Search

In this paper, different contents of molybdenum oxide/tungsten oxide (MoO/WO) composite nanostructures were synthesized by hydrothermal method. Field emission scanning electron microscopy images revealed that the morphologies of WO nanostructures were significantly influenced by the Mo amount. Furthermore, the introduction strategy of MoO into WO could effectively improve the gas sensing properties. Especially, the sensor based on the 4 mol% MoO/WO composite nanostructures exhibited enhanced gas sensing performance, giving a low limit of detection (500 ppb). It shows high responses of 28.5 and 18.2-100 ppm ethanol and acetone at the operating temperature of 320 °C, which were about 2.3 and 1.7 times higher than those of the pure WO, respectively. The enhanced sensing properties of MoO/WO gas sensor can be attributed to the addition of MoO, which has been discussed in relation to the gas sensing mechanism. [ABSTRACT FROM AUTHOR]

In recent years, online social networks (OSNs) have thoroughly penetrated people's lives. Since information always flows along with various online relations in OSNs, analysing these relations becomes one of the most fundamental problems in various applications. Unfortunately, limited by the privacy concerns, data availability and the pow-law distributions of most OSNs, we can not observe enough relation links all the time, which is difficult to improve downstream tasks. To address this problem, many studies try to predict potential relations in online social networks via existing pair-wise links. However, when the observable pair-wise links are extremely sparse, most of them fail to learn the smoothness on the networks and make their proposed methods brittle. In light of this, we go beyond pair-wise relations and leverage hypergraphs to learn higher relations in the graphs. A hypergraph allows one hyperedge to connect multiple nodes, which is perfect to include more potential pair-wise links and can guarantee smooth node embeddings for better link prediction performance. In this paper, we aim at predicting potential links in sparsely observed networks. To achieve this goal, we first start from some blurry hyperedges and then proposed a novel hyperedge shrinking method to make the learned hyperedges more hierarchical. This method can learn hypergraph structure automatically from the given sparsely observed links and rely less on manual design. Following this, we further propose a novel hypergraph-based graph neural network to learn potential links in the graph. To address semantic fusion in the heterogeneous networks, we put forward multi-level reconstruction methods to preserve both specific semantics denoted by meta-paths, and high-level semantics denoted by hypergraphs. We compare our method with four state-of-the-art baselines. Extensive evaluations demonstrate that our method can achieve the best linking prediction results, especially when the networks are sparse. [ABSTRACT FROM AUTHOR]

Deep modeling and analysis of human big data deepens our understanding of human activities. Periodic time-series signals, e.g., electrocardiographs, collected by health monitoring sensors reflect human health status and assist in disease diagnosis. However, long-term prediction of these signals using deep learning models poses three challenges, namely, sparse features, conservative prediction of extreme peaks, and varying periodic intervals. We address these issues by proposing a prediction framework called EPIC with extreme peak and interval calibrations. EPIC consists of a triple-channel prediction network and a calibration network. The prediction network learns the time-domain, frequency-domain, and deformable features of time-series patterns simultaneously. Amplitude residuals of extreme peaks are emphasized in the designed training loss function. In addition, to alleviate the problem of unaligned predictions resulting from inaccurate periodic intervals, we further design a calibration module to reduce the deviation of periodic intervals. The experimental results and ablation studies indicate that EPIC achieves excellent performance in long-term prediction tasks. [ABSTRACT FROM AUTHOR]

Graph learning methods have boosted brain analysis for user healthcare, disease detection, and behavioral modeling. Spatially separated brain regions are functionally connected with different weights, enabling the classification of brain networks from the perspective of graph learning. However, existing methods based on graph neural networks mainly rely on the calculation of node feature correlation and manual threshold selection to obtain the graph structure, which disregards the temporal features of nodes and the latent information in the implicit graph structure. To address this problem, we propose a structure adaptive graph neural network with temporal representation and residual connections (TR-SAGNN) for brain network classification. First, we design a temporal attention learning module to learn the temporal features of the node itself. We design an end-to-end adaptive graph structure learning module based on the product-moment self-attention mechanism, which avoids manual threshold selection and obtains a more accurate graph structure. Second, we design a graph representation learning module based on a residual connection strategy to avoid the problem of insufficient propagation of node features. Last, we design a loss function to consider both the graph classification task and node classification task, which makes the model obtain better graph representation learning ability under the supervision of the node classification label. We conduct extensive experiments on the ANDI dataset. The results show that our model has better end-to-end adaptive graph construction capability as well as feature learning and classification performance. [ABSTRACT FROM AUTHOR]

POI group recommendation is one of the hottest research topics in location-based social networks, which recommends the most agreeable places for a group of users. However, traditional POI group recommendation methods only generate a consensus function to aggregate individual preference into group preference and they do not consider all the factors that can determine the results of POI group recommendation, which leads to a low recommendation accuracy. What's more, these methods have a long running time. Therefore, in this paper, we propose a new POI group recommendation method with an extreme learning machine (ELM) called PGR-ELM. The PGR-ELM method regards POI group recommendation as a binary classification problem. First, three features are extracted from three factors: POI popularity, group members' distance to POI, members' interest preferences combined affinity between group members. These features simultaneously consider all the factors that can determine the results of recommendation and guarantee the effectiveness of POI group recommendation. Then, the extracted features are input to train an ELM classifier because of its fast learning speed, which guarantees the efficiency of POI group recommendation. Finally, extensive experiments verify the accuracy and efficiency of PGR-ELM method. [ABSTRACT FROM AUTHOR]

