91 research outputs found
Improving the performances of the LIBS technique for the analysis of solid and liquid samples using noble metal nanoparticles
Phase shifting speckle interferometry for dynamic phenomena.
The paper presents an algorithm able to retrieve the phase in speckle interferometry by a single intensity pattern acquired in a deformed state, provided that the integrated speckle field is resolved in the reference condition in terms of mean intensity, modulation amplitude and phase. The proposed approach, called throughout the paper "one-step", can be applied for studying phenomena whose rapid evolution does not allow the application of a standard phase-shifting procedure, which, on the other hand, must be applied at the beginning of the experiment. The approach was proved by an experimental test reported at the end of the paper
A novel operating principle in speckle interferometry: the double-focusing.
The present paper describes in details the operating principle of a completely new family of speckle interferometers: the double-focusing. This type of interferometer is sensitive to the same components of displacement given by holographic interferometry, i.e. the component along the bisector of the angle identified by the illumination and the observation directions. In addition, no external reference beam is necessary, with a consequent reduction of the complexity of the experimental setup. The only requirement for the correct functioning of this family of interferometers is that only a portion of the illuminated area undergoes a sensible deformation. The implementation can be indifferently carried out by adopting the classical Michelson or Mach-Zender configurations, but also a particularly compact in-line implementation can be realized
Multi-scale Laplacian community detection in heterogeneous networks
Heterogeneous and complex networks represent the intertwined interactions
between real-world elements or agents. A fundamental problem of complex network
theory involves finding inherent partitions, clusters, or communities. By
taking advantage of the recent Laplacian Renormalization Group approach, we
scrutinize information diffusion pathways throughout networks to shed further
light on this issue. Based on inter-node communicability, our definition
provides a unifying framework for multiple partitioning measures: multi-scale
Laplacian (MSL) community detection algorithm. This new framework permits to
introduce a scale-dependent optimal partition in communities and to determine
the existence of a particular class of nodes, called metastable nodes, that
switching community at different scales are expected to play a central role in
the communication between different communities and, therefore in the control
of the whole network.Comment: 14 pages, 12 figure
Emergence of collective self-oscillations in minimal lattice models with feedback
The emergence of collective oscillations and synchronization is a widespread
phenomenon in complex systems. While widely studied in dynamical systems
theory, this phenomenon is not well understood in the context of
out-of-equilibrium phase transitions. Here we consider classical lattice
models, namely the Ising, the Blume-Capel and the Potts models, with a feedback
among the order and control parameters. With linear response theory we derive
low-dimensional dynamical systems for mean field cases that quantitatively
reproduce many-body stochastic simulations. In general, we find that the usual
equilibrium phase transitions are taken over by complex bifurcations where
self-oscillations emerge, a behavior that we illustrate by the feedback Landau
theory. For the case of the Ising model, we obtain that the bifurcation that
takes over the critical point is non-trivial in finite dimensions. We provide
numerical evidence that in 2D the most probable value of the amplitude follows
the Onsager law. We illustrate multi-stability for the case of discontinuously
emerging oscillations in the Blume-Capel model, whose tricritical point is
substituted by the Bautin bifurcation. For the Potts model with q = 3 colors we
highlight the appearance of two mirror stable limit cycles at a bifurcation
line and characterize the onset of chaotic oscillations that emerge at low
temperature through either the Feigenbaum cascade of period doubling or the
Aifraimovich-Shilnikov scenario of a torus destruction. We show that entropy
production singularities as a function of the temperature correspond to change
in the spectrum of Lyapunov exponents. Our results show that mean-field
behaviour can be described by the bifurcation theory of low-dimensional
dynamical systems, which paves the way for the definition of universality
classes of collective oscillations.Comment: 25 pages 10 figure
A new approach to non-linear multivariate calibration in laser-induced breakdown spectroscopy analysis of silicate rocks
In this paper a new approach to quantitative Laser-Induced Breakdown Spectroscopy (LIBS) analysis of silicate rocks is presented. The method is adapted from the Franzini and Leoni algorithm, a method widely used in X-Ray Fluorescence analysis for correcting the matrix effects in the determination of the composition of geological materials. To illustrate the features of the new method proposed, nine elements were quantified in 19 geological standards by building linear univariate calibration curves, linear multivariate calibration surfaces (PLS) and using Artificial Neural Networks. The results were then compared with the predictions derived from the application of the algorithm here proposed. It was found that the Franzini and Leoni approach gives results much more precise than linear uni- and multivariate approaches, and comparable with the ones derived from the application of Artificial Neural Networks. A definite advantage of the proposed approach is the possibility of building multivariate non-linear calibration surfaces using linear optimization algorithms, a feature which makes the application of the Franzini and Leoni method in LIBS analysis much simpler (and controllable) with respect to the algorithms based on Artificial Neural Networks
Shock waves in laser-induced plasmas
The production of a plasma by a pulsed laser beam in solids, liquids or gas is often associated with the generation of a strong shock wave, which can be studied and interpreted in the framework of the theory of strong explosion. In this review, we will briefly present a theoretical interpretation of the physical mechanisms of laser-generated shock waves. After that, we will discuss how the study of the dynamics of the laser-induced shock wave can be used for obtaining useful information about the laser-target interaction (for example, the energy delivered by the laser on the target material) or on the physical properties of the target itself (hardness). Finally, we will focus the discussion on how the laser-induced shock wave can be exploited in analytical applications of Laser-Induced Plasmas as, for example, in Double-Pulse Laser-Induced Breakdown Spectroscopy experiments
Improvement of the performances of a commercial hand-held laser-induced breakdown spectroscopy instrument for steel analysis using multiple artificial neural networks.
In this article, we present a study on the optimization of the analytical performance of a commercial hand-held laser-induced breakdown spectroscopy instrument for steel analysis. We show how the performances of the instrument can be substantially improved using a non-linear calibration approach based on a set of Artificial Neural Networks (ANNs), one optimized for the determination of the major elements of the alloy, and the others specialized for the analysis of minor components. Tests of the instrument on steel samples used for instrument internal calibration demonstrate a comparable accuracy with the results of the ANNs, while the latter are considerably more accurate when unknown samples, not used for calibration/training, are tested
Tenebrio molitor as a Simple and Cheap Preclinical Pharmacokinetic and Toxicity Model
The progression of drugs into clinical phases requires proper toxicity assessment in animals and the correct identification of possible metabolites. Accordingly, different animal models are used to preliminarily evaluate toxicity and biotransformations. Rodents are the most common models used to preliminarily evaluate the safety of drugs; however, their use is subject to ethical consideration and elevated costs, and strictly regulated by national legislations. Herein, we developed a novel, cheap and convenient toxicity model using Tenebrio molitor coleoptera (TMC). A panel of 15 drugs-including antivirals and antibacterials-with different therapeutic applications was administered to TMC and the LD50 was determined. The values are comparable with those already determined in mice and rats. In addition, a TMC model was used to determine the presence of the main metabolites and in vivo pharmacokinetics (PK), and results were compared with those available from in vitro assays and the literature. Taken together, our results demonstrate that TMC can be used as a novel and convenient preliminary toxicity model to preliminarily evaluate the safety of experimental compounds and the formation of main metabolites, and to reduce the costs and number of rodents, according to 3R principles
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