In this paper, we report a mode-interference-based approach for the efficient and reliable diameter measurement of micro/nanofibers (MNFs), enabling the in situ monitoring of MNFs fabricated from both single-mode fibers (SMFs) and multimode fibers (MMFs). The proposed method integrates automated signal processing with parameter-corrected flamebrush models, establishing a real-time closed-loop feedback mechanism during the fabrication process. Within the 524-1778 nm range, measurement accuracies better than 8 nm (< 1.25%) for SMF and 5 nm (< 0.78%) for MMF are demonstrated. Furthermore, to address the challenge of reconstructing complex taper profiles, we introduce a one-dimensional convolutional neural network (1D-CNN). Trained on a physics-enhanced data set, this network enables the end-toend precision measurement of taper morphology. Within the diameter range of 1.9-10 & micro;m, the maximum relative error is maintained below 0.35%, with a maximum absolute error of less than 9 nm. This method demonstrates broad applicability, offering a reliable solution for the fabrication of high-performance MNF-based photonic devices.
Structured light, with its multidimensional control over amplitude, phase, space and frequency, is a key enabler for advanced technologies such as high-capacity communications, quantum information, and super-resolution imaging. Here, we propose a unified inverse-design methodology for arbitrary on-chip vectorial structured-light. Inspired by quantum-state representations, we describe complex vector fields as finite-dimensional vectors in a Hilbert space and introduce a transmission-matrix formalism that links input waveguide modes to target topological edge states. By combining this mapping with adjoint-based topology optimization, we obtain the permittivity distribution within a compact design window that realizes the desired vector transformation while preserving topological transport. We experimentally demonstrate two representative domain-wall configurations on a valley photonic crystal (VPC) platform, termed Type-I and Type-II topological couplers, which efficiently couple the fundamental mode into valley pseudospin edge states. Simulations of the ideally designed device show insertion losses of 0.04 dB and 0.09 dB at 1550 nm with 3-dB bandwidths of 132 nm and 65 nm, respectively. Experimentally, the fabricated device, which was designed accounting for fabrication tolerances, maintains a broadband low-loss performance, with measured losses of < 0.6 dB at 1550 nm with 3-dB bandwidth over > 60 nm and < 0.8 dB at 1550 nm with 3-dB bandwidth over 87 nm. Mirror-symmetric designs further validate selective excitation of orthogonal pseudospin states. Our results establish this inverse-design methodology as a powerful tool for strictly controlling on-chip vectorial light, paving the way toward compact, broadband, and multifunctional photonic integrated circuits for optical computing, communications, and beyond.
Over the past decade, perovskite light-emitting diodes (PeLEDs) have garnered extensive attention due to their remarkable progress in external quantum efficiency (EQE). The EQE for red and green emission has surpassed 30%, while blue PeLEDs have also exceeded 20%, rendering them a highly competitive technology for next-generation displays. This article provides a comprehensive review of milestones for PeLEDs, recording the key performances including EQE, peak luminance, and operational lifetime. Furthermore, we discuss the prevailing challenges that PeLEDs must overcome in their near-future development. This work aims to outline encouraging achievements of PeLEDs and draw a roadmap to accelerate their commercialization.
Two-dimensional (2D) all-inorganic halide perovskites exhibit promise for optoelectronic applications, yet selective exfoliation along specific crystallographic planes remains a critical challenge for performance optimization. Using first-principles calculations combined with device simulations, we systematically investigated the structural stability, exfoliation feasibility, and optoelectronic properties of 24 all-inorganic 2D perovskites derived from the (100) and (111) planes of cubic perovskites, specifically the A2BX4 and A3B ' 2X9 series (A = Cs, Rb; B = Pb, Sn; B ' = Bi, Sb; X = Cl, Br, I). Our results demonstrate that (111)-derived A3B ' 2X9 perovskites exhibit significantly lower exfoliation energies (23.1-62.1 meV/& Aring;2) than (100)-derived A2BX4 counterparts (59.7-174.0 meV/& Aring;2), attributed to weaker van der Waals interlayer coupling in the former. Rb3Bi2I9 possesses an ultralow exfoliation energy of 23.1 meV/& Aring;2, rivaling that of graphene and demonstrating exceptional potential for mechanical exfoliation of high-quality monolayers. A2BX4 monolayers exhibit direct band gaps, which are favorable for optoelectronic applications; whereas A3B ' 2X9 monolayers display indirect band gaps. Among all investigated materials, monolayer Rb2SnBr4 emerges as an outstanding candidate, featuring an ideal direct band gap of 1.34 eV (HSE06) that perfectly matches the Shockley-Queisser limit for single-junction solar cells. SCAPS-1D device simulations further predict that optimized Rb2SnBr4-based solar cells can achieve a remarkable theoretical power conversion efficiency of 27.10% under defect densities below 1014 cm-3. This work establishes that (111) plane cleavage is optimal for synthesizing exfoliable 2D perovskites, while (100) plane orientation enables superior direct band gap characteristics for photovoltaic applications, providing critical design principles for crystallographic plane engineering in halide perovskite devices.
