Search Results (271)

Analysis of the EPR of dilute transition-ion complexes and metalloproteins in random phases, such as frozen solutions, powders, glasses, and gels, requires a model for the spectral ‘powder’ shape. Such a model comprises a description of the line shape and the linewidth of individual molecules as well as a notion of their physical origin. Spectral features sharpen up with decreasing temperature until the limit of constant linewidth of inhomogeneous broadening. At and below this temperature limit, each molecule has a linewidth that slightly differs from those of its congeners, and which is not related in a simple way to lifetime broadening. Choice of the model not only affects precise assignment of g-values, but also concentration determination (‘spin counting’), and therefore, calculation of stoichiometries in multi-center complexes. Forty years ago, the theoretically and experimentally well-founded statistical theory of g-strain was developed as a prime model for EPR powder patterns. In the intervening years until today, this model was universally ignored in favor of models that are incompatible with physical reality, resulting in many mistakes in EPR spectral interpretation. The purpose of this review is to outline the differences between the models, to reveal where analyses went astray, and thus to turn a very long standstill in EPR powder shape understanding into a new start towards proper methodology. Full article

Accurately simulating the asymmetric spectral profiles of blue LEDs is crucial for photobiological research, yet it remains a challenge for traditional symmetric models. This study proposes a novel spectral simulation model that effectively captures these asymmetries. The proposed model structure is partly motivated by known broadening and dispersion mechanisms observed in real LED spectra; it employs a ‘base model + correction’ framework, where an Exponentially Modified Gaussian (EMG) function captures the primary spectral shape and falling edge and an Asymmetric Pseudo-Voigt (APV) function corrects the deviations on the rising edge. Requiring only the central wavelength and bandwidth as user inputs, the simulation results exhibit a high degree of agreement with the experimental data spectra. The model provides a rapid and robust tool for pre-evaluating light sources against regulatory criteria (e.g., >99% of the spectral intensity is in the 400–500 nm band), thereby enhancing the efficiency of experimental design in blue light protection studies. Full article

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article

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

The thermal Doppler broadening of spectral profiles for particle populations in the absence or presence of potential fields can be described by kappa distributions. The kappa distribution provides a replacement for the Maxwell–Boltzmann distribution, which can be considered as a generalization for describing systems characterized by local correlations among their particles, as found in space and astrophysical plasmas. This paper presents all special cases of kappa distributions as members of a general pathway family of densities introduced by Mathai. The aim of the present paper is to bring to attention the application of various forms of the kappa distribution, its various special cases and its generalizations, which, in scalar-variable and multivariate situations, belong to a general family of distributions known as Mathai’s pathway models, comprising three different families of functions, namely the generalized type-1 beta, type-2 beta and gamma families. Through one parameter, known as the pathway parameter, one will be able to reach all the three families of functions and the stages of transitioning from one family to another. After pointing out the connection of multivariate (vector-variate) kappa distributions to the multivariate pathway model, the multivariate kappa distribution is extended to the real matrix-variate case by working out the various forms and by evaluating the normalizing constants of the various forms of the matrix-variate case explicitly. It is also pointed out that the pathway models are available for the scalar, vector and rectangular matrix-variate cases in the real domain as well as in the complex domain. Full article

Vibrational analysis of peptides in solution and the theoretical determination of the effects of the microenvironment on infrared and Raman spectra are of key importance in many fields of chemical interest. In this work, we present a computational study combining static quantum mechanical calculations with ab initio molecular dynamics simulations to investigate the vibrational behavior of three peptide models in both the gas phase and in explicit water, under non-periodic boundary conditions. The vibrational spectra of the main amide bands, namely amide I-III and A, were analyzed using a time–frequency approach based on the wavelet transform, which allows the resolution of transient frequency shifts and mode couplings along the trajectories. This combined approach enabled us to perform a time-resolved vibrational analysis revealing how vibrational frequencies, especially of the C=O and N–H stretching modes, evolve over time due to dynamical microsolvation. These fluctuations modulate vibrational couplings and lead to spectral broadening and frequency shifts that correlate with the local structuring of the solvent. In conclusion, our results highlight how the proposed protocol allows for the direct connection between vibrational modes and local structural changes, providing a link from the spectroscopic observable to the structure, the peptide backbone, and its microenvironment. Full article

