The structural and electronic properties of a series of SrMnO 3 thin films grown on different substrates leading to different biaxial strains are examined by means of aberration-corrected high-angle annular dark-field scanning transmission electron microscopy and electron energy-loss spectroscopy (EELS). Here, we report a comprehensive theoretical investigation on the impact of oxygen nonstoichiometry and strain on the O-K and Mn-L 2,3 electron energy-loss near-edge structures of SrMnO 3 with the aid of the all-electron density functional theory code WIEN2K. Our experimental and theoretical EELS data reveal pronounced peak-height changes and energy shifts as a function of strain, which are ascribed to differences in the oxygen vacancy density rather than to a direct effect of lattice distortions. In addition, we find that the formation of oxygen vacancies in SrMnO 3 is likely to be enhanced by tensile strain. Compared to the unstrained material, the vacancy formation energy is reduced by 0.25 eV under 3.78% tensile strain.

Reprinted with permission from the American Physical Society: Guo, J. W., Wang, P. S., Yuan, Y., He, Q., Lu, J. L., Chen, T. Z., Yang, S. Z., Wang, Y. J., Erni, R., Rossell, M. D., Gopalan, V., Xiang, H. J., Tokura, Y. Yu, P. (2018). Strain-induced ferroelectricity and spin-lattice coupling in SrMnO3 thin lms. Physical Review B 97(23): 235135 c 2018 by the American Physical Society. Readers may view, browse, and/or download material for temporary copying purposes only, provided these uses are for noncommercial personal purposes. Except as provided by law, this material may not be further reproduced, distributed, transmitted, modied, adapted, performed, displayed, published, or sold in whole or part, without prior written permission from the American Physical Society.Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.

As essential units in an artificial neural network (ANN), artificial synapses have to adapt to various environments. In particular, the development of synaptic transistors that can work above 125 °C is desirable. However, it is challenging due to the failure of materials or mechanisms at high temperatures. Here, we report a synaptic transistor working at hundreds of degrees Celsius. It employs monolayer MoS 2 as the channel and Na + -diffused SiO 2 as the ionic gate medium. A large on/off ratio of 10 6 can be achieved at 350 °C, 5 orders of magnitude higher than that of a normal MoS 2 transistor in the same range of gate voltage. The short-term plasticity has a synaptic transistor function as an excellent low-pass dynamic filter. Long-term potentiation/depression and spike-timing-dependent plasticity are demonstrated at 150 °C. An ANN can be simulated, with the recognition accuracy reaching 90%. Our work provides promising strategies for high-temperature neuromorphic applications.

This study proposes a novel auditory brain-computer interface paradigm, which allows the subject to mentally select a target among a random sequence of spoken digits. The subject's voluntary recognition of the property of the target digits enhances the discriminability between brain responses to target and nontarget digits. EEG data from 14 subjects has shown that the amplitude of N2 and the late positive component (LPC) elicited by target digits was significantly higher than that of nontarget ones. Three classification methods, i.e., N2/LPC area comparison, Fisher discriminant analysis and support vector machine (SVM), were adopted to assess the target detection accuracy using EEG data from a single electrode. For SVM classification, a mean accuracy of 85% was achieved with five trials averaged. This new paradigm could be useful for locked-in patients with vision impairment.

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