Being one of the most frequent atmospheric contaminants, nitrogen dioxide (NO 2) was accused to be the culprit for a serious of public environmental hazards and respiratory diseases. In the present study, SnS 2 /Mo 4/3 B 2 MBene microcomposites with different component ratios were successfully developed for ambient temperature sensing of NO 2. The optimized microcomposite showed outstanding detection properties, such as a response as high as 4.82–1 ppm NO 2 and a detection limit as low as 0.1 ppb, which was demonstrably improved compared with those of SnS 2 /Ti 3 C 2 MXene sharing a similar microstructure and component ratio. Besides, the SnS 2 /MBene microcomposites also revealed excellent selectivity and repeatability for NO 2 sensing. The outstanding sensing properties of SnS 2 /MBene microcomposites were examined via density functional theory computation on adsorption performance, Bader transferred charges and charge density difference compared with SnS 2 /MXene theoretically. Hopefully, the present research may open up an innovative sensing design strategy for constructing NO 2 sensors at room temperature, as well as upgrade the sensing properties of MXene-based sensor. [Display omitted] • SnS 2 /Mo 4/3 B 2 MBene microcomposite was synthetized for NO 2 sensing at RT. • The material showed an improved response compared with that of SnS 2 /Ti 3 C 2 MXene. • The limit of detection of SnS 2 /Mo 4/3 B 2 MBene reached to a low value of 0.1 ppb. • Mechanism was explained theoretically by DFT calculation compared with SnS 2 /MXene. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ni/Co c-MOFs decoration with Au and Co 3 O 4 NPs were synthesized by a facile method. • The Au 3 @Co 3 O 4 NPs/Ni 1 Co 2 (HITP) immunosensor was optimized toward HBsAg detection. • High-performance of immunosensor can be ascribed to the synergistic effect. • The optimum immunosensor exhibits a low detection limit of 15 fg mL−1 for HBsAg. Herein, highly sensitive electrochemical immunosensor based on noble metal and metal oxide nanoparticles functionalized bimetal conductive metal–organic frameworks (c-MOFs) was proposed toward HBsAg detection. The nanorod-like architecture of Ni 3 (HITP) 2 c-MOFs was synthesised via a simple and facile route, followed by dopant coble ions formation cobaltosic oxide (Co 3 O 4) nanoparticles decorated bimetal NiCo(HITP) c-MOFs nanocomposites. Benefiting from large specific surface area, high electrical conductivity and abundant active sites of desirable nanostructures, the resultant Co 3 O 4 NPS/NiCo(HITP) bimetal c-MOFs nanostructure exhibits fast electron transfer capability and effective specific binding between antigen and antibody. Meanwhile, different amounts of Au nanoparticles were further functionalized on Co 3 O 4 NPS/NiCo(HITP) nanocomposites to further improve the electrochemical properties. Under optimum condition, the as-proposed Au@Co 3 O 4 NPS/NiCo(HITP) electrochemical immunosensor exhibits superior sensing performances toward HBsAg including wide linear response range from 1 pg mL−1 to 100 ng mL−1 and low detection limit of 15 fg mL−1 (S/N = 3). Meanwhile, the as-fabricated electrochemical immunosensors also displays outstanding selectivity, repeatability and stability. Besides, quantitative detection of HBsAg also achieves satisfactory results in real sample detection. Thereby, our work not only provides a promising strategy to construct highly sensitive electrochemical immunosensor using hybrid c-MOFs nanomaterials but also presents a potential application in the early diagnosis of hepatitis B-related diseases. [ABSTRACT FROM AUTHOR]

[Display omitted] • SnS 2 /Ti 3 C 2 MXene microcomposite was decorated by AuNPs for NO 2 sensing at RT. • A 5 times of improvement to 1 ppm NO 2 was achieved by 6 wt% of AuNPs decoration. • The limit of detection dropped from 500 ppb to 5 ppb owing to AuNPs decoration. • The material had good selectivity, stability and potential flexible application. • Sensing mechanism was explained by the involving of DFT calculations. The continuous discharging of nitrogen dioxide (NO 2) significantly damaged global environment and human health. In this study, SnS 2 microflowers/Ti 3 C 2 MXene heterostructure was modified by Au nanoparticles (AuNPs) for the detection of NO 2 at room temperature. The microcomposite decorated with 6 wt% AuNPs exhibited excellent sensing properties including response value of 5.34 to 1 ppm NO 2 , which was almost 5 times higher than that of undecorated one. The limit of detection lowered from 500 ppb to 5 ppb with the addition of AuNPs. Besides, the AuNPs decorated SnS 2 /MXene microcomposite revealed good selectivity, fatigue resistance, as well as potential application towards flexible sensing. The NO 2 gas sensing mechanism of AuNPs decorated with SnS 2 /Ti 3 C 2 MXene microcomposite was discussed through density functional theory calculation on adsorption behavior and transferred charges in detail. This work may provide an important pathway for the future development of NO 2 sensors at room temperature. [ABSTRACT FROM AUTHOR]

