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In this study, the CO2 sorption mechanisms and regeneration properties of alumina-based sorbent using K2CO3 loading under high-pressure and moderate temperature conditions were examined. To investigate the mechanism of CO2 sorption, a zirconium-based sorbent was compared with an alumina-based sorbent. The CO2 capture capacities of the PAI10, 20, 30, and 40 were 32.3, 63.0, 95.4, and 124.5 mg CO2/g sorbent, respectively. To investigate the CO2 sorption mechanism of an alumina-based sorbent, we performed XRD, TG/DTG, and FTIR analyses after CO2 sorption in the presence of 10 vol% CO2 and H2O each at 200 °C and 20 atm. For PAI10–40 sorbents, KHCO3 and KAl(CO3)(OH)2 phases were observed by TG/DTG and FTIR analysis. For PAI-x sorbents, it was confirmed that the captured CO2 is desorbed completely at a temperature below 400 °C at 20 atm.

Highlights A multifunctional Janus film is fabricated by integrating highly-crystalline and oxidation-resistant Ti3C2T x MXene with carbon nanotube (CNT) film through strong hydrogen bonding, which exhibits high electrical conductivity of 4250 S cm−1 and robust mechanical strength of 77 MPa. The MXene/CNT Janus film of 15 μm thickness demonstrates efficient electromagnetic interference shielding of 72 dB, low infrared (IR) emissivity of 0.09 and hence superior thermal camouflage performance, and outstanding IR detection capability, while maintaining its integrity equally at room temperature as well as under extreme conditions. This multifunctional MXene/CNT Janus film offers a practical solution for electromagnetic shielding and IR shielding/detection in challenging conditions.

Localization is essential for the autonomous navigation of a mobile robot. Because the error of the internal dead-reckoning localization increases with time, the localization error should be corrected periodically using external environmental information. This study presents a practical application of Quick Response (QR) codes to correct the localization error by combining the external QR code information and the internal encoder values. Each QR code contains the information of its own global coordinates and is attached as a one-dimensional array on vertical, thus walls reducing the number of QR codes required for localization. Whenever a QR code is detected and decoded by the mobile robot, the relative transformation between the QR code and the robot can be computed and the pose estimation for the robot is corrected by the extended Kalman filter. Experimental results demonstrate the performance of the localization and autonomous navigation of the mobile robot. The influence of the distance between adjacent QR codes in the environment on the localization performance was also addressed and verified by experiments.

Gamma-alumina (γ-Al2O3) is an essential support material in dry sorbents used to capture CO2 from flue gas. This study explores the deactivation of potassium-based γ-Al2O3 sorbents due to by-products such as KAl(CO3)(OH)2 during CO2 capture. We synthesized sorbents with K2CO3 loadings of 5, 10, 20, and 30 wt% and subjected them to repeated capture and regeneration cycles. The results show significant variations in the deactivation degree: the sorbent with 5 wt% K2CO3 exhibited a 100% deactivation rate, while the 30 wt% variant showed a markedly reduced rate of 44.6%. These findings highlight the impact of the formation of KAl(CO3)(OH)2 at the interface between K2CO3 and γ-Al2O3 on sorbent deactivation. An equation that can be used to predict the final CO2 capture capacity based on the ratio of active material to support was proposed using these results. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic altered buyers' concerns and resulted in more complex demand for high-involvement goods, such as financial products. These changes seem likely to persist after the pandemic. Additionally, other unforeseen events, although likely not as pervasive as the pandemic, could occur at any time. In this context, the ability of salespeople to keep their mental abilities sharp is vital in overcoming challenges and moving forward. This study highlights the emerging psychological capacities that help salespeople cope with new work circumstances following the COVID-19 pandemic, as well as dealing with other unforeseen events, and identifies ways to develop these psychological capabilities. Findings from in-depth interviews with 20 salespeople suggest that patience (P), consideration (C), and responsibility (R) are the emerging psychological capacities that motivate salespeople to achieve positive outcomes during and after the pandemic. These capacities can be developed through the exercise of passion and purpose, gratitude, long-term thinking, self-reflection, and self-compassion. We found the P.C.R. framework to fit the existing psychological capital (PsyCap) model, providing nuances to our understanding of PsyCap by proposing a framework with the emerging psychological capacities that salespeople require to succeed in the post-COVID personal selling environment. The study's limitations and future research directions are also discussed. [ABSTRACT FROM AUTHOR]

A rapidly aging population, combined with technological advancement, is creating strong latent motivators for older women to learn and use technology. While many past studies have provided insights into older women's perceptions and impacts of their technology adoption, the psychological capabilities underpinning their motivation to learn are often overlooked. This study draws on interview data from 16 older women in a less developed country to explore the psychological capabilities of older women that drive them to learn and use technology. Our analysis reveals five themes (i.e. sense of purpose, self-efficacy, continuous learning, self-care, and social support seeking) that reflect the psychological capabilities of older women in relation to their technology learning and adoption. As our findings provide new evidence of the importance of fusing the elements of psychological capabilities with content delivery in education and training for older women in less developed countries, this study has practical and theoretical implications for gerontechnology. [ABSTRACT FROM AUTHOR]

Recently, the identification of inertia and damping matrices (IIDM) and safety issues, as well as natural cooperation, are interestingly considered to enhance the quality of the physical human–robot interaction (pHRI). To cover all of these issues, advanced admittance controllers, such as those based on fuzzy logic or hedge algebras, have been formulated and successfully applied in several industrial problems. However, the inference mechanism of those kinds of controllers causes the discreteness of the super surface describing the input–output relationship in the Cartesian coordinates. As a consequence, the quality of the safe-natural cooperation between humans and robots is negatively affected. This paper presents an alternative admittance controller for pHRI by using a combination of hedge algebras and multilayer perceptron neural network (MLP), whose purpose is to create a more accurate inference mechanism for the admittance controller. To our best knowledge, this is the first time that such a neural network is considered for the inference mechanism of hedge algebras and also the first time that such an admittance controller is used for pHRI. The proposed admittance controller is verified on a teaching task using a 6-DOF manipulator. Experimental results have shown that the proposed method provides better cooperation compared with previous methods.