Search Results (665)

This work provides the first systematic survey of the two–photon properties of 97 natural 9,10–anthraquinones from plants and fungi. A comprehensive computational dataset of two–photon absorption properties calculated using RI–CC2/aug–cc–pVDZ is presented. Single degenerate photon energies are required for two–photon excitation span 575.8–1007.9 nm across the five lowest singlet states, with all S₀→S₁ transitions falling within the biological therapeutic window. Remarkably, S₃ state exhibits systematically enhanced TPA efficiency, with 60% of compounds surpassing 1 GM and achieving a mean cross–section of 29.9 GM–substantially higher than S₁ (mean: 7.5 GM) or S₅ (mean: 12.2 GM). Three compounds demonstrate exceptional performance: cynodontin (73.6 GM, S₂), dermocybin (68.7 GM, S₄), and morindone (50.7 GM, S₃). Natural transition orbital analysis reveals that these excitations possess high configurational purity (82.5–94.2% single–excitation character) and diagnostics validating the single–reference treatment. The observed spatial separation between hole and particle NTOs, combined with extreme transition dipole anisotropy along the molecular long axis, indicates dipolar charge–transfer enhancement. Comprehensive structure–property analysis establishes that strategic peri–hydroxylation (1,5 or 1,8), alkoxylation, and multi–site donor substitution maximise TPA cross–sections through enhanced charge–transfer character and longitudinal polarisability. Comparison with aqueous–phase calculations for three compounds reveals non–systematic solvent–induced redistributions of TPA activity across excited states, indicating that gas–phase outcomes serve primarily as internal benchmarks and intrinsic descriptors of structure–property relationships rather than quantitative predictors of photoactivity. Full article

The exploration of chalcogenides is on the rise owing to their desirable optical, electronic, thermoelectric, and thermal properties. Chalcogenide materials have been investigated for possible applications in areas such as non-linear optics and solar cells. Among these materials are BaTiS₃ and BaTiSe₃. BaTiS₃ has shown promise in the above-mentioned applications due to its low thermal conductivity. However, neither the thermal properties of BaTiSe₃ nor the mechanical properties of both BaTiS₃ and BaTiSe₃ have been reported. In this work, we performed a computational study of the mechanical and thermal properties of both materials within the density functional theory using Quantum Espresso and BoltzTrap2 codes, employing generalized gradient approximation. The results showed that the computed thermal conductivity of BaTiS₃ at 0.43 W/m/K is comparable to the literature values. The computed elastic constants of BaTiS₃ (bulk modulus of 44.7 GPa, shear modulus of 11.2 GPa, Young’s modulus of 29.6 GPa, and Vickers hardness of 1.053 GPa) were higher than those of BaTiSe₃. The calculated properties obtained in this work add to the literature on the properties of BaTiS₃ and BaTiSe₃. However, since the work was computational, the results can be verified by an experimental investigation. Full article

Cu₂ZnSnSe₄ is a promising light-absorbing material for cost-effective and eco-friendly thin-film solar cells; however, its synthesis often leads to secondary phases that limit device efficiency. To overcome these challenges, we devised a straightforward and efficient method to obtain single-phase Cu₂ZnSnSe₄ nanocrystalline powders directly from the elements Cu, Zn, Sn, and Se via mechanochemical synthesis followed by vacuum annealing at 450 °C. Phase evolution monitored by X-ray diffraction (XRD) and Raman spectroscopy at two-hour milling intervals confirmed the formation of phase-pure kesterite Cu₂ZnSnSe₄ and enabled tracking of transient secondary phases. Raman spectra revealed the characteristic A₁ vibrational modes of the kesterite structure, while XRD peaks and Rietveld refinement (χ² ~ 1) validated single-phase formation with crystallite sizes of 10–15 nm and dislocation densities of 3.00–3.20 10¹⁵ lines/m². Optical analysis showed a direct bandgap of ~1.1 eV, and estimated linear and nonlinear optical constants validate its potential for photovoltaic applications. Scanning electron microscopy (SEM) analysis showed uniformly distributed particles 50–60 nm, and energy dispersive X-ray (EDS) analysis confirmed a near-stoichiometric Cu:Zn:Sn:Se ratio of 2:1:1:4. X-ray photoelectron spectroscopy (XPS) identified the expected oxidation states (Cu⁺, Zn²⁺, Sn⁴⁺, and Se²⁻). Electrical characterization revealed p-type conductivity with a mobility (μ) of 2.09 cm²/Vs, sheet resistance (ρ) of 4.87 Ω cm, and carrier concentrations of 1.23 × 10¹⁹ cm⁻³. Galvanostatic charge–discharge testing (GCD) demonstrated an energy density of 2.872 Wh/kg⁻¹ and a power density of 1083 W kg⁻¹, highlighting the material’s additional potential for energy storage applications. Full article

