CO2 capture over steam and KOH activated biochar: Effect of relative humidity

Carbon dioxide (CO2) capture is critical for emission reduction. Biochar is a promising adsorbent for CO2 capture. In this work, the effect of relative humidity and biochar activation with steam or KOH treatment on CO2 capture was investigated. The results demonstrate that the biochar sample activated by KOH has a high CO2 capture capacity (50.73 mg g−1). In addition, the biochar after 1.0 h of steam treatment showed a carbon capture capacity of 38.84 mg g−1. The results also show that the capture ability of biochar decreased as CO2 concentration decreased from 100% to 15%. The relative humidity had a negative impact on CO2 capture over biochar. The CO2 capture capability of biochar materials treated by steam decreased by a range of 31.38%–62.89% as the relative humidity rose from 8.8% to 87.9%. Furthermore, the lifetime of biochar samples at various relative humidity shows that increased relative humidity had a negative impact on CO2 adsorption due to water molecules occupying active sites

Potassium-promoted limestone for preferential direct hydrogenation of carbonates in integrated CO 2 capture and utilization

Integrated CO2 capture and utilization (ICCU) via the reverse water–gas shift (RWGS) reaction offers a particularly promising route for converting diluted CO2 into CO using renewable H2. Current ICCU-RWGS processes typically involve a gas–gas catalytic reaction whose efficiency is inherently limited by the Le Chatelier principle and side reactions. Here, we show a highly efficient ICCU process based on gas–solid carbonate hydrogenation using K promoted CaO (K-CaO) as a dual functional sorbent and catalyst. Importantly, this material allows ∼100% CO2 capture efficiency during carbonation and bypasses the thermodynamic limitations of conventional gas-phase catalytic processes in hydrogenation of ICCU, achieving >95% CO2-to-CO conversion with ∼100% selectivity. We showed that the excellent functionalities of the K-CaO materials arose from the formation of K2Ca­(CO3)2 bicarbonates with septal K2CO3 and CaCO3 layers, which preferentially undergo a direct gas–solid phase carbonates hydrogenation leading to the formation of CO, K2CO3 CaO and H2O. This work highlights the immediate potential of K-CaO as a class of dual-functional material for highly efficient ICCU and provides a new rationale for designing functional materials that could benefit the real-life application of ICCU processes

Integrated CO2 capture and methanation on Ru/CeO2-MgO combined materials: Morphology effect from CeO2 support

Integrated CO2 capture and methanation (ICCM) is attracting more attention to promote the reduction of CO2 emission. This work developed and applied a set of combined materials using Ru/CeO2 as catalyst and physically mixed Li, Na, K-doped MgO as adsorbent for the ICCM process. The influences of morphologies of CeO2 (rod, particle, and cube) in combined materials are investigated explicitly in terms of CO2 conversion and CH4 yield. Compared to the CeO2 with cube morphology, the CeO2 with rod and particle morphologies showed better Ru dispersion and more abundant support-metal interaction (SMI). The combined materials with rod and particle morphologies CeO2 (Ru/rod-CeO2-MgO and Ru/particle-CeO2-MgO) show more superior catalytic performance (0.33 and 0.29 mmol/g for CH4 yield and 55.7% and 59.8% for CO2 conversion, respectively) than that with Ru/cube-CeO2-MgO. Furthermore, the Ru/rod-CeO2-MgO shows excellent catalytic stability and reusability during 9 cyclic ICCM evaluations. In situ DRIFTS of Ru/CeO2-MgO revealed that the formates and dissociated CO2 (Ru-CO) might be the critical methanation intermediates in ICCM

A Deep Learning-Based Encrypted VPN Traffic Classification Method Using Packet Block Image

Network traffic classification has great significance for network security, network management and other fields. However, in recent years, the use of VPN and TLS encryption had presented network traffic classification with new challenges. Due to the great performances of deep learning in image recognition, many solutions have focused on the deep learning-based method and achieved positive results. A traffic classification method based on deep learning is provided in this paper, where the concept of Packet Block is proposed, which is the aggregation of continuous packets in the same direction. The features of Packet Block are extracted from network traffic, and then transformed into images. Finally, convolutional neural networks are used to identify the application type of network traffic. The experiment is conducted using captured OpenVPN dataset and public ISCX-Tor dataset. The results shows that the accuracy is 97.20% in OpenVPN dataset and 93.31% in ISCX-Tor dataset, which is higher than the state-of-the-art methods. This suggests that our approach has the ability to meet the challenges of VPN and TLS encryption