Multiple Spectra Research Articles

Design and analysis of a compact millimeter-wave pentaband antenna for 5G FR-2 band wireless technologies

This paper examines a short-range Millimeter-wave antenna characterized by its compact design, coplanar waveguide feeding, and folded dipole shape, that operates across multiple frequency spectrums. The radiator is designed on a Rogers TMM4 material having the dimensions of 1.05λL × 1.05λL × 0.057λL (λL: wavelength associated with the bottommost cut-off frequency). Adoption of a folded shape serves to augment the electrical dimension of the antenna by expanding the current-carrying path, thereby facilitating the attainment of the necessary frequency spectrums. The proposed antenna operates at 22.44–23.77 GHz (5.75 %), 26.68–30.35 GHz (12.87 %), 38.1–43.46 GHz (13.14 %), 49.13–54.48 GHz (10.33 %) and 57.63–67.85 GHz (16.29 %) spectrums with maximum gain and radiation efficiency of 8dBi and 94.5 % respectively. The work further delves into an in-depth analysis of antenna characteristics, encompassing aspects such as radiation pattern, surface current, group delay, and transfer function. All examined characteristics affirm that the proposed antenna exhibits versatility for diverse frequency bands, including but not limited to n257, n259, n260, n261, and various other 5G/beyond 5G wireless technologies.

Rapid monitoring of the spatial distribution of soil organic matter using unmanned aerial vehicle imaging spectroscopy

ABSTRACT An existing technique for quickly obtaining the spatial distribution data of soil organic matter (SOM) involves combining an unmanned aerial vehicle (UAV) platform and imaging spectroscopy technology; this technique is suitable for precision agricultural management and real-time monitoring. In this study, the average spectral data were first extracted via circular regions of interest (ROIs) of different radii and modelled using the measured SOM to determine the optimal extraction range for the spectral data. Partial least-squares regression (PLSR) and support vector machine (SVM) models were constructed based on the raw spectra, standard normal transform spectra, multiple scattering-corrected spectra, and first-order differential spectra. The difference index, ratio index, and normalized index were calculated and analysed for their correlation with SOM content, and the spectral indices were screened to establish the SOM prediction model. Finally, the accuracy of the model was evaluated, and we selected the optimal model to map the spatial distribution of SOM. The results of our study show that 1) the radius of the ROI significantly affects the model’s accuracy; 2) the accuracy of the nonlinear SVM model is much higher than that of the linear PLSR model; 3) the prediction model based on spectral indices is better than full-band model. This study can serve as a model for the application of UAV imaging spectroscopy technology to field-scale SOM estimations and rapid monitoring, as well as provide technical support for precision agriculture and climate change studies.

Neural consequences of 5-Hz transcranial alternating current stimulation over right hemisphere: An eLORETA EEG study

IntroductionTranscranial alternating current stimulation (tACS) at 5-Hz to the right hemisphere can effectively alleviate anxiety symptoms. This study aimed to explore the neural mechanisms that drive the therapeutic benefits. MethodsWe collected electroencephalography (EEG) data from 24 participants with anxiety disorders before and after a tACS treatment session. tACS was applied over the right hemisphere, with 1.0 mA at F4, 1.0 mA at P4, and 2.0 mA at T8 (10–10 EEG convention). With eLORETA, we transformed the scalp signals into the current source density in the cortex. We then assessed the differences between post- and pre-treatment brain maps across multiple spectra (delta to low gamma) with non-parametric statistics. ResultsWe observed a trend of heightened power in alpha and reduced power in mid-to-high beta and low gamma, in accord with the EEG markers of anxiolytic effects reported in previous studies. Additionally, we observed a consistent trend of de-synchronization at the stimulating sites across spectra. ConclusiontACS 5-Hz over the right hemisphere demonstrated EEG markers of anxiety reduction. The after-effects of tACS on the brain are intricate and cannot be explained solely by the widely circulated entrainment theory. Rather, our results support the involvement of plasticity mechanisms in the offline effects of tACS.

