Purpose: In this work, an analytical model describing the noise in the retrieved three contrast channels, transmission, refraction, and ultra small‐angle scattering, obtained with edge illumination ...X‐ray phase‐based imaging system is presented and compared to experimental data.
Methods: In EI, images acquired at different displacements of the presample mask (i.e., different illumination levels referred to as points on the “illumination curve”), followed by pixel‐wise curve fitting, are exploited to quantitatively retrieve the three contrast channels. Therefore, the noise in the final image will depend on the error associated with the fitting process. We use a model based on the derivation of the standard error on fitted parameters, which relies on the calculation of the covariance matrix, to estimate the noise and the cross‐channel correlation as a function of the position of the sampling points. In particular, we investigated the most common cases of 3 and 5 sampling points. In addition, simulations have been used to better understand the role of the integration time for each sampling point. Finally, the model is validated by comparison with the experimental data acquired with an edge illumination setup based on a tungsten rotating anode X‐ray source and a photon counting detector.
Results: We found a good match between the predictions of the model and the experimental data. In particular, for the investigated cases, an arrangement of the sampling points leading to minimum noise and cross‐channel correlation can be found. Simulations revealed that, given a fixed overall scanning time, its distribution into the smallest possible number of sampling points needed for phase retrieval leads to minimum noise thanks to higher statistics per point.
Conclusions: This work presents an analytical model describing the noise in the various contrast channels retrieved in edge illumination as a function of the illumination curve sampling. In particular, an optimal sampling scheme leading to minimum noise has been determined for the case where 3 or 5 sampling points are used, which represent two of the most common acquisition schemes. In addition, the correlation between noise in the different channels and the role of the number of points and exposure time have been also investigated. In general, our results suggest a series of procedures that should be followed in order to optimize the experimental acquisitions.
Plastics have a considerable contribution on volumetric portion of the worldwide municipal solid waste. Gasification of plastic waste is an efficient thermo-chemical approach with a bright outlook. ...In the present study, the steam gasification of polyethylene, polypropylene, polycarbonate and polyethylene terephthalate waste was modeled. The effects of key features including steam to plastic waste ratio, temperature, moisture content, and pressure were assessed on hydrogen-rich syngas compositions and exergy destruction rate. Taguchi approach was utilized to investigate and optimize the process. The findings revealed that the gasification of polypropylene waste led to the highest hydrogen production at all processing conditions. However, the lowest exergy destruction rates belonged to polyethylene waste gasification. The results of analysis of variance showed that steam to plastic waste ratio had the highest contributions on hydrogen production and exergy destruction rate with 53.13% and 59.53%, respectively. Signal to noise ratio analysis was hired to multi-objective optimize the plastic waste gasification. Polyethylene waste, 1.75 of steam to plastic waste ratio, 1300 K of temperature, 10% of moisture, and 400 kPa of pressure were the optimum conditions. Hydrogen production was 66.71% and exergy destruction rate was 54.86 kW at the optimum conditions.
•Hydrogen rich syngas of plastic waste gasification was studied versus key features.•Gasification of PE, PP, PC and PET wastes was conducted.•Taguchi approach was utilized to optimize the plastic waste gasification process.•Multi-objective optimization was performed for hydrogen and exergy destruction.•Optimum hydrogen production was 66.71% and exergy destruction rate was 54.86 kW.
We propose a peaking-free low-power high-gain observer that preserves the main feature of standard high-gain observers in terms of arbitrarily fast convergence to zero of the estimation error, while ...overtaking their main drawbacks, namely the “peaking phenomenon” during the transient and the numerical implementation issue deriving from the high-gain parameter that is powered up to the order of the system. Moreover, the new observer is proved to have superior features in terms of sensitivity of the estimation error to high-frequency measurement noise when compared with standard high-gain observers. The proposed observer structure has a high-gain parameter that is powered just up to two regardless the dimension of the observed system and adopts saturations to prevent the peaking of the estimates during the transient. As for the classical solution, the new observer is robust with respect to uncertainties in the observed system dynamics in the sense that practical estimation in the high-gain parameter can be proved.
We develop a perturbation theory for stochastic differential equations (SDEs) by which we mean both stochastic ordinary differential equations (SODEs) and stochastic partial differential equations ...(SPDEs). In particular, we estimate the Lp
-distance between the solution process of an SDE and an arbitrary Itô process, which we view as a perturbation of the solution process of the SDE, by the Lq
-distances of the differences of the local characteristics for suitable p, q > 0. As one application of the developed perturbation theory, we establish strong convergence rates for numerical approximations of a class of SODEs with nonglobally monotone coefficients. As another application of the developed perturbation theory, we prove strong convergence rates for spatial spectral Galerkin approximations of solutions of semilinear SPDEs with nonglobally monotone nonlinearities including Cahn–Hilliard–Cook-type equations and stochastic Burgers equations. Further applications of the developed perturbation theory include regularity analyses of solutions of SDEs with respect to their initial values as well as small-noise analyses for ordinary and partial differential equations.
