Thermodynamic and kinetic study of CO2 adsorption/desorptionon amine-functionalized sorbents

The thermodynamic and kinetic characteristics of CO2 adsorption of SBA-16 loaded with pentaethylenehexamine (PEHA) have been investigated using adsorption column system. The Langmuir isotherm model fitts the CO2 adsorption isotherms well, and the average isosteric heat of adsorption is 59.6 kJ/mol, indicating that the CO2 adsorption on PEHA-loaded SBA-16 is chemisorption. The Avrami fractional dynamics model is very suitable for illustrating the adsorption behaviour of CO2 adsorption, and the results of kinetic analysis show that increasing the partial pressure of CO2 or the working temperature is beneficial to the adsorption of CO2. Three desorption methods were evaluatedto achieve the optimal desorption method. The results show that VTSA and steam stripping method are effective methods for industrial CO2 desorption. Steam stripping may be more suitable for plants that already have low-cost steam. The activation energy Ea of CO2 adsorption/desorption is calculated using Arrhenius equation. The activation energy Ea of CO2 adsorption/desorption was calculated using the Arrhenius equation. The results show that the absolute value of Ea (adsorption) decreases with the increase of CO2 partial pressure. In addition, the Ea value of vacuum rotary regeneration method and steam stripping method is smaller than the Ea value of temperature swing regeneration

Thermodynamic and kinetic study of CO2 adsorption/desorptionon amine-functionalized sorbents

473-482The thermodynamic and kinetic characteristics of CO2 adsorption of SBA-16 loaded with pentaethylenehexamine (PEHA) have been investigated using adsorption column system. The Langmuir isotherm model fitts the CO2 adsorption isotherms well, and the average isosteric heat of adsorption is 59.6 kJ/mol, indicating that the CO2 adsorption on PEHA-loaded SBA-16 is chemisorption. The Avrami fractional dynamics model is very suitable for illustrating the adsorption behaviour of CO2 adsorption, and the results of kinetic analysis show that increasing the partial pressure of CO2 or the working temperature is beneficial to the adsorption of CO2. Three desorption methods were evaluatedto achieve the optimal desorption method. The results show that VTSA and steam stripping method are effective methods for industrial CO2 desorption. Steam stripping may be more suitable for plants that already have low-cost steam. The activation energy Ea of CO2 adsorption/desorption is calculated using Arrhenius equation. The activation energy Ea of CO2 adsorption/desorption was calculated using the Arrhenius equation. The results show that the absolute value of Ea (adsorption) decreases with the increase of CO2 partial pressure. In addition, the Ea value of vacuum rotary regeneration method and steam stripping method is smaller than the Ea value of temperature swing regeneration

How empowering leadership influences medical workers' work–family conflict in the post-pandemic era: A moderated mediation model of leadership “black box”

After experiencing the COVID-19 pandemic, the status and mechanisms of leadership, and the challenges for medical workers in terms of family–work conflicts, have caused widespread concern. In the post-pandemic era, based on role theory and the stressor-detachment model, this paper seeks to break the “black box” of negative effects that can be caused by leadership, research the mechanism and boundary conditions of those negative effects, and explore factors to reduce those negative effects. We recruited 1,010 Chinese medical workers fighting COVID-19 on the frontline. Our study results showed that there was a significant negative correlation between empowering leadership and work–family conflict, and this relationship was completely mediated by role stress, while psychological detachment moderated the relationship between role stress and work–family conflict. Moreover, psychological detachment moderated the mediating effect of empowering leadership on work–family conflict through role stress. Therefore, higher levels of psychological detachment were less conducive to medical workers' family–work conflict. This study has important theoretical significance and practical value for revealing the negative effects and mechanisms of empowering leadership and for medical workers to better deal with work–family relations

Robust elbow angle prediction with aging soft sensors via output-level domain adaptation

Wearable devices equipped with soft sensors provide a promising solution for body movement monitoring. Specifically, body movements like elbow flexion can be captured by monitoring the stretched soft sensors’ resistance changes. However, in addition to stretching, the resistance of a soft sensor is also influenced by its aging, which makes the resistance a less stable indicator of the elbow angle. In this paper, we leverage the recent progress in Deep Learning and address the aforementioned issue by formulating the aging-invariant prediction of elbow angles as a domain adaption problem. Specifically, we define the soft sensor data (i.e., resistance values) collected at different aging levels as different domains and adapt a regression neural network among them to learn domain-invariant features. However, unlike the popular pairwise domain adaptation problem that only involves one source and one target domain, ours is more challenging as it has “infinite” target domains due to the non-stop aging. To address this challenge, we novelly propose an output-level domain adaptation approach which builds on the fact that the elbow angles are in a fixed range regardless of aging. Experimental results show that our method enables robust and accurate prediction of elbow angles with aging soft sensors, which significantly outperforms supervised learning ones that fail to generalize to aged sensor data

Facile Fabrication of Sandwich Structural Membrane With a Hydrogel Nanofibrous Mat as Inner Layer for Wound Dressing Application

A common problem existing in wound dressing is to integrate the properties of against water erosion while maintaining a high water-uptake capacity. To tackle this issue, we imbedded one layer of hydrogel nanofibrous mat into two hydrophobic nanofibrous mats, thereafter, the sandwich structural membrane (SSM) was obtained. Particularly, SSM is composed of three individual nanofibrous layers which were fabricated through sequential electrospinning technology, including two polyurethane/antibacterial agent layers, and one middle gelatin/rutin layer. The obtained SSM is characterized in terms of morphology, component, mechanical, and functional performance. In addition to the satisfactory antibacterial activity against Staphylococcus aureus and Escherichia coli, and antioxidant property upon scavenging DPPH free radicals, the obtained SSM also shows a desirable thermally regulated water vapor transmission rate. More importantly, such SSM can be mechanically stable and keep its intact morphology without appearance damage while showing a high water-absorption ratio. Therefore, the prepared sandwich structural membrane with hydrogel nanofibrous mat as inner layer can be expected as a novel wound dressing

A new multi-anticipative car-following model with consideration of the desired following distance

We propose in this paper an extension of the multi-anticipative optimal velocity car-following model to consider explicitly the desired following distance. The model on the following vehicle’s acceleration is formulated as a linear function of the optimal velocity and the desired distance, with reaction-time delay in elements. The linear stability condition of the model is derived. The results demonstrate that the stability of traffic flow is improved by introducing the desired following distance, increasing the time gap in the desired following distance or decreasing the reaction-time delay. The simulation results show that by taking into account the desired following distance as well as the optimal velocity, the multi-anticipative model allows longer reaction-time delay in achieving stable traffic flows