Bistable response and quasi-periodicity excitation of the internal dynamics of soliton molecules.

Soliton molecules, a frequently observed phenomenon in most mode-locked lasers, have intriguing characteristics comparable to their matter molecule counterparts. However, there are rare explorations of the deterministic control of the underlying physics within soliton molecules. Here, we demonstrate the bistable response of intramolecular motion to external stimuli and identify a general approach to excite their quasi-periodic oscillations. By introducing frequency-swept gain modulation, the intrinsic resonance frequency of the soliton molecule is observed in the simulation model. Applying stronger modulation, the soliton molecule exhibits divergent response susceptibility to up- and down-sweeping, accompanied by a jump phenomenon. Quasi-periodic intramolecular oscillations appear at the redshifted resonance frequency. Given the leading role of bistability and quasi-periodic dynamics in nonlinear physics, our research provides insights into the complex nonlinear dynamics within dissipative soliton molecules. It may pave the way to related experimental studies on synchronization and chaos at an ultrafast time scale.

Combined Mutual Learning Net for Raman Spectral Microbial Strain Identification.

Infectious diseases pose a significant threat to global health, yet traditional microbiological identification methods suffer from drawbacks, such as high costs and long processing times. Raman spectroscopy, a label-free and noninvasive technique, provides rich chemical information and has tremendous potential in fast microbial diagnoses. Here, we propose a novel Combined Mutual Learning Net that precisely identifies microbial subspecies. It demonstrated an average identification accuracy of 87.96% in an open-access data set with thirty microbial strains, representing a 5.76% improvement. 50% of the microbial subspecies accuracies were elevated by 1% to 46%, especially for E. coli 2 improved from 31% to 77%. Furthermore, it achieved a remarkable subspecies accuracy of 92.4% in the custom-built fiber-optical tweezers Raman spectroscopy system, which collects Raman spectra at a single-cell level. This advancement demonstrates the effectiveness of this method in microbial subspecies identification, offering a promising solution for microbiology diagnosis.

On-Chip Fabrication-Tolerant Exceptional Points Based on Dual-Scatterer Engineering.

The intriguing and anomalous optical characteristics of exceptional points (EPs) in optical resonators have attracted significant attention. While EP-related phenomena have been observed by perturbing resonators with off-chip components, implementing EPs fully on-chip remains challenging due to their extreme susceptibility to fabrication errors. In this Letter, we propose a succinct and compact approach to reach EP in an on-chip integrated silicon microring resonator by manipulating the evolution of backscatterings with two nanocylinders of disparate diameters. The theoretical analysis unveils that the fabrication constraints could be significantly relieved by increasing the difference in diameters of the nanocylinders. The evolution from non-EP to EP is traced experimentally through the step-by-step tuning of the angular and radial positions of nanocylinders. The proposed method opens a pathway toward the on-chip high-density integration of non-Hermitian devices.

Self-supervised Self2Self denoising strategy for OCT speckle reduction with a single noisy image.

Optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiple scattered photons owing to its low-coherence interferometry property. Although various deep learning schemes have been proposed for OCT despeckling, they typically suffer from the requirement for ground-truth images, which are difficult to collect in clinical practice. To alleviate the influences of speckles without requiring ground-truth images, this paper presents a self-supervised deep learning scheme, namely, Self2Self strategy (S2Snet), for OCT despeckling using a single noisy image. Specifically, in this study, the main deep learning architecture is the Self2Self network, with its partial convolution being updated with a gated convolution layer. Specifically, both the input images and their Bernoulli sampling instances are adopted as network input first, and then, a devised loss function is integrated into the network to remove the background noise. Finally, the denoised output is estimated using the average of multiple predicted outputs. Experiments with various OCT datasets are conducted to verify the effectiveness of the proposed S2Snet scheme. Results compared with those of the existing methods demonstrate that S2Snet not only outperforms those existing self-supervised deep learning methods but also achieves better performances than those non-deep learning ones in different cases. Specifically, S2Snet achieves an improvement of 3.41% and 2.37% for PSNR and SSIM, respectively, as compared to the original Self2Self network, while such improvements become 19.9% and 22.7% as compared with the well-known non-deep learning NWSR method.

