Blind Source Separation Research Articles

Separation and Extraction of Compound-Fault Signal Based on Multi-Constraint Non-Negative Matrix Factorization.

To solve the separation of multi-source signals and detect their features from a single channel, a signal separation method using multi-constraint non-negative matrix factorization (NMF) is proposed. In view of the existing NMF algorithm not performing well in the underdetermined blind source separation, the β-divergence constraints and determinant constraints are introduced in the NMF algorithm, which can enhance local feature information and reduce redundant components by constraining the objective function. In addition, the Sine-bell window function is selected as the processing method for short-time Fourier transform (STFT), and it can preserve the overall feature distribution of the original signal. The original vibration signal is first transformed into time-frequency domain with the STFT, which describes the local characteristic of the signal from the time-frequency distribution. Then, the multi-constraint NMF is applied to reduce the dimensionality of the data and separate feature components in the low dimensional space. Meanwhile, the parameter WK is constructed to filter the reconstructed signal that recombined with the feature component in the time domain. Ultimately, the separated signals will be subjected to envelope spectrum analysis to detect fault features. The simulated and experimental results indicate the effectiveness of the proposed approach, which can realize the separation of multi-source signals and their fault diagnosis of bearings. In addition, it is also confirmed that the proposed method, juxtaposed with the NMF algorithm of the traditional objective function, is more applicable for compound fault diagnosis of the rotating machinery.

Non‐Tidal Ocean Loading Signals of the North and Baltic Sea From Terrestrial Gravimetry, GNSS, and High‐Resolution Modeling

AbstractNon‐tidal ocean loading (NTOL) signals are known to be a significant source of geophysically induced noise in gravimetric and geodetic observations also far‐away from the coast and especially during extreme events such as storm surges. Operationally available corrections suffer from a low temporal and spatial resolution and reveal too small amplitudes on continental stations. Dedicated high‐resolution sea‐level modeling of the North and Baltic Sea provides an improved prediction of NTOL signals. Superconducting gravimeter and Global Navigation Satellite Systems observations on the small offshore island of Heligoland in the North Sea are used for an evaluation of the model values revealing largely increased correlations of up to 0.9 and signal reductions of up to 50% during a storm surge period of one month in January and February 2022. Evaluations on additional continental superconducting gravimeter stations also show significant improvements through the recommended high‐resolution modeling for improved signal separation further away from the coast.

Scott-Russel Linkage-Based Triboelectric Self-Powered Sensor for Contact Material-Independent Force Sensing and Tactile Recognition.

The rapid growth of Internet of Things (IoT) in recent years has increased demand for various sensors to collect a wide range of data. Among various sensors, the demand for force sensors that can recognize physical phenomena in 3D space has notably increased. Recent research has focused on developing energy harvesting methods for sensors to address their maintenance problems. Triboelectric nanogenerator (TENG) based force sensors are a promising solution for converting external motion into electrical signals. However, conventional TENG-based force sensors that use the signal peak can negatively affect data accuracy. In this study, a Scott-Russell linkage-inspired TENG (SRI-TENG) is developed. The SRI-TENG has completely separate signal generation and measurement sections, and the number of peaks in the electrical output is measured to prevent disturbing output signals. In addition, the lubricant liquid enhances durability, enabling stable force signal measurements for 270000 cycles. The SRI system demonstrates consistent peak counts and high accuracy across different contacting surfaces, indicating that it can function as a contact material-independent self-powered force sensor. Furthermore, using a deep learning method, it is demonstrated that it can function as a multimodal sensor by realizing the tactile properties of various materials.

Fluorescence lifetime multiplexing with fluorogen activating protein FAST variants

In this paper, we propose a fluorescence-lifetime imaging microscopy (FLIM) multiplexing system based on the fluorogen-activating protein FAST. This genetically encoded fluorescent labeling platform employs FAST mutants that activate the same fluorogen but provide different fluorescence lifetimes for each specific protein-dye pair. All the proposed probes with varying lifetimes possess nearly identical and the smallest-in-class size, along with quite similar steady-state optical properties. In live mammalian cells, we target these chemogenetic tags to two intracellular structures simultaneously, where their fluorescence signals are clearly distinguished by FLIM. Due to the unique structure of certain fluorogens under study, their complexes with FAST mutants display a monophasic fluorescence decay, which may facilitate enhanced multiplexing efficiency by reducing signal cross-talks and providing optimal prerequisites for signal separation upon co-localized and/or spatially overlapped labeling.

