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24 pages, 5018 KiB  
Article
Machine Learning for the Photonic Evaluation of Cranial and Extracranial Sites in Healthy Individuals and in Patients with Multiple Sclerosis
by Antonio Currà, Riccardo Gasbarrone, Davide Gattabria, Nicola Luigi Bragazzi, Giuseppe Bonifazi, Silvia Serranti, Paolo Missori, Francesco Fattapposta, Carlotta Manfredi, Andrea Maffucci, Luca Puce, Lucio Marinelli and Carlo Trompetto
Appl. Sci. 2025, 15(15), 8534; https://doi.org/10.3390/app15158534 (registering DOI) - 31 Jul 2025
Viewed by 178
Abstract
This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify [...] Read more.
This study aims to characterize short-wave infrared (SWIR) reflectance spectra at cranial (at the scalp overlying the frontal cortex and the temporal bone window) and extracranial (biceps and triceps) sites in patients with multiple sclerosis (MS) and age-/sex-matched controls. We sought to identify the diagnostic accuracy of wavelength-specific patterns in distinguishing MS from normal controls and spectral markers associated with disability (e.g., Expanded Disability Status Scale scores). To achieve these objectives, we employed a multi-site SWIR spectroscopy acquisition protocol that included measurements from traditional cranial locations as well as extracranial reference sites. Advanced spectral analysis techniques, including wavelength-dependent absorption modeling and machine learning-based classification, were applied to differentiate MS-related hemodynamic changes from normal physiological variability. Classification models achieved perfect performance (accuracy = 1.00), and cortical site regression models showed strong predictive power (EDSS: R2CV = 0.980; FSS: R2CV = 0.939). Variable Importance in Projection (VIP) analysis highlighted key wavelengths as potential spectral biomarkers. This approach allowed us to explore novel biomarkers of neural and systemic impairment in MS, paving the way for potential clinical applications of SWIR spectroscopy in disease monitoring and management. In conclusion, spectral analysis revealed distinct wavelength-specific patterns collected from cranial and extracranial sites reflecting biochemical and structural differences between patients with MS and normal subjects. These differences are driven by underlying physiological changes, including myelin integrity, neuronal density, oxidative stress, and water content fluctuations in the brain or muscles. This study shows that portable spectral devices may contribute to bedside individuation and monitoring of neural diseases, offering a cost-effective alternative to repeated imaging. Full article
(This article belongs to the Special Issue Artificial Intelligence in Medical Diagnostics: Second Edition)
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16 pages, 993 KiB  
Article
Optical and Photoconversion Properties of Ce3+-Doped (Ca,Y)3(Mg,Sc)2Si3O12 Films Grown via LPE Method onto YAG and YAG:Ce Substrates
by Anna Shakhno, Vitalii Gorbenko, Tetiana Zorenko, Aleksandr Fedorov and Yuriy Zorenko
Materials 2025, 18(15), 3590; https://doi.org/10.3390/ma18153590 - 30 Jul 2025
Viewed by 186
Abstract
This work presents a comprehensive study of the structural, luminescent, and photoconversion properties of epitaxial composite phosphor converters based on single crystalline films of Ce3+-activated Ca2−xY1+xMg1+xSc1−xSi3O12:Ce (x = 0–0.25) [...] Read more.