Accurate and real-time state of charge (SOC) monitoring is critical for the safe, efficient, and stable long-term operation of vanadium redox flow batteries (VRFBs). Traditional monitoring methods are susceptible to errors arising from side reactions, cumulative drift, and electrolyte imbalance. This study develops a non-invasive optical sensor module for the negative electrolyte (anolyte), utilizing the favorable spectral properties of V(II)/V(III) ions at 850 nm for real-time SOC tracking. A fifth-order polynomial model was employed for calibration, successfully managing the non-linear optical response of highly concentrated electrolytes and achieving exceptional accuracy (adjusted R² > 0.9999). The optical sensor reliably tracked capacity degradation over 50 galvanostatic cycles, yielding a degradation curve that showed a high correlation with the conventional coulomb counting method, thus confirming its feasibility for assessing battery’s state of health. Contrary to initial expectations, operating at higher current densities resulted in a lower capacity degradation rate (CDR). This phenomenon is primarily attributed to the time-dependent nature of parasitic side reactions. Higher current densities reduce the cycle duration, thereby minimizing the temporal exposure of active species to degradation mechanisms and mitigating cumulative ion imbalance. This mechanism was corroborated by physicochemical analysis via UV-Vis spectroscopy, which revealed a strong correlation between the severity of spectral deviation and the CDR ranking. This non-invasive optical technology offers a low-cost and effective solution for precise VRFB management and preventative maintenance. Full article

Visible light positioning (VLP) has emerged as a promising indoor positioning technology, owing to its high accuracy and cost-effectiveness. In practical scenarios, signal attenuation, multiple light reflections, or light-deficient regions, particularly near room corners or furniture, can significantly degrade the light quality. In addition, the non-uniform light distribution by light-emitting diode (LED) luminaires can also introduce errors in VLP estimation. To mitigate these challenges, recent studies have increasingly explored the use of machine learning (ML) techniques to model the complex nonlinear characteristics of indoor optical channels and improve VLP performance. Convolutional neural networks (CNNs) have demonstrated strong potential in reducing positioning errors and improving system robustness under non-ideal lighting conditions. However, the performance of CNN-based systems is highly sensitive to their hyperparameters, including learning rate, dropout rate, batch size, and optimizer selection. Manual tuning of these parameters is not only time-consuming but also often suboptimal, particularly when models are applied to new or dynamic environments. Therefore, there is a growing need for automated optimization techniques that can adaptively determine optimal model configurations for VLP tasks. In this work, we propose and demonstrate a VLP system that integrates received signal strength (RSS) signal pre-processing, a CNN, and particle swarm optimization (PSO) for automated hyperparameter tuning. In the proof-of-concept VLP experiment, three different height layer planes (i.e., 200, 225, and 250 cm) are employed for the comparison of three different ML models, including linear regression (LR), an artificial neural network (ANN), and a CNN. For instance, the mean positioning error of a CNN + pre-processing model at the 200 cm receiver (Rx)-plane reduces from 9.83 cm to 5.72 cm. This represents an improvement of 41.81%. By employing a CNN + pre-processing + PSO, the mean error can be further reduced to 4.93 cm. These findings demonstrate that integrating PSO-based hyperparameter tuning with a CNN and RSS pre-processing significantly enhances positioning accuracy, reliability, and model robustness. This approach offers a scalable and effective solution for real-world indoor positioning applications in smart buildings and Internet of Things (IoT) environments. Full article