Method of field measurement of electromagnetic environment of high speed maglev train

Abstract In order to reduce the complexity of the magnetic field test of maglev trains and improve the accuracy of measurement data, this paper proposes a field measurement method of maglev train radiation emission based on high-speed parallel adaptive filter denoising technology. This measurement method is not limited by special measurement sites, and has the characteristics of high-speed measurement, real-time data processing, and large-capacity data storage. It breaks through the technical bottleneck of the traditional measurement system. The electromagnetic radiation emission of high-speed maglev trains is measured in real-time through multi-antenna synchronous measurement technology, and multi-channel high-speed RF signal measurement and acquisition of external signals are further carried out by blind source separation technology, environmental pre-scanning is carried out, environmental interference characteristics are obtained through feature statistics, and the radiation signal to be measured is estimated by blind source separation algorithm. It helps to achieve the purpose of accurately measuring the electromagnetic radiation emission of high-speed maglev trains. Through field testing, the denoising performance of the method reached 20 dB. Through field testing, the denoising performance of the method reached 20 dB.

Decomposing the AIA 304 Å Channel into Its Cool and Hot Components

The AIA 304 Å channel on board the Solar Dynamics Observatory (SDO) offers a unique view of ≈105 K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\approx 10^{5}\ ext{ K}$\\end{document} plasma emitting in the He ii 304 Å line. However, when observing off-limb, the emission of the (small) cool structures in the solar atmosphere (such as spicules, coronal rain and prominence material) can be of the same order as the surrounding hot coronal emission from other spectral lines included in the 304 Å passband, particularly over active regions. In this paper, we investigate three methods based on temperature and morphology that are able to distinguish the cool and hot emission within the 304 Å passband. The methods are based on the Differential Emission Measure (DEM), a linear decomposition of the AIA response functions (RFit) and the Blind Source Separation (BSS) technique. All three methods are found to produce satisfactory results in both quiescent and flaring conditions, largely removing the diffuse corona and leading to images with cool material off-limb in sharp contrast with the background. We compare our results with co-aligned data from the Interface Region Imaging Spectrograph (IRIS) in the SJI 1400 Å and 2796 Å channels, and find the RFit method to best match the quantity and evolution of the cool material detected with IRIS. Some differences can appear due to plasma emitting in the logT=5.1–5.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\log T=5.1\\,\ ext{--}\\,5.5$\\end{document} temperature range, particularly during the catastrophic cooling stage prior to rain appearance during flares. These methods are, in principle, applicable to any passband from any instrument suffering from similar cool and hot emission ambiguity, as long as there is good coverage of the high-temperature range.

Application of Lock-in Amplifier Technique in Signal Blind Source Separation

Abstract In the realm of signal processing, frequency error stands as a critical challenge that cannot be overlooked, particularly in scenarios characterized by noise disturbances and constantly changing environmental variables, as it significantly undermines the precision of waveform separation. Faced with a spectrum of frequency error phenomena stemming from hardware instability, such as clock drift, transmission distortion, and environmental changes, this paper proposes a waveform separation scheme centered around a core closed-loop control architecture aimed at addressing such challenges. Leveraging the high-performance STM32F407 microcontroller platform, the system harnesses the powerful algorithmic advantages of Fast Fourier Transform (FFT) to precisely locate and delineate the spectral characteristics of signals. Furthermore, we creatively integrate lock-in amplifier technology into the meticulously designed closed-loop control system, coupled with state-of-the-art zero-crossing detection algorithms for in-depth optimization. The research findings demonstrate outstanding signal-to-noise ratio performance within predefined target frequency bands for the separation device based on lock-in amplifier technology, showcasing not only remarkable resilience against interference but also the ability to accurately separate and reconstruct target signal components with high fidelity from complex and dynamic signal environments. This significantly enhances the robustness and accuracy of the entire system.

