Search

Piezoresistive sensor based on flexible three-dimensional graphene oxide (3DGO) is regarded as one of the most significant components for the next generation of wearable electronics. Despite the promising potential, the sensitivity of 3DGO piezoresistive sensors is severely restricted by its disordered microstructure and internal elastic defects to capture tiny signals. Although considerable efforts have been dedicated to improving its performances by chemical reduction and subsequent freeze-drying processes, it is still a huge challenge to achieve a 3D GO-based sensor coupled with ultra-high sensitivity and low detection limit simultaneously. Here, a simple and cost-effective strategy is proposed to reconstruct its pristine disordered skeleton by inducing GO liquid crystals (GO LCs) with a daily-used surfactant of sodium dodecyl benzenesulfonate (SDBS), and then the flexible and ultralight 3D GO/SBDS (GS) composite aerogel with double-walled honeycomb-like architecture is successfully fabricated. The prepared 3D GS composite aerogel exhibits significantly improved sensing performances with a fine sensitivity of 1.48 kPa−1, a low detection limit of 0.05% strain, a fatigue resistance exceeding 10,000 cycles, and a fast response time of 20.86 ms. Importantly, the sensitive sensor has been applied in monitoring real-time fingertip pulse, offering a competitive alternative for the existed complex health monitoring systems.

Sensing materials that are ultrastrong but still superelastic and highly sensitive are crucial for meeting the requirements of future flexible sensors. However, these requirements are challenging to satisfy simultaneously due to the internal constraints among these properties. Here, an ultrastrong and superelastic eutectogel is designed and prepared using a waterborne polyurethane (WPU) network enhanced by two-dimensional (2D) nanosheets in a deep eutectic solvent. The 2D nanosheet-induced noncovalent cross-linking endows the prepared eutectogel with superelasticity and flexibility, and its elongation at break reaches 2071%, higher than those of most polymers (1000%). Meanwhile, this eutectogel also exhibits a high tensile strength (21.6 MPa), which is strong enough to support 20 000 times its own weight. Such a composite design provides a feasible route for preparing eutectogels with outstanding comprehensive functions without trade-offs among these features. In addition, the eutectogel-assembled sensor possesses a high ionic conductivity of 0.225 S/m and a high strain sensitivity of 1.18 kPa

Hollow mesoporous polydopamine (HMPDA) microcapsules were prepared by a template method using silica nanoparticles as the templates. The template method was also used for the formation of mesoporous polydopamine (PDA), which is driven by π–π interactions of trimethyl-benzene and PDA, in which the PDA wall with mesoporous structure can be obtained after template removal by extraction. Because of its surface mesoporous structure and large central cavity structure, HMPDA microcapsules have unique adsorption properties. Compared with other porous materials, PDA has better biosafety because dopamine itself is a bionic material. The above properties are of great significance for the application of HMPDA microcapsules in the field of biologic medicine, especially in drug carriers.

The submicron-sized Fe₃O₄ particles were synthesized by solvothermal method. Then polydopamine (PDA) was used to modify the surface of Fe₃O₄ particles. And then the core-shell Fe₃O₄@PDA@BSA (single layer and multilayer) microspheres were prepared by the layer by layer self-assembly method (SAM). In addition, Ag nanospheres were grafted onto the surface of Fe₃O₄@PDA particles to obtain Fe₃O₄@PDA-Ag core-shell nanoparticles using silver mirror reaction. The morphology and component of the obtained core-shell particles were characterized by transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The biocompatibility of the microspheres was researched in vitro by MTT method and its magnetic properties were also evaluated. The results showed that the microsphere has excellent magnetic properties and good biological safety. Then the catalytic performance of Fe₃O₄@PDA-Ag microspheres for methylene blue (MB) was studied. And the antimicrobial properties of Fe₃O₄@PDA-Ag microspheres for Escherichia coli and Bacillus subtilis were also be discussed. The results indicated good catalytic properties and antibacterial properties.

Three-dimensional (3D) graphene oxide/MXene (GO/MX) composite aerogels with characters of lightweight, porous structure, and superior elasticity have attracted great attention in electromagnetic shielding, oil–water separation, and chemical catalysis, especially smart electronic sensors. However, due to the lack of adequate interaction force between GO and MX nanosheets, it is challenging to construct a high strength and long-range ordered GO/MX aerogel, resulting in inferior mechanical properties and poor conductivity. Here, a facile gelation strategy is developed to form long-range ordered GO/MX aerogel by ethylenediamine (EDA) induced self-assembly of MX and GO liquid crystals (LCs). In this system, EDA plays dual roles of inducing ordered self-assembly of GO LCs nanosheets and strengthening interaction between GO and MX nanosheets. As a result, the obtained GO/MX aerogel with long-range ordered microstructure presents an outstanding mechanical elasticity of 9.5 kPa and a high conductivity of 0.99 S m−1. Especially, the assembled GO/MX-based sensor has an extremely low detection limit of ~ 0.2% strain, prominent physiological signal capture capability, and excellent durability over 10,000 compression/release cycles. This work paves the way for the potential applications of GO/MX composite aerogel in real-time human motion monitor, artificial skins, and flexible smart devices.

