Topology meets MOF chemistry for pore-aperture fine tuning: ftw-MOF platform for energy-efficient separations via adsorption kinetics or molecular sieving

2018 ◽  
Vol 54 (49) ◽  
pp. 6404-6407 ◽  
Author(s):  
Dong-Xu Xue ◽  
Amandine Cadiau ◽  
Łukasz J. Weseliński ◽  
Hao Jiang ◽  
Prashant M. Bhatt ◽  
...  

The MBB approach permitted the construction of a highly stable ftw-MOF for intricate separations.

2018 ◽  
Vol 54 (52) ◽  
pp. 7251-7251
Author(s):  
Dong-Xu Xue ◽  
Amandine Cadiau ◽  
Łukasz J. Weseliński ◽  
Hao Jiang ◽  
Prashant M. Bhatt ◽  
...  

Correction for ‘Topology meets MOF chemistry for pore-aperture fine tuning: ftw-MOF platform for energy-efficient separations via adsorption kinetics or molecular sieving’ by Dong-Xu Xue et al., Chem. Commun., 2018, DOI: 10.1039/c8cc03841d.


Author(s):  
Daniil M. Polyukhov ◽  
Artem S. Poryvaev ◽  
Aleksandr S. Sukhikh ◽  
Sergey A. Gromilov ◽  
Matvey V. Fedin

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bandar Almaslukh

Early detection of pneumonia disease can increase the survival rate of lung patients. Chest X-ray (CXR) images are the primarily means of detecting and diagnosing pneumonia. Detecting pneumonia from CXR images by a trained radiologist is a challenging task. It needs an automatic computer-aided diagnostic system to improve the accuracy of diagnosis. Developing a lightweight automatic pneumonia detection approach for energy-efficient medical systems plays an important role in improving the quality of healthcare with reduced costs and speedier response. Recent works have proposed to develop automated detection models using deep learning (DL) methods. However, the efficiency and effectiveness of these models need to be improved because they depend on the values of the models’ hyperparameters. Choosing suitable hyperparameter values is a critical task for constructing a lightweight and accurate model. In this paper, a lightweight DL approach is proposed using a pretrained DenseNet-121-based feature extraction method and a deep neural network- (DNN-) based method with a random search fine-tuning technique. The DenseNet-121 model is selected due to its ability to provide the best representation of lung features. The use of random search makes the tuning process faster and improves the efficiency and accuracy of the DNN model. An extensive set of experiments are conducted on a public dataset of CXR images using a set of evaluation metrics. The experiments show that the approach achieved 98.90% accuracy with an increase of 0.47% compared to the latest approach on the same dataset. Moreover, the experimental results demonstrate the approach that the average execution time for detection is very low, confirming its suitability for energy-efficient medical systems.


2021 ◽  
Author(s):  
Nanwen Li ◽  
Xiuling Chen ◽  
Yanfang Fan ◽  
Linzhou Zhang ◽  
Dong Guan ◽  
...  

Abstract Highly permeable and selective membranes that exceed the conventional permeability-selectivity upper bound are attractive for energy-efficient gas separations. In the context microporous polymers have gained increasing attention owing to their high porosity and exceptional permeability. However, the moderate selectivity of microporous polymers caused by inherent broad distribution of cavities leads to a loss of valuable gas products, making them unfavorable for separating similarly sized gas mixtures. Here we report a new approach to designing polymeric molecular sieve membranes via multi-covalent-crosslinking of miscible blends of Polymer of Intrinsic Microporosity, i.e. bromomethyl (PIM-BM) and Tröger's Base (TB), enabling simultaneously high permeability and selectivity. Selective gas permeation is achieved via adjusting reaction temperature, reaction time and the oxygen concentration with occurrences of polymer chain scissor, rearrangement and thermal oxidative crosslinking reaction simultaneously. Upon a thermal treatment at 300 oC for 5h, membranes exhibit an O2/N2, CO2/CH4 and H2/CH4 selectivity as high as 11.1, 155.7 and 814.1, respectively, with an O2, H2 and CO2 permeability of 18.2, 358.2 and 67.6 Barrer, respectively, transcending the state-of-art upper bounds. The design strategy represents a generalizable approach to creating molecular-sieving polymer membranes with enormous potentials for energy-efficient separation processes.