The optical diffractive neural network (ODNN), based on the free-space propagation of light waves, exhibits significant ad-vantages, including ultra-high speed, low power consumption, and parallel computation. However, this technology faces challenges in practical applications, particularly concerning fabrication and alignment accuracy, with stringent requirements on manufacturing processes. In this paper, a class of hybrid optical diffractive neural networks (H-ODNNs) is designed by constructing continuous passive phase modulation layers using diffraction neurons of varying sizes. Three representative tasks (digit recognition, image processing, and wavelength multiplexing) substantiate its superior performance in enhancing robustness. Compared with network with vaccination (a common method for enhancing robustness), the H-ODNN does not need vaccination training, the average training time is reduced by approximately 50%, and even achieves superior performance. Additionally, the larger size of some diffraction neurons, the H-ODNN reduces the complexity of fabrication and improves manufacturing yield. This work provides a new concept for the design of ODNN.
In this paper, we report multi-component gas analysis from a narrow overlapping near-infrared spectral window using second-harmonic photothermal interferometry (PTI) combined with partial least-squares regression (PLSR). The analytes in a 5 cm hollow core Fabry-P & eacute;rot probe are pumped by a 40 mW DFB laser tuned from 1680 nm to 1681.2 nm and probed at 1570 nm. A total of 460 spectra for the gases CH4, C2H6, and C2H4 at different concentrations were automatically recorded, with each spectrum containing 520 points. Using 80%/20% train/validation splits and 5-fold cross-validation, the PLSR model exhibits an overall relative error of 0.319%. The model predictions can maintain a good relative error of about 0.5% with only 180 training samples, or 150 attention-focused points, or 33-point down-sampled spectra. This narrow-band single-laser fiber-integrated PTI with PLSR would enable accurate gas component prediction for industrial and medical applications.
In this paper, we demonstrate a compact broadband on-chip radio-frequency (RF) termination based on pure titanium thinfilm resistors integrated into coplanar waveguide (CPW) structures. The terminated transmission lines exhibit good impedance matching, with simulated reflection coefficients below -20 dB over 100 GHz bandwidth and experimentally measured reflections below -10 dB up to 65 GHz. Additional measurements evaluate the stability and power-handling behavior of the Ti resistors. The results show that thin-film titanium can operate as an effective on-chip RF termination in integrated CPW structures, maintaining stable RF performance over repeated measurements. Such terminations are relevant for high-speed photonic platforms where compact integration and reduced packaging complexity are desirable, including dense modulator architectures.
Wavefront coding technology employs a phase mask to modulate the phase of incident light, thereby dispersing the laser spot on the detector and achieving laser protection for optical systems. Current research has predominantly concentrated on validating laser damage at a single imaging distance, neglecting the evolution of protective capability across varying distances in the wavefront coding imaging system. To address this limitation, this study establishes a wavefront coding imaging system based on a cubic phase function and experimentally elucidates the variation of laser suppression capacity with transmission distance. Under conditions of pulsed laser-induced point damage in the visible spectrum, a strong correlation is observed between the laser suppression ratio and the laser damage threshold improvement value. Additionally, the NAFNet model is utilized to restore encoded images, resulting in high-fidelity reconstruction. The PSNR for both simulated and experimentally decoded images consistently surpasses 23 dB. Furthermore, under laser irradiation conditions, the model adeptly eliminates laser artifacts and recovers image content. This study possesses considerable practical value for the design and implementation of laser protection mechanisms in optical systems.
