Theoretical physicist Jonathan Heckman and Ph.D. student Rebecca Hicks at University of Pennsylvania put a spin on ideas related to testing string theory: Rather than looking to verify it, the team and their collaborators sought a novel way to falsify it with data from the Large Hadron Collider at CERN. “We're not rooting for string theory to fail,” Hicks says. “We're stress-testing it, applying more pressure to see if it holds up." “If string theory survives, fantastic," Heckman says. "If it snaps, we'll learn something profound about nature.”

An interdisciplinary research team including mechanical science and engineering professor Mickey Clemon in The Grainger College of Engineering at the University of Illinois Urbana-Champaign is investigating cooling methods for heat sinks by performing experiments onboard a satellite currently orbiting the earth.

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

Can animals share the same space peacefully high above the ground in the treetops? A research team at the University of Göttingen has found that forests combining both deciduous and conifer trees make it easier for red squirrels and dormice to coexist. Using cameras placed high in the canopy, scientists discovered that red squirrels tend to prefer coniferous forests, while dormice are more commonly found in beech forests. However, in mixed forests that include both tree types, both species were observed living side by side. This suggests that mixed forests could play an important role in supporting biodiversity. The findings were published in the European Journal of Wildlife Research.

The origin of life is one of the most fundamental and enduring questions of mankind and one of the three greatest Origin Questions in the natural sciences.  Recently, China has officially launched its Mars Sample Return (MSR) mission, Tianwen-3, marking a significant step forward in planetary exploration. The mission aims to bring Martian samples back to Earth, where advanced laboratory instruments will be employed to conduct comprehensive analyses, seeking to determine whether life ever existed—or may still exist—on Mars. Professor Yiliang LI, an astrobiologist from the Department of Earth Sciences at The University of Hong Kong (HKU), serves as a core member of the Tianwen-3 scientific team and a co-author of a recently published perspective article in Nature Astronomy outlining the mission’s objectives. His role mainly involves leading an HKU group that is working on the selection of the landing site for the Tianwen-3 MSR mission.

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.