Search for: All records

Structural batteries, also known as “massless batteries”, integrate energy storage directly into load-bearing materials, offering a transformative alternative to traditional Li-ion batteries. Unlike conventional systems that serve only as energy storage devices, structural batteries replace passive structural components, reducing overall weight while providing mechanical reinforcement. However, achieving uniform and efficient coatings of active materials on carbon fibers remains a major challenge, limiting their scalability and electrochemical performance. This study investigates ultrasonic spray coating as a precise and scalable technique for fabricating composite cathodes in structural batteries. Using a computer-controlled ultrasonic nozzle, this method ensures uniform deposition with minimal material waste while maintaining the mechanical integrity of carbon fibers. Compared to traditional techniques such as electrophoretic deposition, vacuum bag hot plate processing, and dip-coating, ultrasonic spray coating achieved superior coating consistency and reproducibility. Electrochemical testing revealed a specific capacity of 100 mAh/gLFP with 80% retention for more than 350 cycles at 0.5 C, demonstrating its potential as a viable coating solution. While structural batteries are not yet commercially viable, these findings represent a step toward their practical implementation. Further research and optimization will be essential in advancing this technology for next-generation aerospace and transportation applications. 

I introduce a novel associative memory model named Correlated Dense Associative Memory (CDAM), which integrates both auto- and hetero-association in a unified framework for continuous-valued memory patterns. Employing an arbitrary graph structure to semantically link memory patterns, CDAM is theoretically and numerically analysed, revealing four distinct dynamical modes: auto-association, narrow hetero-association, wide hetero-association, and neutral quiescence. Drawing inspiration from inhibitory modulation studies, I employ anti-Hebbian learning rules to control the range of hetero-association, extract multi-scale representations of community structures in graphs, and stabilise the recall of temporal sequences. Experimental demonstrations showcase CDAM’s efficacy in handling real-world data, replicating a classical neuroscience experiment, performing image retrieval, and simulating arbitrary finite automata. 

Solid electrolytes are critical for structural batteries, combining energy storage with structural strength for applications like electric vehicles and aerospace. However, achieving high ionic conductivity remains challenging, compounded by a lack of standardized testing methodologies. This study examines the impact of experimental setups and data interpretation methods on the measured ionic conductivities of solid polymer electrolytes (SPEs). SPEs were prepared using a polymer-induced phase separation process, resulting in a bi-continuous microstructure for improved ionic transport. Eight experimental rigs were evaluated, including two- and four-electrode setups with materials like stainless steel, copper, and aluminum. Ionic conductivity was assessed using electrochemical impedance spectroscopy, with analysis methods comparing cross-sectional and surface-area-based approaches. Results showed that the four-electrode stainless steel setup yielded the highest ionic conductivity using the cross-sectional method. However, surface-area-based methods provided more consistent results across rigs. Copper setups produced lower conductivities but exhibited less data variability, indicating their potential for reproducible measurements. These findings highlight the critical influence of experimental design on conductivity measurements and emphasize the need for standardized testing protocols. Advancing reliable characterization methods will support the development of high-performance solid electrolytes for multifunctional energy storage applications. 

In modern smarthomes, temperature regulation is achieved through a mix of traditional and emergent technologies including air conditioning, heating, intelligent utilization of the effects of sun, wind, and shade as well as using stored heat and cold. To achieve the desired comfort for the inhabitants while minimizing environmental impact and cost, the home controller must predict how its actions will impact the temperature and other environmental factors in various parts of the home. The question we are investigating in this paper is whether the temperature values in different rooms in a home are predictable based on readings from sensors in the home. We are also interested in whether increased accuracy can be achieved by adding sensors to capture the state of doors and windows of the given room and/or the whole home, and what type of machine learning algorithms can take advantage of the additional information. As experimentation on real-world homes is highly expensive, we use ScaledHome, a 1:12 scale, IoT-enabled model of a smart home for data acquisition. Our experiments show that while additional data can improve the accuracy of the prediction, the type of machine learning models needs to be carefully adapted to the number of data features available. 

Green homes require informed energy management decisions. For instance, it is preferable that a comfortable internal temperature is achieved through natural, energy-efficient means such as opening doors or lowering shades as opposed to turning on the air conditioning. This requires the control agent to understand the complex system dynamics of the home: will opening the window raise or lower the temperature in this particular situation? Unfortunately, developing mathematical models of a suburban home situated in its natural environment is a significant challenge, while performing real-world experiments is costly, takes a long time and depends on external circumstances beyond the control of the experimenter. In this paper, we describe the architecture of a physical, small scale model of a suburban home and its immediate exterior environment. Specific scenarios can be enacted using Internet of Things (IoT) actuators that control the doors and windows. We use a suite of IoT sensors to collect data during the scenario. We use deep learning-based temporal regression models to make predictions about the impact of specific actions on the temperature and humidity in the home.