Thermal Analysis on Bioprotectant Disaccharides by Elastic Incoherent Neutron Scattering

Mavilia

et al. 2020

Materials

Experimental findings obtained by FTIR and Raman spectroscopies on montmorillonite-water mixtures at three concentration values are presented. To get some insight into the hydrogen bond network of water within the montmorillonite network, FTIR and Raman spectra have been collected as a function of time and then analyzed following two complementary approaches: An analysis of the intramolecular OH stretching mode in the spectral range of 2700–3900 cm−1 in terms of two Gaussian components, and an analysis of the same OH stretching mode by wavelet cross-correlation. The FTIR and Raman investigations have been carried as a function of time for a montmorillonite-water weight composition (wt%) of 20–80%, 25–75%, and 35–65%, until the dehydrated state where the samples appear as a homogeneous rigid layer of clay. In particular, for both the FTIR and Raman spectra, the decomposition of the OH stretching band into a “closed” and an “open” contribution and the spectral wavelet analysis allow us to extract quantitative information on the time behavior of the system water content. It emerges that, the total water contribution inside the montmorillonite structure decreases as a function of time. However, the relative weight of the ordered water contribution diminishes more rapidly while the relative weight of the disordered water contribution increases, indicating that a residual water content, characterized by a highly structural disorder, rests entrapped in the montmorillonite layer structure for a longer time. From the present study, it can be inferred that the montmorillonite dehydration process promotes the layer self-assembly.

Section: Analysis and Discussionmentioning

confidence: 99%

Self-Assembly Processes in Hydrated Montmorillonite by FTIR Investigations

Mavilia

et al. 2020

Materials

“…In the last years wavelet transforms find a lot of various applications in numerous field such as physics, meteorology, engineering, mathematics, and spectroscopic data analyses [22][23][24][25][26][27]. Differently from Fourier transform, the wavelet analysis allow to highlight which frequencies are present and where, or at what scale, they are.…”

Section: Wavelet Results Data Analysismentioning

confidence: 99%

Wavelet Study of Meteorological Data Collected by Arduino-Weather Station: Impact on Solar Energy Collection Technology

MATEC Web of Conferences

Calabrò

Cannuli

et al. 2016

Abstract. Meteorological data collected by an automated LSI Lastem weather station connected with an Arduino device for remote acquisition are reported and discussed. Weather station, located at 38° 15' 35.10'' N latitude and 15° 35' 58.86'' E longitude, registered data which were analysed by wavelet transform to obtain time-frequency characterization of the signals. Such an approach allowed to highlight the correlation existing among the registered meteorological data. The results show a positive correlation between the minimum temperature and the maximum temperature values whereas a negative correlation emerges between daily rainfall and minimum temperature values as well as for daily rainfall and maximum temperature values. These results suggest the possibility to estimate the global and diffuse solar radiation using more reliable climatologic parameters for optimizing solar energy collected by solar panels.

“…33–35 Such an approach furnishes an innovative tool, which has recently found an increasing number of applications in different fields, from the research of discontinuities in the trends of physical observables to wavevector spectral analysis of neutron scattering data, and for frequency analysis of many types of data, from meteorological time series to financial datasets. 36–43 More specifically, the wavelet transform enables decomposition of a given signal f(t) onto a set of continuous functions Ψa,b(t) derived by rescaling and shifting a chosen mother wavelet where represents the base wavelet derived by shifting and rescaling a chosen mother wavelet function Ψ(t), * denotes the complex conjugation, a is the parameter of scale ( a > 0) and b is the shift parameter. Many types of mother wavelet can be chosen, but for our study we used the Morlet function; it should be taken into account that wavelets furnish a measure of the affinity between the signal and the chosen mother wavelet.…”

Section: Wavelet Cross Correlationmentioning

confidence: 99%

Ethylene Glycol – Polyethylene Glycol (EG-PEG) Mixtures: Infrared Spectra Wavelet Cross-Correlation Analysis

Magazù

2016

Appl Spectrosc

Infrared spectra were collected on mixtures of ethylene glycol (EG) and polyethylene glycol 600 (PEG600) as a function of weight fraction from pure EG to pure PEG600. In this paper, it will be shown that while the OH vibrational contribution drastically reduces its center frequency from 3450 cm to 3300 cm in the weight fraction range 0-25%, the displacement of the mixture spectral features of the mixtures from ideal behavior, i.e., in the absence of interaction, shows the presence of a non-ideal mixing process. Furthermore, wavelet cross-correlation analysis of the registered pairs of spectra and of the intramolecular O-H stretching contributions reveals how the addition of a small amount of pure EG to PEG600 dramatically influences the structural properties of the polymeric matrix, owing to an increase the intermolecular connectivity. In particular, the wavelet cross-correlation parameters, evaluated between each pair of the registered data as a function of weight fraction, in a linear-logarithmic plot, reveals an inflection point for a weight fraction of about 25% of EG, which confirms that, within the three-dimensional networks of hydrogen-bonded EG-PEG600 molecules, a key role is played by EG in determining an increase in the hydrogen-bond network density.