RSPSSL: A novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization.

Raman spectroscopy has tremendous potential for material analysis with its molecular fingerprinting capability in many branches of science and technology. It is also an emerging omics technique for metabolic profiling to shape precision medicine. However, precisely attributing vibration peaks coupled with specific environmental, instrumental, and specimen noise is problematic. Intelligent Raman spectral preprocessing to remove statistical bias noise and sample-related errors should provide a powerful tool for valuable information extraction. Here, we propose a novel Raman spectral preprocessing scheme based on self-supervised learning (RSPSSL) with high capacity and spectral fidelity. It can preprocess arbitrary Raman spectra without further training at a speed of ~1 900 spectra per second without human interference. The experimental data preprocessing trial demonstrated its excellent capacity and signal fidelity with an 88% reduction in root mean square error and a 60% reduction in infinite norm ([Formula: see text]) compared to established techniques. With this advantage, it remarkably enhanced various biomedical applications with a 400% accuracy elevation (ΔAUC) in cancer diagnosis, an average 38% (few-shot) and 242% accuracy improvement in paraquat concentration prediction, and unsealed the chemical resolution of biomedical hyperspectral images, especially in the spectral fingerprint region. It precisely preprocessed various Raman spectra from different spectroscopy devices, laboratories, and diverse applications. This scheme will enable biomedical mechanism screening with the label-free volumetric molecular imaging tool on organism and disease metabolomics profiling with a scenario of high throughput, cross-device, various analyte complexity, and diverse applications.

Magnetic microspheres enhanced peanut structure cascaded lasso shaped fiber laser biosensor for cancer marker-CEACAM5 detection in serum.

The detection of trace cancer markers in body fluids such as blood/serum is crucial for cancer diseases screening and treatment, which requires high sensitivity and specificity of biosensors. In this study, a peanut structure cascaded lasso (PSCL) shaped fiber sensing probe based on fiber laser demodulation method was proposed to specifically detect the carcinoembryonic antigen related cell adhesion molecules 5 (CEACAM5) protein in serum. Thanks for the narrow linewidth and high signal-to-noise ratio (SNR) of the laser spectrum, it is easier to distinguish small spectral changes than interference spectrum. Adding the antibody modified magnetic microspheres (MMS) to form the sandwich structure of "antibody-antigen-antibody-MMS", and amplified the response caused by biomolecular binding. The limit of detection (LOD) for CEACAM5 in buffer could reach 0.11 ng/mL. Considering the common threshold of 5 ng/mL for CEA during medical screening and the cut off limit of 2.5 ng/mL for some kits, the LOD of proposed biosensor meets the actual needs. Human serum samples from a hospital were used to validate the real sensing capability of proposed biosensor. The deviation between the measured value in various serum samples and the clinical value ranged from 1.9 to 9.8 %. This sensing scheme holds great potential to serve as a point of care testing (POCT) device and extend to more biosensing applications.

A Multimode Microfiber Specklegram Biosensor for Measurement of CEACAM5 through AI Diagnosis.

Carcinoembryonic antigen (CEACAM5), as a broad-spectrum tumor biomarker, plays a crucial role in analyzing the therapeutic efficacy and progression of cancer. Herein, we propose a novel biosensor based on specklegrams of tapered multimode fiber (MMF) and two-dimensional convolutional neural networks (2D-CNNs) for the detection of CEACAM5. The microfiber is modified with CEA antibodies to specifically recognize antigens. The biosensor utilizes the interference effect of tapered MMF to generate highly sensitive specklegrams in response to different CEACAM5 concentrations. A zero mean normalized cross-correlation (ZNCC) function is explored to calculate the image matching degree of the specklegrams. Profiting from the extremely high detection limit of the speckle sensor, variations in the specklegrams of antibody concentrations from 1 to 1000 ng/mL are measured in the experiment. The surface sensitivity of the biosensor is 0.0012 (ng/mL)-1 within a range of 1 to 50 ng/mL. Moreover, a 2D-CNN was introduced to solve the problem of nonlinear detection surface sensitivity variation in a large dynamic range, and in the search for image features to improve evaluation accuracy, achieving more accurate CEACAM5 monitoring, with a maximum detection error of 0.358%. The proposed fiber specklegram biosensing scheme is easy to implement and has great potential in analyzing the postoperative condition of patients.

