Search

In this study, iron oxide (Fe2O3)/graphitic carbon nitride (g-C3N4)/titanium dioxide (TiO2) nanofibers (NFs) were used as an additional layer on photoanode in dye-sensitized solar cells (DSSCs) to improve the photovoltaic performance of DSSCs. The nanofibers were prepared by electrospinning with double jets. The heterogeneous nanofiber composite mainly includes Fe2O3, g-C3N4, and TiO2. The Fe2O3/g-C3N4/TiO2 NFs were characterized by X-ray diffractometer (XRD), and the surface morphology was observed by a field emission scanning electron microscope (Fe-SEM). The electrochemical impedance spectroscopy (EIS) was applied to analyze the impedance of the cell. The incident photon current efficiency (IPCE) measurements were applied to determine the quantum efficiency, and ultraviolet-visible (UV-Vis) spectrophotometer was applied to observe the dye adsorption and absorption wavelength. To understand the performance of the modified photoanodes in DSSCs, we analyzed the photovoltaic parameters. The results suggest that the addition of Fe2O3/g-C3N4/TiO2 NFs is able to improve the conversion efficiency of the modified DSSCs to 4.81%. In addition, the efficiency under different illumination was also analyzed, and the highest conversion efficiency was 6.81% at 30 mW/cm2. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) using smartphone sensors have been recently studied in various applications including healthcare, fitness, and smart home. Their recognition accuracy often depends on high-quality feature design and effectiveness of classification algorithms, where existing work mostly replies on laborious hand-crafted design and shallow feature learning architecture. Recent deep learning techniques demonstrate outstanding effectiveness in performing automatic feature learning and outperform traditional models in terms of accuracy. But their performance is limited by the quality and volumes of available labelled data. It is challenging to achieve accurate multisubject HAR with only smartphone sensing data. This article proposes a novel optimal activity graph generation model incorporating a deep learning framework for automatic and accurate HAR with multiple subjects using only acceleration and gyroscope data. The activity graph generation model presents a multisensory integration mechanism with three-steps sorting algorithms for producing optimal activity graphs containing alignments of neighbored signals in their width and height. Then, we propose a deep convolutional neural network to automatically learn distinguishable features from the graphs for HAR. By leveraging superior presentation of correlations between human activities and neighbored signals alignments via optimal activity graphs, the learned features are endowed with more discriminative power. The experimental evaluation was carried out on several benchmark datasets (i.e., UCI, USCHAD, and UTD-MHAD). The results showed that our approach improved the average recognition accuracy by about 5% when compared with other state-of-the-art HAR methods. Particularly towards multisubject HAR cases (UTD-MHAD dataset with 21 subjects), it achieved up to 10% accuracy gain over other methods. These improvements show the advantage and potential of our method dealing with complex HAR problems with multiple subjects using limited sensing data. [ABSTRACT FROM AUTHOR]

Dye-sensitized solar cells (DSSCs) have great potential in solar power generation due to their advantages of easy fabrication and low fabrication cost. One of the main problems of DSSCs is the loss of recombination between the fluorine-doped tin oxide (FTO) substrate/electrolyte. This is mainly due to the mesoporous nature of the TiO2 film. The recombination effect can be reduced by introducing compact layers (CLs) on the photosensitive layer to prevent the direct contact between the transparent conductive oxide substrate and the redox electrolyte. When compared with TiO2, zinc oxide (ZnO) tends to have more negative conduction band edges. This helps to prevent, electronic recombination reactions and so improves the open-circuit voltage (${V}_{{\text {OC}}}$). ZnO blocking layers (ZBLs) were deposited on the FTO substrate by RF sputtering and used for DSSCs. We employed a field emission scanning electron microscope (FE-SEM) and X-ray diffraction (XRD) to characterize ZBL. Photovoltaic (PV) parameters were measured on the DSSCs samples fabricated in this study under solar simulator illumination at AM 1.5 (100 mW/cm2). Compared with the DSSCs without ZBL, DSSCs with ZBL (31 nm) exhibit higher short-circuit current density (${J}_{{\text {SC}}}$) and photovoltaic conversion efficiency (PCE), which is 21.82% higher than that of the DSSCs without ZBL. [ABSTRACT FROM AUTHOR]

Objective: The m6A modification is the most common ribonucleic acid (RNA) modification, playing a role in prompting the virus's gene mutation and protein structure changes in the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Nanopore single-molecule direct RNA sequencing (DRS) provides data support for RNA modification detection, which can preserve the potential $m^6A$ signature compared to second-generation sequencing. However, due to insufficient DRS data, there is a lack of methods to find m6A RNA modifications in DRS. Our purpose is to identify $m^6A$ modifications in DRS precisely. Methods: We present a methodology for identifying $m^6A$ modifications that incorporated mapping and extracted features from DRS data. To detect $m^6A$ modifications, we introduce an ensemble method called mixed-weight neural bagging (MWNB), trained with 5-base RNA synthetic DRS containing modified and unmodified $m^6A$. Results: Our MWNB model achieved the highest classification accuracy of 97.85% and AUC of 0.9968. Additionally, we applied the MWNB model to the COVID-19 dataset; the experiment results reveal a strong association with biomedical experiments. Conclusion: Our strategy enables the prediction of $m^6A$ modifications using DRS data and completes the identification of $m^6A$ modifications on the SARS-CoV-2. Significance: The Corona Virus Disease 2019 (COVID-19) outbreak has significantly influence, caused by the SARS-CoV-2. An RNA modification called $m^6A$ is connected with viral infections. The appearance of $m^6A$ modifications related to several essential proteins affects proteins’ structure and function. Therefore, finding the location and number of $m^6A$ RNA modifications is crucial for subsequent analysis of the protein expression profile. [ABSTRACT FROM AUTHOR]

In our study, the dye-sensitized solar cells (DSSCs) with double-layer thin films were fabricated. The active bottom layer of the structure was mainly made of the commercially available titanium dioxide (TiO2) nanoparticles. TiO2 hollow microspheres (THS) were doped into the TiO2 composite material and used as the scattering top layer. The THS prepared by the one-pot hydrothermal method was composed of irregularly arranged nanoparticles. Meanwhile, we also characterized the synthesized hollow TiO2 microspheres by both the scanning electron microscope (FE-SEM) and the X-ray diffractometer (XRD). Besides, we produced DSSCs with different weight percentages (0.5wt%, 1wt%, and 2wt%) of THS photoanodes. The DSSCs based on pure TiO2 showed the short-circuit current (JSC) of 8.33 mA/cm2 and the photovoltaic conversion efficiency (PCE) of 3.75%. The DSSCs manufactured using 1wt%-THS photoanode had JSC of 12.02 mA/cm2 and a PCE of 5.01%. When compared with the JSC and PCE based on pure TiO2 DSSCs, a significant improvement in the structure was observed. [ABSTRACT FROM AUTHOR]