Background: Diabetic neuropathy affects almost half of diabetic patients, yet the relative contributions of metabolic, vascular and clinical factors remain controversial. We aimed to investigate which risk factors are more associated with reduced corneal nerve fiber length (CNFL). Methods: This is a cross-sectional study of 30 patients with type 2 diabetes. We assessed metabolic parameters (HbA1c, lipids), vascular measurements (Doppler ultrasonography of carotid and ophthalmic arteries, central vessel density measured by optical coherence tomography angiography), and corneal epithelial thickness. We explored the data using network analysis, then applied penalized mixed-effect regression (in which $\beta$ represents the standardized coefficients with mean 0 and unit standard deviation), followed by generalized additive models and polynomial transformations. Results: Penalized regression identified vascular parameters as dominant predictors: carotid plaques ($\beta$ = −0.609) and intima-media thickness ($\beta$ = −0.574) showed the strongest associations with CNFL. Traditional metabolic markers including HbA1c failed to meet selection thresholds. Bifurcation velocity ($\beta$ = −0.313) and corneal sensitivity measures ($\beta$ = 0.278–0.135) were also significant. The non-linear modeling showed complex vascular–structural interactions. Conclusions: Vascular compromise, particularly carotid disease, had the highest association with CNFL in our cohort. Thus, our study reports a higher effect of vascular parameters than HbA1c in patients with a longer history of diabetes. This may reflect the progression of diabetic complications, where initial metabolic insults are followed by vascular pathology as the primary driver of end-organ damage. Our findings highlight the need for carotid artery screening in diabetic patients for a better estimation of the neuropathy risk. Full article

The non-linear optical and antitumoral properties of cis-Ir(N,C-ppy)₂(O,O-THC) have previously been established (where ppy and THC are the deprotonated forms of 2-phenylpyridine and tetrahydrocurcumin, respectively). In the present study, this complex is investigated as a green phosphorescent emitter for an OLED fabricated by solution processing. The device efficiency is similar to that of an analogue employing the archetypal complex cis-Ir(N,C-ppy)₂(O,O-acac), but shows a higher luminance at low applied voltages (<6 V). In order to explore whether this effect might be observed in the blue region too, a new derivative has been prepared and characterized, namely cis-Ir(N,C-F₂ppy)₂(O,O-THC) (F₂ppyH = 2-(2,4-difluorophenyl)pyridine). It, too, gives an OLED with a particularly high luminance at low voltage, suggesting a beneficial effect of substituting acetylacetonate by tetrahydrocurcuminate. Full article

Optical neuromonitoring has matured from descriptive oxygenation trends to model-informed quantification of cerebral physiology. This review synthesizes evidence on near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and laser Doppler flowmetry (LDF) for monitoring cerebral blood flow (CBF), blood volume (CBV), and cerebral metabolic rate of oxygen (CMRO₂) during cardiac arrest (CA) and cardiopulmonary resuscitation (CPR). We focus on using hemo-metabolic models, especially Coherent Hemodynamic Spectroscopy (CHS) and the BrainSignals models, as a framework to explain what optical signals do (and do not) tell us about microvascular oxygen transport and mitochondrial metabolism. We compare linear vs. non-linear CHS formulations for large perturbations (e.g., CA/CPR), summarize emerging depth-sensitivity and extracerebral-signal suppression strategies, and outline how DCS pairs with NIRS to link oxygen delivery with use. Across animal and human studies, we highlight convergent patterns (rapid oxygenation collapse, partial reperfusion during CPR, lagging metabolic recovery), recurring limitations (extracerebral contamination, calibration to absolutes, motion), and standardization efforts required for translation. We conclude with a pragmatic roadmap for bedside implementation: harmonized physiological endpoints (CBF, CMRO₂, rCCO), reporting standards, and model-informed thresholds to guide resuscitation. This review aims to bridge instrumentation, physiology, and modeling to enhance neuroprotective care in CA/CPR. Full article

Stress, workload, and uncertainty characterize occupational tasks across sports, healthcare, military, and transportation domains. Emerging theory and empirical research suggest that coordinated whole-body movements may reflect these transient mental states. Wearable sensors and optical motion capture offer opportunities to quantify such movement dynamics and classify mental states that influence occupational performance and human–machine interaction. We tested this possibility in a small pilot study (N = 10) designed to test feasibility and identify preliminary movement features linked to mental states. Participants performed a perceptual decision-making task involving facial emotion recognition (i.e., deciding whether depicted faces were happy versus angry) with variable levels of stress (via a risk of electric shock), workload (via time pressure), and uncertainty (via visual degradation of task stimuli). The time series of movement trajectories was analyzed both holistically (full trajectory) and by phase: lowered (early), raising (middle), aiming (late), and face-to-face (sequential). For each epoch, up to 3844 linear and non-linear features were extracted across temporal, spectral, probability, divergence, and fractal domains. Features were entered into a repeated 10-fold cross-validation procedure using 80/20 train/test splits. Feature selection was conducted with the T-Rex Selector, and selected features were used to train a scikit-learn pipeline with a Robust Scaler and a Logistic Regression classifier. Models achieved mean ROC AUC scores as high as 0.76 for stress classification, with the highest sensitivity during the full movement trajectory and middle (raise) phases. Classification of workload and uncertainty states was less successful. These findings demonstrate the potential of movement-based sensing to infer stress states in applied settings and inform future human–machine interface development. Full article

