Ana M. Cubillas

Photonic crystal fibres for chemical sensing and photochemistry

In this review, we introduce photonic crystal fibre as a novel optofluidic microdevice that can be employed as both a versatile chemical sensor and a highly efficient microreactor. We demonstrate that it provides an excellent platform in which light and chemical samples can strongly interact for quantitative spectroscopic analysis or photoactivation purposes. The use of photonic crystal fibre in photochemistry and sensing is discussed and recent results on gas and liquid sensing as well as on photochemical and catalytic reactions are reviewed. These developments demonstrate that the tight light confinement, enhanced light-matter interaction and reduced sample volume offered by photonic crystal fibre make it useful in a wide range of chemical applications.

Methane detection at 1670-nm band using a hollow-core photonic bandgap fiber and a multiline algorithm

The long interaction pathlengths provided by hollow-core photonic bandgap fibers (HC-PBFs) are especially advantageous for the detection of weakly absorbing gases such as methane (CH(4)). In this paper, we demonstrate methane sensing with a 1670-nm band HC-PBF. A multiline algorithm is used to fit the R(6) manifold (near 1645 nm) and, in this way, to measure the gas concentration. With this method, a minimum detectivity of 10 ppmv for the system configuration was estimated.

High resolution spectroscopy of ammonia in a hollow-core fiber

We have demonstrated frequency modulation saturation spectroscopy of the nu(1) +nu(3) band of ammonia in hollow-core photonic bandgap fibers (HC-PBFs). Previously blended lines have been resolved and the corresponding molecular transitions assigned. Cross-over resonances are observed between transitions that do not share a common level. We have measured the pressure dependence of the line shape and determined the collisional self-broadening coefficients for ammonia. The many absorption lines of ammonia in the 1.5 microm wavelength region are potential frequency references lines for optical communication as well as candidates for spectroscopic trace gas monitoring.

Gas Sensor Based on Photonic Crystal Fibres in the 2ν3 and ν2 + 2ν3 Vibrational Bands of Methane

In this work, methane detection is performed on the 2ν3 and ν2 + 2ν3 absorption bands in the Near-Infrared (NIR) wavelength region using an all-fibre optical sensor. Hollow-core photonic bandgap fibres (HC-PBFs) are employed as gas cells due to their compactness, good integrability in optical systems and feasibility of long interaction lengths with gases. Sensing in the 2ν3 band of methane is demonstrated to achieve a detection limit one order of magnitude better than that of the ν2 + 2ν3 band. Finally, the filling time of a HC-PBF is demonstrated to be dependent on the fibre length and geometry.

Chemical and (Photo)‐Catalytical Transformations in Photonic Crystal Fibers

The concept of employing photonic crystal fibers for chemical and (photo)‐catalytical transformations is presented. These optofluidic microdevices represent a versatile platform where light and fluids can interact for spectroscopic or photoactivation purposes. The use of photonic crystal fibers in chemistry and sensing is reviewed and recent applications as catalytic microreactors are presented. Results on homogeneous catalysis and the immobilization of homogeneous and heterogeneous catalysts in the fiber channels are discussed. The examples demonstrate that combining catalysis and the excellent light guidance of photonic crystal fibers provides unique features for example, for photocatalytic activation and quantitative photospectroscopic reaction analysis.

Data Processing Method Applying Principal Component Analysis and Spectral Angle Mapper for Imaging Spectroscopic Sensors

A data processing method to classify hyperspectral images from an imaging spectroscopic sensor is evaluated. Each image contains the whole diffuse reflectance spectra of the analyzed material for all the spatial positions along a specific line of vision. The implemented linear algorithm comes to solve real time constrains typical of industrial systems. This processing method is composed of two blocks: data compression is performed by means of principal component analysis (PCA) and the spectral interpretation algorithm for classification is the spectral angle mapper (SAM). This strategy, applying PCA and SAM, has been successfully tested for online raw material sorting in the tobacco industry, where the desired raw material (tobacco leaves) should be discriminated from other unwanted spurious materials, such as plastic, cardboard, leather, feathers, candy paper, etc. Hyperspectral images are recorded by a sensor consisting of a monochromatic camera and a passive prism-grating-prism device. Performance results are compared with a spectral interpretation algorithm based on artificial neural networks (ANN).

Multi-Line Fit Model for the Detection of Methane at ν2 + 2ν3 Band using Hollow-Core Photonic Bandgap Fibres

Hollow-core photonic bandgap fibres (HC-PBFs) have emerged as a novel technology in the field of gas sensing. The long interaction pathlengths achievable with these fibres are especially advantageous for the detection of weakly absorbing gases. In this work, we demonstrate the good performance of a HC-PBF in the detection of the ν2 + 2ν3 band of methane, at 1.3 μm. The Q-branch manifold, at 1331.55 nm, is targeted for concentration monitoring purposes. A computationally optimized multi-line model is used to fit the Q-branch. Using this model, a detection limit of 98 ppmv (parts per million by volume) is estimated.

Methane sensing using multiple-coupling gaps in hollow-core photonic bandgap fibers

Gas detection and gas sensing based on hollow core photonic bandgap fiber (HC-PBF) is a very promising technique due to the long interaction light-gas lengths that are achievable. However, long path-lengths also imply higher gas filling times of the hollow fiber and higher response times of the detection systems what can constitute a serious practical inconvenience. In this paper, the high sensitivity is maintained but the sensor response time is reduced by using multiple-coupling fiber gaps. The results and conclusions extracted from a systematic experimental study (comparing the spectra and filling time of different HC-PBF lengths and different number of coupling gaps) are presented and discussed. Finally, the maximum number of gaps allowed in the system is modelled.

Adaptive illumination source for multispectral vision system applied to material discrimination

A multispectral system based on a monochrome camera and an adaptive illumination source is presented in this paper. Its preliminary application is focused on material discrimination for food and beverage industries, where monochrome, color and infrared imaging have been successfully applied for this task. This work proposes a different approach, in which the relevant wavelengths for the required discrimination task are selected in advance using a Sequential Forward Floating Selection (SFFS) Algorithm. A light source, based on Light Emitting Diodes (LEDs) at these wavelengths is then used to sequentially illuminate the material under analysis, and the resulting images are captured by a CCD camera with spectral response in the entire range of the selected wavelengths. Finally, the several multispectral planes obtained are processed using a Spectral Angle Mapping (SAM) algorithm, whose output is the desired material classification. Among other advantages, this approach of controlled and specific illumination produces multispectral imaging with a simple monochrome camera, and cold illumination restricted to specific relevant wavelengths, which is desirable for the food and beverage industry. The proposed system has been tested with success for the automatic detection of foreign object in the tobacco processing industry.

Automatic classification of steel plates based on laser induced breakdown spectroscopy and support vector machines

Welding processes are one of the most widely spread industrial activities, and their quality control is an important area of research. The presence of residual traces from the protective antioxidant coating, is a problematic issue since it causes a significant reduction in the welding seam strength. In this work, a solution based on a Laser Induced Breakdown Spectroscopy (LIBS) setup and a Support Vector Machines (SVMs) classifier to detect and discriminate antioxidant coating residues in the welding area without destroying the sample before the welding procedure is proposed. This system could be an interesting and fast tool to detect aluminium impurities.