Cardiomyopathies are often characterized by significant fibrotic remodelling of the heart, marked by an abnormal accumulation of collagen type I. Label free Raman spectroscopy, a non-invasive diagnostic technique, holds promise for monitoring biochemical changes throughout the initiation and progression of different diseases, including cardiomyopathies. This study demonstrates the effectiveness of 70% glycerol as a hyperosmotic immersion liquid for in-depth controlling the optical properties of ex vivo myocardium tissue during deep-UV Raman spectroscopy with 244 nm excitation. The results revealed a considerable enhancement in the intensities of Raman peak, particularly the amide I region after glycerol treatment. This occurred across all depths (0-120 & micro;m) and glycerol treatment durations (30 and 60 min). A noticeable enhancement of the Raman peak at 1647 cm-1 was also observed that is attributable to structural transformations of the collagen due to the dehydration induced by glycerol. This finding suggest that deep-UV Raman can be employed as a specific probe of the collagen environment. As the amide I region reflects structural changes in collagen type I, these findings propose the potential of deep-UV Raman spectroscopy in combination with glycerol as optical clearing agent for monitoring collagen modifications.
All-perovskite tandem solar cells are a promising photovoltaic technology, but their efficiency is strongly limited by the tunnel junction. The tunnel junction enables carrier tunneling and recombination, which depend on the interfacial band alignment. Through quantitative simulations using Silvaco Technology Computer Aided Design (TCAD), we find that hole tunneling is intrinsically more difficult than electron tunneling in the tunnel junction. Efficient tunnel junctions require minimizing the barrier for holes while maintaining a moderate barrier for electrons to balance tunneling. For the SnO2/met-al/PEDOT:PSS tunnel junction in all-perovskite tandem solar cells, tuning the metal work function achieves balanced electron and hole tunneling, reduces junction resistance, and directly enhances performance of tandem solar cells. This work provides quantitative design rules for tunnel junction optimization, offering a clear pathway toward high-performance allperovskite tandem solar cells.
Low-frequency electric field sensors are essential for applications in geophysics, electrical engineering, aerospace, and medical technology. However, conventional technologies often suffer from intrinsic trade-offs among traceability, multidimensional vector detection, and miniaturization, which significantly hinder their scalability and deployment in compact platforms. To address these challenges, we propose a vector-resolved quasi-static electric field sensor based on a Rydberg dipolar chain, where the external field reorients the atomic quantization axis and thereby modulates the angle-dependent dipolar exchange interaction. Using a unified framework combining time-domain propagation, Ramsey-mode spectroscopy, and end-to-end Green's-function analysis, we identify three complementary observables-arrival time, eigenmode frequency shifts, and transmission fringes-that encode both the amplitude and direction of the applied field. The approach operates at micrometer scales compatible with optical-tweezer arrays, offers tunable sensitivity near the magic angle, and provides multi-channel readout within a single platform. Our results establish a compact and experimentally feasible route toward high-resolution, vector-sensitive low-frequency electrometry with the potential for quantum-enhanced performance.
Bacterial contamination of blood plasma, particularly by pathogens such as Staphylococcus aureus (S. aureus), including antibiotic resistant strains (e.g., MRSA), remains a critical challenge in transfusion medicine. Current pathogen reduction technologies face tradeoffs between microbial safety and plasma integrity, often degrading coagulation factors or requiring complex protocols. This study demonstrates a novel photodynamic inactivation (PDI) strategy using the photosensitizer Photogem (R) activated by 630 nm red light to achieve effective plasma decontamination while preserving functionality. Through systematic optimization of photosensitizer concentration (25-50 mu g/mL) and light doses (15-60 J/cm2), we achieved a 3-log CFU/mL reduction of S. aureus in artificial plasma at 50 mu g/mL with 60 J/cm2 irradiation, matching FDA sterilization thresholds for blood products. Crucially, plasma components enhanced Photogem (R) stability, reducing photo-bleaching rates by 1.5-2.5 x compared to PBS (decay constants: 0.025-0.07 min-1 vs. 0.045-0.1 min-1) through protein mediated molecular interactions. Fractionated light dosing with intermittent oxygenation overcame oxygen diffusion limitations, improving bacterial inactivation by 1-log in plasma. Fluorescence microscopy revealed 2 x greater photosensitizer retention in plasma versus PBS, attributed to albumin binding and porphyrin protein stabilization. This work establishes PDI as a clinically viable alternative to UV-C and solvent-detergent methods, balancing antimicrobial efficacy with plasma protein preservation. Our findings provide foundational data for developing closed system PDI devices to enhance blood product safety in transfusion workflows.
