Can the Large Hadron Collider snap string theory?
Peer-Reviewed Publication
Updates every hour. Last Updated: 10-Jul-2025 06:10 ET (10-Jul-2025 10:10 GMT/UTC)
Researchers have developed an analytical model for Stirling engines based on an optimized Simple analysis method, significantly improving the accuracy of performance predictions. This provides optimization and analysis tools for more efficient and reliable Stirling engines, which is crucial for applications ranging from space nuclear power to terrestrial energy systems.
- University of Leicester’s science and innovation park to lead on a European Space Agency project to build a Double-Walled Isolator (DWI) to support analysis of extra-terrestrial samples
- Samples could be stored and handled and initially analysed in the DWI, to reduce the risk of cross contamination on Earth
- Work has started with funding of €5 million
Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.