Au/FeNiPO4‐Based Multiple Spectra Optoacoustic Tomography/CT Dual‐Mode Nanoprobe for Systemic Screening of Atherosclerotic Vulnerable Plaque

AbstractCurrent diagnostic technique in direct identification of multi‐site plaques and simultaneous assessment of plaque vulnerability remains a challenge, which is crucial for indicating the risk of atherosclerotic cardiovascular diseases (ASCVD). Herein, an osteopontin (OPN)‐specific nanoprobe (OPN Ab‐Au/FeNiPO4@ICG) with both multiple spectra optoacoustic tomography (MSOT) and computed tomography (CT) imaging, is constructed successfully realizing systemic screening of vulnerable plaque. OPN Ab‐Au/FeNiPO4@ICG nanoprobe specifically targeted OPN‐overexpressed foam cells and recognized the vulnerable plaque at the molecular level. In AS mice, CT imaging exhibits that OPN Ab‐Au/FeNiPO4@ICG nanoprobe effectively avoid interference from calcification and accurately visualized AS plaque. MSOT functional imaging results reveals that after the injection of OPN Ab‐Au/FeNiPO4@ICG nanoprobe, the carotid plaque exhibited a much higher MSOT signal than the aortic arch plaque (P = 0.0291). Further pathological analysis displays that the carotid plaque possessed a much higher vulnerability score (P = 0.0247), in agreement with the MSOT signals. More importantly, the linear regression analysis confirms the high correlation between the MSOT signals and plaque vulnerability with R = 0.7095 (P = 0.0216), demonstrating the potential of the proposed nanoprobe in systematic evaluation of plaque vulnerability. This work employs the dual‐model nanoprobe strategy for both plaque localization and vulnerability assessment, greatly advancing the accurate diagnosis of ASCVD.

Radiologic Lag and Brain MRI Lesion Dynamics During Attacks in MOG Antibody-Associated Disease.

Knowledge of the evolution of CNS demyelinating lesions within attacks could assist diagnosis. We evaluated intra-attack lesion dynamics in patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) vs multiple sclerosis (MS) and aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorder (AQP4+NMOSD). This retrospective observational multicenter study included consecutive patients from Mayo Clinic (USA) and Great Ormond Street Hospital for Children (UK). Inclusion criteria were as follows: (1) MOGAD, MS, or AQP4+NMOSD diagnosis; (2) availability of ≥2 brain MRIs (within 30 days of attack onset); and (3) brain involvement (i.e., ≥1 T2 lesion) on ≥1 brain MRI. The initial and subsequent brain MRIs within a single attack were evaluated for the following: new T2 lesions(s); resolved T2 lesion(s); both; or no change. This was compared between MOGAD, MS, and AQP4+NMOSD attacks. We used the Mann-Whitney U test and χ2/Fisher exact test for statistical analysis. Our cohort included 55 patients with MOGAD (median age, 14 years; interquartile range [IQR] 5-34; female sex, 29 [53%]) for a total of 58 attacks. The comparison groups included 38 patients with MS, and 19 with AQP4+NMOSD. In MOGAD, the initial brain MRI (median of 5 days from onset [IQR 3-9]) was normal in 6/58 (10%) attacks despite cerebral symptoms (i.e., radiologic lag). The commonest reason for repeat MRI was clinical worsening or no improvement (33/56 [59%] attacks with details available). When compared with the first MRI, the second intra-attack MRI (median of 8 days from initial scan [IQR 5-13]) showed the following: new T2 lesion(s) 27/58 (47%); stability 24/58 (41%); resolution of T2 lesion(s) 4/58 (7%); or both new and resolved T2 lesions 3/58 (5%). Findings were similar between children and adults. Steroid treatment was associated with resolution of ≥1 T2 lesion (6/28 [21%] vs 1/30 [3%], p = 0.048) and reduced the likelihood of new T2 lesions (9/28 vs 18/30, p = 0.03). Intra-attack MRI changes favored MOGAD (34/58 [59%]) over MS (10/38 [26%], p = 0.002) and AQP4+NMOSD (4/19 [21%], p = 0.007). Resolution of ≥1 T2 lesions was exclusive to MOGAD (7/58 [12%]). Radiologic lag is common within MOGAD attacks. Dynamic imaging with frequent appearance and occasional disappearance of lesions within a single attack suggest MOGAD diagnosis over MS and AQP4+NMOSD. These findings have implications for clinical practice, clinical trial attack adjudication, and understanding of MOGAD pathogenesis.

