Curvature sensing is essential in robotics, aerospace, and structural health monitoring, where precise deformation measurements are critical. However, existing curvature sensing technologies—such as strain gauges, inertial measurement units, and various fiber-optic approaches—suffer from limitations including electromagnetic interference, bulky form factors, low spatial resolution, temperature drift, complex fabrication, and restricted measurement ranges, especially for large-curvature and full-direction recognition. Here, we present a Michelson interferometer (MI)-based fiber curvature sensor composed of single-mode fiber, multimode fiber, and three-core fiber (SMF–MMF–TCF). The spatially distributed cores of the TCF inherently provide a directional reference, eliminating the need for inscribed gratings or asymmetric cladding, and enabling ultra-wide curvature measurement from 0 to 5.49 m⁻¹ with a maximum directional sensitivity of 1.07 dB/m⁻¹. Experimental results reveal a sinusoidal curvature sensitivity pattern, allowing precise bending angle recognition with excellent linearity (R² > 0.98) even at maximum curvature. The temperature sensitivity is −0.073 dB/°C within the temperature range of 25–95°C, and the corresponding temperature cross-sensitivity is −0.068 m⁻¹/°C, indicating that the proposed sensor exhibits reliable curvature sensing performance in the presence of moderate temperature fluctuations. This plug-and-play design, requiring only simple fusion splicing, offers a low-cost, high-performance, and robust solution for full 360° vector curvature sensing, paving the way for practical deployment in demanding industrial applications.

A feature extraction method for distributed optical fiber strain monitoring data of tunnel structures based on an LDSA-TCN

Compact optical fiber sensor system featuring tunable sensitivity and real-time wireless interrogation

Self-assembled 3D/0D quasi-core-shell sensitized optical fiber biosensor for ultrasensitive detection of Helicobacter pylori.

A vestibular-inspired optical fiber sensor based on orthogonal hook-core structure for curvature measurement

Research on the rapid measurement of the three-dimensional refractive index profile of active optical fiber preforms based on regional reconstruction from a small number of holograms

A TOPAS-based simulation and reconstruction framework for sparse-view 2D photon dosimetry with scintillating optical fibers

Smartphone-integrated LSPR-based U-shaped optical fiber sensor for refractive index and salinity sensing

Biosensing of malachite green in fish using a high-stability fiber-optic SERS probe biosensor fabricated via laser-induced self-assembly and silica encapsulation.

High-sensitivity and high-specificity optical fiber SPR doxorubicin sensor enabled by Ti3C2 MXene sensitization.

Tunable-sensitivity velocimeter based on asymmetric optical fiber bionic cilium

1,10-phenanthroline-modified silver-coated D-shaped optical fiber SPR sensor for selective and rapid detection of Fe²⁺ ions in corrosion environments

Advances in optical fiber SERS sensor for biochemical sensing applications

Efficient Automatic Dispersion Compensation Network for Intensity Modulation Direct Detection in optical fiber communication system

The interatomic potential is key to understanding all properties of materials. Yet, conventional atomic force microscopes do not usually measure the interatomic potential, because they lack control of the tip-sample distance. Here, we propose a simple methodology for measuring tip-surface interactions directly as a function of tip-sample distance, rather than deflection as a function of sample displacement. We use an AC interferometer to monitor the absolute tip displacement. When a force is applied on the tip a negative feedback loop displaces the cantilever anchoring point, deflecting the lever such that the tip position remains constant. This feedback loop actively maintains the tip at a distance from an optical fibre where the interferometer sensitivity is maximum. As a result, the tip-sample distance or the indentation is directly given by the sample motion and the tip does not jump-to-contact.

Metrological comparison of fiber-optic and conventional spectrophotometry for visible spectra of liquid dye solutions.