When analyzing insulator surfaces using X-ray photoelectron spectroscopy (XPS), spectral shifts and deformations often arise due to surface charging. Although neutralization techniques have been widely adopted to achieve reliable XPS measurements, their effectiveness remains limited, highlighting the need for innovative neutralization strategies. Here, ultraviolet (UV) light irradiation was introduced into XPS measurements. Although it was still impossible to perfectly eliminate the charging effect, stable XPS spectra with reduced and consistent spectral shifts, as well as minimal deformation and broadening, were successfully obtained. Our findings demonstrate that UV light irradiation not only significantly mitigates the intensity of surface charging but also markedly enhances both its temporal stability and spatial uniformity during XPS measurements. Further investigation reveals that the suppression of charging is primarily attributed to the adsorption of UV-excited photoelectrons onto the X-ray-irradiated region. This innovative neutralization method, termed UV-assisted neutralization in this article, was found to be at least as effective as and even superior in maintaining sample integrity to the most commonly used dual-beam charge neutralization, and therefore is expected to become a promising alternative for addressing the charging issues in XPS measurements. Full article

Swept-source optical coherence tomography (SS-OCT) is a widely used imaging technique, particularly in medical diagnostics, due to its ability to provide high-resolution cross-sectional images. However, one of the main challenges in SS-OCT systems is the nonlinearity in wavelength sweeping, which leads to degraded depth resolution after Fourier transform. Correcting for this nonlinearity typically requires complex re-sampling and chirp compensation methods. In this paper, we introduce the first ultrafast time-stretch optical coherence tomography (TS-OCT) system that utilizes reservoir computing (RC) to perform direct temporal signal analysis without relying on Fourier transform techniques. By focusing solely on the temporal characteristics of the interference signal, regardless of frequency chirp, we demonstrate a more efficient solution to address the nonlinear wavelength sweeping issue. By leveraging the dynamic temporal processing capabilities of RC, the proposed system effectively bypasses the challenges faced by Fourier analysis, maintaining high-resolution depth measurement without being affected by chirp-introduced spectral broadening. The system operates by categorizing the interference signals generated by variations in sample position. This classification-based approach simplifies the data processing pipeline. We developed an RC-based model to interpret the temporal patterns in the interferometric signals, achieving high classification accuracy. A proof-of-the-concept experiment demonstrated that this method allows for precise depth resolution, independent of system chirp. With an A-scan rate of 50 MHz, the classification model yielded 100% accuracy with a root mean square error (RMSE) of 0.2416. This approach offers a robust alternative to Fourier-based analysis, particularly in systems prone to nonlinearities during signal acquisition. Full article

The rapid advancements in optical communication and sensing technologies have significantly increased the demand for advanced tunable spectral systems. This study presents a dual-band terahertz transmission and manipulation approach by leveraging the topologically protected properties of valley-topological photonic crystal edge states. The designed structure facilitates the excitation of the K valley within the range of 0.851–0.934 THz and the K′ valley from 1.604 to 1.686 THz, while also demonstrating anomalous refraction and birefringence. The calculated emission angles, derived through momentum matching, enable transitions between single-wave and dual-wave emissions and allow for precise angle control. The introduction of the liquid crystal material NJU-LDn-4 enables continuous tuning of the dual-band spectral range under a varying electric field, broadening the operating frequency bands to the ranges of 0.757–0.996 THz and 1.426–1.798 THz, respectively. These findings suggest promising applications in tunable filter design, optical communication, photonic computing, optical sensing, and high-resolution imaging, particularly in novel optical devices requiring precise control over spectral characteristics and light propagation. Full article

