Free radical EPR spectroscopy analysis using blind source separation

Deciphering Evolution Pathway of Supported NO3 • Enabled via Radical Transfer from •OH to Surface NO3 - Functionality for Oxidative Degradation of Aqueous Contaminants

NO₃ ^• can compete with omnipotent ^•OH/SO₄ ^•- in decomposing aqueous pollutants because of its lengthy lifespan and significant tolerance to background scavengers present in H₂O matrices, albeit with moderate oxidizing power. The generation of NO₃ ^•, however, is of grand demand due to the need of NO₂ ^•/O₃, radioactive element, or NaNO₃/HNO₃ in the presence of highly energized electron/light. This study has pioneered a singular pathway used to radicalize surface NO₃ ^- functionalities anchored on polymorphic α-/γ-MnO₂ surfaces (α-/γ-MnO₂-N), in which Lewis acidic Mn^2+/3+ and NO₃ ^- served to form ^•OH via H₂O₂ dissection and NO₃ ^• via radical transfer from ^•OH to NO₃ ^- (^•OH → NO₃ ^•), respectively. The elementary steps proposed for the ^•OH → NO₃ ^• route could be energetically favorable and marginal except for two stages such as endothermic ^•OH desorption and exothermic ^•OH-mediated NO₃ ^- radicalization, as verified by EPR spectroscopy experiments and DFT calculations. The Lewis acidic strength of the Mn^2+/3+ species innate to α-MnO₂-N was the smallest among those inherent to α-/β-/γ-MnO₂ and α-/γ-MnO₂-N. Hence, α-MnO₂-N prompted the rate-determining stage of the ^•OH → NO₃ ^• route (^•OH desorption) in the most efficient manner, as also evidenced by the analysis on the energy barrier required to proceed with the ^•OH → NO₃ ^• route. Meanwhile, XANES and in situ DRIFT spectroscopy experiments corroborated that α-MnO₂-N provided a larger concentration of surface NO₃ ^- species with bi-dentate binding arrays than γ-MnO₂-N. Hence, α-MnO₂-N could outperform γ-MnO₂-N in improving the collision frequency between ^•OH and NO₃ ^- species and in facilitating the exothermic transition of NO₃ ^- functionalities to surface NO₃ ^• analogues per unit time. These were corroborated by a greater efficiency of α-MnO₂-N in decomposing phenol, in addition to scavenging/filtration control runs and DFT calculations. Importantly, supported NO₃ ^• species provided 5-7-fold greater efficiency in degrading textile wastewater than conventional ^•OH and supported SO₄ ^•- analogues we discovered previously.

The Noninvasive Analysis of Paint Mixtures on Canvas Using an EPR MOUSE

Many artists create the variety of colors in their paintings by mixing a small number of primary pigments. Therefore, analytical techniques for studying paintings must be capable of determining the components of mixtures. Electron paramagnetic resonance (EPR) spectroscopy is one of many techniques that can achieve this, however it is invasive. With the recent introduction of the EPR mobile universal surface explorer (MOUSE), EPR is no longer invasive. The EPR MOUSE and a least squares regression algorithm were used to noninvasively identify pairwise mixtures of seven different paramagnetic pigments in paint on canvas. This capability will help art conservators, historians, and restorers to study paintings with EPR spectroscopy.

Study of the heat stability of sunflower oil enriched in natural antioxidants by different analytical techniques and front-face fluorescence spectroscopy combined with Independent Components Analysis

The aim of this study was to find objective analytical methods to study the degradation of edible oils during heating and thus to suggest solutions to improve their stability. The efficiency of Nigella seed extract as natural antioxidant was compared with butylated hydroxytoluene (BHT) during accelerated oxidation of edible vegetable oils at 120 and 140 °C. The modifications during heating were monitored by 3D-front-face fluorescence spectroscopy along with Independent Components Analysis (ICA), (1)H NMR spectroscopy and classical physico-chemical methods such as anisidine value and viscosity. The results of the study clearly indicate that the natural seed extract at a level of 800 ppm exhibited antioxidant effects similar to those of the synthetic antioxidant BHT at a level of 200 ppm and thus contributes to an increase in the oxidative stability of the oil.

