In this work, Fe–Ni alloy nanoclusters (Fe–Ni ANCs) anchored on N, S co‐doped carbon aerogel (Fe–Ni ANC@NSCA catalysts) are successfully prepared by the optimal pyrolysis of polyaniline (PANI) aerogels derived from the freeze‐drying of PANI hydrogel obtained by the polymerization of aniline monomers in the co‐presence of tannic acid (TA), Fe3+, and Ni2+ ions. In addition, the optimal molar ratio of the TA, Fe3+, and Ni2+ ions for synthesis of Fe–Ni ANC@NSCA catalysts are 1:2:5, which can guarantee the formation of carbon aerogel composed of quasi‐2D porous carbon sheets and the formation of high‐density Fe–Ni ANCs with an ultrasmall size between 2 to 2.8 nm. These Fe–Ni ANCs consisting of N4–Fe–O–Ni–N4 moiety are proposed as a new type of active species for the first time, to the best of the authors’ knowledge. Thanks to their unique features, the Fe–Ni ANC@NSCA catalysts show excellent performance in oxygen reduction reaction with a half‐wave potential (E1/2) of 0.891 V and oxygen evolution reaction (260 mV @ 10 mA cm−2) in alkaline media as bifunctional catalysts, which are better than the state‐of‐the‐art commercial Pt/C catalysts and RuO2 catalysts. Moreover, Zn–air battery assembled with the Fe–Ni ANC@NSCA catalysts also shows a remarkable performance and exceptionally high stability over 500 h at 5 mA cm−2.

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To accurately identify atoms on noisy transmission electron microscope images, a deep learning (DL) approach is employed to estimate the map of probabilities at each pixel for being an atom with element discernment. Thanks to a delicately-designed loss function and the ability to extract features, the proposed DL networks can be trained by a small dataset created from approximately 30 experimental images, each with a size of 256 × 256 pixels2. The accuracy and robustness of the network were verified by resolving the structural defects of graphene and polar structures in PbTiO3/SrTiO3 multilayers from both the general TEM images and their imitated images on which intensities of some pixels lost randomly. Such a network has the potential to identify atoms from very few images of beam-sensitive material and explosive images recorded in a dynamical atomic process. The idea of using a small-dataset-trained DL framework to resolve a specific problem may prove instructive for practical DL applications in various fields.

In ferroelectrics, complex interactions among various degrees of freedom enable the condensation of topologically protected polarization textures. Known as ferroelectric solitons, these particle-like structures represent a new class of materials with promise for beyond-CMOS technologies due to their ultrafine size and sensitivity to external stimuli. Such polarization textures have scarcely been demonstrated in multiferroics. Here, we present evidence for ferroelectric solitons in (BiFeO3)/(SrTiO3) superlattices. High-resolution piezoresponse force microscopy and Cs-corrected high-angle annular dark-field scanning transmission electron microscopy reveal a zoo of topologies, and polarization displacement mapping of planar specimens reveals center-convergent/divergent topological defects as small as 3 nm. Phase-field simulations verify that some of these structures can be classed as bimerons with a topological charge of ±1, and first-principles-based effective Hamiltonian computations show that the coexistence of such structures can lead to non-integer topological charges, a first observation in a BiFeO3-based system. Our results open new opportunities in multiferroic topotronics.