Optical neural networks (ONNs) hold great promise for low-latency, energy-efficient inference. However, the absence of a fully real-valued end-to-end ONN, in which the inputs, weight matrices, and nonlinear activations are all represented in the real-number domain and can be optically cascaded, remains a key bottleneck. Existing approaches either rely on electrical post-processing of photodetector outputs to extend the number field in the linear layers, which breaks optical cascadability, or employ photodiode–driven micro-ring modulators (MRMs) to implement nonlinearities, constraining subsequent-layer inputs to the nonnegative domain and thereby limiting network expressivity and architectural flexibility. Here, we employ two MRMs biased at different resonance wavelengths to achieve real-valued optical encoding, together with a dual-MRM activation element driven by the differential photocurrent of photodiodes, which provides optically cascadable real-valued nonlinear activation. Combined with a real-valued Mach–Zehnder interferometer mesh for matrix computation, this architecture realizes a fully real-valued end-to-end ONN. We experimentally demonstrate a tanh-like nonlinear activation function and validate it on an iris classification task, achieving an accuracy of 98%. We further model the generator of a generative adversarial network based on this structure, in which the nonlinear activation is based on the experimentally measured nonlinear transfer curve. The generator can use natural optical noise as its input, thereby eliminating electro-optic conversion and digital-to-analog conversion at the input stage. With the above merits, the proposed ONN achieves successful optical-to-optical on-chip image generation, validating the superiority of optical computing.
In recent years, the utilization of nanoparticles with varying morphologies in optical coherence tomography (OCT) has gained prominence, primarily aimed at enhancing imaging contrast and depth. Various factors associated with nanoparticles, encompassing their shape, orientation, and distribution within biological tissues, significantly influence OCT performance. A thorough investigation of these parameters has yielded substantial findings, particularly regarding the enhancement of OCT images facilitated by the presence of nanorods (NRs). In this study, we conducted OCT imaging of chicken breast tissue employing Fe3O4 NRs under different polarization states, utilizing solenoids to apply a magnetic field to the nanoparticles. The results demonstrate that orienting nanoparticles can improve the Contrast-to-Noise Ratio (CNR) and signal-to-noise ratio (SNR) of OCT signal more than twofold compared to scenarios lacking specified orientation. Furthermore, this article addresses the challenge of prolonged nanoparticle distribution in tissue when using ultrasound probes, successfully reducing the distribution time from approximately 45 min to about 5 min. The findings presented herein show significant promises for advancing optical coherence tomography across a variety of applications.
Red blood cells (RBCs) are vital components of human blood, and their morphological abnormalities serve as reliable indicators of various disease pathophysiologies. As a novel label-free optical technique, Mueller matrix (MM) polarimetry is gaining recognition for its value in disease diagnosis and pathological analysis. In this study, we integrate a dual-angle MM measurement system with single-cell polarized light scattering modeling to establish specific polarization feature parameters (PFPs) characterizing cellular microphysical properties. The PFPs quantitatively describe morphological and optical changes in individual RBCs undergoing complex deformations. Experimental results demonstrate that PFPs can effectively distinguish differences in size, shape, refractive index, and surface spicules between deformed and normal RBCs. Moreover, by incorporating PFPs into a Random Forest classifier, we accurately quantify the proportion of abnormal RBCs in mixed suspensions. This study confirms the capability of polarization measurement for label-free, high-throughput analysis of RBC microphysical properties at the single-cell level.
Age is a limiting factor in the efficacy of photobiomodulation (PBM) for brain drainage and cognitive functions. Meningeal lymphatic vessels (MLVs) are "tunnels" for removal of toxins from the brain and the target of PBM. Age-related decline in the MLV functions is one of the mechanisms by which the effects of PBM on brain drainage and cognitive process are limited. Sleep is a time of natural activation of brain drainage. Recent findings have shown that PBM during sleep has greater effects on lymphatic clearance of beta-amyloid and cognitive function in young and middle-age mice. Based on these data, this study tested the hypothesis that sleep enhances the effects of PBM on MLVs and cognitive function in the aging brain. Indeed, the results revealed that PBM during sleep, but not during wakefulness, has stimulatory effects on lymphatic clearance of beta-amyloid from the brain of old mice that improves memory. In sleep deficit experiments, it was found that chronic sleep deprivation is accompanied by suppression of brain drainage and removal of metabolites from the brain, such as beta-amyloid, tau, glutamate, lactate and glucose in young, middle-aged and most significantly in old mice. The course of PBM during sleep contributed better than in wakefulness to the restoration of the brain level of tested metabolites in young and middle-aged mice, while in old mice only PBM during sleep was effective. These results open a new strategy for the use of PBM during sleep to improve the efficacy of PBM on clearance of toxic metabolites from the brain, especially in aged subjects in whom the efficacy of PBM during wakefulness is limited.