This work presents a comprehensive study of the structural, luminescent, and photoconversion properties of epitaxial composite phosphor converters based on single crystalline films of Ce3+-activated Ca2−xY1+xMg1+xSc1−xSi3O12:Ce (x = 0–0.25) (CYMSSG:Ce) garnet, grown using the liquid phase epitaxy (LPE) method on single-crystal Y3Al5O12 (YAG) and YAG:Ce substrates. The main goal of this study is to elucidate the structure–composition–property relationships that influence the photoluminescence and photoconversion efficiency of these film–substrate composite converters, aiming to optimize their performance in high-power white light-emitting diode (WLED) applications. Systematic variation in the Y3+/Sc3+/Mg2+ cationic ratios within the garnet structure, combined with the controlled tuning of film thickness (ranging from 19 to 67 µm for CYMSSG:Ce/YAG and 10–22 µm for CYMSSG:Ce/YAG:Ce structures), enabled the precise modulation of their photoconversion properties. Prototypes of phosphor-converted WLEDs (pc-WLEDs) were developed based on these epitaxial structures to assess their performance and investigate how the content and thickness of SCFs affect the colorimetric properties of SCFs and composite converters. Clear trends were observed in the Ce3+ emission peak position, intensity, and color rendering, induced by the Y3+/Sc3+/Mg2+ cation substitution in the film converter, film thickness, and activator concentrations in the substrate and film. These results may be useful for the design of epitaxial phosphor converters with tunable emission spectra based on the epitaxially grown structures of garnet compounds. Full article
(This article belongs to the Section Materials Physics)
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45 pages, 770 KiB  
Review
Neural Correlates of Burnout Syndrome Based on Electroencephalography (EEG)—A Mechanistic Review and Discussion of Burnout Syndrome Cognitive Bias Theory
by James Chmiel and Agnieszka Malinowska
J. Clin. Med. 2025, 14(15), 5357; https://doi.org/10.3390/jcm14155357 - 29 Jul 2025
Viewed by 343
Abstract
Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies [...] Read more.
Introduction: Burnout syndrome, long described as an “occupational phenomenon”, now affects 15–20% of the general workforce and more than 50% of clinicians, teachers, social-care staff and first responders. Its precise nosological standing remains disputed. We conducted a mechanistic review of electroencephalography (EEG) studies to determine whether burnout is accompanied by reproducible brain-function alterations that justify disease-level classification. Methods: Following PRISMA-adapted guidelines, two independent reviewers searched PubMed/MEDLINE, Scopus, Google Scholar, Cochrane Library and reference lists (January 1980–May 2025) using combinations of “burnout,” “EEG”, “electroencephalography” and “event-related potential.” Only English-language clinical investigations were eligible. Eighteen studies (n = 2194 participants) met the inclusion criteria. Data were synthesised across three domains: resting-state spectra/connectivity, event-related potentials (ERPs) and longitudinal change. Results: Resting EEG consistently showed (i) a 0.4–0.6 Hz slowing of individual-alpha frequency, (ii) 20–35% global alpha-power reduction and (iii) fragmentation of high-alpha (11–13 Hz) fronto-parietal coherence, with stage- and sex-dependent modulation. ERP paradigms revealed a distinctive “alarm-heavy/evaluation-poor” profile; enlarged N2 and ERN components signalled hyper-reactive conflict and error detection, whereas P3b, Pe, reward-P3 and late CNV amplitudes were attenuated by 25–50%, indicating depleted evaluative and preparatory resources. Feedback processing showed intact or heightened FRN but blunted FRP, and affective tasks demonstrated threat-biassed P3a latency shifts alongside dampened VPP/EPN to positive cues. These alterations persisted in longitudinal cohorts yet normalised after recovery, supporting trait-plus-state dynamics. The electrophysiological fingerprint differed from major depression (no frontal-alpha asymmetry, opposite connectivity pattern). Conclusions: Across paradigms, burnout exhibits a coherent neurophysiological signature comparable in magnitude to established psychiatric disorders, refuting its current classification as a non-disease. Objective EEG markers can complement symptom scales for earlier diagnosis, treatment monitoring and public-health surveillance. Recognising burnout as a clinical disorder—and funding prevention and care accordingly—is medically justified and economically imperative. Full article
(This article belongs to the Special Issue Innovations in Neurorehabilitation)
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29 pages, 3064 KiB  
Review
Inelastic Electron Tunneling Spectroscopy of Molecular Electronic Junctions: Recent Advances and Applications
by Hyunwook Song
Crystals 2025, 15(8), 681; https://doi.org/10.3390/cryst15080681 - 26 Jul 2025
Viewed by 379
Abstract
Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing [...] Read more.