Compact optical fiber sensor based on Vernier effect with speckle patterns.

We propose a Vernier effect-based sensor for temperature and salinity measurements. This sensor utilizes the correlation speckle pattern generated by spatial multimode interference and has undergone testing to validate its effectiveness. The speckle demodulation method is used to solve the problem of inconsistent envelope measurement when tracking with different upper and lower envelopes. The device consists of two Fabry Perot interferometers (FPIs) created by connecting hole core fiber (HCF) and erbium-doped fiber (EDF) in series. The speckle image produced by the interferometers is analyzed using the Zero means normalized cross-correlation (ZNCC) technique. The ZNCC value demonstrates a linear relationship with salinity and temperature, allowing for the measurement of these parameters. The sensor exhibits a temperature detection sensitivity of -0.0224 /°C and a salinity detection sensitivity of -0.0439/%. The sensor offers several advantageous features, including its compact size, low-cost manufacturing, high sensitivity, stability, and convenient reflection measurements. These characteristics make it a valuable tool for various applications. The proposed Vernier effect-based temperature and salinity sensor shows great potential for simultaneous monitoring and measurement of temperature and salinity in environments such as marine settings or industrial processes where accurate control of these parameters is crucial.

SNR enhancement of quasi-distributed weak acoustic signal detection by elastomers and MMF integrated Φ-OTDR.

We have proposed and demonstrated a weak acoustic signal detection technology based on phase-sensitive optical time-domain reflectometry (Φ-OTDR). Non-contact acoustic signals transmitting through air gap between the sound source and the receiver are difficult to detect due to fast attenuation. In order to improve the detection ability of non-contact weak acoustic signals, we demonstrate that multi-mode fiber (MMF) is a better solution than single-mode fiber (SMF) benefiting from its larger core and higher Rayleigh backscattering (RBS) capture coefficient. The frequency signal-to-noise ratio (SNR) has been enhanced by 9.26 dB. Then, with the help of 3D printing technology, elastomers have been designed to further enhance the detection ability due to the high-sensitive response to acoustic signals. Compared with the previous reported "I" type elastomer, the location and frequency SNR enhancement caused by our new proposed "n" type elastomer are 8.39 dB and 11.02 dB in SMF based system. The values are further improved to 10.51 dB and 13.38 dB in MMF and "n" type elastomer integrated system. And a phase-pressure sensitivity of -94.62 dB re rad/µPa has been achieved at 2.5 kHz. This non-contact weak acoustic signal detection technique has great application potential in the quasi-distributed partial discharge (PD) detection of smart grid.

Generation of 35 fs, 20 µJ, GHz pulse burst by hybrid fiber amplification technique.

We have proposed and demonstrated the generation of a high-energy, ultrashort pulse duration, GHz pulse burst polarization-maintaining fiber amplification system that utilizes both chirped-pulse amplification and self-similar amplification techniques. Such hybrid fiber amplification system produces 22 µJ-energy bursts of 200 pulses with a 1.02-GHz intra-burst pulse repetition rate and a 1-MHz inter-burst repetition rate. The center wavelength of the amplified compressed pulse is 1065 nm, with a 3 dB spectral bandwidth of 65 nm. The pulse duration of optimal compression is ∼35 fs, which represents the shortest pulse duration reported to date for any multi-microjoule class amplification system with a repetition rate at the GHz level. At the same time, only common double-cladding Yb3+-doped fiber is used as the gain fiber, without any large-mode-area Yb3+-doped photonic crystal fiber, makes the system compact and reliable by the simple fusion operation.