Eleven novel asymmetric pyrimidine derivatives were synthesized. The pyrimidine core was functionalized with a coumarin chromophore and a pro-mesogenic fragment bearing either chiral or linear alkyl chains of variable length and substitution patterns. The thermal properties were investigated using polarized optical microscopy, differential scanning calorimetry, and small-angle X-ray scattering, revealing that only selected derivatives exhibited liquid crystalline phases with ordered columnar or smectic organizations. Linear and nonlinear optical properties were characterized by UV–Vis absorption, fluorescence spectroscopy, two-photon absorption, and second-harmonic generation. Optical responses were found to be highly sensitive to the substitution pattern: derivatives functionalized at the 4 and 3,4,5 positions exhibited enhanced 2PA cross-sections and pronounced SHG signals, whereas variations in alkyl chain length exerted only a minor influence. Notably, compounds forming highly ordered non-centrosymmetric mesophases produced robust SHG-active thin films. Importantly, strong SHG responses were obtained without the need for a chiral center, as the inherent asymmetry of the linear alkyl chain derivatives was sufficient to drive self-organization into non-centrosymmetric materials. These results demonstrate that asymmetric pyrimidine-based architectures combining π-conjugation and controlled supramolecular organization are promising candidates for nonlinear optical applications such as photonic devices, multiphoton imaging, and optical data storage. Full article

With the increasing application of oxide films in nuclear fuel assemblies, the accurate measurement of thin-film thickness has become increasingly critical. Traditional spectral interferometry techniques have limitations when dealing with new materials and complex structures; therefore, this study proposes a multi-channel wide-spectrum high-resolution analysis technique. This technique optimizes the utilization of photosensitive elements through multi-channel spectral sampling, combined with precision spectroscopic components and an independent optical focusing and imaging system. Simultaneously, it adopts optical correction technologies such as coma optimization and astigmatism correction to improve imaging quality and spectral resolution. Additionally, it enhances data accuracy by means of multi-channel calibration based on the least squares method and non-linear correction. The technique enables high-precision measurement ranging from the nanometer to the millimeter scale, resulting in a significantly wider measurement range compared to traditional spectrometers. Simulation verification shows that this technique outperforms existing technologies in information acquisition, analysis accuracy, and detection efficiency, and has broad application prospects in fields such as semiconductor chip manufacturing and optical coating. In the future, focus will be placed on expanding the spectral range, improving resolution, and enhancing real-time measurement capabilities. Full article

Optical fiber radiation sensing probes made using inorganic scintillator materials have notable advantages in achieving high spatial resolution and building sensing arrays due to their small size and excellent linearity, serving as a key tool for dose measurement in precision radiotherapy. This study establishes a theoretical model for scintillator luminescence coupling into optical fibers, and derives a fluorescence intensity calculation formula based on the fiber’s numerical aperture and fluorescence self-absorption. The light intensity response to scintillator length for different absorption coefficients is established based on numerical simulation, providing a nonlinear fitting equation, resulting in a novel “effective length of scintillator” concept. Five probes with scintillator lengths of 0.2 mm, 0.5 mm, 1.0 mm, 1.5 mm, and 2.0 mm were prepared in the laboratory using a 3:1 mass ratio mixture of UV-setting epoxy and Gd₂O₂S:Tb powder. Tests in a clinical radiation delivery setting showed good agreement between experimental data and theory, confirming optimum effective length of the scintillator as 0.62 mm. This study indicates that inorganic scintillators for end-constructed probes do need not need to be excessively long. Analyzing the effective length can reduce scintillator usage, simplify fabrication and processing, and enhance the probe’s spatial resolution without decreasing the signal-to-noise ratio, thus offering new insights for optimizing optical fiber radiation probes. Full article