Wearable sensors hold significant potential for managing lower-limb dysfunction in neurological disorders, but current systems remain constrained by unimodal sensing, external power dependence, and limited diagnostic capabilities. Here, we present a wireless wearable dual-mode sensor (WDMS) integrating three polyurethane-based flexible optical strain (PFOS) components with a contact-separation mode triboelectric nanogenerator (CS-TENG). The PFOS components are used for muscle signal monitoring, while the CS-TENG simultaneously monitors plantar pressure and harvests biomechanical energy to power the WDMS, eliminating external power dependence. Furthermore, leveraging gait data from 60 individuals, an embedded Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model achieved 94.23% accuracy in distinguishing Parkinson's disease (PD) and stroke, while quantitatively evaluating rehabilitation progress after pharmacological and physical therapy interventions. By synergizing multimodal sensing, AI-driven analysis, and clinical validation, this technology advances beyond passive monitoring to provide intelligent diagnostic support. Its self-sufficiency and scalability facilitate transformative home-based management of neurological disorders.

Stimulated Raman scattering (SRS) plays a pivotal role in applications such as optical communications, fiber optic sensing, and spectral analysis. However, traditional modeling methods like the split-step Fourier method (SSFM) are computationally demanding. In response to these challenges, we propose a novel deep learning framework based on a hybrid neural network, specifically architected to capture the complex spatio-temporal dependencies inherent in nonlinear pulse propagation. Our model offers rapid and precise predictions of SRS behavior, alleviating the need for computationally expensive simulations like SSFM. To validate the model's performance, we conducted experiments using chalcogenide microstructured optical fibers (MOFs), which are attracting attention due to their high Raman gain coefficient and wide spectral range in the mid-infrared (MIR) region. Specifically, we demonstrate the first successful generation of MIR SRS in a 2- $\mu $ m direct-pumped suspended-core As2S3 MOF, which provides a crucial real-world dataset for model validation. The results demonstrate that our hybrid neural network is 116 times faster on a GPU and 44 times faster on a CPU compared to SSFM while maintaining accuracy and generalization. This significant acceleration paves the way for real-time analysis and inverse design of nonlinear photonic systems, tasks previously intractable with traditional methods.

The generalized fractional Kundu-Mukherjee-Naskar equation (gFKMNE) is a nonlinear fractional partial differential equation (NFPDE) that models nonlinear pulse transmission in communication systems and optical fibers. This work investigates the gFKMNE in (2+1) dimensions, seeking optical soliton solutions. Using a wave transformation, the gFKMNE is converted into nonlinear ordinary differential equations (NODEs) of integer order. The modified extended direct algebraic approach is then applied to solve the NODE, yielding nonlinear algebraic equations and series-form solutions. The solutions, obtained using Maple-13, reveal optical soliton solutions for the gFKMNE. These soliton solutions can be stacked to produce black lattices in optical media, visible in contour plots and 3D photographs. These dark soliton lattices are crucial in telecommunications, optical signal processing, and nonlinear optics. Further analysis examines the influence of various parameters on soliton behavior, demonstrating that soliton solutions exhibit distinct features depending on parameters like amplitude, width, and velocity. This study showcases the effectiveness of the modified extended direct algebraic technique in solving complex NFPDEs, providing new insights into soliton behavior in optical media.

Simultaneous monitoring of multiple biomarkers in tissues is critical for biomedical applications. However, few existing platforms enable concurrent in vivo detection. This study presents a compact mid-infrared transflection optical fiber probe for label-free, simultaneous monitoring of three physiologically relevant biomarkers - ethanol, glucose, and lactate. The probe comprises two silver halide fibers - one with an angled tip and one gold-coated as mirror - housed in polyetheretherketone tubing and surrounded by a semi-permeable membrane. With an outer diameter of only 1.1 mm, this is the smallest mid-infrared transflection probe reported to date. Coupled with aquantum cascade laser, the probe achieves ~1 mM detection limits for the three compounds. Peak deconvolution was deployed to resolve overlapping spectral features, enabling quantification of individual compounds in mixtures. Validation was performed in ex vivo human skin against microdialysis. Additionally, monitoring of the concentration changes for all three compounds in the skin was demonstrated.