Analysis of imaging features of rare tumors in nasal cavity and paranasal sinus

Objective:To explore the imaging features of rare tumors of nasal cavity and sinuses, and to improve the understanding of these diseases, thereby aiding clinical diagnosis and treatment. Methods:The CT and MRI findings of 79 cases of rare neoplasm of nasal cavity and sinuses confirmed by pathology were retrospectively analyzed, and the imaging features were summarized. Results:Among the 79 cases, there were 16 cases of neuroendocrine carcinoma, most showing expansive and infiltrative bone destruction without hyperosteogeny and sclerosis. The sphenoid sinus exhibited a "pigeon" shape. In 28 cases of malignant melanoma, MRI signals were diverse, typical signals were rare, but mixed signals were more common. In 12 cases of rhabdomyosarcoma, MRI enhancement mostly showed "grape-like" enhancement and partial ring enhancement; There were 10 cases of olfactory neuroblastoma, the lesions were consistent with the distribution area of olfactory mucosa, most of them were lobulated, marginal nodules, and "flower ring" enhancement, and 2 cases grew across intracranial and external, with multiple cystic lesions and surrounding flaky edema bands. In 5 cases of solitary fibrous tumor, Benign tumors had regular shape and uniform density, while malignant tumors had irregular shape and uneven density, The enhancement was obviously uneven and showed a "pattern" change. There were 2 cases of sarcomatoid carcinoma, both with lobed appearance, uneven density, lamellar low-density shadow, and osteolytic bone destruction. In 4 cases of schwannoma, the enhancement showed obvious inhomogeneous enhancement. One case showed cystic necrosis, one case showed calcification, and the surrounding structure was compressed without damage. There was 1 case of neurofibroma, with many cystic components, low signal separation and compartmentalized enhancement. One case of paraganglioma showed moderate enhancement in the arterial phase and progressive enhancement in the venous phase, accompanied by significant swelling bone destruction. Conclusion:Rare tumors of nasal cavity and paranasal sinuses have distinctive imaging features. CT and MRI can effectively show the extent of the lesions and the degree of infiltration into adjacent tissues and organs, which is helpful for early clinical diagnosis and staging. However, definitive diagnosis still depends on pathology and immunohistochemistry.

Characterization of high-pressure-treated Bacillus subtilis spore populations using flow cytometry — Shedding light on spore superdormancy at 550 MPa

Mild spore inactivation can be challenging in industry because of the remarkable resistance of bacterial spores. High pressure (HP) can trigger spore germination, which reduces the spore's resistance, and thereby allows mild spore inactivation. However, spore germination is heterogenous. Some slowly germinating or non-germinating spores called superdormant spores remain resistant and can survive. Therefore, superdormant spores need to be characterized to understand the causes of their germination deficiency.Bacillus subtilis spores were pressurized for 50 s – 6 min at a very high pressure (vHP) level of 550 MPa and 60 °C in buffer to trigger germination. For a rapid quantification of the remaining ungerminated superdormant spores, flow cytometry (FCM) analysis was validated using single cell sorting and growth analysis. FCM based on propidium iodide (PI) and SYTO16 can be used for 550 MPa-superdormant spores after short vHP treatments of ≤1 min and post-HP incubation at 37 °C or 60 °C. The need for a post-HP incubation is particular for vHP treatments. The incubation was successful to separate FCM signals from superdormant and germinated spores, thus allowing superdormant spore quantification. The SYTO16 and PI fluorescence levels did not necessarily indicate superdormancy or apparent viability. This highlights the general need for FCM validation for different HP treatment conditions.The ∼7 % of ungerminated, i.e., superdormant, spores were isolated after a vHP treatment (550 MPa, 60 °C, 43–52 s). This allowed the characterization of vHP superdormant spores for the first time. The superdormant spores had a similar dipicolinic acid content as spores of the initial dormant population. Descendants of superdormant spores had a normal vHP germination capacity. The causes of vHP superdormancy were thus unlikely linked to the dipicolinic acid content or a permanent genetic change. Isolated superdormant spores germinated better in a second vHP treatment compared to the initial spore population. This has not been observed for other germination stimuli so far. In addition, the germination capacity of the initial spore population was time-dependent. A vHP germination deficiency can therefore be lost over time and seems to be caused by transient factors. Permanent cellular properties played a minor role as causes of superdormancy under chosen HP treatment conditions.The study gained new fundamental insights in vHP superdormancy which are of applied interest. Understanding superdormancy helps to efficiently develop a strategy to avoid superdormant spores and hence to inactivate all spores. The development of a mild HP spore germination-inactivation process aims at better preserving the food quality.