Generation of sub-100 fs pulses from a SESAM-NPE hybrid mode-locked Yb-doped fiber laser at a fundamental repetition rate of 1.15 GHz.

Supercontinuum generation via direct pumping of unamplified high-repetition-rate, sub-100 fs pulses with a pulse energy lower than 50 pJ is superior in noise performance and features a high acquisition speed. We demonstrate a novel, to the best of our knowledge, gigahertz-repetition-rate, mode-locked Yb-doped fiber laser, where the hybrid mode-locking approach is employed. The laser has a low initiating threshold of 300 mW and a broad mode-locking range of 600 mW (300-900 mW) in terms of pump power. The shortest obtained pulse width of the laser after compression is 95 fs, and the highest output pulse energy is 92.9 pJ at a fundamental repetition rate of 1.15 GHz. Moreover, the laser's output polarization states are switchable, and it has a polarization extinction ratio of 17.9 dB.

Loss-balanced parallel decoding network for retinal fluid segmentation in OCT.

As a leading cause of blindness worldwide, macular edema (ME) is mainly determined by sub-retinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelial detachment (PED) accumulation, and therefore, the characterization of SRF, IRF, and PED, which is also known as ME segmentation, has become a crucial issue in ophthalmology. Due to the subjective and time-consuming nature of ME segmentation in retinal optical coherence tomography (OCT) images, automatic computer-aided systems are highly desired in clinical practice. This paper proposes a novel loss-balanced parallel decoding network, namely PadNet, for ME segmentation. Specifically, PadNet mainly consists of an encoder and three parallel decoder modules, which serve as segmentation, contour, and diffusion branches, and they are employed to extract the ME's characteristics, the contour area features, and to expand the ME area from the center to edge, respectively. A new loss-balanced joint-loss function with three components corresponding to each of the three parallel decoding branches is also devised for training. Experiments are conducted with three public datasets to verify the effectiveness of PadNet, and the performances of PadNet are compared with those of five state-of-the-art methods. Results show that PadNet improves ME segmentation accuracy by 8.1%, 11.1%, 0.6%, 1.4% and 8.3%, as compared with UNet, sASPP, MsTGANet, YNet, RetiFluidNet, respectively, which convincingly demonstrates that the proposed PadNet is robust and effective in ME segmentation in different cases.

Spatially Modulated Fiber Speckle for High-Sensitivity Refractive Index Sensing.

A fiber speckle sensor (FSS) based on a tapered multimode fiber (TMMF) has been developed to measure liquid analyte refractive index (RI) in this work. By the lateral and axial offset of input light into TMMF, several high-order modes are excited in TMMF, and the speckle pattern is spatially modulated, which affects an asymmetrical speckle pattern with a random intensity distribution at the output of TMMF. When the TMMF is immersed in the liquid analyte with RI variation, it influences the guided modes, as well as the mode interference, in TMMF. A digital image correlations method with zero-mean normalized cross-correlation coefficient is explored to digitize the speckle image differences, analyzing the RI variation. It is found that the lateral- and axial-offsets-induced speckle sensor can enhance the RI sensitivity from 6.41 to 19.52 RIU-1 compared to the one without offset. The developed TMMF speckle sensor shows an RI resolution of 5.84 × 10-5 over a linear response range of 1.3164 to 1.3588 at 1550 nm. The experimental results indicate the FSS provides a simple, efficient, and economic approach to RI sensing, which exhibits an enormous potential in the image-based ocean-sensing application.

Integrated Fiber Ring Laser Temperature Sensor Based on Vernier Effect with Lyot-Sagnac Interferometer.

