Carbon Tube-Based Cathode for Li-CO2 Batteries: A Review

Metal–air batteries are considered the research, development, and application direction of electrochemical devices in the future because of their high theoretical energy density. Among them, lithium–carbon dioxide (Li–CO2) batteries can capture, fix, and transform the greenhouse gas carbon dioxide while storing energy efficiently, which is an effective technique to achieve “carbon neutrality”. However, the current research on this battery system is still in the initial stage, the selection of key materials such as electrodes and electrolytes still need to be optimized, and the actual reaction path needs to be studied. Carbon tube-based composites have been widely used in this energy storage system due to their excellent electrical conductivity and ability to construct unique spatial structures containing various catalyst loads. In this review, the basic principle of Li–CO2 batteries and the research progress of carbon tube-based composite cathode materials were introduced, the preparation and evaluation strategies together with the existing problems were described, and the future development direction of carbon tube-based materials in Li–CO2 batteries was proposed

Effect of Bonding Temperature on Microstructure and Mechanical Properties during TLP Bonding of GH4169 Superalloy

The effect of bonding temperature on the microstructure and mechanical properties of transient liquid phase (TLP) joints of GH4169 superalloy was investigated. Joining processes were carried out at 1040–1100 °C for 30 min using BNi-2 solder paste. The results showed that three distinct microstructural zones were formed in the joint region: an athermal solidification zone (ASZ), consisting of eutectic compounds; an isothermal solidification zone (ISZ), consisting of γ solid solution; and a diffusion affected zone (DAZ), consisting of Ni-Cr rich boride and Cr-Nb-Mo-rich boride compounds. With increasing bonding temperature, the amounts of eutectic compounds in ASZ first decreased and then increased. A eutectic-free joint centerline was obtained at 1080 °C. The maximum bonding shear strength reached 728.03 MPa due to the completion of isothermal solidification. Fractographic studies revealed that the boride compounds in ASZ and the intermetallic compounds in DAZ were the main causes for the failure of joints. The fracture mode of the sample bonded at 1040 °C was brittle, and the fracture path was along the ASZ. However, the fracture mode of the sample bonded at 1080 °C was ductile, and the fracture occurred along the DAZ

A dependability modeling and analysis approach for an IoP-based service system

The increasing use of the internet of people (IoP) based service systems has the potential to bring about substantial benefits to individuals. An IoP-based service system includes components such as cell phones, wearable devices, smart homes, cars etc., together with information processing sub-systems that are able to provide personalised services, such as medical diagnosis and assistance to disabled individuals. Such systems are safety-critical since even minor faults in devices could cause harm or even loss of life. Therefore, it is imperative to analyse the dependability of IoP-based service systems. The dependability analysis of an IoP-based service system is, however, difficult. The IoP-based service system typically consists of a large number of devices, processes and tasks, connected together to form a large and complex system architecture. The analysis to determine which components or processes can cause the system to fail is difficult and error-prone if undertaken manually. For this reason, some automation is required. This paper describes a model-based dependability analysis method that can be applied to IoP-based service systems. The proposed method is illustrated by using an IoP-based living support system. We conclude that the proposed approach can be used to improve the dependability of IoP-based service systems

An Optimized Clustering Approach to Investigate the Main Features in Predicting the Punching Shear Capacity of Steel Fiber-Reinforced Concrete

We developed an optimized system for solving engineering problems according to the characteristics of data. Because data analysis includes different variations, the use of common features can increase the performance and accuracy of models. Therefore, this study, using a combination of optimization techniques (K-means algorithm) and prediction techniques, offers a new system and procedure that can identify and analyze data with similarity and close grouping. The system developed using the new sparrow search algorithm (SSA) has been updated as a new hybrid solution to optimize development engineering problems. The data for proposing the mentioned techniques were collected from a series of laboratory works on samples of steel fiber-reinforced concrete (SFRC). To investigate the issue, the data were first divided into different clusters, taking into account common features. After introducing the top clusters, each cluster was developed using three predictive models, i.e., multi-layer perceptron (MLP), support vector regression (SVR), and tree-based techniques. This process continues until the criteria are met. Accordingly, the K-means–artificial neural network 3 structure shows the best performance in terms of accuracy and error. The results also showed that the structure of hybrid models with cluster numbers 2, 3, and 4 is higher than the baseline models in terms of accuracy for assessing the punching shear capacity (PSC) of SFRC. The K-means–ANN3-SSA generated a new methodology for optimizing PSC. The new proposed model/procedure can be used for a similar situation by combining clustering and prediction methods

