Abstract
This paper presents an approach to enhancing sensitivity in optical sensors by integrating self-image theory and graphene oxide coating. The sensor is specifically engineered to quantitatively assess glucose concentrations in aqueous solutions that simulate the spectrum of glucose levels typically encountered in human saliva. Prior to sensor fabrication, the theoretical self-image points were rigorously validated using Multiphysics COMSOL 6.0 software. Subsequently, the sensor was fabricated to a length corresponding to the second self-image point (29.12 mm) and coated with an 80 mu m/mL graphene oxide film using the Layer-by-Layer technique. The sensor characterization in refractive index demonstrated a wavelength sensitivity of 200 +/- 6 nm/RIU. Comparative evaluations of uncoated and graphene oxide-coated sensors applied to measure glucose in solutions ranging from 25 to 200 mg/dL showed an eightfold sensitivity improvement with one bilayer of Polyethyleneimine/graphene. The final graphene oxide-based sensor exhibited a sensitivity of 10.403 +/- 0.004 pm/(mg/dL) and demonstrated stability with a low standard deviation of 0.46 pm/min and a maximum theoretical resolution of 1.90 mg/dL.

Abstract
Spectral imaging is a powerful technology that uses spatially referenced spectral signatures to create informative visual maps of sample surfaces that can reveal more than what conventional RGB-visual images can show. Indeed, different spectroscopy modalities can provide different information about the same sample: for instance, Laser-Induced Breakdown Spectroscopy (LIBS) imaging can detect the presence of specific elements on the surface, while Raman imaging can identify the molecular structures and compositions of the sample, both of which have potential applications in various industrial processes, from quality control to material sorting. In the path from science to technology, the increasing accessibility to such solutions and the strong market pull have opened a window of opportunity for innovative multimodal imaging solutions, where information from distinct sources is set to be combined in order to enhance the capabilities of the single modality system. However, the practical implementation of multimodal spectral imaging is still a challenge, despite its theoretical potential, and as such, it is yet to be achieved. In this work, we will go over multimodal spectral knowledge distillation, a disruptive approach to multimodal spectral imaging techniques that tries to explore the combination of two techniques to capitalize on their individual strengths. In specific, this approach allows us to utilize one technique as an autonomous supervisor for the other, leveraging the higher degree of knowledge and interpretability of one of the techniques to increase the performance and transparency of the other. We present some example scenarios with LIBS and HSI and Raman spectroscopy and LIBS, discussing the impact of this new approach for scientific and technological applications.

Abstract
As lithium-bearing minerals become critical raw materials for the field of energy storage and advanced technologies, the development of tools to accurately identify and differentiate these minerals is becoming essential for efficient resource exploration, mining, and processing. Conventional methods for identifying ore minerals often depend on the subjective observation skills of experts, which can lead to errors, or on expensive and time-consuming techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Optical Emission Spectroscopy (ICPOES). More recently, Raman Spectroscopy (RS) has emerged as a powerful tool for characterizing and identifying minerals due to its ability to provide detailed molecular information. This technique excels in scenarios where minerals have similar elemental content, such as petalite and spodumene, by offering distinct vibrational information that allows for clear differentiation between such minerals. Considering this case study and its particular relevance to the lithium- mining industry, this manuscript reports the development of an unsupervised methodology for lithium-mineral identification based on Raman Imaging. The deployed machine-learning solution provides accurate and interpretable results using the specific bands expected for each mineral. Furthermore, its robustness is tested with additional blind samples, providing insights into the unique spectral signatures and analytical features that enable reliable mineral identification.

Abstract
The generation of short pulses in fiber lasers using saturable absorbers made of graphene oxide (GO), focusing on film thickness, was studied and optimized. The saturable absorber comprised a GO thin film deposited onto a single-mode fiber using the spray coating technique. Water-dispersed GO with a concentration of 4 mg/mL, characterized by a high proportion of monolayer flakes, was employed. This thin film was integrated into a cavity ring laser featuring an erbium-doped fiber amplifier (EDFA), resulting in a fiber laser emitting at a central emission wavelength of approximately 1564 nm and having a total cavity length of approximately 120 m. By controlling intracavity polarization, short-pulsed light was generated through mode-locking, Q switching, or a combination of both regimes. This work presents a comprehensive characterization of the cavity ring laser operating under the mode-locking regime. It encompasses an analysis of the spectral behavior, focusing on the evolution of the Kelly's sidebands with increasing pump power, as well as an assessment of its temporal stability. Moreover, the effects of the aging of the saturable absorber material were studied after a time period of 6 months after the fabrication. It was observed that the general characteristics of spectral signal of the laser were maintained, with long-term stability .

Abstract
This work provides a method that combines graphene oxide coating and self-image theory to improve the sensitivity of optical sensors. The sensor is designed specifically to measure the amount of glucose present quantitatively in aqueous solutions that replicate the range of glucose concentrations found in human saliva. COMSOL Multiphysics 6.0 was used to simulate the self-imaging phenomenon using a coreless silica fiber (CSF). For high-quality self-imaging, the second and fourth self-imaging points are usually preferred because of their higher coupling efficiency, which increases the sensor sensitivity. However, managing the fourth self-image is more difficult because it calls for a longer CSF length. As a result, the first and second self-image points were the focus of the simulation in this work. After the simulation, using the Layerby-Layer method, the sensor was constructed to a length that matched the second self-image point (29.12 mm) and coated with an 80 mu m/mL graphene oxide layer. When comparing uncoated and graphene oxide-covered sensors to measure glucose in liquids ranging from 25 to 200 mg/dL, one bilayer of polyethyleneimine/graphene demonstrated an eight-fold improvement in sensitivity. The final sensor, built on graphene oxide, showed stability with a low standard deviation of 0.6 pm/min. It also showed sensitivity at 10.403 +/- 0.004 pm/(mg/dL) with a limit of detection of 9.15 mg/dL.