Three-wave resonant interaction equations can model nonlinear dynamics in many fields, e.g., fluids, optics, and plasma. Rogue waves, i.e., modes algebraically localized in both space and time, are obtained analytically. The aim of this paper is to study degenerate three-wave resonant interaction equations, where two out of the three interacting wave packets have identical group velocities. Physically, degenerate resonance typically occurs for dispersion relation, possessing many branches, e.g., internal waves in a continuously stratified fluid. Here, the Nth-order rogue wave solutions for this dynamical model are presented. Based on these solutions, we examine the effects of the group velocity on the width and structural profiles of the rogue waves. The width of the rogue waves exhibit a linear increase as the group velocity increases, a feature well-correlated with the prediction made using modulation instability. In terms of structural profiles, first-order rogue waves display ‘four-petal’ and ‘eye-shaped’ patterns. Second-order rogue waves can reveal intriguing configurations, e.g., ‘butterfly’ patterns and triplets. To ascertain the robustness of these modes, numerical simulations with random initial conditions were performed. Sequences of localized modes resembling these analytical rogue waves were observed. A spectral jump was observed, with the jump broadening in the case of rogue wave triplets. Furthermore, we predict new rogue waves based on information from two existing ones obtained using the deep learning technique in the context of rogue wave triplets. This predictive model holds potential applications in ocean engineering. Full article

Piezoelectric materials are fundamental elements in modern science and technology due to their unique ability to convert mechanical and electrical energy bidirectionally. They are widely employed in sensors, actuators, and energy-harvesting systems. In this work, we investigate the behavior of commercial lead zirconate titanate (PZT) sensors under frequency-mode excitation using a combined approach of impedance spectroscopy and optical interferometry. The impedance spectra reveal distinct resonance–antiresonance features that strongly depend on geometry, while interferometric measurements capture dynamic strain fields through fringe displacement analysis. The strongest deformation occurs near the first kilohertz resonance, directly correlated with the impedance phase, enabling the extraction of an effective piezoelectric constant (~40 pC/N). Moving beyond the linear regime, laser-induced excitation demonstrates optically driven activation of piezoelectric modes, with a frequency-dependent response and nonlinear scaling with optical power, characteristic of coupled pyroelectric–piezoelectric effects. These findings introduce a frequency-mode approach that combines impedance spectroscopy and optical interferometry to simultaneously probe electrical and mechanical responses in a single setup, enabling non-contact, frequency-selective sensing without surface modification or complex optical alignment. Although focused on macroscale ceramic PZTs, the non-contact measurement and activation strategies presented here offer scalable tools for informing the design and analysis of piezoelectric behavior in micro- and nanoscale systems. Such frequency-resolved, optical-access approaches are particularly valuable in the development of next-generation nanosensors, MEMS/NEMS devices, and optoelectronic interfaces where direct electrical probing is challenging or invasive. Full article

Non-perturbative approaches to linear and nonlinear responses (NLR) of atoms, molecules, and molecular aggregates are reviewed in relation to low and high harmonic generations (HG) by laser fields. These response properties are effective for the generation of entangled light pairs for quantum information processing by spontaneous parametric downconversion (SPDC) and stimulated four-wave mixing (SFWM). Quasi-energy derivative (QED) methods, such as QED Møller–Plesset (MP) perturbation, are reviewed as time-dependent variational methods (TDVP), providing analytical expressions of time-dependent linear and nonlinear responses of open-shell atoms, molecules, and molecular aggregates. Numerical Liouville methods for the low HG (LHG) and high HG (HHG) regimes are reviewed to elucidate the NLR of molecules in both LHG and HHG regimes. Three-step models for the generation of HHG in the latter regime are reviewed in relation to developments of attosecond science and spectroscopy. Orbital tomography is also reviewed in relation to the theoretical and experimental studies of the amplitudes and phases of wave functions of open-shell atoms and molecules, such as molecular oxygen, providing the Dyson orbital explanation. Interactions between quantum lights and molecules are theoretically examined in relation to derivations of several distribution functions for quantum information processing, quantum dynamics of molecular aggregates, and future developments of quantum molecular devices such as measurement-based quantum computation (MBQC). Quantum dynamics for energy transfer in dendrimer and related light-harvesting antenna systems are reviewed to examine the classical and quantum dynamics behaviors of photosynthesis. It is shown that quantum coherence plays an important role in the well-organized arrays of chromophores. Finally, applications of quantum optics to molecular quantum information and quantum biology are examined in relation to emerging interdisciplinary frontiers. Full article