Inelastic electron tunneling spectroscopy (IETS) has emerged as a powerful vibrational spectroscopy technique for molecular electronic junctions, providing unique insights into molecular vibrations and electron–phonon coupling at the nanoscale. In this review, we present a comprehensive overview of IETS in molecular junctions, tracing its development from foundational principles to the latest advances. We begin with the theoretical background, detailing the mechanisms by which inelastic tunneling processes generate vibrational fingerprints of molecules, and highlighting how IETS complements optical spectroscopies by accessing electrically driven vibrational excitations. We then discuss recent progress in experimental techniques and device architectures that have broadened the applicability of IETS. Central focus is given to emerging applications of IETS over the last decade: molecular sensing (identification of chemical bonds and conformational changes in junctions), thermoelectric energy conversion (probing vibrational contributions to molecular thermopower), molecular switches and functional devices (monitoring bias-driven molecular state changes via vibrational signatures), spintronic molecular junctions (detecting spin excitations and spin–vibration interplay), and advanced data analysis approaches such as machine learning for interpreting complex tunneling spectra. Finally, we discuss current challenges, including sensitivity at room temperature, spectral interpretation, and integration into practical devices. This review aims to serve as a thorough reference for researchers in physics, chemistry, and materials science, consolidating state-of-the-art understanding of IETS in molecular junctions and its growing role in molecular-scale device characterization. Full article
(This article belongs to the Special Issue Advances in Multifunctional Materials and Structures)
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26 pages, 6051 KiB  
Article
A Novel Sound Coding Strategy for Cochlear Implants Based on Spectral Feature and Temporal Event Extraction
by Behnam Molaee-Ardekani, Rafael Attili Chiea, Yue Zhang, Julian Felding, Aswin Adris Wijetillake, Peter T. Johannesen, Enrique A. Lopez-Poveda and Manuel Segovia-Martínez
Technologies 2025, 13(8), 318; https://doi.org/10.3390/technologies13080318 - 23 Jul 2025
Viewed by 373
Abstract
This paper presents a novel cochlear implant (CI) sound coding strategy called Spectral Feature Extraction (SFE). The SFE is a novel Fast Fourier Transform (FFT)-based Continuous Interleaved Sampling (CIS) strategy that provides less-smeared spectral cues to CI patients compared to Crystalis, a predecessor [...] Read more.
This paper presents a novel cochlear implant (CI) sound coding strategy called Spectral Feature Extraction (SFE). The SFE is a novel Fast Fourier Transform (FFT)-based Continuous Interleaved Sampling (CIS) strategy that provides less-smeared spectral cues to CI patients compared to Crystalis, a predecessor strategy used in Oticon Medical devices. The study also explores how the SFE can be enhanced into a Temporal Fine Structure (TFS)-based strategy named Spectral Event Extraction (SEE), combining spectral sharpness with temporal cues. Background/Objectives: Many CI recipients understand speech in quiet settings but struggle with music and complex environments, increasing cognitive effort. De-smearing the power spectrum and extracting spectral peak features can reduce this load. The SFE targets feature extraction from spectral peaks, while the SEE enhances TFS-based coding by tracking these features across frames. Methods: The SFE strategy extracts spectral peaks and models them with synthetic pure tone spectra characterized by instantaneous frequency, phase, energy, and peak resemblance. This deblurs input peaks by estimating their center frequency. In SEE, synthetic peaks are tracked across frames to yield reliable temporal cues (e.g., zero-crossings) aligned with stimulation pulses. Strategy characteristics are analyzed using electrodograms. Results: A flexible Frequency Allocation Map (FAM) can be applied to both SFE and SEE strategies without being limited by FFT bandwidth constraints. Electrodograms of Crystalis and SFE strategies showed that SFE reduces spectral blurring and provides detailed temporal information of harmonics in speech and music. Conclusions: SFE and SEE are expected to enhance speech understanding, lower listening effort, and improve temporal feature coding. These strategies could benefit CI users, especially in challenging acoustic environments. Full article
(This article belongs to the Special Issue The Challenges and Prospects in Cochlear Implantation)
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17 pages, 2635 KiB  
Article
Effects of Vibration Direction, Feature Selection, and the SVM Kernel on Unbalance Fault Classification
by Mine Ateş and Barış Erkuş
Machines 2025, 13(8), 634; https://doi.org/10.3390/machines13080634 - 22 Jul 2025
Viewed by 240
Abstract
In this study, the combined influence of vibration direction, feature selection strategy, and the support vector machine (SVM) kernel on the classification accuracy of unbalance faults was investigated. Experiments were carried out on a Jeffcott rotor test rig at a constant speed and [...] Read more.
