)^(–).理论计算结果表明,FeNC比CuPc表现出更强的NO_(2)^(–)吸附活化能力,说明NO_(2)^(−)在FeNC上更容易进行加氢还原.NO_(3)^(−)RR反应全路径分析结果表明,对于^(*)NO_(3)还原到*NO2过程,CuPc相对于FeNC位点具有明显降低的反应自由能,说明CuPc有利于NO_(2)^(−)的生成;而FeNC位点在后续的^(*)NO_(2)还原合成^(*)NH_(3)过程中具有更低的反应自由能,这与实验结果一致.一系列非原位和原位表征证明了CuPc催化剂在高电位下存在少量金属颗粒析出,与CuPc催化剂在高电位下NH_(3)分电流密度快速增加结果一致.综上,本工作中CuPc和FeNC催化剂之间的协同作用弥补了各自的不足,通过串联反应机制,在低过电位下有效增加了NH_(3)的法拉第效率和电流密度,实现了高效的协同催化转化,为设计和合成高效催化剂提供了新思路.
基金The National Natural Science Foundation of China(No.61375118)the Program for New Century Excellent Talents in University of China(No.NCET-12-0115)
文摘This study addresses the problem of classifying emotional words based on recorded electroencephalogram （EEG） signals by the single-trial EEG classification technique. Emotional two-character Chinese words are used as experimental materials. Positive words versus neutral words and negative words versus neutral words are classified, respectively, using the induced EEG signals. The method of temporally regularized common spatial patterns （TRCSP） is chosen to extract features from the EEG trials, and then single-trial EEG classification is achieved by linear discriminant analysis. Classification accuracies are between 55% and 65%. The statistical significance of the classification accuracies is confirmed by permutation tests, which shows the successful identification of emotional words and neutral ones, and also the ability to identify emotional words. In addition, 10 out of 15 subjects obtain significant classification accuracy for negative words versus neutral words while only 4 are significant for positive words versus neutral words, which demonstrate that negative emotions are more easily identified.
