A research team from the Dalian Institute of Chemical Physics of the Chinese Academy of Sciences uncovered that syn-CH₃CHOO's reaction with atmospheric water vapor is approximately 100 times faster than previously predicted by theoretical models.

Scientists from China, the UK and the USA have collaborated to analyse the inner workings of Bolivia’s “zombie” volcano, Uturuncu. By combining seismology, physics models and analysis of rock composition, researchers identify the causes of Uturuncu’s unrest, alleviating fears of an imminent eruption. The findings have been published today (28 April) in the journal PNAS.

Rice researchers have developed a new machine learning algorithm that excels at interpreting optical spectra, potentially enabling faster and more precise medical diagnoses and sample analysis.

No more rancid hazelnuts: a research team at the URV has developed a method that can identify nuts that have gone bad due to oxidation. The technique uses infrared light to determine the chemical composition of hazelnuts without even removing them from their shells. The new system overcomes the limitations of traditional methods and makes it possible to identify the condition of all the hazelnuts in a packet in a single analysis, without the need to prepare or destroy the sample. The authors argue that the application of this technology would help to improve packaging techniques and distribution systems and significantly reduce losses in the nut trade, while offering new quality standards to the sector.

A research team from the Institute of Modern Physics (IMP), Chinese Academy of Sciences (CAS), has developed a machine learning (ML) model enhanced with expert knowledge to classify eight types of faults in superconducting radio-frequency (SRF) cavities at the China Accelerator Facility for superheavy Elements (CAFE2) facility. This model helps operators quickly identify fault patterns after incidents, speeding recovery and supporting long-term stable SRF accelerator operation.