The Vernier effect created using an incorporated Lyot-Sagnac loop is used to create an ultra-high sensitivity temperature sensor based on a ring laser cavity. Unlike standard double Sagnac loop systems, the proposed sensor is fused into a single Sagnac loop by adjusting the welding angle between two polarization-maintaining fibers (PMFs) to achieve effective temperature sensitivity amplification. The PMFs are separated into two arms of 0.8 m and 1 m in length, with a 45° angle difference between the fast axes. The sensor's performance is examined both theoretically and experimentally. The experimental results reveal that the Vernier amplification effect can be achieved via PMF rotating shaft welding. The temperature sensitivity in the laser cavity can reach 2.391 nm/°C, which is increased by a factor of more than eight times compared with a single Sagnac loop structure (0.298 nm/°C) with a length of 0.8 m without the Vernier effect at temperatures ranging from 20 °C to 30 °C. Furthermore, unlike traditional optical fiber sensing that uses a broadband light source (BBS) for detection, which causes issues such as low signal-to-noise ratio and broad bandwidth, the Sagnac loop can be employed as a filter by inserting itself into the fiber ring laser (FRL) cavity. When the external parameters change, the laser is offset by the interference general modulation, allowing the external temperature to be monitored. The superior performance of signal-to-noise ratios of up to 50 dB and bandwidths of less than 0.2 nm is achieved. The proposed sensor has a simple structure and high sensitivity and is expected to play a role in biological cell activity monitoring.

Fiber Laser-Based Lasso-Shaped Biosensor for High Precision Detection of Cancer Biomarker-CEACAM5 in Serum.

Detection of trace tumor markers in blood/serum is essential for the early screening and prognosis of cancer diseases, which requires high sensitivity and specificity of the assays and biosensors. A variety of label-free optical fiber-based biosensors has been developed and yielded great opportunities for Point-of-Care Testing (POCT) of cancer biomarkers. The fiber biosensor, however, suffers from a compromise between the responsivity and stability of the sensing signal, which would deteriorate the sensing performance. In addition, the sophistication of sensor preparation hinders the reproduction and scale-up fabrication. To address these issues, in this study, a straightforward lasso-shaped fiber laser biosensor was proposed for the specific determination of carcinoembryonic antigen (CEA)-related cell adhesion molecules 5 (CEACAM5) protein in serum. Due to the ultra-narrow linewidth of the laser, a very small variation of lasing signal caused by biomolecular bonding can be clearly distinguished via high-resolution spectral analysis. The limit of detection (LOD) of the proposed biosensor could reach 9.6 ng/mL according to the buffer test. The sensing capability was further validated by a human serum-based cancer diagnosis trial, enabling great potential for clinical use. The high reproduction of fabrication allowed the mass production of the sensor and extended its utility to a broader biosensing field.

Alterable interferential fineness for high temperature sensing calibration based on Bragg hollow core fiber.

We propose, what we believe to be, a novel method for high temperature sensing calibration based on the mechanism of alterable interferential fineness in Bragg hollow core fiber (BHCF). To verify the proof-of-concept, the fabricated sensing structure is sandwiched by two sections with different length of BHCF. Two interferential fineness fringes dominate the transmission spectrum, where the high-fineness fringes formed by anti-resonant reflecting optical waveguide (ARROW) plays the role for high temperature measurement. Meanwhile, the low-fineness fringes induced by short Fabry-Perot (F-P) cavity are exploited as temperature calibration. The experimental results show that the ARROW mechanism-based temperature sensitivity can reach 26.03 pm/°C, and the intrinsic temperature sensitivity of BHCF is 1.02 pm/°C. Here, the relatively lower magnitude of the temperature sensitivity is considered as the standard value since it merely relies on the material properties of silicon. Additionally, a large dynamic temperature range from 100 °C to 800 °C presents linear response of the proposed sensing structure, which may shine the light on the sensing applications in the harsh environment.

Strong bulk photovoltaic effect in engineered edge-embedded van der Waals structures.