In this study, the combined influence of vibration direction, feature selection strategy, and the support vector machine (SVM) kernel on the classification accuracy of unbalance faults was investigated. Experiments were carried out on a Jeffcott rotor test rig at a constant speed and under three operating conditions. The overlapping sliding window method was used for raw sample expansion. Features extracted from time domain signals and from the order and power spectra obtained in the frequency domain were ranked using the Kruskal–Wallis algorithm. Based on the feature-ranking results, the three most discriminative features for each domain–axis combination, as well as all nine most discriminative features for each axis in a hybrid manner, were fed into SVM classifiers with different kernels, and their performance was evaluated using ten-fold cross-validation. Classification using vibration signals in the vertical direction had higher accuracy rates than those using signals in the horizontal direction for the feature sets obtained in the same domains. According to the statistical results, feature set selection had a much greater impact on classification accuracy than SVM kernel choice. Power spectrum-based features allowed higher classification accuracies in all SVM algorithms compared to both the time domain features and the order spectrum-based features for detecting unbalance faults. Increasing the number of features or employing hybrid feature selection did not result in a consistent or significant enhancement in overall classification performance. Selecting the right SVM kernel shapes both the model’s flexibility and its fit to the chosen feature space; when this fit is inadequate, classification accuracy may decrease. Consequently, by selecting the appropriate vibration direction, feature set, and SVM kernel, an improvement of up to 67% in unbalance fault classification accuracy was achieved. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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20 pages, 1461 KiB  
Article
Vulnerability-Based Economic Loss Rate Assessment of a Frame Structure Under Stochastic Sequence Ground Motions
by Zheng Zhang, Yunmu Jiang and Zixin Liu
Buildings 2025, 15(15), 2584; https://doi.org/10.3390/buildings15152584 - 22 Jul 2025
Viewed by 236
Abstract
Modeling mainshock–aftershock ground motions is essential for seismic risk assessment, especially in regions experiencing frequent earthquakes. Recent studies have often employed Copula-based joint distributions or machine learning techniques to simulate the statistical dependency between mainshock and aftershock parameters. While effective at capturing nonlinear [...] Read more.
Modeling mainshock–aftershock ground motions is essential for seismic risk assessment, especially in regions experiencing frequent earthquakes. Recent studies have often employed Copula-based joint distributions or machine learning techniques to simulate the statistical dependency between mainshock and aftershock parameters. While effective at capturing nonlinear correlations, these methods are typically black box in nature, data-dependent, and difficult to generalize across tectonic settings. More importantly, they tend to focus solely on marginal or joint parameter correlations, which implicitly treat mainshocks and aftershocks as independent stochastic processes, thereby overlooking their inherent spectral interaction. To address these limitations, this study proposes an explicit and parameterized modeling framework based on the evolutionary power spectral density (EPSD) of random ground motions. Using the magnitude difference between a mainshock and an aftershock as the control variable, we derive attenuation relationships for the amplitude, frequency content, and duration. A coherence function model is further developed from real seismic records, treating the mainshock–aftershock pair as a vector-valued stochastic process and thus enabling a more accurate representation of their spectral dependence. Coherence analysis shows that the function remains relatively stable between 0.3 and 0.6 across the 0–30 Rad/s frequency range. Validation results indicate that the simulated response spectra align closely with recorded spectra, achieving R2 values exceeding 0.90 and 0.91. To demonstrate the model’s applicability, a case study is conducted on a representative frame structure to evaluate seismic vulnerability and economic loss. As the mainshock PGA increases from 0.2 g to 1.2 g, the structure progresses from slight damage to complete collapse, with loss rates saturating near 1.0 g. These findings underscore the engineering importance of incorporating mainshock–aftershock spectral interaction in seismic damage and risk modeling, offering a transparent and transferable tool for future seismic resilience assessments. Full article
(This article belongs to the Special Issue Structural Vibration Analysis and Control in Civil Engineering)
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21 pages, 2817 KiB  
Article
A Handheld IoT Vis/NIR Spectroscopic System to Assess the Soluble Solids Content of Wine Grapes
by Xu Zhang, Ziquan Qin, Ruijie Zhao, Zhuojun Xie and Xuebing Bai
Sensors 2025, 25(14), 4523; https://doi.org/10.3390/s25144523 - 21 Jul 2025
Viewed by 324
Abstract
The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, [...] Read more.