The design of a highly effective isopropanol gas sensor with high response and trace detection capability is extremely important for environmental surveillance and human health. Here, novel flower-like PtO x @ZnO/In 2 O 3 hollow microspheres were prepared by a three-step approach. The hollow structure was composed of an In 2 O 3 shell inside and layered ZnO/In 2 O 3 nanosheets outside with PtO x nanoparticles (NPs) on the surface. Meanwhile, the gas sensing performances of the ZnO/In 2 O 3 composite with different Zn/In ratios and PtO x @ZnO/In 2 O 3 composites were evaluated and compared systematically. The measurement results indicated that the ratio of Zn/In affected the sensing performance and the ZnIn 2 sensor presented a higher response, which was then modified with PtO x NPs to further enhance its sensing property. The Pt@ZnIn 2 sensor exhibited outstanding isopropanol detection performance with ultrahigh response values under 22 and 95% relative humidity (RH). In addition, it also showed a rapid response/recovery speed, good linearity, and low theoretical limit of detection (LOD) regardless of being under a relatively dry or ultrahumid atmosphere. The enhancement of isopropanol sensing properties might be ascribed to the unique structure of PtO x @ZnO/In 2 O 3 , heterojunctions between the components, and catalytic effect of Pt NPs.

Hierarchical three-dimensional (3D) flower-like n -ZnO/ p -NiO heterostructures with various Zn x Ni y molar ratios (Zn 5 Ni 1 , Zn 2 Ni 1 , Zn 1 Ni 1 , Zn 1 Ni 2 and Zn 1 Ni 5 ) were synthesized by a facile hydrothermal method. Their crystal phase, surface morphology, elemental composition and chemical state were comprehensively investigated by XRD, SEM, EDS, TEM and XPS techniques. Gas sensing measurements were conducted on all the as-developed Zn x Ni y -based sensors toward ammonia (NH 3 ) detection under various working temperatures from 160 to 340 °C. In particular, the as-prepared Zn 1 Ni 2 sensor exhibited superior NH 3 sensing performance under optimum working temperature (280 °C) including high response (25 toward 100 ppm), fast response/recovery time (16 s/7 s), low detection limit (50 ppb), good selectivity and long-term stability. The enhanced NH 3 sensing capabilities of Zn 1 Ni 2 sensor could be attributed to both the specific hierarchical structure which facilitates the adsorption of NH 3 molecules and produces much more contact sites, and the improved gas response characteristics of p-n heterojunctions. The obtained results clear demonstrated that the optimum n -ZnO/ p -NiO heterostructure is indeed very promising sensing material toward NH 3 detection for different applications.

Clustered indium oxide/copper oxide (In 2 O 3 /CuO) nanospheres with different CuO amounts were successfully synthesized as sensing materials for the carbon monoxide (CO) detection. Component and morphological characterizations were performed by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). Sensing performance for CO of the clustered In 2 O 3 and In 2 O 3 /CuO nanospheres were investigated under different temperatures and humidity conditions. The results show that the sensors based on 2 mol % In 2 O 3 /CuO (InCu2) exhibit about threefold improvement in response to CO compared to that of In 2 O 3 with quick response and recovery time, wide linearity, and low detection limit at 200 °C under 25% relative humidity (RH). Moreover, it shows tiny resistance and response declines despite the wide range of humidity variation from 25 to 95% RH. Meanwhile, the mechanism of enhanced gas-sensing performances and antihumidity properties of InCu2 were systematically investigated. We speculated that most of the water-driven species are predominantly adsorbed by CuO due to its high affinity to the hydroxyl group, which suppresses the interaction between moisture and In 2 O 3 . InCu2 is a new and promising material to sense CO in a highly sensitive and fast manner with negligible interference from ambient humidity.

Pure WO 3 sensors and Mn 3 O 4 /WO 3 composite sensors with different Mn concentrations (1 atom %, 3 atom % and 5 atom %) were successfully prepared through a facile hydrothermal method. As gas sensing materials, their sensing performance at different temperatures was systematically investigated for gas detection. The devices displayed different sensing responses toward different gases at specific temperatures. The gas sensing performance of Mn 3 O 4 /WO 3 composites (especially at 3 atom % Mn) were far improved compared to sensors based on pure WO 3 , where the improvement is related to the heterojunction formed between the two metal oxides. The sensor based on the Mn 3 O 4 /WO 3 composite with 3 atom % Mn showed a high selective response to hydrogen sulfide (H 2 S), ammonia (NH 3 ) and carbon monoxide (CO) at working temperatures of 90 °C, 150 °C and 210 °C, respectively. The demonstrated superior selectivity opens the door for potential applications in gas recognition and detection.