Redox-Active Azulene-based 2D Conjugated Covalent Organic Framework for Organic Memristors

As a conjugated and unsymmetric building block composed of an electron-poor seven-membered sp² carbon ring and an electron-rich five-membered carbon ring, azulene and its derivatives have been recognized as one of the most promising building blocks for novel electronic devices due to its intrinsic redox activity. By using 1,3,5-tris(4-aminophenyl)-benzene and azulene-1,3-dicarbaldehyde as the starting materials, an azulene(Azu)-based 2D conjugated covalent organic framework, COF-Azu, is prepared through liquid-liquid interface polymerization strategy for the first time. The as-fabricated Al/COF-Azu/indium tin oxide (ITO) memristor shows typical non-volatile resistive switching performance due to the electric filed induced intramolecular charge transfer effect. Associated with the unique memristive performance, a simple convolutional neural network is built for image recognition. After 8 epochs of training, image recognition accuracy of 80 % for a neutral network trained on a larger data set is achieved.     

COVID-19 Epidemic Prediction Based on Deep Learning

In this paper, a multi-layer gated recurrent unit neural network (multi-head GRU) model is proposed to predict the confirmed cases of the new crown epidemic (COVID-19). We extract the time series relationship in the data, and the rolling prediction method is adopted to ensure the simple structure of the model and achieve higher precision and interpretability. The prediction results of this model are compared with the LSTM model, the Transformer model and the infectious disease model (SIR). The results show that the proposed model has higher prediction accuracy. The mean absolute error (MAE) of epidemic prediction in most countries (the United States, Brazil, India, the United Kingdom and Russia) is respectively 197.52, 68.02, 200.67, 24.78 and 123.50, which is much smaller than the prediction error of the SIR model, LSTM model and Transformer model. For the spread of the COVID-19 epidemic, traditional infectious disease models and machine learning models cannot achieve more accurate predictions. In this paper, we use a GRU model to predict the real-time spread of COVID-19, which has fewer parameters and reduces the risk of overfitting to train faster. Meanwhile, it can make up for the shortcoming of the transformer model to capture local features.     

Interactions of potassium vapor with reactor tubes made of different materials and their impacts on particulate matter emission during pulverized biomass combustion

During the combustion of biomass in drop-tube furnace (DTF) systems, the released alkali metal (e.g., potassium, K) inevitably reacts with reactor tube at high temperatures, affecting the experimental results on the emission of particulate matter with aerodynamic diameters of <10 μm (PM₁₀). This study reports the interactions between K vapor and tube reactors made of silicon carbide, corundum, and mullite and their impacts on PM₁₀ emission. Demineralized wood samples loaded with potassium chloride (KCl) or ion-exchanged K respectively were combusted in a DTF at 1300 °C under air or oxy-fuel atmosphere. Another series of experiments was conducted to collect and analyze the PM₁₀ from the combustion of KCl-loaded wood, K-exchanged wood, and two typical biomass samples (cotton stalk and wheat straw) in the three reactor tubes under air atmosphere. Experimental results show that 4.1‒72.5% of K is retained in the three tubes when burning the KCl-loaded wood in air, and the combustion in oxy-fuel atmosphere slightly increases the K retention. For K-exchanged wood combustion in air, only 3.7‒23.6% of K is released from the reactor tubes. In all conditions, the reactivity of the reactor tubes with K vapor follows a sequence of mullite > corundum > silicon carbide. The retained K is unstable, 49.0‒64.8% of which can be re-released during polyvinyl chloride combustion. In addition, the results demonstrate that, compared with silicon carbide tube, the use of corundum and mullite tubes leads to a 16.2‒54.3% decrease in PM₁ yields and a significant drop in fine mode peaks in PM₁₀ during the combustion of biomass samples in air, while the PM_1–10 yields and the coarse mode peaks remain largely unchanged. These are attributed to the enhanced retentions of alkali metals in corundum and mullite tubes, which reduce the yields of Na, K, and Cl in PM₁₀, but has negligible effect on those of refractory elements such as Mg and Ca.