N2O is
a greenhouse gas with tremendous global warming
potential, and more importantly it also causes ozone depletion; thus,
the separation of N2O from industrial processes has gained
significant attention. We have demonstrated that N2O can
be selectively separated from CO2 using the zeolite imidazolate
framework ZIF-7. The adsorption/desorption isotherms of both N2O and CO2 in ZIF-7 indicate the gate-opening mechanism
of this material, and surprisingly, the threshold pressure for the
gate opening with N2O is lower than that with CO2. Theoretical calculations indicate that both gas–host and
gas–gas interaction energies for N2O are more favorable
than those for CO2, giving rise to the difference in the
threshold pressure between N2O and CO2 in ZIF-7.
Breakthrough experiments for N2O/CO2 mixtures
confirm that ZIF-7 is capable of separating N2O and CO2 mixtures under the optimized conditions, in reasonable agreement
with simulation results, making it a promising material for industrial
applications.
We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest X-Ray images. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest X-Ray images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest X-Ray images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy.
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