Ultrasonication is the most widely used technique for the dispersion of a range of nanomaterials, but the intrinsic mechanism which leads to stable solutions is poorly understood with procedures quoted in the literature typically specifying only extrinsic parameters such as nominal electrical input power and exposure time. Here we present new insights into the dispersion mechanism of a representative nanomaterial, single-walled carbon nanotubes (SW-CNTs), using a novel upscalable sonoreactor and an in situ technique for the measurement of acoustic cavitation activity during ultrasonication. We distinguish between stable cavitation, which leads to chemical modification of the surface of the CNTs, and inertial cavitation, which favors CNT exfoliation and length reduction. Efficient dispersion of CNTs in aqueous solution is found to be dominated

The availability of digital technologies, such as 3D printing, allow members of the public rather than only producers, to innovate. Makerspaces, where communities of individuals share access to such technologies may therefore support the democratisation of innovation. Yet little is known about how and why makerspace members use 3D printing to realise their creative and commercial ambitions. Through an ethnographic study, we identify a bricolage approach whereby makerspace members combine 3D printing with whatever resources are at hand in a makerspace, to generate innovations that otherwise may not be realised. In this context, we find bricolage entails synergy -combining resources in creative ways -and openness -a willingness to gather and share resources. Yet, we confirm that bricolage restricts commercial growth such 2 that a need for more structured processes and perhaps a move away from makerspaces eventually becomes necessary. We contribute to theory by presenting makerspaces as a route to innovation in resource constrained contexts, or those in which neither a problem nor solution are clearly defined. This contrasts with crowdsourcing where problems but not solutions are defined, and R&D where both problem and expected solution are defined.

The technological leap of smart technologies has brought the conventional electrical grid in a new digital era called Smart Grid (SG), providing multiple benefits, such as two-way communication, pervasive control and self-healing. However, this new reality generates significant cybersecurity risks due to the heterogeneous and insecure nature of SG. In particular, SG relies on legacy communication protocols that have not been implemented having cybersecurity in mind. Moreover, the advent of the Internet of Things (IoT) creates severe cybersecurity challenges. The Security Information and Event Management (SIEM) systems constitute an emerging technology in the cybersecurity area, having the capability to detect, normalise and correlate a vast amount of security events. They can orchestrate the entire security of a smart ecosystem, such as SG. Nevertheless, the current SIEM systems do not take into account the unique SG peculiarities and characteristics like the legacy communication protocols. In this paper, we present the Secure and PrivatE smArt gRid (SPEAR) SIEM, which focuses on SG. The main contribution of our work is the design and implementation of a SIEM system capable of detecting, normalising and correlating cyberattacks and anomalies against a plethora of SG application-layer protocols. It is noteworthy that the detection performance of the SPEAR SIEM is demonstrated with real data originating from four real SG use case (a) hydropower plant, (b) substation, (c) power plant and (d) smart home.

The technological leap of smart technologies and the Internet of Things has advanced the conventional model of the electrical power and energy systems into a new digital era, widely known as the Smart Grid. The advent of Smart Grids provides multiple benefits, such as self-monitoring, self-healing and pervasive control. However, it also raises crucial cybersecurity and privacy concerns that can lead to devastating consequences, including cascading effects with other critical infrastructures or even fatal accidents. This paper introduces a novel architecture, which will increase the Smart Grid resiliency, taking full advantage of the Software-Defined Networking (SDN) technology. The proposed architecture called SDN-microSENSE architecture consists of three main tiers: (a) Risk assessment, (b) intrusion detection and correlation and (c) self-healing. The first tier is responsible for evaluating dynamically the risk level of each Smart Grid asset. The second tier undertakes to detect and correlate security events and, finally, the last tier mitigates the potential threats, ensuring in parallel the normal operation of the Smart Grid. It is noteworthy that all tiers of the SDN-microSENSE architecture interact with the SDN controller either for detecting or mitigating intrusions.

Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result.