Investigation of the dynamic response of h-type anti-slide pile based on shaking table test

The h-type anti-slide piles (hTPs) are extensively utilized in slope stabilization projects, yet their response to seismic activities remains inadequately understood. This study investigated the dynamic response of hTPs with circular cross-sections (hTCPs) through a shaking table test, focusing on seismic events along Jiuzhaigou-Mianyang highway slopes (ZK142 + 124 to ZK142 + 274). Time-frequency analysis using Fast Fourier Transform (FFT) and Hilbert-Huang Transform (HHT) was conducted. The peak acceleration and its direction, dynamic earth pressure and its sharing ratio, and moment were characterized, and the variations of pile displacement and the seismic damage on different parts of hTCPs were analyzed under varying loading conditions. Results reveal a distinct pressure distribution and damage characteristics between the long and short piles in hTCPs. A positive correlation between peak accelerations and loading conditions was observed in both long and short piles, with peak amplification occurring only at the top of long piles. Long piles exhibited diverse acceleration directions under different loading conditions, contrasting with the infrequent occurrence in short piles. The peak dynamic earth pressure distribution varied between the front and back side of pile bodies, with opposite overall trends between the long and short piles. The sharing ratio displayed an increasing followed by decreasing trend in long piles, but an opposite trend in short piles, with the turn point occurring at a loading condition of 0.3 g. These differences were significantly influenced by El Centro waves within a low frequency band (13.34 Hz–16.18 Hz), as a single large peak HHT energy spectrum was observed in long piles, but multiple and destructive low-frequency energy spectra in short piles. The results suggest an independent dynamic response occurred in the long and short piles of hTCPs during shaking table test, providing insights for their field engineering application in seismic active regions.

When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization

BackgroundSurface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems. ResultsHerein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification. SignificanceThe proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

Adaptive multi-spectral mimicking with 2D-material nanoresonator networks

Active nanophotonic materials that can emulate and adapt between many different spectral profiles—with high fidelity and over a broad bandwidth—could have a far-reaching impact, but are challenging to design due to a high-dimensional and complex design space. Here, we show that a metamaterial network of coupled 2D-material nanoresonators in graphene can adaptively match multiple complex absorption spectra via a set of input voltages. To design such networks, we develop a semi-analytical auto-differentiable dipole-coupled model that allows scalable optimization of high-dimensional networks with many elements and voltage signals. As a demonstration of multi-spectral capability, we design a single network capable of mimicking four spectral targets resembling select gases (nitric oxide, nitrogen dioxide, methane, nitrous oxide) with very high fidelity ( >90% ). Our results could impact the design of highly reconfigurable optical materials and platforms for applications in sensing, communication and display technology, and signature and thermal management.

Quantitative modelling of type Ia supernovae spectral time series: Constraining the explosion physics

Abstract Multiple explosion mechanisms have been proposed to explain type Ia supernovae (SNe Ia). Empirical modelling tools have also been developed that allow for fast, customised modelling of individual SNe and direct comparisons between observations and explosion model predictions. Such tools have provided useful insights, but the subjective nature with which empirical modelling is performed makes it difficult to obtain robust constraints on the explosion physics or expand studies to large populations of objects. Machine learning accelerated tools have therefore begun to gain traction. In this paper, we present riddler, a framework for automated fitting of SNe Ia spectral sequences up to shortly after maximum light. We train a series of neural networks on realistic ejecta profiles predicted by the W7 and N100 explosion models to emulate full radiative transfer simulations and apply nested sampling to determine the best-fitting model parameters for multiple spectra of a given SN simultaneously. We show that riddler is able to accurately recover the parameters of input spectra and use it to fit observations of two well-studied SNe Ia. We also investigate the impact of different weighting schemes when performing quantitative spectral fitting and show that best-fitting models and parameters are highly dependent on the assumed weighting schemes and priors. As spectroscopic samples of SNe Ia continue to grow, automated spectral fitting tools such as riddler will become increasingly important to maximise the physical constraints that can be gained in a quantitative and consistent manner.

