This work provides a comprehensive picture of the advances that the exponential expansion theory (EET) of autocorrelation functions relevant to liquids dynamics made possible in the last decade up to very recent times. The role of both longitudinal and transverse collective excitations in liquids is investigated by studying the main autocorrelation functions typically obtained either experimentally (when possible) or through molecular dynamics simulations. Examples for some classes of liquids are provided, especially intended for the understanding of dispersion curves, i.e., the collective mode frequencies as a function of the wavevector Q, which is inversely proportional to the length scale at which microscopic processes are probed. The main result of this work is the ubiquitous observation that the EET method works extremely well for all considered autocorrelation functions or spectra, either experimental or simulated. This paper provides also, in its final part, important hints for future research, based on an integration of the EET lineshape description within Bayesian inference analysis.

The vibrational dynamics of crystalline a-phase methanol and its three isotopic substitutes has been investigated by inelastic neutron scattering, classical molecular dynamics, and ab initio lattice dynamics. Experimental data were collected on the TOSCA neutron spectrometer at low temperature (T <= 15 K). Classical molecular dynamics trajectories were obtained using a modified OPLS-AA effective force field. Ab initio lattice dynamics simulations were performed in the DFT framework using a high-level hybrid functional. Methanol vibrational bands have been divided into two main intervals: "external'' (i.e., including lattice phonons and molecular librations) and intramolecular. The former domain showed a large dispersion of the vibrational excitations and, in the case of CH3OH and CH3OD, high-resolution H-projected vibrational densities of states have been obtained. Such physical quantities favorably compared with the respective infrared/Raman data, while only a semi-quantitative agreement with simulated spectra has been achieved. Above 200 meV, large multiphonon components in the experimental data masked all the weak fundamental vibrational bands. Still, in spite of these difficulties, the reconstruction of the intramolecular methanol spectra has been attempted producing globally good results for CH3OH and CH3OD, while those pertaining to CD3OH and CD3OD included small discrepancies in the band positions and intensities. DFT lattice dynamics calculations compare reasonably well with the neutron scattering spectra, but still worse than what classical molecular dynamics simulations do. We underline the importance of the present results for interpreting spectral data of amorphous water-methanol mixtures.

Controlled generation of coherent spin waves with highest possible frequencies and shortest possible wavelengths is a cornerstone of spintronics and magnonics. Here, using Heisenberg antiferromagnet RbMnF3, we demonstrate that laser-induced THz spin dynamics corresponding to pairs of mutually coherent counter-propagating spin waves with the wavevectors up to the edge of the Brillouin zone cannot be understood in terms of magnetization and antiferromagnetic (Néel) vectors, conventionally used to describe spin waves. Instead, we propose to model such spin dynamics using the spin correlation function. We derive a quantum-mechanical equation of motion for the latter and emphasize that unlike the magnetization and antiferromagnetic vectors the spin correlations in antiferromagnets do not exhibit inertia.

Since 2012, the Italian Society of Neutron Spectroscopy has been organizing Advanced Summer Schools on neutron scattering techniques for the study of condensed and soft matter. They are open to Graduate and PhD and to PostDo cs and researchers working in scientific disciplines as Biology, Chemistry, Earth and similar. After a first three-year cycle focused on Neutron Diffraction and ReflecNeutron Imaging (2013), in 2015, the Schools were devoted to Neutron Scattering tering Data Handling, Numerical Methods, Statistical and Computational Tools in 2018, to Neutrons and Muons for Magnetism in 2019 and, finally, to Applications of X-rays and Neutron Scattering in Biology in 2020. The current project is a threeof the 2022 edition focused on structural properties is reported.

We used inelastic x-ray scattering methods to measure the terahertz spectrum of density fluctuations of ethanol in both liquid and solid phases. The results of a Bayesian inference-based lineshape analysis with a multiple excitation model and the comparison with a previous similar analysis on water indicate that the different structures induced by hydrogen bonds in ethanol and water have a profound influence on the respective dynamic responses, the latter being characterized by longer living and better resolved high-frequency acoustic excitations. In addition, we compare these findings with those obtained with an alternative approach based on the exponential expansion theory and ensuring sum rules fulfillment, demonstrating that the model's choice directly impacts the number of spectral modes detected.

Understanding how molecules engage in collective motions in a liquid where a network of bonds exists has both fundamental and applied relevance. On the one hand, it can elucidate the "ordering" role of long-range correlations and inspire new avenues to control such order to implement sound manipulation. Water represents an ideal investigation case to unfold these general aspects, and, across the decades, it has been the focus of thorough scrutiny. Despite this investigative effort, the spectrum of terahertz density fluctuations of water largely remains a puzzle for condensed matter physicists. To unravel it, we compare previous scattering measurements of water spectra with new ones on ice. Owing to the unique asset of Bayesian inference, we draw a more detailed portrayal of the phonon response of ice. The comparison with the one of liquid water challenges the current understanding of density fluctuations in water, or more in general, of any networked liquid.