This paper describes a general strategy that uses dopamine as a stable anchor to present protein molecules on the surface of silicon microspheres via self-assembled method (SAM), and after the removal of templates, the bovine serum albumin (BSA) microcapsules were successfully prepared. Two self assembling methods are described here, one is using glutaraldehyde (GA) as crosslinking agent to modify multilayer protein on the surface of silicon microspheres; In another way, dopamine is used as a anchor layer to graft the BSA monolayers onto silicon templates. The BSA coated silicon microspheres and BSA hollow microcapsules were characterized by transmission electron microscopy (TEM) and field emission scanning electron microscopy (FESEM). The biocompatibility of the BSA coated silicon microspheres was evaluated in vitro by MTT method. Then the polydopamine (PDA) hollow microcapsules was prepared using SiO 2 nanoparticles as the templates, and Ag namoparticles was then modified on the PDA capsules. The catalytic performance of SiO 2 @ PDA@Ag microspheres for methylene blue (MB) was studied. And the Escherichia coli and Staphylococcus aureus was used as the indicator bacteria to investigate the antimicrobial activity of the obtained SiO 2 @ PDA@Ag microspheres.

Estimation of six degrees of freedom (6DoF) attitude of rigid bodies is an essential issue in such fields as robotics and virtual reality. This paper proposed a method that could accurately estimate 6DoF attitude of known rigid bodies with RGB and RGB-D input image data. Additionally, the method could also work well in the scenes in which objects exhibit mutual similarities and occlusion. As one of the contributions made by this paper, a modularized assembly line method was proposed, which integrated deep learning and multi-view geometry method. At first, a neural network for instance segmentation was used to identify the general locations of known objects in the images and give the bounding boxes and masks. Then 6DoF attitude was estimated roughly according to the local features of RGB-D images and templates. Finally, purely geometric method was used to refine the estimation. Another contribution of this paper was the correction of misclassification with the help of some information reserved in the process of training the network. The proposed method achieves a superior performance on a challenging public dataset.

The submicron-sized Fe₃O₄ particles were synthesized by solvothermal method. Then polydopamine (PDA) was used to modify the surface of Fe₃O₄ particles. And then the core-shell Fe₃O₄@PDA@BSA (single layer and multilayer) microspheres were prepared by the layer by layer self-assembly method (SAM). In addition, Ag nanospheres were grafted onto the surface of Fe₃O₄@PDA particles to obtain Fe₃O₄@PDA-Ag core-shell nanoparticles using silver mirror reaction. The morphology and component of the obtained core-shell particles were characterized by transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The biocompatibility of the microspheres was researched

The magnetically core-shell Fe3O4@mesoporous polydopamine (Fe3O4@MPDA) nanospheres were prepared by a facile and efficient self-assembly approach, the key mechanism for generating the mesoporous structure of PDA is the π-π stacking interactions between dopamine molecules and 1,3,5-trimethylbenzene (TMB) molecules. Because of the mesoporous structure of the surface, the Fe3O4@MPDA particles exhibited a large specific surface area of 17.27 m2/g and total pore volumes of 0.057 cm3/g. Besides, it maintained good magnetic properties. The adsorption properties and mechanism of organic dyes methylene blue (MB) and rhodamine B (RhB) on Fe3O4@MPDA nanoparticles were studied. And a model anticancer drug of doxorubicin hydrochloride (DOX) was used as a target drug to research the delivery and sustained-release properties of the Fe3O4@MPDA particles. The average loading capacities toward DOX could reach 295.2 mg/g, and the release amounts were about 41% within 60 h. Meanwhile, the in vitro cytotoxicity of the DOX loaded Fe3O4@MPDA nanoparticles against hela cells and osteoblast cells were also being researched. The DOX-loaded Fe3O4@MPDA nanoparticles showed greater toxicity than Fe3O4 nanoparticles and Fe3O4@MPDA nanoparticles, even than free DOX. Compared with osteoblasts, the inhibitory effect on hella cells was more obvious. The above properties are of great significance for its application in the field of biological medicine, especially the target drug carriers.

We present an optical study on high-quality and single-phase LiTisub2/subOsub4/sub(LTO) superconductor thin films grown on MgAlsub2/subOsub4/subsubstrates by pulsed laser deposition. The near infrared (NIR) reflectivity is measured for samples with (001) and (111) lattice orientations. The temperature-induced metal-superconductor transition can be observed, and the superconducting transition temperature can be measured for both samples. We find that the NIR reflection experiment can reflect rightly the basic features of LTO superconductor thin films. Furthermore, the results obtained from this simple optical measurement suggest that the photo-induced electronic localization effect can be present in LTO thin films in a metallic state. Such information cannot be obtained directly from conventional transport and magneto-transport measurements. These interesting and important findings demonstrate that the NIR reflection experiment is a powerful optical technique for contactless characterizations and investigations of superconductor materials.

For mobile photos annotation, users are more interested in the context information behind the photos. The user's social circle can provide valuable information for it. However, the accompanying textual information of social network is sparse and ambiguous in nature. In this paper, we propose a personalized annotation framework for mobile photos leveraging the user's social circle. To address the unreliability problem of social network, we present an algorithm to generate reliable tags for social photos before assigning tags to the user's unlabeled photos. In the tag generation stage, a multi-modality hierarchical clustering algorithm is performed to detect social events. Besides, we use "Album" instead of individual photo as the basic unit for clustering. Finally, we employ a weighted nearest neighbor model for label propagation. We evaluate our framework on a large-scale, real-world dataset from Renren, the largest Facebook-like social network in China. Our evaluation results show promising results of our proposed framework.