SPECIFICS OF DESIGNING AN INFRARED PYROMETER-REFLECTOMETER FOR SEMICONDUCTOR HETEROSTRUCTURE FABRICATION

There are general technical requirements for all types of reactors for chemical vapour deposition technology using AIII- BV metalorganic compounds. Among them, it is worth highlighting the large temperature gradients that cause the origin of convection loops, which in turn, taking into account the high speed of the gas flow, lead to turbulence in the reactor instead of the expected laminar flow. It is also important to take into account the change in parameters of the wafer surface during the growth process and the need for signal separation between the useful signal from the wafer surface and the background signal from the wafer carrier, which rotates at fixed speed for uniform deposition of compounds. To obtain high-quality heterostructures with reproducible parameters, it is important to have a system of precise temperature control on the wafer surface directly in the deposition area, since the deposition process for many complex semiconductor devices (for example, laser diodes, LEDs, photodiodes, transistors on heterojunctions) is very sensitive to temperature changes. The method of optical pyrometry is a non-contact method that allows to precisely determine the temperature of the wafer surface and meets the technical requirements of CVD epitaxy growth reactors. This article is devoted to the analysis of the features of the development of optoelectronic systems for precise temperature measurement during epitaxial growth in order to determine the criteria for the selection or development of components of the optoelectronic system of the pyrometer-reflectometer. The main physical processes, electro-optical characteristics of Si photodiode, AlGaAs/GaAs LED and parameters of bandpass interference filters were investigated. Based on the analysis of the obtained research and measurement results, scientific recommendations have been developed. The recommendations aimed at the selection and optimization of the parameters of the components of the pyrometer-reflectometer (photodetectors, light emitting diodes, optical filters) in order to improve the accuracy and temperature stability of measurements in the pyrometer’s operation conditions, which take into account the compensation of emissivity change from the surface of the wafer.

Low-rank prior-based Fast-RPCA for clutter filtering and noise suppression in non-contrast ultrasound microvascular imaging

Accurate and real-time separation of blood signal from clutter and noise signals is a critical step in clinical non-contrast ultrasound microvascular imaging. Despite the widespread adoption of singular value decomposition (SVD) and robust principal component analysis (RPCA) for clutter filtering and noise suppression, the SVD’s sensitivity to threshold selection, along with the RPCA’s limitations in undersampling conditions and heavy computational burden often result in suboptimal performance in complex clinical applications. To address those challenges, this study presents a novel low-rank prior-based fast RPCA (LP-fRPCA) approach to enhance the adaptability and robustness of clutter filtering and noise suppression with reduced computational cost. A low-rank prior constraint is integrated into the non-convex RPCA model to achieve a robust and efficient approximation of clutter subspace, while an accelerated alternating projection iterative algorithm is developed to improve convergence speed and computational efficiency. The performance of the LP-fRPCA method was evaluated against SVD with a tissue/blood threshold (SVD1), SVD with both tissue/blood and blood/noise thresholds (SVD2), and the classical RPCA based on the alternating direction method of multipliers algorithm through phantom and in vivo non-contrast experiments on rabbit kidneys. In the slow flow phantom experiment of 0.2 mm/s, LP-fRPCA achieved an average increase in contrast ratio (CR) of 10.68 dB, 9.37 dB, and 8.66 dB compared to SVD1, SVD2, and RPCA, respectively. In the in vivo rabbit kidney experiment, the power Doppler results demonstrate that the LP-fRPCA method achieved a superior balance in the trade-off between insufficient clutter filtering and excessive suppression of blood flow. Additionally, LP-fRPCA significantly reduced the runtime of RPCA by up to 94-fold. Consequently, the LP-fRPCA method promises to be a potential tool for clinical non-contrast ultrasound microvascular imaging.

О выделении сигнала высокочастотного ЭМ-поля литосферного происхождения: практический аспект

The work is devoted to the practical aspect of separation of pulse-type signals of lithospheric origin in the recorded electromagnetic field by their shape. Experiments on registration of electromagnetic field signals in a wide frequency range have been carried out. In the frequency range from 5 to 20 kHz, pulses consistent with the generalized shape of the pulse of lithospheric origin were detected. Special hardware and software was developed for registration and further processing of electromagnetic field signals in the kilohertz range. With its help, prototypes of pulses of lithospheric origin were identified. An algorithm was proposed and tested, which makes it possible to automatically select pulses similar to the prototype from the entire set of observed data. Field experiments were carried out to isolate pulses of electromagnetic field of lithospheric origin and to determine their energy and quantitative characteristics. Daily variations of the average energy and activity of electromagnetic field pulses of lithospheric origin are revealed.

