Electroreduction of CO2 in a highly selective and efficient manner is a crucial step towards CO2 utilization. Nanostructured Ag catalysts have been found to be effective candidates for CO2 to CO conversion. In this report, we combine experimental and computational efforts to explore the electrocatalytic reaction mechanism of CO2 reduction on nanostructured Ag catalyst surfaces in an aqueous electrolyte. In contrast to bulk Ag catalysts, both nanoparticle and nanoporous Ag catalysts show enhanced ability to reduce the activation energy of the CO2 to intermediate step through the low coordinated Ag surface atoms, resulting in a reaction mechanism involving a fast first electron and proton transfer followed by a slow second proton transfer as the rate limiting step. Experimental SectionComputational Modeling

Robotic Operating System(ROS) security research is currently in a preliminary state, with limited research in tools or models. Considering the trend of digitization of robotic systems, this lack of foundational knowledge increases the potential threat posed by security vulnerabilities in ROS. In this article, we present a new tool to assist further security research in ROS, ROSploit. ROSploit is a modular two-pronged offensive tool covering both reconnaissance and exploitation of ROS systems, designed to assist researchers in testing exploits for ROS.

In robotic systems, the physical world is highly coupled with cyberspace. New threats affect cyber-physical systems as they rely on several sensors to perform critical operations. The most sensitive targets are their location systems, where spoofing attacks can force robots to behave incorrectly. In this paper, we propose a novel anomaly detection approach for sensor spoofing attacks, based on an auto-encoder architecture. After initial training, the detection algorithm works directly on the compressed data by computing the reconstruction errors. We focus on spoofing attacks on Light Detection and Ranging (LiDAR) systems. We tested our anomaly detection approach against several types of spoofing attacks comparing four different compression rates for the auto-encoder. Our approach has a 99% True Positive rate and a 10% False Negative rate for the 83% compression rate. However, a compression rate of 41% could handle almost all of the same attacks while using half the data.

The Robot Operating System (ROS) are being deployed for multiple life critical activities such as self-driving cars, drones, and industries. However, the security has been persistently neglected, especially the image flows incoming from camera robots. In this paper, we perform a structured security assessment of robot cameras using ROS. We points out a relevant number of security flaws that can be used to take over the flows incoming from the robot cameras. Furthermore, we propose an intrusion detection system to detect abnormal flows. Our defense approach is based on images comparisons and unsupervised anomaly detection method. We experiment our approach on robot cameras embedded on a self-driving car.

In this paper we propose ROS-Defender, a holistic approach to secure robotics systems, which integrates a Security Event Management System (SIEM), an intrusion prevention system (IPS) and a firewall for a robotic system. ROS-Defender combines anomaly detection systems at application (ROS) level and network level, with dynamic policy enforcement points using software defined networking (SDN) to provide protection against a large class of attacks. Although SIEMs, IPS, and firewall have been previously used to secure computer networks, ROS-Defender is applying them for the specific use case of robotic systems, where security is in many cases an afterthought.