Post-combustion CO 2 capture from the flue gas is one of the key technology options to reduce greenhouse gases, because this can be potentially retrofitted to the existing fleet of coal-fired power stations. Adsorption processes using solid sorbents capable of capturing CO 2 from flue gas streams have shown many potential advantages, compared to other conventional CO 2 capture using aqueous amine solvents. In view of this, in the past few years, several research groups have been involved in the development of new solid sorbents for CO 2 capture from flue gas with superior performance and desired economics. A variety of promising sorbents such as activated carbonaceous materials, microporous/mesoporous silica or zeolites, carbonates, and polymeric resins loaded with or without nitrogen functionality for the removal of CO 2 from the flue gas streams have been reviewed. Different methods of impregnating functional groups, including grafting techniques and modifying the support materials, have been discussed to enhance the performance of the sorbents. The performance characteristics of the solid sorbents are assessed in terms of various desired attributes, such as their equilibrium adsorption capacity, selectivity, regeneration, multicycle durability, and adsorption/ desorption kinetics. The potential of metal-organic frameworks (MOFs) is also recognized to determine whether these novel materials provide better CO 2 adsorption capacity under low CO 2 partial pressure. A comprehensive critical review and analysis of the literature on this subject has been carried out to update the recent progress in this arena. A comparison of different solid sorbents at different stages is made. It also includes a brief review on techno-economic analysis and design aspects of sorbent bed contactor configuration. Finally, a few recommendations have been proposed for further research efforts to progress post-combustion carbon capture.

In this work, an experimental and theoretical investigation was conducted on the adsorptive removal of CO 2 onto tetraethylenepentamine (TEPA) functionalized mesoporous SBA-15. The functionalization of SBA-15 silica with TEPA was achieved using a conventional wet impregnation technique. The structural properties of the mesoporous silica sorbents were characterized by nitrogen adsorption/desorption, SAXS, SEM, TEM, and FTIR techniques. The adsorption of CO 2 on the amine-impregnated sorbent was measured by thermogravimetric method over a CO 2 partial pressure range of 10−100 kPa and a temperature range of 30−100 °C under atmospheric pressure. The effects on CO 2 adsorption capacity of temperature, partial pressure of CO 2 , amine loading, and moisture were evaluated. All the impregnated SBA-15 sorbents showed reversible CO 2 adsorption behaviors with fast adsorption kinetics. The CO 2 adsorption capacity measured at different temperatures suggests that the optimal adsorption temperature is 75 °C. The CO 2 uptake of the amine-impregnated sorbent increased significantly in the presence of moisture. SBA-15 containing 60 wt % TEPA showed the highest CO 2 adsorption capacity of 5.22 mmol/g in pure and humid CO 2 at 75 °C. Temperature swing adsorption/desorption cycles were also explored using simulated flue gas in both dry and humid conditions, and it was found that CO 2 uptake after ten cycles was within 90% of CO 2 uptake of the first cycle. Different adsorption kinetic models have also been investigated to analyze the experimental data of CO 2 uptake. The model was validated with the experimental results of isothermal adsorption measurements of CO 2 on SBA-15/TEPA. It has been found that Fractional Order kinetic model (Chem. Eng. J. 2011, 173, 72) is very good over the entire adsorption region of the study with a maximum average absolute deviation between experimental CO 2 uptake and that calculated from the model of about 2.42%.

Automated deception detection (ADD) from real-life videos is a challenging task. It specifically needs to address two problems: (1) Both face and body contain useful cues regarding whether a subject is deceptive. How to effectively fuse the two is thus key to the effectiveness of an ADD model. (2) Real-life deceptive samples are hard to collect; learning with limited training data thus challenges most deep learning based ADD models. In this work, both problems are addressed. Specifically, for face-body multimodal learning, a novel face-focused cross-stream network (FFCSN) is proposed. It differs significantly from the popular two-stream networks in that: (a) face detection is added into the spatial stream to capture the facial expressions explicitly, and (b) correlation learning is performed across the spatial and temporal streams for joint deep feature learning across both face and body. To address the training data scarcity problem, our FFCSN model is trained with both meta learning and adversarial learning. Extensive experiments show that our FFCSN model achieves state-of-the-art results. Further, the proposed FFCSN model as well as its robust training strategy are shown to be generally applicable to other human-centric video analysis tasks such as emotion recognition from user-generated videos.

Please cite this article as: E. Gudmundsson et al., Pleuroparenchymal fibroelastosis in idiopathic pulmonary fibrosis: Survival analysis using visual and computer-based computed tomography assessment, EClinicalMedicine (2021),

We study the Anderson transition with interactions in one dimension from the perspective of quantum entanglement. Extensive numerical calculations of the entanglement entropy (EE) of the systems are carried out through the density matrix renormalization group algorithm. We demonstrate that the EE can be used for the finite-size scaling (FSS) to characterize the Anderson transition in both noninteracting and interacting systems. From the FSS analysis we can obtain a precise estimate of the critical parameters of the transition. The method can be applied to various one-dimensional models, either interacting or noninteracting, to quantitatively characterize the Anderson transitions.

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