High output flexible polyvinylidene fluoride based piezoelectric device incorporating cellulose nanofibers/BaTiO3@TiO2 piezoelectric core-shell structure.

Flexible composite film has gained increasing attention in the fields of wearable devices and portable electronic products. In this work, a novel core-shell structure of cellulose nanofibers/BaTiO3@TiO2 (CNF/BTO@TiO2) was synthesized with the assistant of the biological macromolecule material of cellulose nanofibril (CNF), in which the CNF can improve the stability and dispersibility of BaTiO3 (BTO) in the aqueous phase and elevate the integrity of the core-shell structure. The agglomeration of nanoparticles and the structural defects of the composite film can be improved when the core-shell structure was uniformly dispersed in polyvinylidene fluoride (PVDF). Meanwhile, the core-shell structure can promote the polarization of the electric dipole and the formation of ß phase in PVDF due to the generated interface spatial polarization between the shell of TiO2 and the core of BTO. When the content of the core-shell structure was 5 wt%, the ß phase content reaches 61.89 %, and the piezoelectric coefficient of composite film reaches 84.29 pm/V. Thus the maximum output open-circuit voltage (VOC) and short-circuit current (ISC) of the piezoelectric composite film is as high as 13.10 V and 464.3 nA. In addition, its excellent pressure sensing capability allows for its application in various flexible electronic devices.

High thermal conductivity regenerated cellulose/carboxylated carbon nanotubes composite films with semi-insulating properties prepared via ionic coordination and hydrothermal synthesis of zinc oxide.

With the rapid development of miniaturization and integration of electronic products, its heat dissipation has become the focus of research. In order to improve the heat dissipation efficiency of electronic components, flexible thermal conduction materials are constantly studied. Cellulose has good flexibility and load capacity, which is often used in the preparation of thermal conduction materials. In this paper, carboxylated multi-walled carbon nanotubes (C-MWCNTs) were modified by metal ion coordination and hydrothermal synthesis of zinc oxide (ZnO) to prepare semi-insulating thermal conduction fillers, which were dispersed into regenerated cellulose (RC) to cast to be composite films. The results show that the two modification methods can reduce the probability of phonon scattering and block the electron transport path, so as to improve the thermal conductivity (TC) and electrical insulation properties of the composite films. Especially for the RC/C-MWCNTs@ZnO composite films, when the total filler content is 20 wt%, the in-plane TC can reach 11.89 ± 0.19 (W/(m·K)), and the surface electrical resistivity (ρs) is (5.24 ± 0.17) × 106 Ω. Compared with the RC/C-MWCNTs composite films, the in-plane TC and ρs of the RC/C-MWCNTs@ZnO composites films are increased by about 94.92 % and 555 %, respectively. Therefore, the developed RC-based composite film has broad application prospects in thermal management.

Stepwise construction of Ag29 nanocluster-based hydrogen evolution electrocatalysts.

Although several silver-based nanoclusters have been controllably prepared and structurally determined, their electrochemical catalytic performances have been relatively unexplored (or showed relatively weak ability towards electro-catalysis). In this work, we accomplished the step-by-step enhancement of the electrocatalytic hydrogen evolution reaction (HER) efficiency based on an Ag29 cluster template. A combination of atomically precise operations, including the kernel alloying, ligand engineering, and surface activation, was exploited to produce a highly efficient Pt1Ag28-BTT-Mn(10) nano-catalyst towards HER, derived from both experimental characterization and theoretical modelling. The precision characteristic of the Ag29-based cluster system enables us to understanding the correlations between nanocluster structures and HER performances at the atomic level. Overall, the findings of this work will hopefully provide more opportunities for the customization of new cluster-based nano-catalysts with enhanced electrocatalytic capacities.

Evolution of Electrocatalytic CO2 Reduction Activity Induced by Charge Segregation in Atomically Precise AuAg Nanoclusters Based on Icosahedral M13 Unit 3D Assembly.

The precise self-assembly of building blocks at atomic level provides the opportunity to achieve clusters with advanced catalytic properties. However, most of the current self-assembled materials are fabricated by 1/2D assembly of blocks. High dimensional (that is, 3D) assembly is widely believed to improve the performance of cluster. Herein, the effect of 3D assembly on the activity for electrocatalytic CO2 reduction reaction (CO2 RR) is investigated by using a range of clusters (Au8 Ag55 , Au8 Ag57 , Au12 Ag60 ) based on 3D assembly of M13 unit as models. Although three clusters have almost the same sizes and geometric structures, Au8 Ag55 exhibits the best CO2 RR performance due to the strong CO2 adsorption capacity and effective inhibition of H2 evolution competition reaction. The deep insight into the superior activity of Au8 Ag55 is the unique electronic structure attributed to the charge segregation. This study not only demonstrates that the assembly mode greatly affects the catalytic activity, but also offers an idea for rational designing and precisely constructing catalysts with controllable activities.

[An improved Vision Transformer model for the recognition of blood cells].

Leukemia is a common, multiple and dangerous blood disease, whose early diagnosis and treatment are very important. At present, the diagnosis of leukemia heavily relies on morphological examination of blood cell images by pathologists, which is tedious and time-consuming. Meanwhile, the diagnostic results are highly subjective, which may lead to misdiagnosis and missed diagnosis. To address the gap above, we proposed an improved Vision Transformer model for blood cell recognition. First, a faster R-CNN network was used to locate and extract individual blood cell slices from original images. Then, we split the single-cell image into multiple image patches and put them into the encoder layer for feature extraction. Based on the self-attention mechanism of the Transformer, we proposed a sparse attention module which could focus on the discriminative parts of blood cell images and improve the fine-grained feature representation ability of the model. Finally, a contrastive loss function was adopted to further increase the inter-class difference and intra-class consistency of the extracted features. Experimental results showed that the proposed module outperformed the other approaches and significantly improved the accuracy to 91.96% on the Munich single-cell morphological dataset of leukocytes, which is expected to provide a reference for physicians' clinical diagnosis.