Review on All-Fiber Online Raman Sensor with Hollow Core Microstructured Optical Fiber

3D nanoprinted fiber-interfaced hollow-core waveguides for high-accuracy nanoparticle tracking analysis

The integration of functional components into flexible photonic environments is a critical area of research in integrated photonics and is essential for high-precision sensing. This work presents a novel concept of interfacing square-core hollow-core waveguides with commercially available optical fibers using 3D nanoprinting, and demonstrates its practical relevance through a nanoscience-based characterization technique. In detail, this innovative concept results in a monolithic, fully fiber-integrated device with key advantages such as alignment-free operation, high-purity fundamental mode excitation, full polarization control, and a unique handling flexibility. For the first time, the application potential of a fiber-interfaced waveguide in nanoscale analysis is demonstrated by performing nanoparticle-tracking-analysis experiments. These experiments involve the tracking and analysis of individual gold nanospheres diffusing in the hollow core waveguide, enabled by nearly aberration-free imaging, extended observation times, and homogeneous light-line illumination. The study comprehensively covers design strategy, experimental implementation, key principles, optical characterization, and practical applications. The fiber-interfaced hollow-core waveguide concept offers significant potential for applications in bioanalytics, environmental sciences, quantum technologies, optical manipulation, and life sciences. It also paves the way for the development of novel all-fiber devices that exploit enhanced light-matter interaction in a monolithic form suitable for flexible and remote applications.

High-precision prediction of blood glucose concentration utilizing Fourier transform Raman spectroscopy and an ensemble machine learning algorithm

Raman spectroscopy has gained popularity in analyzing blood glucose levels due to its non-invasive identification and minimal interference from water. However, the challenge lies in how to accurately predict blood glucose concentrations in human blood using Raman spectroscopy. This paper researches a novel integrated machine learning algorithm called Bagging-ABC-ELM. The optimal input weights and biases of extreme learning machine (ELM) model are obtained by artificial bee colony (ABC) algorithm. The bagging algorithm is used to obtain a better the stability of the model and higher performance than ELM algorithm. The results show that the mean value of coefficient of determination is 0.9928, and root mean square error is 0.1928. Compared to other regression models, the Bagging-ABC-ELM model exhibited superior prediction accuracy, robustness, and generalization capability. The Bagging-ABC-ELM model presents a promising alternative for analyzing blood glucose levels in clinical and research settings.

A New Gas Analysis Method Based on Single-Beam Excitation Stimulated Raman Scattering in Hollow Core Photonic Crystal Fiber Enhanced Raman Spectroscopy

We previously developed a hollow-core photonic crystal fiber (HCPCF) based Raman scattering enhancement technique for gas/human breath analysis. It enhances photon-gas molecule interactions significantly but is still based on CW laser excitation spontaneous Raman scattering, which is a low-probability phenomenon. In this work, we explored nanosecond/sub-nanosecond pulsed laser excitation in HCPCF based fiber enhanced Raman spectroscopy (FERS) and successfully induced stimulated Raman scattering (SRS) enhancement. Raman measurements of simple and complex gases were performed using the new system to assess its feasibility for gas analysis. We studied the gas Raman scattering characteristics, the relationship between Raman intensities and pump energies, and the energy threshold for the transition from spontaneous Raman scattering to SRS. H2, CO2, and propene (C3H6) were used as test gases. Our results demonstrated that a single-beam pulsed pump combined with FERS provides an effective Raman enhancement technique for gas analysis. Furthermore, an energy threshold for SRS initiation was experimentally observed. The SRS-capable FERS system, utilizing a single-beam pulsed pump, shows great potential for analyzing complex gases such as propene, which is a volatile organic compound (VOC) gas, serving as a biomarker in human breath for lung cancer and other human diseases. This work contributes to the advancement of gas analysis and opens alternative avenues for exploring novel Raman enhancement techniques.

Online trace detection of mercury ions with enhanced fluorescence excitation within metal-lined hollow-core fiber

In this Letter, we present a portable all-fiber fluorescent detection system based on metal-lined hollow-core fiber (MLHCF) for the ultra-sensitive real-time monitoring of mercury ions (Hg2+). The system employs a rhodamine derivative as the probe. The hollow core of the MLHCF serves as both the flow channel of the liquid sample and the waveguide of the optical path. The metal coating in the intermediate layer between the capillary and the polyimide (PI) coating in the MLHCF provides good light confinement, enhancing the interaction between the sample and the incident light for better fluorescence excitation and collection efficiency. Additionally, further enhancement is achieved by placing an inserted filter along the light path to reflect the excitation light back to the MLHCF. A 3-cm length of MLHCF enables simultaneous excitation of a 40-µL sample volume and collection of most of its fluorescent signal in all directions, thereby significantly contributing to its exceptional sensitivity with a limit of detection (LOD) of 2.3 ng/L. The all-fiber fluorescence-enhanced detection device also shows rapid response time, excellent reusability, and selectivity. This system presents an online, reproducible, and portable solution for the trace detection of Hg2+ and provides a promising way for detecting other heavy metal ions.

Raman Spectroscopy Techniques for the Investigation and Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is the most common neurodegenerative disorder, resulting in memory loss, cognitive decline, bodily function impairment, and finally death. The growing number of people suffering from AD increasingly urges the development of effective early diagnosis and monitoring techniques. Here, we review the most recent developments in the field of Raman-based techniques, which have shown a significant potential in identifying AD by detecting specific biomarkers in biological fluids, as well as in providing fundamental insights into key molecules involved in the disease progression or in the analysis of histological specimens of patients with AD. These techniques comprise spontaneous and resonant Raman spectroscopies, exploit plasmon- or fiber- enhanced effects, such as surface-, tip- or fiber- enhanced Raman spectroscopies, or involve non-linear techniques like coherent Raman scattering. The scientific efforts employed up to now as well as the rapid technological advancements in optical detection instruments (spectrometers, lasers, substrates for analysis, etc.) and the diffusion of advanced data processing methods suggest a leading role of Raman techniques in the perspective of a preclinical or clinical detection of AD.

Special Issue “Novel Specialty Optical Fibers and Applications”: An Overview

Novel specialty optical fibers refer to optical fibers that have been engineered in terms of design, material and structure, and have been post-processed for novel functionalities and applications [...]

Aerogel-Lined Capillaries for Raman Signal Gain of Aqueous Mixtures

We report an experimental study on the gain of the Raman signal of aqueous mixtures and liquid water when confined in aerogel-lined capillaries of various lengths of up to 20 cm and various internal diameters between 530 and 1000 µm. The lining was made of hydrophobised silica aerogel, and the carrier capillary body consisted of fused silica or borosilicate glass. Compared to the Raman signal detected from bulk liquid water with the same Raman probe, a Raman signal 27 times as large was detected when the liquid water was confined in a 20 cm-long capillary with an internal diameter of 700 µm. In comparison with silver-lined capillaries of the same length and same internal diameter, the aerogel-lined capillaries featured a superior Raman signal gain and a longer gain stability when exposed to mixtures of water, sugar, ethanol and acetic acid.