Excitation, detection, and control of coherent THz magnetic excitation in antiferromagnets are challenging problems that can be addressed using ever shorter laser pulses. We study experimentally excitation of magnetic dynamics at THz frequencies in an antiferromagnetic insulator CoF2by sub-10 fs laser pulses. Time-resolved pump-probe polarimetric measurements at different temperatures and probe polarizations reveal laser-induced transient circular birefringence oscillating at the frequency of 7.45 THz and present below the Néel temperature. The THz oscillations of circular birefringence are ascribed to oscillations of the magnetic moments of Co2+ions induced by the laser-driven coherentEgphonon mode via the THz analogue of the transverse piezomagnetic effect. It is also shown that the same pulse launches coherent oscillations of the magnetic linear birefringence at the frequency of 3.4 THz corresponding to the two-magnon mode. Analysis of the probe polarization dependence of the transient magnetic linear birefringence at the frequency of the two-magnon mode enables identifying its symmetry.

Femtosecond laser excitation heats a ferrimagnetic iron garnet across the compensation temperature and decreases the magnetizations of the constituting Fe3+ sublattices. Here, we explore the heat-induced magnetization dynamics in the ferrimagnet at different points in the H-T phase diagram. For magnetic field strengths high enough to promote a state with non-collinear magnetizations of the sublattices, the dynamics occurs on a sub-ns timescale, governed by the effective spin–lattice interaction throughout the whole Brillouin zone of the spin excitations. When the field is low and the magnetizations are collinear, the heating alone is not sufficient to initiate the dynamics. In that case, the dynamics can only start after the magnetizations experience an initial kick, which occurs on the timescale of the spin–lattice interaction in the center of the Brillouin zone, leading to a substantial delay of the response of the spins to the thermal excitation.

Atmospheric particulate matter or PM is one of the pollutants certainly most considered by professionals, both for the impact it has on the environment and for the impact, it has on human health. The official measurement methods for the detection of PM give information at long intervals and in pre-established sites giving a poor resolution from this point of view. For years, sensor manufacturers have tried to address this shortcoming with new portable devices, which have a very low sampling time (a few seconds) and a low price called the Low-Cost PM Sensor (LCPMS). This work shows a comparison of the performance of an LCPMS set. The test is carried out in the laboratory where a test chamber has been created capable of providing a controlled environment in which to test the devices.

The work deals with a technique adopted to calibrate in laboratory chemiresistor gas sensing film based on graphene that work at room temperature installed on a micro sensor board for applications in open air and IOT scenario. From the study in controlled environment the beginning of poisoning due to chemisorption can be estimated for the sensing layer and is possible to avoid harmful exposure to the analite during the calibration.

Accurate sensors validation is a fundamental requirement in air quality monitoring. Validated multisensor could in fact be used as tools to provide indicative measurements to compliment data coming from regulatory monitoring stations. The sparseness of the conventional analyzers, do not allow to have a dense background and prevent the possibility to achieve a high resolution picture of pollutants concentrations in cities and validated Air Quality multisensor could provide a solution to the lack of resolution. Currently, the most accredited validation procedure involves in field data recording in co-location with reference instrumentation. In this work, we show the results of a validation experiment implemented co-locating the ENEA MONICA platform together with an ARPAC (Campania Regional Agency for Environmental Protection) conventional analyzer, during 8 months. The obtained results encourage the possible use of MONICA multisensor platform as a backup tool for reference analyzers.

This work explains a technique for the parallel calibration in laboratory of several embedded sensor systems for the air quality monitoring. Thanks to a Large Volume Test Chamber and to a precise injection of the target gas in the chamber is possible to measure with the right precision and accuracy the sensitivity curve of the gas sensors contained in embedded systems for the Internet of Things.

We report on the photoinduced dynamics of the magneto-optical Faraday effect and the transmittivity of ferromagnetic semiconducting phase of EuO. Excitation with 8-fs laser pulses launches significantly different dynamics of the Faraday effect compared to magnetic refraction. It is argued that the effects are dynamic probes of the magnetization and the exchange interaction in the material which have distinctly different dynamics at the sub-100 fs time scale.

