Abstract
Surface plasmon-polaritons are electromagnetic modes that can be excited at a conducting-dielec-tric interface [1]. The engineering of surface plasmon resonance (SPR) based devices is a milestone in the development of optical sensors. The ability to construct an all-optical system to confine lightwave power at subwavelength dimensions with higher levels of sensitivity and resolution in a broad spectral range are the central features that have attracted a rapid-growing interest in SPR sensors [2]. Particularly, minute variations in the refractive index of the surrounding medium (also known as analyte) change significantly the characteristics of the electromagnetic fields of a surface plasmon mode. As a consequence, the spectral shifts in the mode phase and also losses variations in the associated confined power can be used to detect analyte properties that are described in terms of the refractive index [3].

Abstract
We present a giant sensitivity displacement sensor combining the push-pull method and enhanced Vernier effect. The displacement sensor consists in two interferometers that are composed by two cleaved standard optical fibers coupled by a 3 dB coupler and combined with a double-sided mirror. The push pull-method is applied to the mirror creating a symmetrical change to the length of each interferometer. Furthermore, we demonstrate that the Vernier effect has a maximum sensitivity of two-fold that obtained with a single interferometer. The combination of the push-pull method and the Vernier effect in the displacement sensors allows a sensitivity of 60 +/- 1 nm/mu m when compared with a single interferometer working in the same free spectral range. In addition, exploring the maximum performance of the displacement sensors, a sensitivity of 254 +/- 6 nm/mu m is achieved, presenting a M-factor of 1071 and M-Vernier of 1.9 corresponding to a resolution of 79 pm. This new solution allows the implementation of giant-sensitive displacement measurement for a wide range of applications.

Abstract
In this study, we used machine learning techniques to reconstruct the wavelength dependence of the absorption coefficient of human normal and pathological colorectal mucosa tissues. Using only diffuse reflectance spectra from the ex vivo mucosa tissues as input to algorithms, several approaches were tried before obtaining good matching between the generated absorption coefficients and the ones previously calculated for the mucosa tissues from invasive experimental spectral measurements. Considering the optimized match for the results generated with the multilayer perceptron regression method, we were able to identify differentiated accumulation of lipofuscin in the absorption coefficient spectra of both mucosa tissues as we have done before with the corresponding results calculated directly from invasive measurements. Considering the random forest regressor algorithm, the estimated absorption coefficient spectra almost matched the ones previously calculated. By subtracting the absorption of lipofuscin from these spectra, we obtained similar hemoglobin ratios at 410/550 nm: 18.9-fold/9.3-fold for the healthy mucosa and 46.6-fold/24.2-fold for the pathological mucosa, while from direct calculations, those ratios were 19.7-fold/10.1-fold for the healthy mucosa and 33.1-fold/17.3-fold for the pathological mucosa. The higher values obtained in this study indicate a higher blood content in the pathological samples used to measure the diffuse reflectance spectra. In light of such accuracy and sensibility to the presence of hidden absorbers, with a different accumulation between healthy and pathological tissues, good perspectives become available to develop minimally invasive spectroscopy methods for in vivo early detection and monitoring of colorectal cancer.

Abstract
In this work, X-ray fluorescence (XRF) and Laser-induced breakdown spectroscopy (LIBS) analyses were applied to samples of quartz, montebrasite, and turquoise hydrothermal veins in the Argemela Tin Mine (Central Portugal). Montebrasite (LiAl(PO4)(OH,F)) is potentially the main ore mineral; with its alteration, lithium (Li) can disseminate into other minerals. A hand sample was cut and analyzed by XRF and LIBS for several elements of interest including Cu, P, Al, Si, and Li. Although XRF cannot measure Li, results from its analysis are effective for distinguishing turquoise from montebrasite. LIBS analysis complemented this study, making it possible to conclude that turquoise does not contain any significant Li in its structure. The difference in spot size between the techniques (5 mm vs. 300 mu m for XRF and LIBS, respectively) resulted in a poorer performance by XRF in accurately identifying mixed minerals. A thin section was petrographically characterized and mapped using LIBS. The mapping results demonstrate the possibility of the successful identification of minerals and their alterations on a thin section. The results of XRF analysis and LIBS mapping in petrographic sections demonstrate the efficacy of these methods as tools for element and mineral identification, which can be important in exploration and mining phases, complementing more traditional techniques.

Abstract
Optical waveguides were fabricated at the surface of Eagle2000 glass substrates, using femtosecond laser direct writing and wet etching, and their potential as intensity-modulated refractometers was assessed. Through the analysis of their broadband spectral response to different refractive index oils, we observed that mode mismatch is present when the guided mode reaches the surface of the substrate and interacts with the external medium, thus enabling the use of such optical waveguides in refractive index sensing. Refractive indices equal to or greater than that of the substrate also induced a coupling mechanism that was shown not to be suitable in these devices. The device's wavelength of operation was found to be tunable by controlling the distance between the surface and the center of the optical waveguide. However, the sensitivity was seen to diminish by increasing the latter, being nonexistent for distances greater than 5.5 mu m. In this study, the maximum sensitivity values were found for a surface to core center distance between 1 and 2 mu m, in the biological range, and 2.5 to 3 mu m, for a refractive index nearing that of the substrate. Accordingly, maximum sensitivities of approximate to 25 dB/RIU and approximate to 1200 dB/RIU were found between 1.300 < n(D)(25)degrees(C) < 1.400 and 1.490 < n(D)(25)degrees(C) < 1.500, respectively.

Abstract
The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.