Gallic acid improves color quality and stability of red wine via physico-chemical interaction and chemical transformation as revealed by thermodynamics, real wine dynamics and benchmark quantum mechanical calculations

The aim of this study was to explore the copigmentation effect of gallic acid on red wine color and to dissect its mechanism at the molecular level. Three-dimensional studies, e.g., in model wine, in real wine and in silico, and multiple indicators, e.g., color, spectrum, thermodynamics and phenolic dynamics, were employed. The results showed that gallic acid significantly enhanced the color quality and stability of red wine. Physico-chemical interactions and chemical transformations should be the most likely mechanism, and physico-chemical interactions are also a prerequisite for chemical transformations. QM calculations of the physico-chemical interactions proved that the binding between gallic acid and malvidin-3-O-glucoside is a spontaneous exothermic reaction driven by hydrogen bonding and dispersion forces. The sugar moiety of malvidin-3-O-glucoside and the phenolic hydroxyl groups of gallic acid affect the formation of hydrogen bonds, while the dispersion interaction was related to the stacking of the molecular skeleton.

Potential-Modulated Surface-Enhanced Raman Spectroscopy of Tolmetin at Gold Nanoparticle Film Functionalized Polarizable Liquid-Liquid Interfaces.

An aqueous colloidal suspension of gold nanoparticles (AuNPs) may be condensed into a thin fractal film at the polarizable liquid-liquid interface formed between two immiscible electrolyte solutions upon injection of millimolar concentrations of sodium chloride to the aqueous phase. By adjusting the interfacial polarization conditions (negative, intermediate, and positive open-circuit potentials), the morphology of the film is modified, resulting in unique surface plasmon properties of the film, which enable in situ surface-enhanced Raman spectroscopy (SERS). Intense SERS signals are observed at the polarizable liquid-liquid interface when micromolar concentrations of tolmetin, a nonsteroidal anti-inflammatory drug, are entrapped in the AuNP fractal film. The change in the signal intensity, averaged over multiple spectra, with respect to the concentration of tolmetin, depends on the polarization conditions and suggests the presence of chemical-induced damping effects on the surface plasmons of the gold film.

Research on asymmetric spatial heterodyne spectroscopy for velocimetry navigation

The further development of deep space exploration contributes to the exploration of the universe and the origin and evolution of life on Earth, and is a prerequisite and foundation for the development and utilization of space resources. Autonomous navigation of deep space probes, one of the key technologies for deep space exploration, can significantly reduce ground support costs and improve the autonomous operation, management, and on-orbit survivability of deep space probes. Among them, autonomous astronomical navigation methods based on velocity measurements directly measure velocity information, effectively avoiding the impact of using calculus to solve velocity on response time and navigation accuracy in navigation methods based on angle and distance measurements. The passive radial velocity measurement technique of asymmetric spatial heterodyne spectroscopy has the advantages of compact structure, large luminous flux, and multiple spectra detected simultaneously. Taking the Sun as the navigation target source, we carried out the selection of observational spectral lines and the parameter design of the velocimetry navigation system, designed the calculation of phases for the absorption characteristic line under the complex polychromatic strong background, carried out the simulation analysis of velocimetry under the typical relative velocimetry.

Analysis of Land Use and Land Cover Change Detection for Indore District of Malwa Plateau Region Using Supervised Machine Learning

The main aim of this work is to find out LULC classes for the Indore region. For the last seven years, Indore has been crowned the cleanest city in India according to the latest ranking of 2023. In the Indian state of Madhya Pradesh, the city and district of Indore are situated on the southern edge of the Malwa plateau. The municipalities of the Indore district majorly cover Manpur, Depalpur, Goutampura, Mhow, Hatod, Betma, Rau and Sanwer, with an area of approximately 3900 square kilometers. In contrast, Indore covers a land area of approximately 530 square kilometres. It is in charge of the nation’s slow but steady industrialization and population growth found in the last three decades. The city was much smaller and had a far smaller population thirty years ago. However, to evaluate the changes and decide on Indore city's future planning, Indore has taken over responsibility for the city's economic activities in recent years. New appropriate patterns should be recommended based on local factors like topography characteristics. The satellite image with multiple spectrums was used for the investigation. ArcGIS is based on pixel-by-pixel supervised classification using the maximum likelihood approach of Landsat satellite images from 2003 and 2023. The ground area, agriculture, and urbanization are linked to the LULC features. The various categories of classified land use and land cover features include the area of built-up regions, water bodies, cropland, forest, and barren land taken to predict the broad changes. Remotely sensed Landsat 5 images of 2003 and 8 images of 2023 were utilized to identify changes to accomplish this goal. This work clarifies the comparison of LULC classes for the Indore region.