The quality of wine largely depends on the quality of wine grapes, which is determined by their chemical composition. Therefore, measuring parameters related to grape ripeness, such as soluble solids content (SSC), is crucial for harvesting high-quality grapes. Visible–Near-Infrared (Vis/NIR) spectroscopy enables effective, non-destructive detection of SSC in grapes. However, commercial Vis/NIR spectrometers are often expensive, bulky, and power-consuming, making them unsuitable for on-site applications. This article integrated the AS7265X sensor to develop a low-cost handheld IoT multispectral detection device, which can collect 18 variables in the wavelength range of 410–940 nm. The data can be sent in real time to the cloud configuration, where it can be backed up and visualized. After simultaneously removing outliers detected by both Monte Carlo (MC) and principal component analysis (PCA) methods from the raw spectra, the SSC prediction model was established, resulting in an RV2 of 0.697. Eight preprocessing methods were compared, among which moving average smoothing (MAS) and Savitzky–Golay smoothing (SGS) improved the RV2 to 0.756 and 0.766, respectively. Subsequently, feature wavelengths were selected using UVE and SPA, reducing the number of variables from 18 to 5 and 6, respectively, further increasing the RV2 to 0.809 and 0.795. The results indicate that spectral data optimization methods are effective and essential for improving the performance of SSC prediction models. The IoT Vis/NIR Spectroscopic System proposed in this study offers a miniaturized, low-cost, and practical solution for SSC detection in wine grapes. Full article
(This article belongs to the Section Chemical Sensors)
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24 pages, 4004 KiB  
Article
Assessing the Impact of Solar Spectral Variability on the Performance of Photovoltaic Technologies Across European Climates
by Ivan Bevanda, Petar Marić, Ante Kristić and Tihomir Betti
Energies 2025, 18(14), 3868; https://doi.org/10.3390/en18143868 - 21 Jul 2025
Viewed by 256
Abstract
Precise photovoltaic (PV) performance modeling is essential for optimizing system design, operational monitoring, and reliable power forecasting—yet spectral correction is often overlooked, despite its significant impact on energy yield uncertainty. This study employs the FARMS-NIT model to assess the impact of spectral irradiance [...] Read more.
Precise photovoltaic (PV) performance modeling is essential for optimizing system design, operational monitoring, and reliable power forecasting—yet spectral correction is often overlooked, despite its significant impact on energy yield uncertainty. This study employs the FARMS-NIT model to assess the impact of spectral irradiance on eight PV technologies across 79 European sites, grouped by Köppen–Geiger climate classification. Unlike previous studies limited to clear-sky or single-site analysis, this work integrates satellite-derived spectral data for both all-sky and clear-sky scenarios, enabling hourly, tilt-optimized simulations that reflect real-world operating conditions. Spectral analyses reveal European climates exhibit blue-shifted spectra versus AM1.5 reference, only 2–5% resembling standard conditions. Thin-film technologies demonstrate superior spectral gains under all-sky conditions, though the underlying drivers vary significantly across climatic regions—a distinction that becomes particularly evident in the clear-sky analysis. Crystalline silicon exhibits minimal spectral sensitivity (<1.6% variations), with PERC/PERT providing highest stability. CZTSSe shows latitude-dependent performance with ≤0.7% variation: small gains at high latitudes and losses at low latitudes. Atmospheric parameters were analyzed in detail, revealing that air mass (AM), clearness index (Kt), precipitable water (W), and aerosol optical depth (AOD) play key roles in shaping spectral effects, with different parameters dominating in distinct climate groups. Full article
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20 pages, 6178 KiB  
Article
Time Evolution of Bacterial Resistance Observed with Principal Component Analysis
by Claudia P. Barrera Patiño, Mitchell Bonner, Andrew Ramos Borsatto, Jennifer M. Soares, Kate C. Blanco and Vanderlei S. Bagnato
Antibiotics 2025, 14(7), 729; https://doi.org/10.3390/antibiotics14070729 - 20 Jul 2025
Viewed by 425
Abstract
Background/Objectives: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical [...] Read more.
