Southeast University

About: Southeast University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: MIMO & Control theory. The organization has 66363 authors who have published 79434 publications receiving 1170576 citations. The organization is also known as: SEU.

High-Performance Pseudocapacitive Microsupercapacitors from Laser-Induced Graphene

Abstract: All-solid-state, flexible, symmetric, and asymmetric microsupercapacitors are fabricated by a simple method in a scalable fashion from laser-induced graphene on commercial polyimide films, followed by electrodeposition of pseudocapacitive materials on the interdigitated in-plane architectures. These microsupercapacitors demonstrate comparable energy density to commercial lithium thin-film batteries, yet exhibit more than two orders of magnitude higher power density with good mechanical flexibility.

SERS-Activated Platforms for Immunoassay: Probes, Encoding Methods, and Applications

Abstract: Owing to their excellent multiplexing ability, high sensitivity, and large dynamic range, immunoassays using surface-enhanced Raman scattering (SERS) as the readout signal have found prosperous applications in fields such as disease diagnosis, environmental surveillance, and food safety supervision. Various ever-increasing demands have promoted SERS-based immunoassays from the classical sandwich-type ones to those integrated with fascinating automatic platforms (e.g., test strips and microfluidic chips). As recent years have witnessed impressive progress in SERS immunoassays, we try to comprehensively cover SERS-based immunoassays from their basic working principles to specific applications. Focusing on several basic elements in SERS immunoassays, typical structures of SERS nanoprobes, productive optical spectral encoding strategies, and popular immunoassay platforms are highlighted, followed by their representative biological applications in the last 5 years. Moreover, despite the vast advances achieved ...

Symmetry breaking in molecular ferroelectrics

Abstract: Ferroelectrics are inseparable from symmetry breaking. Accompanying the paraelectric-to-ferroelectric phase transition, the paraelectric phase adopting one of the 32 crystallographic point groups is broken into subgroups belonging to one of the 10 ferroelectric point groups, i.e. C1, C2, C1h, C2v, C4, C4v, C3, C3v, C6 and C6v. The symmetry breaking is captured by the order parameter known as spontaneous polarization, whose switching under an external electric field results in a typical ferroelectric hysteresis loop. In addition, the responses of spontaneous polarization to other external excitations are related to a number of physical effects such as second-harmonic generation, piezoelectricity, pyroelectricity and dielectric properties. Based on these, this review summarizes recent developments in molecular ferroelectrics since 2011 and focuses on the relationship between symmetry breaking and ferroelectricity, offering ideas for exploring high-performance molecular ferroelectrics.

Label Distribution Learning

Abstract: Although multi-label learning can deal with many problems with label ambiguity, it does not fit some real applications well where the overall distribution of the importance of the labels matters. This paper proposes a novel learning paradigm named label distribution learning (LDL) for such kind of applications. The label distribution covers a certain number of labels, representing the degree to which each label describes the instance. LDL is a more general learning framework which includes both single-label and multi-label learning as its special cases. This paper proposes six working LDL algorithms in three ways: problem transformation, algorithm adaptation, and specialized algorithm design. In order to compare the performance of the LDL algorithms, six representative and diverse evaluation measures are selected via a clustering analysis, and the first batch of label distribution datasets are collected and made publicly available. Experimental results on one artificial and 15 real-world datasets show clear advantages of the specialized algorithms, which indicates the importance of special design for the characteristics of the LDL problem.