Microbial fuel cells (MFCs), which can generate low‐pollution power through microbial decomposition, have become a potentially important technology with applications in environmental protection and energy recovery. The electrode materials used in MFCs are crucial determinants of their capacity to generate electricity. In this study, we investigate the performance of using carbon nanotube (CNT) and graphene‐modified carbon‐cloth electrodes in a single‐chamber MFC. We develop a process for fabricating carbon‐based modified electrodes and Escherichia coli HB101 in an air‐cathode MFC. The results show that the power density of MFCs can be improved by applying a coat of either graphene or CNT to a carbon‐cloth electrode, and the graphene‐modified electrode exhibits superior performance. In addition, the enhanced performance of anodic modification by CNT or graphene was greater than that of cathodic modification. The internal resistance decreased from 377 kΩ for normal electrodes to 5.6 kΩ for both electrodes modified by graphene with a cathodic catalyst. Using the modified electrodes in air‐cathode MFCs can enhance the performance of power generation and reduce the associated costs.

This paper proposes a new approach to defect detection system design focused on exact damaged areas demonstrated through visual data containing gear wheel images. The main advantage of the system is the capability to detect a wide range of patterns of defects occurring in datasets. The methodology is built on three processes that combine different approaches from unsupervised and supervised methods. The first step is a search for anomalies, which is performed by defining the correct areas on the controlled object by using the autoencoder approach. As a result, the differences between the original and autoencoder-generated images are obtained. These are divided into clusters using the clustering method (DBSCAN). Based on the clusters, the regions of interest are subsequently defined and classified using the pre-trained Xception network classifier. The main result is a system capable of focusing on exact defect areas using the sequence of unsupervised learning (autoencoder)–unsupervised learning (clustering)–supervised learning (classification) methods (U2S-CNN). The outcome with tested samples was 177 detected regions and 205 occurring damaged areas. There were 108 regions detected correctly, and 69 regions were labeled incorrectly. This paper describes a proof of concept for defect detection by highlighting exact defect areas. It can be thus an alternative to using detectors such as YOLO methods, reconstructors, autoencoders, transformers, etc.

Microbial fuel cells (MFCs) generate low-pollution power by feeding organic matter to bacteria; MFC applications have become crucial for energy recovery and environmental protection. The electrode materials of any MFC affect its power generation capacity. In this research, nine single-chamber MFCs with various electrode configurations were investigated and compared with each other. A fabrication process for carbon-based electrode coatings was proposed, and Escherichia coli HB101 was used in the studied MFC system. The results show that applying a coat of either graphene or carbon nanotubes (CNTs) to a stainless steel mesh electrode can improve the power density and reduce the internal resistance of an MFC system. Using the proposed surface modification method, CNTs and graphene used for anodic and cathodic modification can increase power generation by approximately 3–7 and 1.5–4.5 times, respectively. Remarkably, compared to a standard MFC with an untreated anode, the internal resistances of MFCs with CNTs- and graphene-modified anodes were reduced to 18 and 30% of standard internal resistance. Measurements of the nine systems we studied clearly presented the performance levels of CNTs and graphene applied as surface modification of stainless steel mesh electrodes.

Origami, the ancient paper folding art has inspired the engineering equipment and design for decades. The basic concept of origami is very general, which leads to applications ranging from small scale to large scale. Recently, researchers are interested in being able to create self-folding structures. Such a structure enables kinematic manipulation by external forces or moments without folding and/or unfolding operations. This is a beneficial application for many fields including aerospace systems, robots, small devices and self-assembly systems. In this paper, the investigation and analyses of the previous literatures on the key driving force of the actuation structure, including the heat, light, electricity, gas and other actuation methods. The aims are to provide researchers and practitioners with the support to systematically understand the latest technologies in this important and evolving field, with inspiration and direction for follow-up.