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We report an investigation of the structural, morphological, the ac-dc electrical, magnetic, and Mössbauer spectroscopy properties of 20MnFe2O4-80SiO2 nanocomposite prepared using a one-step and facile auto-combustion approach. XRD pattern shows the formation of MnFe2O4 nanocrystallite without any crystallization of the SiO2 phase. However, the presence of a secondary phase of the nanosized α-Fe2O3 particles was also detected. The morphological analysis showed aggregation of polygonal magnetic nano-crystallites dispersed non-uniformly in a silica matrix. The dc electrical measurements performed on a wide range of temperatures from 120 to 400 K showed the semiconducting nature of the nanocomposite. The temperature dependence of dc conductivity could be perfectly fitted to the nearest neighborhood hopping model with activation energy, ΔENNH, of 0.45 eV. The Nyquist plots demonstrated a nonmonotonous thermally activated trend and non-Debye relaxation behavior. An equivalent circuit was successfully fitted to the complex impedance spectra. The variation of both grain and grain boundary conductivities as a function of temperature exhibited three distinct regions, semiconducting-metallic-semiconducting with different activation energies over the measured temperature window. The provided description of such behavior is further advocated by the ac conductivity and dielectric modulus studies. VSM measurements revealed that the nanocomposite magnetic behavior deviates from the ideal non-interacting superparamagnetic picture, due to the presence of α-Fe2O3 nanocrystalline impurities and relatively intensive exchange interactions between ions. Mössbauer spectra showed the presence of Fe3+ ions with sixfold environment and also confirmed the existence of a sextet related to α-Fe2O3 with a quantity of about 20% out of magnetic components. Fe3+ superparamagnetic doublets were also found within the fitting procedure. [ABSTRACT FROM AUTHOR]

In the present work, a combination of conventional dc magnetron sputtering technique and gas condensation process was utilized to deposit a Ag–Au/a:C nanocomposite coating for application in stents. Quantitative evaluation of Ag+ release to the solution revealed a sharp increase of Ag+ releasing in the first 2 days of immersion, which continues but with a slower rate up to 14 days reaching a value close to 50 ppb. Atomic force microscopy surface morphology depicted that the roughness of the coating, Srms, increases after immersion from 5.86 to 14.22. X-ray photoelectron spectroscopy analysis showed two wide bands at 368.30 and 374.30 eV corresponding to the Ag 3d spin-orbit doublets for non-immersed coating. It was found that the Ag content drops to 5% of the initial concentration as the coating is exposed to artificial urine, due to the high affinity of Ag surface nanoparticles to solution anions. This Ag dissolution to artificial urine is followed by reprecipitating Ag salt clusters with a size less than 3 nm on the surface and the formation of a highly oxidized surface. The formation of the oxidation layer as a result of exposure of the coating to the solution is further confirmed by electrochemical impedance spectroscopy and polarization measurements. It was depicted the resistance of the coating increase from 1.80 to 2.86 MΩ as the coating is immersed into the solution for 14 days. The corrosion potential and corrosion current values collected from the polarization plot of the immersed coating were found to be −0.73 and 0.14, respectively, depicting the protecting character of the coating against the deteriorating solution. It was suggested that the formation of the oxidation layer on the coating surface seals the pores of the coating avoiding solution penetration to the substrate and consequently protecting it against corrosion. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this paper is to demonstrate that a new algorithm for estimating arterial oxygen saturation can be effective even with data corrupted by motion artifacts (MAs). Methods: OxiMA, an algorithm based on the time-frequency components of a photoplethysmogram (PPG), was evaluated using 22-min datasets recorded from 10 subjects during voluntarily-induced hypoxia, with and without subject-induced MAs. A Nellcor OxiMax transmission sensor was used to collect an analog PPG while reference oxygen saturation and pulse rate (PR) were collected simultaneously from an FDA-approved Masimo SET Radical RDS-1 pulse oximeter. Results: The performance of our approach was determined by computing the mean relative error between the PR/oxygen saturation estimated by OxiMA and the reference Masimo oximeter. The average estimation error using OxiMA was 3 beats/min for PR and 3.24% for oxygen saturation, respectively. Conclusion: The results show that OxiMA has great potential for improving the accuracy of PR and oxygen saturation estimation during MAs. Significance: This is the first study to demonstrate the feasibility of a reconstruction algorithm to improve oxygen saturation estimates on a dataset with MAs and concomitant hypoxia. [ABSTRACT FROM AUTHOR]

