Nowadays, a dye-sensitized solar cell (DSSC) attracts attention to its development widely due to its several advantages, such as simple processes, low costs, and flexibility. In this work, we demonstrate the difference in device structures between small size and large size cells (5 cm × 5 cm, 10 cm × 10 cm and 10 cm × 15 cm). The design of the photoanode and dye-sensitized process plays important roles in affecting the cell efficiency and stability. The effects of the TiO electrode, using TiCl pretreatment and post-treatment processes, are also discussed, whereas, the open-circuit voltage (Voc), short-circuit current density (Jsc), and module efficiency are successfully improved. Furthermore, the effects on module performances by some factors, such as dye solution concentration, dye soaking temperature, and electrolyte injection method are also investigated. We have demonstrated that the output power of a 5 cm × 5 cm DSSC module increases from 86.2 mW to 93.7 mW, and the module efficiency achieves an outstanding performance of 9.79%. Furthermore, enlarging the DSSC modules to two sizes (10 cm × 10 cm and 10 cm × 15 cm) and comparing the performance with different module designs (C-DSSC and S-DSSC) also provides the specific application of polymer sealing and preparing high-efficiency large-area DSSC modules.
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http://dx.doi.org/10.3390/nano11082125 | DOI Listing |
Int J Gynecol Cancer
January 2025
Institute of Image-Guided Surgery, IHU Strasbourg, France; University of Strasbourg, ICube, Laboratory of Engineering, Computer Science and Imaging, Department of Robotics, Imaging, Teledetection and Healthcare Technologies, CNRS, UMR, Strasbourg, France.
Objective: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.
Methods: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus.
J Chem Inf Model
January 2025
School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China.
Efficient and accurate drug-target affinity (DTA) prediction can significantly accelerate the drug development process. Recently, deep learning models have been widely applied to DTA prediction and have achieved notable success. However, existing methods often encounter several common issues: first, the data representations lack sufficient information; second, the extracted features are not comprehensive; and third, most methods lack interpretability when modeling drug-target binding.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFFront Neurorobot
January 2025
College of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, Shanxi, China.
Accurate building segmentation has become critical in various fields such as urban management, urban planning, mapping, and navigation. With the increasing diversity in the number, size, and shape of buildings, convolutional neural networks have been used to segment and extract buildings from such images, resulting in increased efficiency and utilization of image features. We propose a building semantic segmentation method to improve the traditional Unet convolutional neural network by integrating attention mechanism and boundary detection.
View Article and Find Full Text PDFJ Med Biochem
November 2024
Shanghai Shuguang Hospital Affiliated with Shanghai University of TCM, Department of Laboratory Medicine, Shanghai, China.
Background: The present study aims to (1) evaluate the autoanalyser quality control (QC) module of the Kehua Polaris c2000 automatic modularised biochemical analyser and (2) verify the impact of the analyser's automatic implementation of internal QC (IQC) testing at a set time point on the results of IQC and turnaround time (TAT).
Methods: For 5 consecutive days, three different methods were used to conduct IQC. Method 1: Internal QC was carried out at 8:00 every day.
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