First-principles calculations were conducted to examine the impact of three sulfonamide-containing molecules (H₄N₂O₂S, CH₈N₄O₃S, and C₂H₂N₆O₄S) adsorbed on the FAPbI₃(001) perovskite surface, aiming to establish a significant positive correlation between the molecular structures and their regulatory effects on the perovskite surface. A systematic comparison was conducted to evaluate the adsorption stability of the three molecules on the two distinct surface terminations. The results show that all three molecules exhibit strong adsorption on the FAPbI₃(001) surface, with C₂H₁₂N₆O₄S demonstrating the most favorable binding stability due to its extended frameworks and multiple electron-donating/withdrawing groups. Simpler molecules lacking carbon skeletons exhibit weaker adsorption and less dependence on surface termination. Ab initio molecular dynamics simulations (AIMD) further corroborated the thermal stability of the stable adsorption configurations at elevated temperatures. Electronic structure analysis reveals that molecular adsorption significantly reconstructs the density of states (DOS) on the PbI₂-terminated surface, inducing shifts in band-edge states and enhancing energy-level coupling between molecular orbitals and surface states. In contrast, the FAI-terminated surface shows weaker interactions. Charge density difference (CDD) analysis indicates that the molecules form multiple coordination bonds (e.g., Pb–O, Pb–S, and Pb–N) with uncoordinated Pb atoms, facilitated by –SO₂–NH₂ groups. Bader charge and work function analyses indicate that the PbI₂-terminated surface exhibits more pronounced electronic coupling and interfacial charge transfer. The C₂H₁₂N₆O₄S adsorption system demonstrates the most substantial reduction in work function. Optical property calculations show a distinct red-shift in the absorption edge along both the XX and YY directions for all adsorption systems, accompanied by enhanced absorption intensity and broadened spectral range. These findings suggest that sulfonamide-containing molecules, particularly C₂H₁₂N₆O₄S with extended carbon skeletons, can effectively stabilize the perovskite interface, optimize charge transport pathways, and enhance light-harvesting performance. Full article

This investigation of hydroxyl and polysiloxane absorption peaks in elastic polymer composites reveals significant spectral shifts within the fingerprint region of FTIR spectra. Using poly(vinyl butyral) (PVB) as the base polymer and poly(vinyl acetate) (PVAc) and poly(vinyl alcohol) (PVA) as reference materials, solvent effects on polymer–solvent interactions were systematically analyzed. Among the tested alcohol solvents, PEG 400 induced the most pronounced spectral changes, with the C=O stretching band shifting from 1740 to 1732 cm⁻¹ and the O–H band significantly broadening and downshifting to around 3300 cm⁻¹, reflecting strong hydrogen-bonding interactions. Wavelet-based noise reduction effectively enhanced the signal-to-noise ratio, reducing the baseline standard deviation by over 90%. This study introduces a novel noise-enhanced FTIR recognition model that integrates baseline noise metrics to improve detection sensitivity. The model successfully uncovers subtle structural variations in polymer–solvent systems that are typically masked by conventional FTIR techniques, advancing materials analysis and providing a robust framework for future FTIR-based diagnostics and material characterization. Full article

Soil nitrate nitrogen (NO₃⁻-N) is a key indicator of agricultural non-point source pollution. The ultraviolet (UV) dual-wavelength method is widely used for NO₃⁻-N detection, but interference from complex soil organic matter affects its accuracy. This study investigated how organic matter influences NO₃⁻-N detection by optimizing UV dual-wavelength combinations. Density functional theory (DFT) calculations showed slight spectral broadening of fulvic and humic acids in the presence of NO₃⁻-N under UV spectrum. Standard solutions and soil samples were used to compare the detection performance of different wavelength pairs. The findings indicated that the dual-wavelength combination of 235 nm/275 nm is optimal rather than 220 nm/275 nm for measuring soil samples at NO₃⁻-N concentrations exceeding 5 mg·L⁻¹. The 235/275 nm method gave an average calibration coefficient of 1.57. Compared to the national standard and flow analysis methods, the average relative errors were 19.7% and 22.3% (p < 0.001), respectively, indicating its suitability for practical soil applications. These results demonstrate the method’s potential for rapid and accurate NO₃⁻-N detection in real soil samples, supporting its application in environmental monitoring and agricultural management. Full article