This work presents the results of the crowdfunding campaign devised for MONICA, an air quality monitoring portable device. The initiative is strongly focused on the increased awareness and involvement of citizens in the air pollution issue solution. Specifically, MONICA is an architecture composed by a portable device based on an array of commercial electrochemical sensors calibrated in lab, an Android App for smartphone, a web portal (MENA) and a NOSQL backend. This infrastructure is able to manage data communication/storage and map visualization of personal exposure to air pollutants. Two associated calibration procedures are depicted, one based on in-lab recordings while the second, based on the emerging on field calibration paradigm, will refine the performance of the node. The successful, both in financial and participatory terms, campaign has reached a crowdfunded contribution of 8000 € (145% of expected 6000 €) by 102 supporters. Among them, 44 users, have opted to become part of a small fleet of human-sensors able to produce air quality data during their daily mobility routine.

Air quality is a source of increasing concern in several cities, due to the adverse health effects of significant pollution levels. For this reason, the need to assess the concentration of pollutants at high temporal and spatial resolution is perceived as very urgent. As such, there is a growing interest in building pervasive networks integrating different sensing technologies to achieve this capability. In this view, low cost smart sensors data could be fused together with fixed but more accurate multisensor devices and certified analyzers data to build hi-res maps (<100 m) of pollutant concentrations. Low cost UAVs are versatile platforms capable to host playloads integrating multi sensor technologies for short term, mobile monitoring tasks. The recently proposed tethered UAV platforms can be deemed as interesting solutions to build and rapidly deploy impromptu networks of air quality analyzers for mid-term environmental monitoring actions. In this work, we propose the integration of the ENEA MONICA multisensor platform as a measurement payload for the TopView SAV-ES UAV. The prototype has been flow on board of the SAV-ES platform for test flight targeting the measurement of a plume generated with a brushwood controlled fire. The functional proof of concept flight have confirmed the possibility to use MONICA as a measurement payload for the targeted platform.

Lightweight green aircraft development goals rely on the massive use of CFRP - Carbon Fiber Reinforced Polymers - for aircraft assembly. However, the lack of a validated NDT methodology for bond quality assurance is slowing down their adoption in primary structures. In particular, during pre-bond phase it is necessary to check for surface contaminations that could hamper the effectiveness of the adhesive bond. Among different techniques under screening, electronic noses (e-noses) remain a very promising tool for detection and identification of surface contaminants. In this work, we report the results of a test campaign conducted in the framework of COMBONDT project. An ad-hoc designed platform is tested in realistic conditions, i.e. using curved samples or real aircraft parts for assessing the tool capability. Design of the e-nose architecture as well as data processing techniques are also presented.

Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, especially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming linear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed.

This electronic Air quality is nowadays a primary concern in several cities. More generally pollutant and toxic gases continuously, incidentally or purposedly released are now considered to be extremely relevant for their severe adverse health effects to exposed citizens. In this view, low cost smart chemical multisensors are arising as a reliable source for indicative data on Air Quality. Low cost UAVs are versatile platforms capable to host playloads integrating multi sensor technologies for short term, mobile monitoring tasks. The recently proposed tethered UAV platforms can be deemed as interesting solutions to build and rapidly deploy impromptu networks of air quality analyzers for short-term and focused environmental monitoring actions. In this work, we propose the integration of the ENEA MONICA multisensor platform as a measurement payload for the TopView SAV-ES UAV. The prototype has been flown on board of the SAV-ES platform for a test flight targeting the measurement of a plume generated with a brushwood controlled fire. The functional proof of concept flight have confirmed the possibility to use MONICA as a measurement payload for the targeted platform.

Neutron inelastic scattering has been used to measure the magnetic excitations in powdered NiPS3, a quasitwo-dimensional antiferromagnet with spin S = 1 on a honeycomb lattice. The spectra show clear, dispersive magnons with a similar to 7 meV gap at the Brillouin zone center. The data were fitted using a Heisenberg Hamiltonian with a single-ion anisotropy assuming no magnetic exchange between the honeycomb planes. Magnetic exchange interactions up to the third intraplanar nearest neighbor were required. The fits show robustly that NiPS 3 has an easy-axis anisotropy with Delta = 0.3 meV and that the third nearest neighbor has a strong antiferromagnetic exchange of J(3) = -6.90 meV. The data can be fitted reasonably well with either J(1) < 0 or J(1) > 0, however, the best quantitative agreement with high-resolution data indicates that the nearest-neighbor interaction is ferromagnetic with J(1) = 1.9 meV and that the second nearest-neighbor exchange is small and antiferromagnetic with J(2) = -0.1 meV. The dispersion has a minimum in the Brillouin zone corner that is slightly larger than that at the Brillouin zone center, indicating that the magnetic structure of NiPS3 is close to being unstable.