A porous metal-organic framework with ultrahigh acetylene uptake capacity under ambient conditions

Acetylene, an important petrochemical raw material, is very difficult to store safely under compression because of its highly explosive nature. Here we present a porous metal-organic framework named FJI-H8, with both suitable pore space and rich open metal sites, for efficient storage of acetylene under ambient conditions. Compared with existing reports, FJI-H8 shows a record-high gravimetric acetylene uptake of 224 cm(3) (STP) g(−1) and the second-highest volumetric uptake of 196 cm(3) (STP) cm(−3) at 295 K and 1 atm. Increasing the storage temperature to 308 K has only a small effect on its acetylene storage capacity (∼200 cm(3) (STP) g(−1)). Furthermore, FJI-H8 exhibits an excellent repeatability with only 3.8% loss of its acetylene storage capacity after five cycles of adsorption–desorption tests. Grand canonical Monte Carlo simulation reveals that not only open metal sites but also the suitable pore space and geometry play key roles in its remarkable acetylene uptake

Formation of PbSe Nanocrystals: A Growth toward Nanocubes

In this paper we report an electron microscopic observation of crystal shape development when PbSe nanocrystals were synthesized using a dynamic injection technique at different temperatures in the presence of oleic acid. A two-step evolution mechanism was proposed, indicating that the shape evolution of PbSe nanocrystals is dependent on the growth time, whereas the crystalline size can be tuned by varying the growth temperature under the studied conditions. It also implies that a higher growth rate in the 〈111 〉 direction compared to that in the 〈100 〉 direction results in the formation of nanocubes. Lead chalcogenides are inspiring semiconductors with a narrow band gap. Size- and shape-controlled nanocrystals (NCs) of this family have demonstrated unique properties1-5 and can potentially be employed in numerous applications, such as near-IR luminescence6 and thermoelectric devices.7,8 To produce monodisperse NCs of lead chalcogenides with high quality and tunable size and shape, it is significant to understand their NC formation process. Since the growth of PbS9,10 and PbTe NCs11 has been investigated previously, similar exploration on PbS

Cardiac biophysical detailed synergetic modality rendering and visible correlation

The heart is a vital organ in the human body. Research and treatment for the heart have made remarkable progress, and the functional mechanisms of the heart have been simulated and rendered through the construction of relevant models. The current methods for rendering cardiac functional mechanisms only consider one type of modality, which means they cannot show how different types of modality, such as physical and physiological, work together. To realistically represent the three-dimensional synergetic biological modality of the heart, this paper proposes a WebGL-based cardiac synergetic modality rendering framework to visualize the cardiac physical volume data and present synergetic correspondence rendering of the cardiac electrophysiological modality. By constructing the biological detailed interactive histogram, users can implement local details rendering for the heart, which could reveal the cardiac biology details more clearly. We also present cardiac physical-physiological correlation visualization to explore cardiac biological association characteristics. Experimental results show that the proposed framework can provide favorable cardiac biological detailed synergetic modality rendering results in terms of both effectiveness and efficiency. Compared with existing methods, the framework can facilitate the study of the internal mechanism of the heart and subsequently deduce the process of initiation, development, and transformation from a healthy heart to an ill one, and thereby improve the diagnosis and treatment of cardiac disorders

Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique

Objectives/Hypothesis: To develop a deep-learning–based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings. Study Design: Retrospective study. Methods: A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNN-based classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. Results: In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P <.001), polyps (91% vs. 86%, P <.001), leukoplakia (91% vs. 65%, P <.001), and malignancy (90% vs. 54%, P <.001). Conclusions: The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions. Level of Evidence: NA Laryngoscope, 130:E686–E693, 2020