An Optimized Clustering Approach to Investigate the Main Features in Predicting the Punching Shear Capacity of Steel Fiber-Reinforced Concrete

We developed an optimized system for solving engineering problems according to the characteristics of data. Because data analysis includes different variations, the use of common features can increase the performance and accuracy of models. Therefore, this study, using a combination of optimization techniques (K-means algorithm) and prediction techniques, offers a new system and procedure that can identify and analyze data with similarity and close grouping. The system developed using the new sparrow search algorithm (SSA) has been updated as a new hybrid solution to optimize development engineering problems. The data for proposing the mentioned techniques were collected from a series of laboratory works on samples of steel fiber-reinforced concrete (SFRC). To investigate the issue, the data were first divided into different clusters, taking into account common features. After introducing the top clusters, each cluster was developed using three predictive models, i.e., multi-layer perceptron (MLP), support vector regression (SVR), and tree-based techniques. This process continues until the criteria are met. Accordingly, the K-means–artificial neural network 3 structure shows the best performance in terms of accuracy and error. The results also showed that the structure of hybrid models with cluster numbers 2, 3, and 4 is higher than the baseline models in terms of accuracy for assessing the punching shear capacity (PSC) of SFRC. The K-means–ANN3-SSA generated a new methodology for optimizing PSC. The new proposed model/procedure can be used for a similar situation by combining clustering and prediction methods

Energy-Efficient Network Transmission between Satellite Swarms and Earth Stations Based on Lyapunov Optimization Techniques

The recent advent of satellite swarm technologies has enabled space exploration with a massive number of picoclass, low-power, and low-weight spacecraft. However, developing swarm-based satellite systems, from conceptualization to validation, is a complex multidisciplinary activity. One of the primary challenges is how to achieve energy-efficient data transmission between the satellite swarm and terrestrial terminal stations. Employing Lyapunov optimization techniques, we present an online control algorithm to optimally dispatch traffic load among different satellite-ground links for minimizing overall energy consumption over time. Our algorithm is able to independently and simultaneously make control decisions on traffic dispatching over intersatellite-links and up-down-links so as to offer provable energy and delay guarantees, without requiring any statistical information of traffic arrivals and link condition. Rigorous analysis and extensive simulations have demonstrated the performance and robustness of the proposed new algorithm

Energy-Efficient Network Transmission between Satellite Swarms and Earth Stations Based on Lyapunov Optimization Techniques

The recent advent of satellite swarm technologies has enabled space exploration with a massive number of picoclass, low-power, and low-weight spacecraft. However, developing swarm-based satellite systems, from conceptualization to validation, is a complex multidisciplinary activity. One of the primary challenges is how to achieve energy-efficient data transmission between the satellite swarm and terrestrial terminal stations. Employing Lyapunov optimization techniques, we present an online control algorithm to optimally dispatch traffic load among different satellite-ground links for minimizing overall energy consumption over time. Our algorithm is able to independently and simultaneously make control decisions on traffic dispatching over intersatellite-links and updown-links so as to offer provable energy and delay guarantees, without requiring any statistical information of traffic arrivals and link condition. Rigorous analysis and extensive simulations have demonstrated the performance and robustness of the proposed new algorithm

Direct DNA crosslinking with CAP-C uncovers transcription-dependent chromatin organization at high resolution.

Determining the spatial organization of chromatin in cells mainly relies on crosslinking-based chromosome conformation capture techniques, but resolution and signal-to-noise ratio of these approaches is limited by interference from DNA-bound proteins. Here we introduce chemical-crosslinking assisted proximity capture (CAP-C), a method that uses multifunctional chemical crosslinkers with defined sizes to capture chromatin contacts. CAP-C generates chromatin contact maps at subkilobase (sub-kb) resolution with low background noise. We applied CAP-C to formaldehyde prefixed mouse embryonic stem cells (mESCs) and investigated loop domains (median size of 200 kb) and nonloop domains (median size of 9 kb). Transcription inhibition caused a greater loss of contacts in nonloop domains than loop domains. We uncovered conserved, transcription-state-dependent chromatin compartmentalization at high resolution that is shared from Drosophila to human, and a transcription-initiation-dependent nuclear subcompartment that brings multiple nonloop domains in close proximity. We also showed that CAP-C could be used to detect native chromatin conformation without formaldehyde prefixing