In this study, nanocrystalline ZnO-SnO mixed metal oxide powder was prepared by co-precipitation using Zn(CHCOO)∙2HO and SnCl∙5HO as precursor materials. The powder was characterized by X-ray diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy. Williamson-Hall method was used to evaluate the micro structural parameters of ZnO-SnO such as crystallite sizes and lattice strain. The photoluminescence property of the sample was studied at different temperatures (10-300 K). Results showed that the emission intensity decreases with temperature increasing. The photocatalytic activity at the gas-solid interface was assessed by monitoring the degradation of nitrogen oxides, a major atmospheric pollutant. The results show that the nanocrystalline ZnO-SnO mixed metal oxide powder exhibits higher and more stable photocatalytic activity against photocorrosion than ZnO alone. Graphical abstract: [ABSTRACT FROM AUTHOR]

Motion and noise artifacts (MNAs) impose limits on the usability of the photoplethysmogram (PPG), particularly in the context of ambulatory monitoring. MNAs can distort PPG, causing erroneous estimation of physiological parameters such as heart rate (HR) and arterial oxygen saturation (SpO2). In this study, we present a novel approach, “TifMA,” based on using the time–frequency spectrum of PPG to first detect the MNA-corrupted data and next discard the nonusable part of the corrupted data. The term “nonusable” refers to segments of PPG data from which the HR signal cannot be recovered accurately. Two sequential classification procedures were included in the TifMA algorithm. The first classifier distinguishes between MNA-corrupted and MNA-free PPG data. Once a segment of data is deemed MNA-corrupted, the next classifier determines whether the HR can be recovered from the corrupted segment or not. A support vector machine (SVM) classifier was used to build a decision boundary for the first classification task using data segments from a training dataset. Features from time–frequency spectra of PPG were extracted to build the detection model. Five datasets were considered for evaluating TifMA performance: (1) and (2) were laboratory-controlled PPG recordings from forehead and finger pulse oximeter sensors with subjects making random movements, (3) and (4) were actual patient PPG recordings from UMass Memorial Medical Center with random free movements and (5) was a laboratory-controlled PPG recording dataset measured at the forehead while the subjects ran on a treadmill. The first dataset was used to analyze the noise sensitivity of the algorithm. Datasets 2-4 were used to evaluate the MNA detection phase of the algorithm. The results from the first phase of the algorithm (MNA detection) were compared to results from three existing MNA detection algorithms: the Hjorth, kurtosis-Shannon entropy, and time-domain variability-SVM approaches. This last is an approach recently developed in our laboratory. The proposed TifMA algorithm consistently provided higher detection rates than the other three methods, with accuracies greater than 95% for all data. Moreover, our algorithm was able to pinpoint the start and end times of the MNA with an error of less than 1 s in duration, whereas the next-best algorithm had a detection error of more than 2.2 s. The final, most challenging, dataset was collected to verify the performance of the algorithm in discriminating between corrupted data that were usable for accurate HR estimations and data that were nonusable. It was found that on average 48% of the data segments were found to have MNA, and of these, 38% could be used to provide reliable HR estimation. [ABSTRACT FROM AUTHOR]