Theoretical and experimental investigation on gearbox vibration signal separation of planetary gear set

The gearbox vibration signal of a planetary gear set contains multiple components caused by different excitations. Separating the gearbox vibration signal to decouple its components can provide core information for operational status monitoring. In this paper, we propose an indirect gearbox vibration signal separation method based on parameter identification. A novel vibration signal model is established using the modal shape of ring gear. A parameter identification method is adopted to determine the parameters of the vibration signal model. The vibration signal components are reconstructed based on the vibration signal model and identified parameters. To evaluate the performance of the proposed method, a ring gear response calculation method derived from the elastic theory of ring gear is presented. The influences of unequal load sharing and meshing position errors on vibration signals are studied through numerical simulations using the calculation method. Experiments are conducted in a planetary gear set test rig. Comparisons between the experimental results obtained by the separation and calculation methods indicate the effectiveness of the proposed separation method.

New breast cancer biomarkers from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models

Currently, magnetic resonance imaging is the most sensitive imaging technique for detecting cancerous processes in early stages. As for breast cancer, due to the tubular structure of the tissue, being formed by ducts, anisotropic diffusion should be considered instead of the general isotropic diffusion. Anisotropic diffusion is studied by applying a technique called Diffusion Tensor Imaging (DTI), where the diffusion gradient is applied by changing the magnetic field in several spatial directions.To date, the application of Multivariate Curve Resolution (MCR) models in diffusion sequences has demonstrated its ability to develop cancer biomarkers of easy clinical interpretation in the case of isotropic tissues, such as the prostate. But so far, it has never been applied in the case of anisotropic tissues, as the breast.Therefore, the main objective of this work is to obtain easy-to-interpret imaging biomarkers useful for early breast cancer diagnosis from diffusion magnetic resonance imaging based on the Diffusion Tensor using multivariate curve resolution (MCR) models. A classification model to identify healthy and tumor affected pixels is also proposed.

Identification for nonlinear systems modelled by deep long short-term memory networks based Wiener model

This paper is concerned with modeling and identification methodology for practical nonlinear system via deep long short-term memory (DLSTM) networks-based Wiener model. To determine the unknown parameters and simplify parameters identification procedure for the Wiener model, a two-step identification scheme is implemented applying hybrid signals involving separable signal and random data. First, the separable signal to separate the two blocks in Wiener model is imported, then parameters of the linear block is estimated using correlation function-based least squares technique, which handles the issue that the intermediate variable information in Wiener model cannot be measured. Moreover, integrating the advantages of adaptive stochastic gradient descent algorithm and root mean square propagation, an adaptive momentum estimation technique is created to optimize the DLSTM networks parameters based on available random data, which improves the accuracy of identified Wiener model. Compared with the other existing schemes, the superiority of the proposed method in terms of predictive performance and control results are illustrated by permanent magnet synchronous motors.

Blind Sound Source Separation by Combining the Convolutional Neural Network and Degree Separator

Blind Sound Source Separation by Combining the Convolutional Neural Network and Degree Separator

Comparing the three-dimensional morphological asymmetries in the ejecta of Kepler and Tycho in X-rays

Aims. Recent simulations have shown that asymmetries in the ejecta distribution of supernova remnants (SNRs) may be a reflection of asymmetries left over from the initial supernova explosion. Thus, SNR studies provide a vital means for testing and constraining model predictions in relation to the distribution of heavy elements, which are key to improving our understanding of the explosion mechanisms in Type Ia supernovae. Methods. The use of a novel blind source separation method applied to the megasecond X-ray observations of the historic Kepler and Tycho supernova remnants has revealed maps of the ejecta distribution. These maps are endowed with an unprecedented level of detail and clear separations from the continuum emission. Our method also provides a three-dimensional (3D) view of the ejecta by individually disentangling red- and blueshifted spectral components associated with images of the Si, S, Ar, Ca, and Fe emission. This approach provides insights into the morphology of the ejecta distribution in those two remnants. Results. Those mappings have allowed us to thoroughly investigate the asymmetries in the intermediate-mass elements and Fe distribution in two Type Ia supernova remnants. We also compared the results with the core-collapse Cassiopeia A remnant, which we had studied previously. The images obtained confirm, as expected for Type Ia SNRs, that the Fe distribution is mostly closer to the core than that of intermediate-mass elements. They also highlight peculiar features in the ejecta distribution, such as the Fe-rich southeastern knot in Tycho.

