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Disruption prediction and mitigation is a crucial topic, especially for future large-scale tokamaks, due to disruption’s concomitant harmful effects on the devices. On this topic, disruption prediction algorithm takes the responsibility to give accurate trigger signal in advance of disruptions, therefore the disruption mitigation system can effectively alleviate the harmful effects. In the past 5 years, a deep learning-based algorithm is developed in HL-2A tokamak. It reaches a true positive rate of 92.2%, a false positive rate of 2.5% and a total accuracy of 96.1%. Further research is implemented on the basis of this algorithm to solve three key problems, i.e., the algorithm’s interpretability, real-time capability and transferability. For the interpretability, HL-2A’s algorithm gives saliency maps indicating the correlation between the algorithm’s input and output by perturbation analysis. The distribution of correlations shows good coherence with the disruption causes. For the transferability, a preliminary disruption predictor is successfully developed in HL-2M, a newly built tokamak in China. Although only 44 shots are used as the training set of this algorithm, it gives reasonable outputs with the help of data from HL-2A and J-TEXT. For the real-time capacity, the algorithm is accelerated to deal with an input slice within 0.3ms with the help of some adjustments on it and TFLite framework. It is also implemented into the plasma control system and gets an accuracy of 89.0% during online test. This paper gives a global perspective on these results and discusses the possible pathways to make HL-2A’s algorithm a more comprehensive solution for future tokamaks.

Regulating catalytic activity plays an important role in further optimizing and developing multifunctional catalysts with high selectivity and high activity. Reversible dual regulation of catalytic activity has always been a challenging task. Here, we prepared poly(N-isopropylacrylamide)-anchored gold nanoparticles (AuNP@CDs-Azo-PNIPAM) through host-guest interaction of cyclodextrin capped gold nanoparticles (AuNP@CDs) and azobenzene-terminated poly(N-isopropylacrylamide) (Azo-PNIPAM). Azo-PNIPAM as thermal and light responsive ligand allows reversible dual regulation of catalytic activity. When the temperature is higher than the lowest critical solution temperature, the PNIPAM chain shrinks rapidly, increasing the steric hindrance around AuNPs and reducing the catalytic activity. Under ultraviolet light irradiation, cis-azobenzene disassembles from cyclodextrin and the number of surface active sites of AuNPs increases, which improves the catalytic activity. The reaction rate of UV irradiation is almost 1.3 times that of visible light irradiation. This work provides a simple and effective strategy for the construction of reversible catalysts.

In-depth research is implemented on the disruption predictor in HL-2A to improve the accuracy and interpretability of the model. For higher accuracy, four adjustments are tried to solve four corresponding problems in a baseline model. Reductive comparison experiments are designed to evaluate their contribution to performance. The result shows that these adjustments together can improve the AUC (area under receiver operating characteristic curve) of the baseline model by 0.039. For interpretability of model, an interpretation method is proposed to evaluate the real-time importance of each input signal. The result of single shot interpretation shows good coherence with the causes of disruptions. To further validate the reasonability of this interpretation method, disruption causes of shot nos. 20000–36000 are analysed to make a disruption cause dataset. Statistical analysis of the output of the interpretation algorithm on this dataset also shows a good coherence with the disruption causes. Then a Bayes classifier is developed to recognize the cause of disruption based on the interpretation algorithm’s output. This classifier has an accuracy of 71.2% on the labelled dataset, which contains 605 disruptive shots categorized into five disruption causes.

For the first time, edge-localized mode (ELM)-free H-mode was realized in the HL-2A tokamak by using electron cyclotron resonance heating and co-current neutral beam injection (NBI) heating. This ELM-free H-mode is associated with the formation of edge particle transport barrier, an increase in density peaking and a significant decrease in edge turbulence. During the stationary ELM-free phase, an edge magnetohydrodynamic mode is identified, which has similar characteristics to an edge harmonic oscillation (EHO), as observed in other tokamaks. This EHO-like mode enhances edge particle transport, and propagates poloidally in the electron diamagnetic drift direction and toroidally in the same direction as the plasma current and NBI. A detailed analysis of this mode and the EHO–ELM transition is presented in this paper.

The all perovskite oxide p-i-n junctions formed by integrating semiconducting p-type (hole-doped) manganite (La,Sr)MnO3 (LSMO) and metallic n-type (electron-doped) cuprate superconductor (La,Ce)2CuO4, in between, the ferroelectric (Ba,Sr)TiO3 (BST) (which is in n-type semiconducting [1]) is sandwiched as the depletion barrier layer (i). It is shown that the perfect interfaces of the integrated layers is the most important factor to determine the rectification function of such p-i-n junction. For the typical p-i-n junction with depletion layer BST~20 nm, under the bias case, the forward current density is in 10?1A cm?2 order. The built-in field is estimated to be ~1.7 V, the depolarization field is ~1.4 V, and the reverse broken field is ~?3.3 V.