This study presents a reverse-optimization framework for supercontinuum (SC) generation in Ge₂₀Sb₁₅Se₆₅ suspended-core fibers (SCFs), integrating neural network modeling with the Nutcracker Optimization Algorithm to co-design optimal fiber structures and pump pulse parameters. A high-nonlinearity SCF structure (γ ≈ 6–7 W⁻¹·m⁻¹) was first designed, and a neural network model was developed to accurately predict effective modal refractive indices and mode-field areas (RMSE < 1%). The generalized nonlinear Schrödinger equation was then used to study spectral broadening influenced by structural and pulse parameters. Global optimization was performed in four-dimensional structural and seven-dimensional combined parameter spaces, significantly enhancing computational efficiency. Simulation results demonstrated that the optimized design achieved a broad and flat SC spectrum extending from 0.7 µm to 25 µm (at –20 dB intensity), with lower peak power requirements compared to previous studies achieving similar coverage. The robustness and manufacturing tolerances of the optimized fiber structure were also analyzed, verifying the reliability of the design. This intelligent reverse-design strategy provides practical guidance and theoretical foundations for mid-infrared SC fiber design. Full article

The inherent brittleness of bio-based poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) significantly restricts its industrial applications despite its industrial compostability. Blending with elastomeric polymers addresses mechanical limitations; however, interfacial incompatibility compromises miscibility as our previous work established. Herein, we investigate coffee oil epoxide (COE) as a bio-based plasticizer for PHBV/natural rubber (NR) blends in sustainable packaging applications. COE, derived from spent coffee grounds, was incorporated into PHBV/NR/peroxide/coagent composites via twin-screw extrusion. FTIR spectroscopy with chemometric analysis confirmed successful COE incorporation (intensified CH₂ stretching: 2847, 2920 cm⁻¹; reduced crystallinity), with PCA and PLS-DA accounting for 67.9% and 54.4% of spectral variance. COE incorporation improved optical properties (7.73% increased lightness; 21.9% reduced yellowness). Rheological characterization through Cole–Cole and Han plots demonstrated enhanced phase compatibility in the PHBV/NR/COE blends. Mechanical testing showed characteristic reductions in flexural properties: strength decreased by 16.5% and modulus by 36.8%. Dynamic mechanical analysis revealed PHBV/NR/COE blends exhibited a single relaxation transition at 32 °C versus distinct glass transition temperatures in PHBV/NR blends. Tan δ deconvolution confirmed the transformation from bimodal distribution to a single broadened peak, indicating enhanced interfacial interactions and improved miscibility. These findings demonstrated COE’s potential as a sustainable additive for biodegradable PHBV-based packaging while valorizing food waste. Full article

The study investigates the spectral, structural, and phase changes occurring in austenitic steel 12Kh18N10T during cathodic electrolytic plasma nitriding (EPN) in a Na₂CO₃–CO(NH₂)₂–NH₄Cl electrolyte at 550 °C for 10 min. Emission spectroscopy revealed active plasma components: N₂⁺, N I, Hα, and O I. The electron density, calculated from the Hα line broadening, was approximately 8.5 × 10¹⁸ cm⁻³. X-ray phase analysis revealed the formation of CrN, Fe₃N phases, and a solid solution of nitrogen in γ-Fe. SEM analysis revealed a three-layer structure of the nitrided layer: a nitride zone, a transition region, and the austenitic matrix. The EDS profile confirmed a decrease in nitrogen concentration, accompanied by a reduction in microhardness from a maximum of 480 HV at the surface, both gradually decreasing with depth. The friction coefficient decreased from ~0.8 (in the initial state) to ~0.6 after EPN. The results confirm the effectiveness of EPN in strengthening and improving the wear resistance of stainless steel. Full article