Blind separation of electron paramagnetic resonance signals using diversity minimization

This paper presents a method for the blind separation of Electron Paramagnetic Resonance (EPR) spectroscopy signals that can aid in the detection of free radicals in living organisms. Observed EPR signals are often mixtures of source signals that are approximately "sparse", with a small number of narrow segments of the signal much larger than the remaining parts. We develop a method to separate the sources through minimizing a p-norm-like diversity measure under some mild assumptions which are generally valid for EPR signals. Simulations demonstrate that the proposed method performs well on EPR signal separation, with better robustness to noise compared to other techniques.

Multi-component analysis: blind extraction of pure components mass spectra using sparse component analysis

The paper presents sparse component analysis (SCA)-based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on l(1) norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds.

De-colorization of Reactive Brilliant Orange X-GN by a novel rotating electrochemical disc process

A novel electrochemical setup for wastewater treatment-rotating electrochemical disc process (RECDP) was developed in this article. The anode and cathode are distributed alternatively and evenly on a flat round disc, which was designed to improve mass transfer of organics from bulk solution to electrode surface, while at the same time increasing oxygen transfer from air to the liquid to benefit the organics oxidization. The color removal of dye Reactive Brilliant Orange X-GN (RBO) was experimentally investigated using the setup. The results show that the RECDP could efficiently remove the color by 99.5% after 60-min electrolysis. The influence of factors such as the disc rotating speed, current intensity, pH, and treating time was also explored, and the de-colorization mechanism was studied with electron paramagnetic resonance (EPR), UV-Vis, and IR. It has been confirmed that the free radicals oxidization leads to the dye de-coloration.

Novel sparse component analysis approach to free radical EPR spectra decomposition

Free radicals play important roles in many physiological and pathological pathways in biological systems. These free radicals can be detected and quantified by their EPR spectra. The measured EPR spectra are often mixtures of pure spectra of several different free radicals and other chemicals. Blind source separation can be applied to estimate the pure spectra of interested free radicals. However, since the pure EPR spectra are often not independent of each other, the approach based on independent component analysis (ICA) cannot accurately extract the required spectra. In this paper, a novel sparse component analysis method for blind source separation, which exploits the sparsity of the EPR spectra, is presented to reliably extract the pure source spectra from their mixtures with high accuracy. This method has been applied to the analysis of EPR spectra of superoxide, hydroxyl, and nitric oxide free radicals, for both simulated data and real world ex vivo experiment. Compared to the traditional self-modeling method and our previous ICA-based blind source separation method, the proposed sparse component analysis approach gives much better results and can give perfect separation for mixtures of superoxide spectrum and hydroxyl spectrum in the ideal noise-free case. This method can also be used in other similar applications of quantitative spectroscopy analysis.

Least-dependent-component analysis based on mutual information

We propose to use precise estimators of mutual information (MI) to find the least dependent components in a linearly mixed signal. On the one hand, this seems to lead to better blind source separation than with any other presently available algorithm. On the other hand, it has the advantage, compared to other implementations of "independent" component analysis (ICA), some of which are based on crude approximations for MI, that the numerical values of the MI can be used for (i) estimating residual dependencies between the output components; (ii) estimating the reliability of the output by comparing the pairwise MIs with those of remixed components; and (iii) clustering the output according to the residual interdependencies. For the MI estimator, we use a recently proposed k -nearest-neighbor-based algorithm. For time sequences, we combine this with delay embedding, in order to take into account nontrivial time correlations. After several tests with artificial data, we apply the resulting MILCA (mutual-information-based least dependent component analysis) algorithm to a real-world dataset, the ECG of a pregnant woman.