Applied Photonics

Abstract
The lower refractive index sensitivity (RIS) of plasmonic nanoparticles (NP) in comparison to their plasmonic thin films counterparts hindered their wide adoption for wavelength-based sensor designs, wasting the NP characteristic field locality. In this context, high aspect-ratio colloidal core-shell Ag@Au nanorods (NRs) are demonstrated to operate effectively at telecommunication wavelengths, showing RIS of 1720 nm/RIU at 1350 nm (O-band) and 2325 nm/RIU at 1550 nm (L-band), representing a five-fold improvement compared to similar Au NRs operating at equivalent wavelengths. Also, these NRs combine the superior optical performance of Ag with the Au chemical stability and biocompatibility. Next, using a side-polished optical fiber, we detected glyphosate, achieving a detection limit improvement from 724 to 85 mg/L by shifting from the O to the C/L optical bands. This work combines the significant scalability and cost-effective advantages of colloidal NPs with enhanced RIS, showing a promising approach suitable for both point-of-care and long-range sensing applications at superior performance than comparable thin film-based sensors in either environmental monitoring and other fields.

Abstract
Combining data from different sensing modalities has been a promising research topic for building better and more reliable data-driven models. In particular, it is known that multimodal spectral imaging can improve the analytical capabilities of standalone spectroscopy techniques through fusion, hyphenation, or knowledge distillation techniques. In this manuscript, we focus on the latter, exploring how one can increase the performance of a Laser-induced Breakdown Spectroscopy system for mineral classification problems using additional spectral imaging techniques. Specifically, focusing on a scenario where Raman spectroscopy delivers accurate mineral classification performance, we show how to deploy a knowledge distillation pipeline where Raman spectroscopy may act as an autonomous supervisor for LIBS. For a case study concerning a challenging Li-bearing mineral identification of spodumene and petalite, our results demonstrate the advantages of this method in improving the performance of a single-technique system. LIBS trained with labels obtained by Raman presents an enhanced classification performance. Furthermore, leveraging the interpretability of the model deployed, the workflow opens opportunities for the deployment of assisted feature discovery pipelines, which may impact future academic and industrial applications.

Abstract
Mineral identification is a challenging task in geological sciences, which often implies multiple analyses of the physical and chemical properties of the samples for an accurate result. This task is particularly critical for the mining industry, where proper and fast mineral identification may translate into major efficiency and performance gains, such as in the case of the lithium mining industry. In this study, a mineral identification algorithm optimized for analyzing lithium-bearing samples using Laser-induced breakdown spectroscopy (LIBS) imaging, is put to the test with a set of representative samples. The algorithm incorporates advanced spectral processing techniques-baseline removal, Gaussian filtering, and data normalization-alongside unsupervised clustering to generate interpretable classification maps and auxiliary charts. These enhancements facilitate rapid and precise labelling of mineral compositions, significantly improving the interpretability and interactivity of the user interface. Extensive testing on diverse mineral samples with varying complexities confirmed the algorithm's robustness and broad applicability. Challenges related to sample granulometry and LIBS resolution were identified, suggesting future directions for optimizing system resolution to enhance classification accuracy in complex mineral matrices. The integration of this advanced algorithm with LIBS technology holds the potential to accelerate the mineral evaluation, paving the way for more efficient and sustainable mineral exploration.

Abstract
This paper presents the examination of a planar waveguide sensor featuring a bimetallic layer, revealing its potential applicability across both the visible and infrared spectrums. The bimetallic layer consists of adjacent gold and silver slabs positioned atop the waveguide's core. This arrangement demonstrates the activation of two distinct plasmon resonances, indicating promising prospects for multiparameter sensing applications.

Abstract
Purpose: Inherited retinal diseases (IRDs) are a group of degenerative disorders of the retina, that can be potentially associated with changes in the anterior segment, but their prevalence and impact are not known. Exploring these concomitant ophthalmologic changes with biomechanical assessment may help identify other non-retina causes of vision loss in these patients, such as corneal ectasia or susceptibility to glaucoma. This study aimed to measure and compare corneal biomechanics in patients with and without IRDs. Methods: A total of 77 patients (154 eyes) with IRD were recruited as the study group. The control group consisted of 77 healthy adults (154 eyes) with matched age and sphere equivalents. All participants underwent a comprehensive assessment including corneal tomography (Pentacam (R)) and biomechanical assessment (Corvis ST (R)). A total of 4 second-generation biomechanical parameters and 3 indexes were collected: Ambrosio Relational Thickness (ARTh), Deflection Amplitude Ratio Max (DARM), Integrated Radius (IR) and Stiffness Parameter at Applanation (SP-A1), the final deviation value D of the Belin/Ambrosio Enhanced Ectasia Display (BADResults: For IRD patients, there was a higher DARM (p < 0.001), lower ARTh (p < 0.001), higher CBI (p < 0.001), higher TBI (p<0.001), and higher BAD-D (p < 0.001) compared to the control group. Regarding discrimination of healthy subjects and IRD patients, ARTh was the most sensitive parameter. Conclusion: The results showed that IRD patients tend to have softer corneal behaviour, compared to eyes without pathology, which may predispose patients to corneal ectasia or glaucoma development. ARTh could be used to screen IRD patients if a non-retina cause of vision loss is suspected.