Transition metal oxyhydroxides have been used as promising electrocatalysts for water splitting however, their catalytic activity is restricted due to low surface area and poor conductivity. Herein, we report novel composite FeOOH@ZIF-12/graphene composite as electrocatalyst for water oxidation, whereby ZIF-12 provide extra surface for the FeOOH dispersion whilst graphene act as excellent electron mediator. The composite shows a low overpotential value of 291 mV to attain a current density of 10 mA cm?2 and a low Tafel slope value of 78 mV dec?1. The catalyst offers a maximum current density of 101 mA cm?2, while it gives a turnover frequency (TOF) value of 0.031 s?1 at an overpotential of 291 mV only. The excellent activity and remarkable stability of composite is attributed to highly conductive and porous support. 相似文献
Text search is a type of strategic reading that involves locating specific goal-relevant information. Previous research has indicated that college and high school students often exhibit inefficient or unsuccessful text-search performance. Consequently, the effects of 2 manipulations on text-search performance were investigated: (1) the presence of indexed terms in a text-search task and (2) the use of a planning prompt before beginning a task. 34 college students engaged in 6 text-search tasks. Half of these tasks contained terms that could be used in an index; half required that a search term be generated. Half of the subjects were randomly assigned to a prompt condition in which they were prompted to plan their search before beginning each task. The results indicated that a planning prompt raised the success level of search performance, as did the presence of terms that were searchable in the textbook's index. (PsycINFO Database Record (c) 2010 APA, all rights reserved) 相似文献
The interpretation of way-finding symbols for healthcare facilities in a multicultural community was assessed in a cross-sectional study. One hundred participants recruited from Al Ain city in the United Arab Emirates were asked to interpret 28 healthcare symbols developed at Hablamos Juntos (such as vaccinations and laboratory) as well as 18 general-purpose symbols (such as elevators and restrooms). The mean age was 27.6 years (16–55 years) of whom 84 (84%) were females. Healthcare symbols were more difficult to comprehend than general-purpose signs. Symbols referring to abstract concepts were the most misinterpreted including oncology, diabetes education, outpatient clinic, interpretive services, pharmacy, internal medicine, registration, social services, obstetrics and gynecology, pediatrics and infectious diseases. Interpretation rates varied across cultural backgrounds and increased with higher education and younger age. Signage within healthcare facilities should be tested among older persons, those with limited literacy and across a wide range of cultures. 相似文献
Automated techniques for Arabic content recognition are at a beginning period contrasted with their partners for the Latin and Chinese contents recognition. There is a bulk of handwritten Arabic archives available in libraries, data centers, historical centers, and workplaces. Digitization of these documents facilitates (1) to preserve and transfer the country’s history electronically, (2) to save the physical storage space, (3) to proper handling of the documents, and (4) to enhance the retrieval of information through the Internet and other mediums. Arabic handwritten character recognition (AHCR) systems face several challenges including the unlimited variations in human handwriting and the leakage of large and public databases. In the current study, the segmentation and recognition phases are addressed. The text segmentation challenges and a set of solutions for each challenge are presented. The convolutional neural network (CNN), deep learning approach, is used in the recognition phase. The usage of CNN leads to significant improvements across different machine learning classification algorithms. It facilitates the automatic feature extraction of images. 14 different native CNN architectures are proposed after a set of try-and-error trials. They are trained and tested on the HMBD database that contains 54,115 of the handwritten Arabic characters. Experiments are performed on the native CNN architectures and the best-reported testing accuracy is 91.96%. A transfer learning (TF) and genetic algorithm (GA) approach named “HMB-AHCR-DLGA” is suggested to optimize the training parameters and hyperparameters in the recognition phase. The pre-trained CNN models (VGG16, VGG19, and MobileNetV2) are used in the later approach. Five optimization experiments are performed and the best combinations are reported. The highest reported testing accuracy is 92.88%.
The Journal of Supercomputing - We present a probabilistic method for classifying chest computed tomography (CT) scans into COVID-19 and non-COVID-19. To this end, we design and train, in an... 相似文献
In this work, the effects of the presence of surfactants in the liquid phase and the hydrodynamic regime of the bubble flow on the oxygen transfer rate were investigated in an electroflotation process in batch mode. The volumetric mass transfer coefficient KLα and the oxygenation capacity were evaluated to improve the performances of the electroflotation process in terms of oxygenation. In order to evaluate the liquid-side mass transfer coefficient KLα the volumetric mass transfer coefficient KLα was dissociated into KL and the specific interracial area (a) since the last one was obtained from the gas hold-up and the bubble diameter. The effect of Reynolds number which define the hydrodynamic of the bubble flow has been also studied. Models of KLa and KL have been established to show the effects of the hydrodynamic parameters and liquid phase characteristics on the oxygen transfer rate. 相似文献
Piezoelectric nanogenerators (PENG) with flexible and simple design have pronounced significance in fabricating sustainable devices for self-powering electronics. This study demonstrates the fabrication of electrospun nanocomposite fibers from polyvinylidene fluoride (PVDF) filled zinc oxide (ZnO)/iron oxide (FeO) nanomaterials. The nanocomposite fiber based flexible PENG shows piezoelectric output voltage of 5.9 V when 3 wt% of ZnO/FeO hybrid nanomaterial is introduced, which is 29.5 times higher than the neat PVDF. No apparent decline in output voltage is observed for almost 2000 s attributed to the outstanding durability. This higher piezoelectric output performance is correlated with the β-phase transformation studies from the Fourier transformation infrared spectroscopy and the crystallinity studies from the differential scanning calorimetry. Both these studies show respective enhancement of 3.79 and 2.16% in the β-phase crystallinity values of PVDF-ZnO/FeO 3 wt% composite. Higher dielectric constant value obtained for the same composite (three times higher than the neat PVDF) confirms the increased energy storage efficiency as well. Thus the proposed soft and flexible PENG is a promising mechanical energy harvester, and its good dielectric properties reveals the ability to use this material as good power sources for wearable and flexible electronic devices.