Dual interfacial modifications of hierarchically structured iodine-doped ZnO photoanodes for high-efficiency dye-sensitized solar cells

A nanocomposite porous electrode structure consisting of hierarchical iodine-doped zinc oxide (I-ZnO) aggregates combined with the two simple solution-processed interfacial modifications i.e. a ZnO compact layer (CL) and a TiO2 protective layer (PL) has been developed in order to understand electron transport and recombination in the photoanode matrix, together with boosting the conversion efficiency of I-ZnO based dye-sensitized solar cells (DSCs). Electrochemical impedance spectra demonstrate that ZnO CL pre-treatment and TiO2 PL post-treatment synergistically reduce charge-transfer resistance and suppress electron recombination. Furthermore, the electron lifetime in two combined modifications of IZnO + CL + PL photoelectrode is the longest in comparison with the other three photoelectrodes. As a consequence, the overall conversion efficiency of I-ZnO + CL + PL DSC is significantly enhanced to 6.79%, with a 36% enhancement compared with unmodified I-ZnO DSC. Moreover, the stability of I-ZnO + CL + PL cell is improved as compared to I-ZnO one. The mechanism of electron transfer and recombination upon the introduction of ZnO CL and TiO2 PL is also proposed in this work.

Economical and coordinated dispatch of CHP based microgrid with renewable energy resources

The advantages of high energy efficiency and economic benefit promote the wide application of combined heat and power system (CHP) based microgrid. Firstly, a mathematical model of the CHP based microgrid is developed. Then, a cost function for the coordination of heat and electric load is proposed. Finally, an optimal dispatch model is developed to achieve the economical and coordinated operation of the CHP based microgrid system. Simulation results verify effectiveness of the proposed dispatch model, which is a powerful tool for the energy management of CHP based microgrid with renewable energy resources.

Identification of Peptide Inhibitors of Enveloped Viruses Using Support Vector Machine

The peptides derived from envelope proteins have been shown to inhibit the protein-protein interactions in the virus membrane fusion process and thus have a great potential to be developed into effective antiviral therapies. There are three types of envelope proteins each exhibiting distinct structure folds. Although the exact fusion mechanism remains elusive, it was suggested that the three classes of viral fusion proteins share a similar mechanism of membrane fusion. The common mechanism of action makes it possible to correlate the properties of self-derived peptide inhibitors with their activities. Here we developed a support vector machine model using sequence-based statistical scores of self-derived peptide inhibitors as input features to correlate with their activities. The model displayed 92% prediction accuracy with the Matthew’s correlation coefficient of 0.84, obviously superior to those using physicochemical properties and amino acid decomposition as input. The predictive support vector machine model for self- derived peptides of envelope proteins would be useful in development of antiviral peptide inhibitors targeting the virus fusion process.