Electrochemical Surface Restructuring of Phosphorus-Doped Carbon@MoP Electrocatalysts for Hydrogen Evolution

The hydrogen evolution reaction(HER) through electrocatalysis is promising for the production of clean hydrogen fuel. However,designing the structure of catalysts,controlling their electronic properties,and manipulating their catalytic sites are a significant challenge in this field. Here,we propose an electrochemical surface restructuring strategy to design synergistically interactive phosphorus-doped carbon@MoP electrocatalysts for the HER. A simple electrochemical cycling method is developed to tune the thickness of the carbon layers that cover on MoP core,which significantly influences HER performance. Experimental investigations and theoretical calculations indicate that the inactive surface carbon layers can be removed through electrochemical cycling,leading to a close bond between the MoP and a few layers of coated graphene. The electronsdonated by the MoP core enhance the adhesion and electronegativity of the carbon layers;the negatively charged carbon layers act as an active surface. The electrochemically induced optimization of the surface/interface electronic structures in the electrocatalysts significantly promotes the HER. Using this strategy endows the catalyst with excellent activity in terms of the HER in both acidic and alkaline environments(current density of 10 mA cm^(-2) at low overpotentials,of 68 mV in 0.5 M H_(2)SO_(4) and 67 mV in 1.0 M KOH).

FeOOH上掺入Ni和Cu引起的羟基和含氮中间物的共吸附行为实现加速氨氧化反应中的脱氢过程

氨是一种很有前途的能源载体,由于其高氢含量和无碳的特点,可用于燃料电池,并可作为电解水制氢装置中水的替代氧化底物.然而,人们对氨电氧化反应(AOR)的机理认识不足,且缺乏廉价、高效的AOR催化剂,因而阻碍了氨基能源系统的发展.在这项工作中,我们通过光诱导化学沉淀法合成的新型Ni和Cu共掺杂的多孔FeOOH纳米棒(NiCu-FeOOH)可以作为AOR催化剂,其具有高效的催化活性(阳极电流密度达到10 mA cm^(-2)时执行电压为1.41 V)和在氨碱溶液中优异的稳定性.实验数据和理论计算结果表明,异质的Ni和Cu原子的协同作用使得NiCu-FeOOH表面的Ni和Fe位点表现出更合适的电子结构,他们可以共同吸附含氮中间产物和羟基,并使其吸附自由能位于火山形曲线的顶部,从而加速AOR脱氢.决速步骤的后移(*NH_(2)+*OH形成步骤移至*N_(2)H_(3)+*OH形成步骤)和决速步骤较低的能垒(0.86 eV)揭示了Ni和Cu的共掺策略使FeOOH晶体对催化AOR更具活性.本文创新地提出了涉及含氮中间物和羟基的共吸附反应途径,以更好地描述和模拟AOR过程,这为设计低成本和稳定的AOR催化剂开辟了新的路径.

Benzimidazole-Linked Porous Polymers:Synthesis and Gas Sorption Properties

A series of benzimidazole-linked porous polymers are obtained by the condensation reaction between the o-aminobenzol end groups of building blocks(2,3,6,7,10,11-hexaaminotriphenylene,3,3'-diaminobenzidine or 1,2,4,5-benzenetetraamine)and the aldehyde groups of building blocks[terephthalicaldehyde,4,4'-biphenyl-dicarboxaldehyde,1,3,5-tris(4-acetylphenyl)benzene or 1,3,5-tris(4-formylbiphenyl)amine]in one-pot synthesis without employing any catalyst or template.The existence of the imidazole ring in the obtained polymers could be identified by Fourier transform infrared and solid-state^(13)C CP/MAS NMR spectroscopy.The sphere-shaped mor-phology of the obtained polymers is observed through scanning electron microscopy.The polymers possess Brunauer-Emmett-Teller specific surface area values over 600 m^(2)·g^(-1),showing hydrogen storage(up to 1.6 wt%,at 77 K and 1×10^(5)Pa)and carbon dioxide capture(up to 12.6 wt%,at 273 K and 1×10^(5)Pa)properties.Such poly-mers would possess good performance in the applications of gas storage and separation.

DeepCount:Crowd Counting with Wi-Fi Using Deep Learning

The ubiquitous Wi-Fi devices and recent research efforts on wireless sensing have led to intelligent environments which can sense people’s locations and activities in a device-free manner.However,current works are mostly designed for single human environment owing to the complexity of multiple human environments,the limited bandwidth of Wi-Fi and in turn,greatly hinder this technology from the real implementation.To realize such device-free sensing in multiple human environments,the first step-stone is to estimate how many targets or in other words crowd counting in a closed environment,which is not only the basis for multiple human environmental sensing but also leads to many potential applications such as crowd control.To this end,we propose DeepCount—a solution using deep learning approach to infer the number of people in an indoor environment with Wi-Fi signals.Our scheme is based on the key intuition that,although with great complexity,the deep learning approaches can somehow be able to build a complex function to fit the correlation between the number of people and channel state information values.Furthermore,to alleviate the inadequate amount of data required and improve the adaptability of the deep learning approach,we add an online transfer learning approach,which utilizes the entering/leaving results to fine-tune the deep learning model.The prototype of Deep-Count is implemented and evaluated on the commercial Wi-Fi device.By the massive training samples,our deep learning model is able to estimate the number of crowd up to 5 with the mean accuracy of 82.3%by this end-to-end learning approach.Meanwhile,by using the amendment mechanism of the activity recognition model to judge door switch to get the variance of the crowd to amend deep learning predicted results,the accuracy is up to 87%in a rather effective manner.