Polymer dielectrics with excellent processability and high breakdown strength (Eb) enable the development of high‐energy‐density capacitors. Although the improvement of dielectric constant (K) of polymer dielectric has been realized by adding high‐K inorganic fillers with high contents (>10 vol%), this approach faces significant challenges in scalable film processing. Here, the incorporation of ultralow ratios (<1 vol%) of low‐K Cd1−xZnxSe1−ySy nanodots into a ferroelectric polymer is reported. The polymer composites exhibit substantial and concurrent increase in both K and Eb, yielding a discharged energy density of 26.0 J cm−3, outperforming the current dielectric polymers and nanocomposites measured at ≤600 MV m−1. The observed unconventional dielectric enhancement is attributed to the structural changes induced by the nanodot fillers, including transformation of polymer chain conformation and induced interfacial dipoles, which have been confirmed by density function theory calculations. The dielectric model established in this work addresses the limitations of the current volume‐average models on the polymer composites with low filler contents and gives excellent agreement to the experimental results. This work provides a new experimental route to scalable high‐energy‐density polymer dielectrics and also advances the fundamental understanding of the dielectric behavior of polymer nanocomposites at atomistic scales.

A progressive learning method with an instrumental variable and bond-valence vector sums was used to improve the bandgap prediction precision.

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There is a noticeable tendency to apply deep convolutional neural network (CNN) in facial identification, since it is able to boost performance in face recognition and verification. However, due to the users have unique facial, exposure of face template to adversaries can severely compromise system security and users' privacy. Here, the authors propose a face template protection technique by using multi-label learning, which maps the facials into low-density parity-check (LDPC) codes. Firstly, a random binary sequence is generated to represent a user and further hashed to produce the protected template. During the training, the random binary sequences are encoded by an LDPC encoder to produce diverse binary codes. Based on carefully designed deep multi-label learning, the facial features of each user are mapped to a diverse binary code. In the process of recognition and verification, the deep CNN mapping architecture is modelled as a Gaussian channel, while the noise brought by intra-variations in the outputs of CNN can be removed by the LDPC decoder. Thus, a robust face template protection scheme is achieved. The simulation results on PIE and extended Yale B indicate that the proposed scheme achieves high genuine accept rate at 1% false accept rate.

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Ammonia detection possesses great potential in atmosphere environmental protection, agriculture, industry, and rapid medical diagnosis. However, it still remains a great challenge to balance the sensitivity, selectivity, working temperature, and response/recovery speed. In this work, Berlin green (BG) framework is demonstrated as a highly promising sensing material for ammonia detection by both density functional theory simulation and experimental gas sensing investigation. Vacancy in BG framework offers abundant active sites for ammonia absorption, and the absorbed ammonia transfers sufficient electron to BG, arousing remarkable enhancement of resistance. Pristine BG framework shows remarkable response to ammonia at 50–110 °C with the highest response at 80 °C, which is jointly influenced by ammonia's absorption onto BG surface and insertion into BG lattice. The sensing performance of BG can hardly be achieved at room temperature due to its high resistance. Introduction of conductive Ti3CN MXene overcomes the high resistance of pure BG framework, and the simply prepared BG/Ti3CN mixture shows high selectivity to ammonia at room temperature with satisfying response/recovery speed.

The effect of hot deformation induced defects and texture on thermoelectric properties of FeSb2 bulk crystals has been investigated. The transport properties of the samples along both parallel and perpendicular direction of pressing were measured from 3 K to 300 K. The results showed that thermal conductivity of the deformed samples was significantly reduced. After twice deformation, the thermal conductivity of the sample along the perpendicular direction of pressing was decreased to 4 W/mK, which was only one third of that before deformation. Transmission electron microscopy observation revealed the presence of high density of lattice defects in the deformed samples. The lattice thermal conductivity was analyzed using the Debye-Callaway approximation, and the results showed that the deformation induced lattice imperfections play an important role in enhancing phonon scattering. In addition, both the electrical resistivity and Seebeck coefficient exhibited a weak anisotropy in the deformed samples. The figure of merit ZT of the bulk FeSb2 was significantly improved from 0.010 to 0.021 after deformation.