In the present article, samples of xAgO-40TeO-(60 − x)VO ternary tellurite glasses with 0 ≤ x ≤ 50 (in mol%) have been prepared using the melt-quenching technique. XRD analysis, density measurement by Archimedes' law, determination of reduced vanadium ions by titration method, and electrical conductivity measurement by using four-probe methods have been done for these glasses. The mixed electronic-ionic conduction of these glasses has been investigated over a wide temperature range of 150-380 K. The experimental results have been analyzed with different theoretical models of hopping conduction. The analysis shows that at high temperatures the conductivity data are consistent with Mott's model of phonon-assisted polaronic hopping, while Mott's variable-range hopping model and Greaves' hopping model are valid at low temperatures. The temperature dependence of the conductivity has been also interpreted in the framework of the percolation model proposed by Triberis and Friedman. The analysis of the conductivity data also indicates that the hopping in these tellurite glasses occurs in the non-adiabatic regime. In each sample, based upon the justified transport mechanism, carrier density and mobility have been determined at different temperatures. The values of oxygen molar volume indicate the effect of AgO concentration on the thermal stability or fragility of understudied samples. [ABSTRACT FROM AUTHOR]

Motion and noise artifacts (MNA) are a serious obstacle in utilizing photoplethysmogram (PPG) signals for real-time monitoring of vital signs. We present a MNA detection method which can provide a clean vs. corrupted decision on each successive PPG segment. For motion artifact detection, we compute four time-domain parameters: (1) standard deviation of peak-to-peak intervals (2) standard deviation of peak-to-peak amplitudes (3) standard deviation of systolic and diastolic interval ratios, and (4) mean standard deviation of pulse shape. We have adopted a support vector machine (SVM) which takes these parameters from clean and corrupted PPG signals and builds a decision boundary to classify them. We apply several distinct features of the PPG data to enhance classification performance. The algorithm we developed was verified on PPG data segments recorded by simulation, laboratory-controlled and walking/stair-climbing experiments, respectively, and we compared several well-established MNA detection methods to our proposed algorithm. All compared detection algorithms were evaluated in terms of motion artifact detection accuracy, heart rate (HR) error, and oxygen saturation (SpO) error. For laboratory controlled finger, forehead recorded PPG data and daily-activity movement data, our proposed algorithm gives 94.4, 93.4, and 93.7% accuracies, respectively. Significant reductions in HR and SpO errors (2.3 bpm and 2.7%) were noted when the artifacts that were identified by SVM-MNA were removed from the original signal than without (17.3 bpm and 5.4%). The accuracy and error values of our proposed method were significantly higher and lower, respectively, than all other detection methods. Another advantage of our method is its ability to provide highly accurate onset and offset detection times of MNAs. This capability is important for an automated approach to signal reconstruction of only those data points that need to be reconstructed, which is the subject of the companion paper to this article. Finally, our MNA detection algorithm is real-time realizable as the computational speed on the 7-s PPG data segment was found to be only 7 ms with a Matlab code. [ABSTRACT FROM AUTHOR]

We introduce a new method to reconstruct motion and noise artifact (MNA) contaminated photoplethysmogram (PPG) data. A method to detect MNA corrupted data is provided in a companion paper. Our reconstruction algorithm is based on an iterative motion artifact removal (IMAR) approach, which utilizes the singular spectral analysis algorithm to remove MNA artifacts so that the most accurate estimates of uncorrupted heart rates (HRs) and arterial oxygen saturation (SpO) values recorded by a pulse oximeter can be derived. Using both computer simulations and three different experimental data sets, we show that the proposed IMAR approach can reliably reconstruct MNA corrupted data segments, as the estimated HR and SpO values do not significantly deviate from the uncorrupted reference measurements. Comparison of the accuracy of reconstruction of the MNA corrupted data segments between our IMAR approach and the time-domain independent component analysis (TD-ICA) is made for all data sets as the latter method has been shown to provide good performance. For simulated data, there were no significant differences in the reconstructed HR and SpO values starting from 10 dB down to −15 dB for both white and colored noise contaminated PPG data using IMAR; for TD-ICA, significant differences were observed starting at 10 dB. Two experimental PPG data sets were created with contrived MNA by having subjects perform random forehead and rapid side-to-side finger movements show that; the performance of the IMAR approach on these data sets was quite accurate as non-significant differences in the reconstructed HR and SpO were found compared to non-contaminated reference values, in most subjects. In comparison, the accuracy of the TD-ICA was poor as there were significant differences in reconstructed HR and SpO values in most subjects. For non-contrived MNA corrupted PPG data, which were collected with subjects performing walking and stair climbing tasks, the IMAR significantly outperformed TD-ICA as the former method provided HR and SpO values that were non-significantly different than MNA free reference values. [ABSTRACT FROM AUTHOR]