2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Dmytro Antypov ◽  
Aleksander Shkurenko ◽  
Prashant M. Bhatt ◽  
Youssef Belmabkhout ◽  
Karim Adil ◽  
...  

AbstractEnergy-efficient approaches to propylene/propane separation such as molecular sieving are of considerable importance for the petrochemical industry. The metal organic framework NbOFFIVE-1-Ni adsorbs propylene but not propane at room temperature and atmospheric pressure, whereas the isostructural SIFSIX-3-Ni does not exclude propane under the same conditions. The static dimensions of the pore openings of both materials are too small to admit either guest, signalling the importance of host dynamics for guest entrance to and transport through the channels. We use ab initio calculations together with crystallographic and adsorption data to show that the dynamics of the two framework-forming units, polyatomic anions and pyrazines, govern both diffusion and separation. The guest diffusion occurs by opening of the flexible window formed by four pyrazines. In NbOFFIVE-1-Ni, (NbOF5)2− anion reorientation locates propane away from the window, which enhances propylene/propane separation.


2021 ◽  
Vol 43 ◽  
pp. e51303
Author(s):  
Aline Bavaresco ◽  
Jhessica Marchini Fonseca ◽  
Fabiano Bisinella Scheufele ◽  
Camila da Silva ◽  
Joel Gustavo Teleken

The objective of this work was to evaluate the ability of CCC as an adsorbent material for the acidity removal of RFO, aiming at the application of the oil in biodiesel production. For that, a RCCD was used for FFA removal by applying the CCC and CAC for comparative purposes. In the RCCD removal assays the effect of the Temperature, Agitation and Mass factors were assessed over acidity removal of the oil. Under the best conditions from RCCD, an evaluation of adsorption kinetics was performed, wherein it was observed the equilibrium was reached within 4 h, for the CCC. Also, the influence of the adsorbent dosage was performed. It was verified that 4 g was sufficient to allow the system to reach the maximum FFA removal. Overall, the CCC presented results approximately twice as high than those obtained by the CAC, mainly due to the pore size distribution which led to a “molecular sieving effect” for the CCC adsorbent. It allowed the major diffusion of the FFA molecules inside its narrow-distributed pores, whereas the CAC with a wider pore distribution (up to 260 Å) resulted in the larger molecules competition for the active sites inside the porous structure. The adsorbents’ characterization also evidenced that CCC adsorbent presented a higher content of oxygenated groups in its surface which acted as potential active sites for the FFA molecules resulting in an enhanced adsorbent-adsorbate affinity. Lastly, the wastes generated in the adsorption experiments, were evaluated as to their calorific power resulting in a value of 31,933 J g-1, suggesting that it could be further used for energetic purposes, such as a solid fuel for boilers and furnaces to generate thermal energy. Based on these results, the CCC stands out as a promising material for RFO acidity removal.


2021 ◽  
Vol 33 (48) ◽  
pp. 2170376
Author(s):  
Hae Sol Lee ◽  
Nam Sun Kim ◽  
Dong‐il Kwon ◽  
Su‐Kyung Lee ◽  
Muhammad Numan ◽  
...  

2021 ◽  
pp. 2105398
Author(s):  
Hae Sol Lee ◽  
Nam Sun Kim ◽  
Dong‐il Kwon ◽  
Su‐Kyung Lee ◽  
Muhammad Numan ◽  
...  

ASHA Leader ◽  
2017 ◽  
Vol 22 (6) ◽  
Author(s):  
Christi Miller
Keyword(s):  

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