Turbulent flow around submerged foundation arrays for ocean energy

Submerged ocean energy foundations experience dynamic loading due to the non-uniform approach velocity and wake propagation behind them. This paper investigates the unsteady turbulent flow dynamics and propagation of large-scale vortical structures around submerged foundations using a large eddy simulation. The computational domain constituted an array of eight foundations arranged in an inline configuration of two columns. Two streamwise spacing, x = 6D and 8D, are investigated at an approach Reynolds number, based on the channel hydraulic diameter, of 8.5 × 104. The parametric study reveals the expected changes in wake recovery and their impact on the downstream foundations. The narrow spacing promotes intense wake interactions, while larger inline spacing causes the entrainment of ambient flow, thereby limiting the wake interaction with the downstream foundations. A two-point correlation analysis reveals larger integral turbulent timescales for the 6D spacing compared to the 8D spacing. The separated flow around the foundations is characterized by eddies shed at varying frequencies. Multiple spectra peaks dominate at low frequencies, signifying dominant energy-containing large-scale vortices coupled with periodic excitation of the vortices as energy is transferred from the large-to small-scale turbulent structures.

Multifractal spectral features enhance classification of anomalous diffusion.

Anomalous diffusion processes, characterized by their nonstandard scaling of the mean-squared displacement, pose a unique challenge in classification and characterization. In a previous study [Mangalam et al., Phys. Rev. Res. 5, 023144 (2023)2643-156410.1103/PhysRevResearch.5.023144], we established a comprehensive framework for understanding anomalous diffusion using multifractal formalism. The present study delves into the potential of multifractal spectral features for effectively distinguishing anomalous diffusion trajectories from five widely used models: fractional Brownian motion, scaled Brownian motion, continuous-time random walk, annealed transient time motion, and Lévy walk. We generate extensive datasets comprising 10^{6} trajectories from these five anomalous diffusion models and extract multiple multifractal spectra from each trajectory to accomplish this. Our investigation entails a thorough analysis of neural network performance, encompassing features derived from varying numbers of spectra. We also explore the integration of multifractal spectra into traditional feature datasets, enabling us to assess their impact comprehensively. To ensure a statistically meaningful comparison, we categorize features into concept groups and train neural networks using features from each designated group. Notably, several feature groups demonstrate similar levels of accuracy, with the highest performance observed in groups utilizing moving-window characteristics and p varation features. Multifractal spectral features, particularly those derived from three spectra involving different timescales and cutoffs, closely follow, highlighting their robust discriminatory potential. Remarkably, a neural network exclusively trained on features from a single multifractal spectrum exhibits commendable performance, surpassing other feature groups. In summary, our findings underscore the diverse and potent efficacy of multifractal spectral features in enhancing the predictive capacity of machine learning to classify anomalous diffusion processes.

A machine learning approach to self-consistent RBS data analysis and combined uncertainty evaluation

We introduce a machine learning approach designed for the self-consistent analysis of data acquired through simultaneous Rutherford backscattering spectrometry (RBS) in multiple scattering geometries, with subsequent determination of the analysis uncertainty. Using a simulated data set, the successful self-consistent evaluation of up to six simultaneous RBS data collections was achieved by employing artificial neural networks (ANN). The results demonstrate enhanced accuracy and precision when concatenating multiple RBS spectra as input to the ANN. The precision was quantified through the combined uncertainty, encompassing the ANN random uncertainty, the ANN systematic uncertainty, and the model robustness. While applied to only RBS analysis, this approach holds promise for advancing the analysis of data collected by next-generation RBS equipment and Total ion beam analysis, offering a robust and efficient framework for future research.

