Publications

The enhanced catalytic activity of permanently polarized hydroxyapatite, which is achieved using a thermally stimulated polarization process, largely depends on both the experimental conditions used to prepare crystalline hydroxyapatite from its calcium and phosphate precursors and the polarization process parameters. A mineral similar to brushite, which is an apatitic phase that can evolve to hydroxyapatite, is found at the surface of highly crystalline hydroxyapatite. It appears after chemical precipitation and hydrothermal treatment performed at 150 degrees C for 24 h followed by a sinterization at 1000 degrees C and a polarization treatment by applying a voltage of 500 Vat high temperature. Both the high crystallinity and the presence of brushite-like phase on the electrophotocatalyst affect the nitrogen and carbon fixation under mild reaction conditions (95 degrees C and 6 bar) and the synthesis of glycine and alanine from a simple gas mixture containing N-2, CO2, CH4 and H2O. Thus, the Gly/Ala ratio can be customized by controlling the presence of brushite on the surface of the catalyst, enabling to develop new strategies to regulate the production of amino acids by nitrogen and carbon fixation. (C) 2021 Elsevier Inc. All rights reserved.

JTD Keywords: Amino acids, Brushite, Carbon, Carbon dioxide fixation, Catalyst activity, Catalytic apatites, Chemical precipitation, Crystalline hydroxyapatite, Crystallinity, Decomposition, Enhanced catalytic activity, Experimental conditions, Heterogeneous catalysis, High crystallinity, Hydrothermal synthesis, Hydrothermal treatments, Hydroxyapatite, Lactic-acid, Mild reaction conditions, Molecular nitrogen fixation, Nitrogen, Nitrogen fixation, Phosphate, Polarization, Precipitation (chemical), Process parameters, Thermally stimulated polarization

Differentiating normal from adventitious respiratory sounds (RS) is a major challenge in the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. This study presents a new classifier that automatically distinguishes normal sounds from CAS. It is based on the multi-scale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD). These techniques have two major advantages over previous techniques: high temporal resolution is achieved by calculating IF-IE and a priori knowledge of signal characteristics is not required for EEMD. The classifier is based on the fact that the IF dispersion of RS signals markedly decreases when CAS appear in respiratory cycles. Therefore, CAS were detected by using a moving window to calculate the dispersion of IF sequences. The study dataset contained 1494 RS segments extracted from 870 inspiratory cycles recorded from 30 patients with asthma. All cycles and their RS segments were previously classified as containing normal sounds or CAS by a highly experienced physician to obtain a gold standard classification. A support vector machine classifier was trained and tested using an iterative procedure in which the dataset was randomly divided into training (65%) and testing (35%) sets inside a loop. The SVM classifier was also tested on 4592 simulated CAS cycles. High total accuracy was obtained with both recorded (94.6% ± 0.3%) and simulated (92.8% ± 3.6%) signals. We conclude that the proposed method is promising for RS analysis and classification.

JTD Keywords: Diseases, Dispersion, Empirical mode decomposition, Feature extraction, Informatics, Support vector machines

Abstract The use of the Hilbert–Huang transform in the analysis of biomedical signals has increased during the past few years, but its use for respiratory sound (RS) analysis is still limited. The technique includes two steps: empirical mode decomposition (EMD) and instantaneous frequency (IF) estimation. Although the mode mixing (MM) problem of EMD has been widely discussed, this technique continues to be used in many RS analysis algorithms. In this study, we analyzed the MM effect in RS signals recorded from 30 asthmatic patients, and studied the performance of ensemble EMD (EEMD) and noise-assisted multivariate EMD (NA-MEMD) as means for preventing this effect. We propose quantitative parameters for measuring the size, reduction of MM, and residual noise level of each method. These parameters showed that EEMD is a good solution for MM, thus outperforming NA-MEMD. After testing different IF estimators, we propose Kay׳s method to calculate an EEMD-Kay-based Hilbert spectrum that offers high energy concentrations and high time and high frequency resolutions. We also propose an algorithm for the automatic characterization of continuous adventitious sounds (CAS). The tests performed showed that the proposed EEMD-Kay-based Hilbert spectrum makes it possible to determine CAS more precisely than other conventional time-frequency techniques.