Background/Objectives: In recent work, we have demonstrated that principal component analysis (PCA) and Fourier Transformation Infrared (FTIR) spectra are powerful tools for analyzing the changes in microorganisms at the biomolecular level to detect changes in bacteria with resistance to antibiotics. Here biochemical structural changes in Staphylococcus aureus were analyzed over exposure time with the goal of identifying trends inside the samples that have been exposed to antibiotics for increasing amounts of time and developed resistance. Methods: All studied data was obtained from FTIR spectra of samples with induced antibiotic resistance to either Azithromycin, Oxacillin, or Trimethoprim/Sulfamethoxazole following the evolution of this development over four increasing antibiotic exposure periods. Results: The processing and data analysis with machine learning algorithms performed on this FTIR spectral database allowed for the identification of patterns across minimum inhibitory concentration (MIC) values associated with different exposure times and both clusters from hierarchical classification and PCA. Conclusions: The results enable the observation of resistance development pathways for the sake of knowing the present stage of resistance of a bacterial sample. This is carried out via machine learning methods for the purpose of faster and more effective infection treatment in healthcare settings. Full article
(This article belongs to the Section Mechanism and Evolution of Antibiotic Resistance)
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14 pages, 26034 KiB  
Article
High-Performance Self-Powered Broadband Photodetectors Based on a Bi2Se3 Topological Insulator/ReSe2 Heterojunction for Signal Transmission
by Yun Wei, Peng Wan, Lijian Li, Tao He, Wanyu Ma, Tong Xu, Bingwang Yang, Shulin Sha, Caixia Kan and Mingming Jiang
Photonics 2025, 12(7), 709; https://doi.org/10.3390/photonics12070709 - 14 Jul 2025
Viewed by 198
Abstract
Topological insulators (TIs) hold considerable promise for the advancement of optoelectronic technologies, including spectroscopy, imaging, and communication, owing to their remarkable optical and electrical characteristics. This study proposes a novel combination of Bi2Se3 TIs and ReSe2 [...] Read more.
Topological insulators (TIs) hold considerable promise for the advancement of optoelectronic technologies, including spectroscopy, imaging, and communication, owing to their remarkable optical and electrical characteristics. This study proposes a novel combination of Bi2Se3 TIs and ReSe2 for self-powered broadband photodetectors with high sensitivity and fast response time. The Bi2Se3/ReSe2 heterojunction photodetector achieves broadband response spectra ranging for 375 nm to 1 μm. It demonstrates a significant responsivity of 64 mA/W at a wavelength of 600 nm (1 mW/cm2), exhibits a rapid response speed of 345 μs rise/336 μs fall time, and has a 3 dB bandwidth of 1.4 kHz under zero-bias conditions. The high performance can be attributed to the suitable energy band structure of Bi2Se3/ReSe2 and high carrier mobility in surface states of Bi2Se3. Excitingly, self-powered TIs photodetectors allow for high-quality signal transmission. The TIs employed in photodetectors can stimulate the production of new optoelectronic features, but they could also be used for highly integrated photonic circuits in the future. Full article
(This article belongs to the Special Issue New Perspectives in Photodetectors)
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17 pages, 8301 KiB  
Article
Composites of Pea Protein Nanofibril and Epigallocatechin Gallate: Formation Mechanism, Structural Characterization, and Antioxidant Activity
by Hailing Zhang, Yangxuan Yang, Yuting Fan and Jiang Yi
Foods 2025, 14(14), 2418; https://doi.org/10.3390/foods14142418 - 9 Jul 2025
Viewed by 311
Abstract
The EGCG/PPN composite, prepared by combining pea protein nanofibrils (PPNs) with epigallocatechin gallate (EGCG), could be used as a multifunctional nanocarrier. Compared to pea protein isolate (PPI), EGCG/PPN composites exhibited remarkably higher turbidity and zeta potential, along with similar UV spectra. Intrinsic fluorescence [...] Read more.