Intraoperative assessment of cerebral hemodynamics is crucial for the success of neurosurgical interventions. This study evaluates the potential of laser speckle contrast imaging (LSCI) and imaging photoplethysmography (IPPG) for contactless perfusion monitoring during neurosurgery. Despite similarities in their hardware requirements, these techniques rely on fundamentally different principles: light scattering for LSCI and light absorption for IPPG. Comparative experiments were conducted using animals (rats) when assessing the reaction of cerebral hemodynamics to adenosine triphosphate infusion. The results show different spatial and temporal characteristics of the techniques: LSCI predominantly visualizes blood flow in large venous vessels, especially in the sagittal and transverse sinuses, showing a pronounced modulation associated with the heart that cannot be explained by venous blood flow alone. In contrast, IPPG quantifies the dynamics of perfusion changes in the parenchyma, showing minimal signal in large venous vessels. We propose that LSCI signal modulation is significantly influenced by the movement of vessel walls in response to mechanical pressure waves propagating through the parenchyma from nearby arteries. A novel algorithm for LSCI data processing was developed based on this interpretation, producing perfusion indices that align well with IPPG measurements. This study demonstrates that the complementary nature of these techniques (LSCI is sensitive to blood cells displacements, while IPPG detects a change in their density) makes their combined application particularly valuable for comprehensive assessment of cerebral hemodynamics during neurosurgery.
Kerr resonator is one of the most popular platforms to produce optical frequency comb and temporal cavity soliton. As an essential method for investigating the nonlinear dynamics of Kerr resonators, traditional numerical simulations rely on solving the Lugiato-Lefever equation (LLE) using the split-step Fourier method (SSFM), which is computationally intensive and time-consuming. To address this challenge, this study proposes a recurrent neural network model with prior information feedback, enabling efficient and accurate prediction of soliton dynamics in Kerr resonator. With the acceleration of graphics processing unit (GPU), the computational efficiency improved by 20 times. We compared various recurrent neural networks and found that the gated recurrent unit (GRU) network demonstrated superior performance in this task. This work highlights the potential of artificial intelligence (AI) for modeling nonlinear optical dynamics in Kerr resonator, paving the way for designing optical frequency comb and generating ultrafast pulse.
High-power fiber oscillators have been widely used in industrial processing, high-end manufacturing, biomedicine and so on. However, as the output power increase, stimulated Raman scattering (SRS) becomes the main factor limiting the performance improvement of fiber oscillators. In this paper, a chirped and tilted fiber Bragg grating (CTFBG) is used to suppress SRS in a high-power fiber oscillator. The CTFBG is fabricated on one side of a low-reflectivity FBG (LRFBG) to form a composite FBG by the femtosecond laser phase mask technology, enhancing the compactness and stability of the fiber oscillator system. SRS is effectively suppressed by CTFBG with a Raman suppression depth and width of 16 dB and 86 nm, respectively, and the Raman light ratio in the output power decreases by an order of magnitude. The output power of fiber oscillators is increased to 9 kW, which is the highest power for fiber oscillators with SRS suppression using CTFBGs, to the best of our knowledge. This work demonstrates that the composite FBG can effectively improve the performance of high-power fiber oscillators, which provides new insights into the development of fiber laser technology.
Three-dimensional reconstruction of tissue architecture is crucial for biomedical research. Tissue optical clearing technology overcomes light scattering limitations in biological tissues, providing an essential tool for high-resolution three-dimensional imaging. Given the high degree of similarity between large model animals (e.g., pigs, non-human primates) and humans in terms of anatomical structure, physiologic function, and disease mechanisms, the application of this technology in these models holds significant value for biomedical research. While well-established tissue clearing protocols exist for tissue sections, whole organs, and even entire bodies in rodents, scaling up to large animal specimens presents substantial challenges due to dimensional effects and compositional variations. This review systematically examines the methodological translation from rodent to large animals, particularly on species-specific differences in brain architecture and parenchymal organ composition that critically impact clearing efficiency. We comprehensively summarize recent applications in large animals, focusing on representative areas including neural circuit mapping, sensory organ imaging, and other related research domains, while proposing optimization strategies to overcome cross-species compatibility barriers. We hope this review will serve as a valuable reference for advancing tissue optical clearing applications in large-animal biomedical research.
