INRiM participates as a partner in the project QUANTAGRID – Next quantum-based traceability and new accuracy description for synchronized multifrequency phasor measurements in modern distribution grids, funded by the Ministry of University and Research (MUR) and running from November 30, 2023, to February 28, 2026.

Distributed measurement systems are essential for the management of modern electrical networks. Phasor Measurement Units (PMUs) provide synchronized measurements of voltage, current, and frequency, but they were designed for transmission networks and are not well suited to distribution networks, which are characterized by harmonics, supraharmonics, and fast dynamics. Current standards, in fact, do not guarantee adequate performance in such scenarios.

Reliable control decisions require realistic harmonic data, which in the future will be provided by specific devices: Distribution PMUs (D-PMUs). Their widespread adoption is considered inevitable, even though traditional PMUs will remain in use in the short term.

The project aims to: define representative reference signals for distribution systems (DS); develop a quantum-based system for the accurate calibration of PMUs and D-PMUs; and enrich output data with information on measurement conditions.

INRiM participates as a partner in the DOMANI project, funded by the Ministry of University and Research (MUR) and running from November 30, 2023, to February 28, 2026.

Submicroplastics (SMP), generated by industrial processes and material degradation, pose significant analytical challenges: they have variable composition and size, cannot be detected with optical instruments, are ubiquitous, and highly heterogeneous. Moreover, they modify their identity in exposure media, since their large surface area and high binding affinity promote the spontaneous and unavoidable formation of eco- and bio-coronas, which depend on environmental conditions. The impact of these coronas on the biological effects of SMP remains poorly explored, making it urgent to develop general or matrix-specific approaches for environmental and food samples.

Available techniques (imaging, spectroscopy, separation) produce large amounts of data that are difficult to correlate; however, multiblock chemometric and machine learning methods are still rarely applied.

The DOMANI project pursues two main objectives: to understand eco-coronas through qualitative and quantitative profiling and characterization of SMP, and to develop predictive models for the rapid recognition of clean or contaminated matrices, thus creating fast and sustainable screening tools.

The approach integrates interdisciplinary expertise, combining metrology, analytical, surface and colloidal chemistry with chemometrics and machine learning.