The China Fusion Engineering Test Reactor (CFETR) is the next device in the roadmap for the realization of fusion energy in China, which aims to bridge the gaps between the fusion experimental reactor ITER and the demonstration reactor (DEMO). CFETR will be operated in two phases. Steady-state operation and self-sufficiency will be the two key issues for Phase I with a modest fusion power of up to 200 MW. Phase II aims for DEMO validation with a fusion power over 1 GW. Advanced H-mode physics, high magnetic fields up to 7 T, high frequency electron cyclotron resonance heating and lower hybrid current drive together with off-axis negative-ion neutral beam injection will be developed for achieving steady-state advanced operation. The recent detailed design, research and development (R&D) activities including integrated modeling of operation scenarios, high field magnet, material, tritium plant, remote handling and future plans are introduced in this paper.

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The peptide quantitative structure−activity relationship (QSAR), also known as the quantitative sequence−activity model (QSAM), has attracted much attention in the bio-and chemoinformatics communities and is a well developed computational peptidology strategy to statistically correlate the sequence/structure and activity/property relationships of functional peptides. Amino acid descriptors (AADs) are one of the most widely used methods to characterize peptide structures by decomposing the peptide into its residue building blocks and sequentially parametrizing each building block with a vector of amino acid principal properties. Considering that various AADs have been proposed over the past decades and new AADs are still emerging today, we herein query the following: is it necessary to develop so many AADs and do we need to continuously develop more new AADs? In this study, we exhaustively collect 80 published AADs and comprehensively evaluate their modeling performance (including fitting ability, internal stability, and predictive power) on 8 QSAR-oriented peptide sample sets (QPSs) by employing 2 sophisticated machine learning methods (MLMs), totally building and systematically comparing 1280 (80 AADs × 8 QPSs × 2 MLMs) peptide QSAR models. The following is revealed: (i) None of the AADs can work best on all or most peptide sets; an AAD usually performs well for some peptides but badly for others. (ii) Modeling performance is primarily determined by the peptide samples and then the MLMs used, while AADs have only a moderate influence on the performance. (iii) There is no essential difference between the modeling performances of different AAD types (physiochemical, topological, 3D-structural, etc.). (iv) Two random descriptors, which are separately generated randomly in standard normal distribution N(0, 1) and uniform distribution U(−1, +1), do not perform significantly worse than these carefully developed AADs. (v) A secondary descriptor, which carries major information involved in the 80 (primary) AADs, does not perform significantly better than these AADs. Overall, we conclude that since there are various AADs available to date and they already cover numerous amino acid properties, further development of new AADs is not an essential choice to improve peptide QSAR modeling; the traditional AAD methodology is believed to have almost reached the theoretical limit nowadays. In addition, the AADs are more likely to be a vector symbol but not informative data; they are utilized to mark and distinguish the 20 amino acids but do not really bring much original property information to these amino acids.

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The hybridization of different materials for energy scavenging techniques based on piezoelectric and triboelectric effects has been studied widely for various applications of nanogenerators. However, there are few reports utilizing the same oxide matrix materials with appropriate doping to simultaneously enhance the piezoelectric and triboelectric outputs. Herein, a hybrid nanogenerator (HG) consisting of a piezoelectric nanogenerator (PENG) and a triboelectric nanogenerator (TENG) was constructed using (Ba0.838Ca0.162)(Ti0.9072Zr0.092)O3 (BCZTO)/polydimethylsiloxane (PDMS) as a piezoelectric layer and Ba(Ti0.8Zr0.2)O3 (BZTO)/PDMS as a triboelectric layer. For the PENG, how the electrical output was related to the BCZTO ratio in the BCZTO/PDMS composite films was systematically investigated. For the TENG, remarkably enhanced output performance is attributed to the ferroelectric polarization and large permittivity of the BZTO/PDMS. The Kelvin probe force microscopy measurements show that the poled BZTO/PDMS composite film with a 20 wt. % mass ratio of BZTO has the highest surface charge potential, in line with the macroscopic electrical outputs of the TENG. Interestingly, the output performance of the PENG in the HG is significantly enhanced compared to the PENG acting alone, which is also verified by COMSOL simulation. After rectification, the HG can produce a maximum output voltage of 390 V and a current density of 47 mA/m2. This work not only provides a feasible solution to enhance the output performance of the HG but also offers an effective approach to develop a small, portable power source with promising application in self-powered electronics.