A novel approach for identifying explosive species is reported, using Raman spectroscopy in suspended core optical fibers. Numerical simulations are presented that predict the strength of the observed signal as a function of fiber geometry, with the calculated trends verified experimentally and used to optimize the sensors. This technique is used to identify hydrogen peroxide in water solutions at volumes less than 60 nL and to quantify microgram amounts of material using the solvent’s Raman signature as an internal calibration standard. The same system, without further modifications, is also used to detect 1,4-dinitrobenzene, a model molecule for nitrobenzene-based explosives such as 2,4,6-trinitrotoluene (TNT).

In this study, a novel surface plasmon resonance (SPR) sensor based on a single-mode polymer-tipped optical fiber (PTOF) is designed and optimized. The refractive index (RI) measurement is achieved by exciting SPR at the interface between gold film and TiO2 layer coated on the polymer-tip. Finite-difference time-domain (FDTD) method based commercial optiFDTD software is used to optimize the design of the proposed sensor for better performance relate to the materials of the metal film, the shape of polymer-tips and the thickness of TiO2 layer. The results show that the sensitivity of optimal single-mode PTOF SPR sensor reaches 3798 nm/RIU when analyte RI is 1.333 and 5708 nm/RIU when analyte RI is 1.383. Compared with conventional optical fiber SPR sensors, the single-mode PTOF SPR sensor proposed in the paper is simpler in fabrication, smaller in size, and higher in sensitivity, which has broad application prospect in biomedical sensing.

A dissolved oxygen sensor made of plastic optical fiber as the substrate and dichlorotris (1, 10-phenanthroline) ruthenium as a fluorescence indicator is studied. Oxygen quenching characteristics of both intensity and phase were measured; the obtained characteristics showed deviation from the linear relation described by the Stern–Volmer equation. A two-layer model is proposed to explain the deviation, and main parameters can be deduced with the model.

Cellulose particles are among the aging products of the insulating paper that are used in power transformers. Too many cellulose particles can cause transformer accidents. Traditional research and detection methods that are used for this problem generally focus on the number and length information of cellulose particles, and it is usually difficult to quantitatively describe the spatial shape of cellulose particles. However, the shape of cellulose particles is also one of the factors affecting the safety of transformer insulation. In this paper, we successfully extracted quantitative information of the spatial shape of cellulose particles in transformer oil using an image processing technique and the transport of intensity equation, providing a new novel approach for the study and detection of the shape of cellulose particles in transformer oil.