The EGCG/PPN composite, prepared by combining pea protein nanofibrils (PPNs) with epigallocatechin gallate (EGCG), could be used as a multifunctional nanocarrier. Compared to pea protein isolate (PPI), EGCG/PPN composites exhibited remarkably higher turbidity and zeta potential, along with similar UV spectra. Intrinsic fluorescence spectroscopy, ThT fluorescence spectroscopy, and surface hydrophobicity analysis suggested that the interactions between EGCG and PPN were primarily driven by hydrophobic forces. UV spectra indicated that the microenvironment of amino acid residues in the tertiary structure of the protein changes upon complexation, and circular dichroism (CD) revealed that the incorporation of EGCG increases the β-sheet content in the protein’s secondary structure. Analyses of DPPH and ABTS radical scavenging activity, as well as reducing power, demonstrated that the synergistic effect between EGCG and PPN did not hinder the inherent antioxidant properties of EGCG but rather enhanced them significantly. Transmission electron microscopy (TEM) images showed that the addition of EGCG reconstructed the fibril morphology, thereby affecting the properties of PPNs. Overall, the composite fabricated through the interaction between PPN and EGCG shows great potential as a nanocarrier in the processing of functional foods. Full article
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23 pages, 4200 KiB  
Article
Thermal Multi-Sensor Assessment of the Spatial Sampling Behavior of Urban Landscapes Using 2D Turbulence Indicators
by Gabriel I. Cotlier, Drazen Skokovic, Juan Carlos Jimenez and José Antonio Sobrino
Remote Sens. 2025, 17(14), 2349; https://doi.org/10.3390/rs17142349 - 9 Jul 2025
Viewed by 284
Abstract
Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface [...] Read more.
Understanding spatial variations in land surface temperature (LST) is critical for analyzing urban climate dynamics, especially within the framework of two-dimensional (2D) turbulence theory. This study assesses the spatial sampling behavior of urban thermal fields across eight metropolitan areas, encompassing diverse morphologies, surface materials, and Köppen–Geiger climate zones. We analyzed thermal infrared (TIR) imagery from two remote sensing platforms—MODIS (1 km) and Landsat (30 m)—to evaluate resolution-dependent turbulence indicators such as spectral slopes and breakpoints. Power spectral analysis revealed systematic divergences across spatial scales. Landsat exhibited more negative breakpoint values, indicating a greater ability to capture fine-scale thermal heterogeneity tied to vegetation, buildings, and surface cover. MODIS, in contrast, emphasized broader thermal gradients, suitable for regional-scale assessments. Seasonal differences reinforced the turbulence framework: summer spectra displayed steeper, more variable slopes, reflecting increased thermal activity and surface–atmosphere decoupling. Despite occasional agreement between sensors, spectral metrics remain inherently resolution-dependent. MODIS is better suited for macro-scale thermal structures, while Landsat provides detailed insights into intra-urban processes. Our findings confirm that 2D turbulence indicators are not fully scale-invariant and vary with sensor resolution, season, and urban form. This multi-sensor comparison offers a framework for interpreting LST data in support of climate adaptation, urban design, and remote sensing integration. Full article
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31 pages, 3723 KiB  
Review
Chemical Profiling and Quality Assessment of Food Products Employing Magnetic Resonance Technologies
by Chandra Prakash and Rohit Mahar
Foods 2025, 14(14), 2417; https://doi.org/10.3390/foods14142417 - 9 Jul 2025
Viewed by 625
Abstract
Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR [...] Read more.
Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) are powerful techniques that have been employed to analyze foodstuffs comprehensively. These techniques offer in-depth information about the chemical composition, structure, and spatial distribution of components in a variety of food products. Quantitative NMR is widely applied for precise quantification of metabolites, authentication of food products, and monitoring of food quality. Low-field 1H-NMR relaxometry is an important technique for investigating the most abundant components of intact foodstuffs based on relaxation times and amplitude of the NMR signals. In particular, information on water compartments, diffusion, and movement can be obtained by detecting proton signals because of H2O in foodstuffs. Saffron adulterations with calendula, safflower, turmeric, sandalwood, and tartrazine have been analyzed using benchtop NMR, an alternative to the high-field NMR approach. The fraudulent addition of Robusta to Arabica coffee was investigated by 1H-NMR Spectroscopy and the marker of Robusta coffee can be detected in the 1H-NMR spectrum. MRI images can be a reliable tool for appreciating morphological differences in vegetables and fruits. In kiwifruit, the effects of water loss and the states of water were investigated using MRI. It provides informative images regarding the spin density distribution of water molecules and the relationship between water and cellular tissues. 1H-NMR spectra of aqueous extract of kiwifruits affected by elephantiasis show a higher number of small oligosaccharides than healthy fruits do. One of the frauds that has been detected in the olive oil sector reflects the addition of hazelnut oils to olive oils. However, using the NMR methodology, it is possible to distinguish the two types of oils, since, in hazelnut oils, linolenic fatty chains and squalene are absent, which is also indicated by the 1H-NMR spectrum. NMR has been applied to detect milk adulterations, such as bovine milk being spiked with known levels of whey, urea, synthetic urine, and synthetic milk. In particular, T2 relaxation time has been found to be significantly affected by adulteration as it increases with adulterant percentage. The 1H spectrum of honey samples from two botanical species shows the presence of signals due to the specific markers of two botanical species. NMR generates large datasets due to the complexity of food matrices and, to deal with this, chemometrics (multivariate analysis) can be applied to monitor the changes in the constituents of foodstuffs, assess the self-life, and determine the effects of storage conditions. Multivariate analysis could help in managing and interpreting complex NMR data by reducing dimensionality and identifying patterns. NMR spectroscopy followed by multivariate analysis can be channelized for evaluating the nutritional profile of food products by quantifying vitamins, sugars, fatty acids, amino acids, and other nutrients. In this review, we summarize the importance of NMR spectroscopy in chemical profiling and quality assessment of food products employing magnetic resonance technologies and multivariate statistical analysis. Full article
(This article belongs to the Special Issue Quantitative NMR and MRI Methods Applied for Foodstuffs)
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10 pages, 297 KiB  
Article
Optimizing Germination, Growth, and Antioxidant Potential of Aegopodium podagraria L. Under Different LED Light Spectra
by Birtukan Tolera Geleta and Jae-Yun Heo
Seeds 2025, 4(3), 32; https://doi.org/10.3390/seeds4030032 - 7 Jul 2025
Viewed by 270
Abstract
Aegopodium podagraria (A. podagraria) L. is a perennial herb valued for its medicinal properties but exhibits poor germination and inconsistent growth under conventional cultivation. To overcome these limitations and enhance its functional potential, this study investigated the effects of various LED [...] Read more.
Aegopodium podagraria (A. podagraria) L. is a perennial herb valued for its medicinal properties but exhibits poor germination and inconsistent growth under conventional cultivation. To overcome these limitations and enhance its functional potential, this study investigated the effects of various LED light spectra on the plant’s physiological and antioxidant responses under controlled indoor conditions. Six light treatments were applied, consisting of different red (R) and blue (B) light ratios (R100, R80:B20, R60:B40, R40:B60, and B100), along with a white-light control. Red-dominant treatments, particularly R80:B20, not only improved germination traits but also significantly promoted shoot growth and biomass accumulation. In contrast, higher proportions of blue light generally inhibited germination performance and reduced growth-related parameters compared to the white-light control. Antioxidant activity was also modulated by light quality: R80:B20 induced the highest levels of total phenolics, ferric reducing antioxidant power, and vitamin C, whereas R40:B60 maximized flavonoid content and DPPH radical scavenging activity. These results suggest that optimizing the red-to-blue light ratio can effectively enhance both the cultivation performance and biofunctional quality of A. podagraria in controlled environments. Full article
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