Origins of Critical Theory and the Exclusion of the Black Philosopher W. E. B. Du Bois

Critical Theory is generative for the advancement of subsequent types of progressive theories that seek justice across multiple spectrums of philosophies. Its origin manifested at the Frankfurt School, among a group of privileged White men seeking to eradicate social ills in Germany during World War II while lacking sufficient global experience and understanding to apply the theory to macro injustices. While critical theory did not redress the limitations of the intellectualized vision, the theory was reconceptualized as more encompassing with its forced relocation to the United States, though still limited in its application to the social ills of woman’s suffrage and the civil rights movement of Blacks in the United States. In flux, the revised critical theory became propagative. Despite its limitations illustrated here in the work of W. E. B. Du Bois, Critical Theory gave rise to scholarships that highlight injustices across areas it did not foresee that include race, ethnicity, disability, gender, and anticolonialism. As a result of Critical Theory across modernities, some silenced voices can be heard, despite what some have labeled an exclusionary canon.

Spectrum conversion and pattern preservation of Airy beams in fractional systems with a dynamical harmonic-oscillator potential

We investigate the dynamics of optical Airy beams in the one-dimensional fractional Schrödinger equation with a harmonic-oscillator (HO) potential subjected to modulation along the propagation distance. Deriving general solutions for propagating beams and particular solutions for Airy waves with/without chirp, we study analytically the spectrum conversion and pattern preservation for the chirp-free and chirped Airy beams in the fractional system including the HO potential with moiré-lattice, hyperbolic-secant, and delta-functional modulation formats. For the HO-moiré-lattice potential, it is found that the chirp-free Airy beam experiences multiple spectrum conversions between the Airy and Gaussian patterns in the momentum space, preserving the Airy pattern in the coordinate space. The chirp magnitude of the chirped Airy beam determines whether the spectrum conversion occurs in the momentum space, and the splitting and evolution direction of the beam in the coordinate space. For the HO-hyperbolic-secant potential, the chirp-free Airy beam undergoes spectrum conversion and tunneling, with the positions of the spectrum conversion and tunneling significantly depending on parameters of the hyperbolic-secant potential; however, the spectrum conversion and pattern preservation of the chirped Airy beam occurs only under a certain relation of the chirp and parameters of the potential. In the case of the HO-delta-functional potential, the chirp-free Airy beam experiences abrupt spectrum conversion and a two-step spectrum shift; however, for the chirped Airy beam, the spectrum conversion is affected by the relation between the chirp and height of the potential. Effects of the fractional Lévy index on the spectrum conversion and pattern preservation of the Airy beams under the action of the three modulation patterns considered here are also explored in detail.

Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration.

Reduction of Cofed Carbon Dioxide Modifies the Local Coordination Environment of Zeolite-Supported, Atomically Dispersed Chromium to Promote Ethane Dehydrogenation.

The reduction of CO2 is known to promote increased alkene yields from alkane dehydrogenations when the reactions are cocatalyzed. The mechanism of this promotion is not understood in the context of catalyst active-site environments because CO2 is amphoteric, and even general aspects of the chemistry, including the significance of competing side reactions, differ significantly across catalysts. Atomically dispersed chromium cations stabilized in highly siliceous MFI zeolite are shown here to enable the study of the role of parallel CO2 reduction during ethylene-selective ethane dehydrogenation. Based on infrared spectroscopy and X-ray absorption spectroscopy data interpreted through calculations using density functional theory (DFT), the synthesized catalyst contains atomically dispersed Cr cations stabilized by silanol nests in micropores. Reactor studies show that cofeeding CO2 increases stable ethylene-selective ethane dehydrogenation rates over a wide range of partial pressures. Operando X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine-structure (EXAFS) spectra indicate that during reaction at 650 °C the Cr cations maintain a nominal 2+ charge and a total Cr-O coordination number of approximately 2. However, CO2 reduction induces a change, correlated with the CO2 partial pressure, in the population of two distinct Cr-O scattering paths. This indicates that the promotional effect of parallel CO2 reduction can be attributed to a subtle change in Cr-O bond lengths in the local coordination environment of the active site. These insights are made possible by simultaneously fitting multiple EXAFS spectra recorded in different reaction conditions; this novel procedure is expected to be generally applicable for interpreting operando catalysis EXAFS data.