JTD Keywords: Hilbert–Huang transform, Ensemble empirical mode decomposition, Instantaneous frequency, Respiratory sounds, Continuous adventitious sounds

The scope of our work focuses on investigating the potential use of the built-in accelerometer of the smartphones for the recording of the respiratory activity and deriving the respiratory rate. Five healthy subjects performed an inspiratory load protocol. The excursion of the right chest was recorded using the built-in triaxial accelerometer of a smartphone along the x, y and z axes and with an external uniaxial accelerometer. Simultaneously, the respiratory airflow and the inspiratory mouth pressure were recorded, as reference respiratory signals. The chest acceleration signal recorded in the z axis with the smartphone was denoised using a scheme based on the ensemble empirical mode decomposition, a noise data assisted method which decomposes nonstationary and nonlinear signals into intrinsic mode functions. To distinguish noisy oscillatory modes from the relevant modes we use the detrended fluctuation analysis. We reported a very strong correlation between the acceleration of the z axis of the smartphone and the reference accelerometer across the inspiratory load protocol (from 0.80 to 0.97). Furthermore, the evaluation of the respiratory rate showed a very strong correlation (0.98). A good agreement was observed between the respiratory rate estimated with the chest acceleration signal from the z axis of the smartphone and with the respiratory airflow signal: Bland-Altman limits of agreement between -1.44 and 1.46 breaths per minute with a mean bias of -0.01 breaths per minute. This preliminary study provides a valuable insight into the use of the smartphone and its built-in accelerometer for respiratory monitoring.

JTD Keywords: Acceleration, Accelerometers, Correlation, Empirical mode decomposition, Fluctuations, Protocols, Time series analysis

Non-invasive evaluation of respiratory activity is an area of increasing research interest, resulting in the appearance of new monitoring techniques, ones of these being based on the analysis of the diaphragm mechanomyographic (MMGdi) signal. The MMGdi signal can be decomposed into two parts: (1) a high frequency activity corresponding to lateral vibration of respiratory muscles, and (2) a low frequency activity related to excursion of the thoracic cage. The purpose of this study was to apply the empirical mode decomposition (EMD) method to obtain the low frequency of MMGdi signal and selecting the intrinsic mode functions related to the respiratory movement. With this intention, MMGdi signals were acquired from a healthy subject, during an incremental load respiratory test, by means of two capacitive accelerometers located at left and right sides of rib cage. Subsequently, both signals were combined to obtain a new signal which contains the contribution of both sides of thoracic cage. Respiratory rate (RR) measured from the mechanical activity (RR_Mmg) was compared with that measured from inspiratory pressure signal (RR_P). Results showed a Pearson's correlation coefficient (r = 0.87) and a good agreement (mean bias = -0.21 with lower and upper limits of -2.33 and 1.89 breaths per minute, respectively) between RR_mmg and RR_P measurements. In conclusion, this study suggests that RR can be estimated using EMD for extracting respiratory movement from low mechanical activity, during an inspiratory test protocol.

JTD Keywords: Accelerometers, Band-pass filters, Biomedical measurement, Empirical mode decomposition, Estimation, IP networks, Muscles

In this paper, the problem of time alignment is revisited by adopting an ensemble-based approach with all signals jointly aligned. It is shown that the maximization of an eigenvalue ratio is synonymous to maximizing the signal-to-jitter-and-noise ratio. Since optimization of this criterion is extremely time consuming, a relaxed optimization procedure is introduced which converges much more quickly. Using simulations based on respiratory flow signals, the results suggest that the time delay error variance of the new method is much lower than that obtained with the well-known Woody’s method.

JTD Keywords: Time alignment, Signal ensemble, Subsample precision, Eigenvalue decomposition

Despite its tremendous scientific and economic impact, the mechanism that triggers metal passive film breakdown in the presence of aggressive ions remains under discussion. We have studied the iron passive film in chloride media using X-ray photoelectron spectroscopy (XPS), electrochemical impedance spectroscopy and electrochemical tunneling spectroscopy (ECTS). Ex situ XPS reveal that the film consists exclusively of an Fe(III) oxide without chloride content. In situ ECTS has been used to build up conductance maps of the Fe electrode during its electrochemical oxidation in a borate buffer solution and its breakdown when the film is grown in the presence of chloride. This conductograms provide direct and in situ experimental evidence of chloride-induced surface states within the band gap of the oxide film (~3.3eV). These states enable new charge exchange pathways that allow hole capture at the surface of the n-type Fe(III) oxide. The blocking of VB processes that occurs in the iron passive film is no longer present in chloride media, and electrode corrosion can proceed through these new states. We propose a simple 3-step mechanism for the process, in which chloride anions form an oxidizing Fe(II) surface intermediate but do not participate directly in the reaction.

JTD Keywords: Electrochemical tunneling spectroscopy, Electronic band structure, Fe passive film, Aqueous chloride corrosion, Semiconductor decomposition, Interface states