Unveiling Hidden Sources of Dynamic Functional Connectome through a Novel Regularized Blind Source Separation Approach

Abstract The investigation of the brain’s functional connectome and its dynamic changes can provide valuable insights into brain organization and its reconfiguration. However, the analysis of dynamic functional connectivity (dFC) using functional magnetic resonance imaging (fMRI) faces major challenges, including the high dimensionality of brain networks, unknown latent sources underlying observed dFC, and the large number of brain connections that increase the risk of spurious findings. In this paper, we propose a new regularized blind source separation (BSS) method called dyna-LOCUS to address these challenges. dyna-LOCUS decomposes observed dFC measures to reveal latent source connectivity traits and their dynamic temporal expression profiles. By utilizing low-rank factorization and novel regularizations, dyna-LOCUS achieves efficient and reliable mapping of connectivity traits underlying the dynamic brain functional connectome, characterizes temporal changes of the connectivity traits that contribute to the reconfiguration in the observed dFC, and generates parsimonious and interpretable results in identifying whole-brain dFC states. We introduce a highly efficient iterative Node-Rotation algorithm that solves the non-convex optimization problem for learning dyna-LOCUS. Simulation studies demonstrate the advantages of our proposed method. Application of dyna-LOCUS to the Philadelphia Neurodevelopmental Cohort (PNC) study unveils latent connectivity traits and key brain connections and regions driving each of these neural circuits, reveals temporal expressions and interactions of these connectivity traits, and generates new findings regarding gender differences in the neurodevelopment of an executive function-related connectivity trait.

A geometric solution to microphone array position calibration and its confidence interval analysis

Wireless acoustic sensor networks (WASNs) comprised of spatially distributed acoustic nodes have been increasingly popular in many areas such as speech enhancement, blind source separation, and audio surveillance, where node position information is generally required to implement relevant processing algorithms. In this article, we focus on the node position calibration only using direction-of-arrival (DOA) information with each node consisting of a microphone array. State-of-the-art techniques translate the position calibration into optimization of DOA-based cost functions, and have shown notable success. However, a compromise between position calibration accuracy and computational load still exists. Furthermore, none of the existing studies can offer a quantitative evaluation of calibration performance before the optimization procedure is finished. To address these problems, a geometric solution to array node position calibration is proposed using DOA measurements. For a given array node, DOAs of three sources are first utilized to construct cylinder, plane, and cone equations. Next, the two-dimensional projection of intersections among these equations is calculated using Sylvesterresultantmatrix. Finally, a closed-form solution of node position in three-dimensional space is established. Moreover, explicit mathematical derivation of the confidence interval analysis on the calibration results under given node angle estimation error condition is presented, indicating that the proposed method can provide predictable performance with corresponding probabilities. The Cramér-Rao bound (CRB) is further investigated. Simulation results reveal that as compared with existing methods our approach achieves comparable calibration accuracy with much less computational burden. Real-world experiments also verify its effectiveness.

Operando UV–Vis diffuse reflectance spectroscopy insights into the methane dehydroaromatization reaction over Mo/H-ZSM-5 catalysts

Methane Dehydroaromatization (MDA) is proposed as a possible way to valorize stranded natural gas, e.g., arising during crude oil drilling. In this study, we used operando UV–Vis Diffuse Reflectance Spectroscopy (DRS) in combination with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS) to distinguish the MDA reaction phases of activation, induction, and deactivation. We identified three spectral components, which describe these distinct reaction phases. When the catalyst was activated in CO, two spectral components alone sufficiently described the operando UV–Vis DRS data. We further found that the preparation method of the Mo/ZSM-5 catalyst has little influence on the observed spectral features. However, the different preparation methods impact the dispersion of Mo, resulting in faster deactivation for the catalyst with more Mo-clusters compared to the catalyst with a higher Mo dispersion. This study shows that operando UV–Vis DRS in combination with the MCR-ALS methodology is a powerful analytical tool to disentangle and study the three MDA reaction phases.