Bulk photovoltaic effect (BPVE), a second-order nonlinear optical effect governed by the quantum geometric properties of materials, offers a promising approach to overcome the Shockley-Quiesser limit of traditional photovoltaic effect and further improve the efficiency of energy harvesting. Here, we propose an effective platform, the nano edges embedded in assembled van der Waals (vdW) homo- or hetero-structures with strong symmetry breaking, low dimensionality and abundant species, for BPVE investigations. The BPVE-induced photocurrents strongly depend on the orientation of edge-embedded structures and polarization of incident light. Reversed photocurrent polarity can be observed at left and right edge-embedded structures. Our work not only visualizes the unique optoelectronic effect in vdW nano edges, but also provides an effective strategy for achieving BPVE in engineered vdW structures.

Self-supervised Blind2Unblind deep learning scheme for OCT speckle reductions.

As a low-coherence interferometry-based imaging modality, optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiply scattered photons. Speckles hide tissue microstructures and degrade the accuracy of disease diagnoses, which thus hinder OCT clinical applications. Various methods have been proposed to address such an issue, yet they suffer either from the heavy computational load, or the lack of high-quality clean images prior, or both. In this paper, a novel self-supervised deep learning scheme, namely, Blind2Unblind network with refinement strategy (B2Unet), is proposed for OCT speckle reduction with a single noisy image only. Specifically, the overall B2Unet network architecture is presented first, and then, a global-aware mask mapper together with a loss function are devised to improve image perception and optimize sampled mask mapper blind spots, respectively. To make the blind spots visible to B2Unet, a new re-visible loss is also designed, and its convergence is discussed with the speckle properties being considered. Extensive experiments with different OCT image datasets are finally conducted to compare B2Unet with those state-of-the-art existing methods. Both qualitative and quantitative results convincingly demonstrate that B2Unet outperforms the state-of-the-art model-based and fully supervised deep-learning methods, and it is robust and capable of effectively suppressing speckles while preserving the important tissue micro-structures in OCT images in different cases.

Phase-tailored assembly and encoding of dissipative soliton molecules.

Self-assembly of particle-like dissipative solitons, in the presence of mutual interactions, emphasizes the vibrant concept of soliton molecules in varieties of laser resonators. Controllable manipulation of the molecular patterns, held by the degrees of freedom of internal motions, still remains challenging to explore more efficient and subtle tailoring approaches for the increasing demands. Here, we report a new phase-tailored quaternary encoding format based on the controllable internal assembly of dissipative soliton molecules. Artificial manipulation of the energy exchange of soliton-molecular elements stimulates the deterministic harnessing of the assemblies of internal dynamics. Self-assembled soliton molecules are tailored into four phase-defined regimes, thus constituting the phase-tailored quaternary encoding format. Such phase-tailored streams are endowed with great robustness and are resistant to significant timing jitter. All these results experimentally demonstrate the programmable phase tailoring and exemplify the application of the phase-tailored quaternary encoding, prospectively promoting high-capacity all-optical storage.

Topological antichiral surface states in a magnetic Weyl photonic crystal.

Chiral edge states that propagate oppositely at two parallel strip edges are a hallmark feature of Chern insulators which were first proposed in the celebrated two-dimensional (2D) Haldane model. Subsequently, counterintuitive antichiral edge states that propagate in the same direction at two parallel strip edges were discovered in a 2D modified Haldane model. Recently, chiral surface states, the 2D extension of one-dimensional (1D) chiral edge states, have also been observed in a photonic analogue of a 3D Haldane model. However, despite many recent advances in antichiral edge states and chiral surface states, antichiral surface states, the 2D extension of 1D antichiral edge states, have never been realized in any physical system. Here, we report the experimental observation of antichiral surface states by constructing a 3D modified Haldane model in a magnetic Weyl photonic crystal with two pairs of frequency-shifted Weyl points (WPs). The 3D magnetic Weyl photonic crystal consists of gyromagnetic cylinders with opposite magnetization in different triangular sublattices of a 3D honeycomb lattice. Using microwave field-mapping measurements, unique properties of antichiral surface states have been observed directly, including the antichiral robust propagation, tilted surface dispersion, a single open Fermi arc connecting two projected WPs and a single Fermi loop winding around the surface Brillouin zone (BZ). These results extend the scope of antichiral topological states and enrich the family of magnetic Weyl semimetals.