Shanghai Jiao Tong University

About: Shanghai Jiao Tong University is a education organization based out in Shanghai, Shanghai, China. It is known for research contribution in the topics: Population & Cancer. The organization has 157524 authors who have published 184620 publications receiving 3451038 citations. The organization is also known as: Shanghai Communications University & Shanghai Jiaotong University.

Surface strategies for catalytic CO2 reduction: from two-dimensional materials to nanoclusters to single atoms

Abstract: Redox catalysis, including photocatalysis and (photo)electrocatalysis, may alleviate global warming and energy crises by removing excess CO2 from the atmosphere and converting it to value-added resources. Nano-to-atomic two-dimensional (2D) materials, clusters and single atoms are superior catalysts because of their engineerable ultrathin/small dimensions and large surface areas and have attracted worldwide research interest. Given the current gap between research and applications in CO2 reduction, our review systematically and constructively discusses nano-to-atomic surface strategies for catalysts reported to date. This work is expected to drive and benefit future research to rationally design surface strategies with multi-parameter synergistic impacts on the selectivity, activity and stability of next-generation CO2 reduction catalysts, thus opening new avenues for sustainable solutions to climate change, energy and environmental issues, and the potential industrial economy.

Identifying Stable Patterns over Time for Emotion Recognition from EEG

Abstract: In this paper, we investigate stable patterns of electroencephalogram (EEG) over time for emotion recognition using a machine learning approach. Up to now, various findings of activated patterns associated with different emotions have been reported. However, their stability over time has not been fully investigated yet. In this paper, we focus on identifying EEG stability in emotion recognition. We systematically evaluate the performance of various popular feature extraction, feature selection, feature smoothing and pattern classification methods with the DEAP dataset and a newly developed dataset called SEED for this study. Discriminative Graph regularized Extreme Learning Machine with differential entropy features achieves the best average accuracies of 69.67 and 91.07 percent on the DEAP and SEED datasets, respectively. The experimental results indicate that stable patterns exhibit consistency across sessions; the lateral temporal areas activate more for positive emotions than negative emotions in beta and gamma bands; the neural patterns of neutral emotions have higher alpha responses at parietal and occipital sites; and for negative emotions, the neural patterns have significant higher delta responses at parietal and occipital sites and higher gamma responses at prefrontal sites. The performance of our emotion recognition models shows that the neural patterns are relatively stable within and between sessions.

Evidence of a gate-tunable Mott insulator in a trilayer graphene moiré superlattice

Abstract: The Mott insulator is a central concept in strongly correlated physics and manifests when the repulsive Coulomb interaction between electrons dominates over their kinetic energy1,2. Doping additional carriers into a Mott insulator can give rise to other correlated phenomena such as unusual magnetism and even high-temperature superconductivity2,3. A tunable Mott insulator, where the competition between the Coulomb interaction and the kinetic energy can be varied in situ, can provide an invaluable model system for the study of Mott physics. Here we report the possible realization of such a tunable Mott insulator in a trilayer graphene heterostructure with a moire superlattice. The combination of the cubic energy dispersion in ABC-stacked trilayer graphene4–8 and the narrow electronic minibands induced by the moire potential9–15 leads to the observation of insulating states at the predicted band fillings for the Mott insulator. Moreover, the insulating states in the heterostructure can be tuned: the bandgap can be modulated by a vertical electrical field, and at the same time the electron doping can be modified by a gate to fill the band from one insulating state to another. This opens up exciting opportunities to explore strongly correlated phenomena in two-dimensional moire superlattice heterostructures. Report of the likely observation of a Mott insulator in trilayer graphene with a moire potential. The Mott state can be tuned between different filling fractions via gating, which will enable the careful study of this paradigmatic many-body state.