The sol-gel method is a very flexible route for producing sieving matrix such as SiO2 with pore radii in nanoscale region to embed oxide particles like Fe2O3. In this work, iron oxide nanoparticles embedded in a SiO2 glassy matrix prepared by the sol-gel method have been investigated as a function of Fe/Si ratio. Different composition of dried gels with 1, 2, 10 and 20 mol% of Fe2O3 in the SiO2 matrix were heat-treated (HT) in air, at temperatures from 400 ºC up to 1400 °C. The structure and morphology of the obtained glasses and glass-ceramics were studied by X-ray powder diffraction (XRD), Raman spectroscopy and scanning electron microscopy (SEM). Impedance spectroscopy measurements and magnetic characterization were performed as function of temperature and the results are related with the microstructure. The X-ray diffraction patterns showed that increasing the amount of Fe2O3 oxide promotes the formation of crystalline phases, in particular silicate phases. The increase of the heat-treatment temperature has a similar behavior. The electrical and dielectric properties were related with these structural modifications and with the sample morphology. (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [ABSTRACT FROM AUTHOR]

This paper proposes a distributed adaptive control algorithm for coverage control in unknown environments with networked mobile sensors. An online neural network weight tuning algorithm is used in order for the robots to estimate the sensory function of the environment, and the control law is derived according to the feedforward neural network estimation of the distribution density function of the environment. It is distributed in that it only takes advantage of local information of each robot. A Lyapunov function is introduced in order to show that the proposed control law causes the network to converge to a near-optimal sensing configuration. Due to neural network nonlinear approximation property, a major advantage of the proposed method is that in contrary to previous well known approaches for coverage, it is not restricted to a linear regression form. Finally the controller is demonstrated in numerical simulations. Simulation results have been shown that the proposed controller outperforms the previous adaptive approaches in the sense of performance and convergence rate. [ABSTRACT FROM AUTHOR]

Ni- and Fe-doped CeO2 nanoparticles were prepared by a chemical co-precipitation method and then heat treated at 300 °C. The structural, morphological, and compositional properties of the prepared nanoparticles were studied by powder X-ray diffraction (XRD), Energy Dispersive Spectroscopy (EDS), Scanning Electron Microscopy (SEM) and Fourier-Transform Infrared (FT-IR) Spectroscopy. The powder XRD results revealed that all the samples exhibit the typical cubic fluorite structure of CeO2. The EDS and FTIR analyses confirmed the formation of CeO2 nanoparticles and the incorporation of Fe and Ni ions in the crystal lattice of CeO2. The photoluminescence (PL) properties were investigated to assess the effect of Fe- and Ni-doping on the emission properties of CeO2 nanoparticles. The impacts of the type of dopant on the dielectric properties and ac conductivity of the prepared nanoparticles were investigated. It was observed that the dielectric constant of Ni-doped CeO2 nanoparticles measured at low-frequency region is much greater than that of Fe-doped CeO2 nanoparticles. The difference was explained on the basis of interfacial/space charge polarization, with Ni-doped CeO2 nanoparticles having a more heterogeneous dielectric structure. The universal Jonscher power law was well adjusted to the ac conductivity spectra. It was shown that the Fe-CeO2 composite nanoparticles are less conductive than the Ni-doped CeO2 ones. [ABSTRACT FROM AUTHOR]