The present study aimed to investigate the effect of molybdate ions to modify the 304 Austenitic Stainless Steel (ASS) surface against localized corrosion in hydrochloric acid medium. In this regard, ...surface characterization, such as Scanning Electron Microscopy (SEM) and X-ray Photoelectron Spectroscopy (XPS) coupled with Electrochemical Noise (EN) measurements, were conducted. Furthermore, molecular simulations were used to study the interactions of molybdate ions in the steel and solution interface. EN analysis showed that by adding 0.10 mM molybdate ions to 0.5 M HCl solution, the current and potential oscillations were drastically reduced compared to the molybdate ion-free medium, and the value of noise resistance has increased from ⁓3–8 kΩ cm2. SEM observations, XPS analysis, as well as Molecular Dynamic (MD) results showed a considerable reduction in localized corrosion owing to the formation of molybdenum-contained compounds, which were chemically absorbed on the passive film and placed at 4.533 Å over the metal surface. Although 304 ASSs are inhibited by various inhibitors, molybdate ions are efficient species for inhibition of the surface against both general and localized corrosion. Investigation of what happens on the 304 ASS surface from both macroscopic and microscopic points of view through electrochemical measurements and molecular simulations is the focus of this work.
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•Using molybdate to produce corrosion resistant surface on 304 ASS.•Based on the XPS analysis, molybdenum-contained compounds formed on the surface.•Molecular simulations illustrated the chemisorption mechanism of molybdate ion.•The best concentration of molybdate ion for corrosion prevention was 0.02 mM.
The presence of various functional groups in the primary molecules of the garlic extract makes this extract a suitable candidate for corrosion inhibition of AISI 304 stainless steel in acidic ...electrolytes. In this study, corrosion of AISI 304 stainless steel in 0.5 M HCl in the presence of the garlic extract was investigated using weight loss measurement, potentiodynamic polarization, electrochemical noise analysis, and computational simulation including density functional theory (DFT), Monte Carlo (MC), and molecular dynamics (MD). The results from the weight loss and electrochemical measurements indicated that the garlic extract reduces the corrosion rate of the stainless steel in HCl solution up to 88%, and the extract works as a mixed type corrosion inhibitor by hindering both cathodic and anodic reactions on the surface. Further investigation illustrated that the inhibitor follows the Langmuir adsorption isotherm during the process of adsorption. Moreover, the computational findings from DFT modeling and molecular simulations based on MC and MD shed more light on the surface adsorption features of the garlic extract.
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•Corrosion of AISI 304 stainless steel was inhibited properly by garlic extract.•General corrosion was investigated using gravimetric, PPL, and EN analysis.•Based on DFT simulation, N, S, O, and C were responsible for the inhibition effect.•Adsorption of extract molecules on the surface followed the Langmuir isotherm.•The PSD of current and potential decreased significantly by adding the inhibitor.
Hopfield model is one of the few neural networks for which analytical results can be obtained. However, most of them are derived under the assumption of random uncorrelated patterns, while in real ...life applications data to be stored show non-trivial correlations. In the present paper we study how the retrieval capability of the Hopfield network at null temperature is affected by spatial correlations in the data we feed to it. In particular, we use as patterns to be stored the configurations of a linear Ising model at inverse temperature β thus limiting our analysis to exponentially decaying spatial correlations. Exploiting the signal to noise technique we obtain a phase diagram in the load of the Hopfield network and the Ising temperature where a fuzzy phase and a retrieval region can be observed. Remarkably, as the spatial correlation inside patterns is increased, the critical load of the Hopfield network diminishes, a result also confirmed by numerical simulations. The analysis is then generalized to Dense Associative Memories with arbitrary odd-body interactions, for which we obtain analogous results.
Solid-state nanopore has the ability to detect proteins at a single-molecule level with its high sensitivity, high-throughput, and low cost. Improvements in fabrication, functionalization, and ...characterization of solid-state nanopores keep evolving. Various analytical methods targeted towards diagnostic applications using nanopore-based devices are appearing. This review article provides an overview of recent progress in the field of solid-state and biological nanopores for protein detection in a complex analyte. The advantages and challenges involved in nanopore sensing have been discussed. Further, the review surpasses the steady-state resistive pulse techniques of sensing and incorporate transient variations in the nanopore conductance. Application of the power spectral density of these fluctuations toward sensing has been highlighted with importance on reducing the detection limit in a complex environment. Lastly, the current problems and future perspectives have been discussed with a perspective to increase nanopores performance towards diagnostic applications in complex medium.
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•Overview of recent developments of solid-state and biological nanopores for protein sensing in a complex analyte.•Functionalization strategies of both solid-state and biological nanopores.•Application of the power spectral density of transient nanopore conductance fluctuations in a complex environment.
Optoelectronic oscillators are nonlinear time-delayed microwave photonic systems capable of producing exceptionally pure microwave signals. In order to improve the spectral purity of the generated ...microwaves, it is important to investigate the origin and magnitude of the noise that drive the oscillator away from optimal operation. This task appears to be challenging above threshold because some of the noise sources are nonlinearly mixed with the signal, and it is very difficult to disambiguate them. In this study, we take advantage from the fact that the signal is expected to be null under the threshold, so that any detected oscillation in the sub-threshold range is solely the result of the noise fluctuations that we need to characterize. We develop the theoretical framework for this approach and obtain a set of stochastic differential equations, allowing us to characterize these noise sources that can thereby be lumped as additive and multiplicative fluctuation terms. We perform experimental measurements that confirm the validity of this approach and offer a generic pathway for the characterization of noise in certain classes of oscillators.
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