Ariel Guerreiro

Abstract
Topological photonics, leveraging concepts from condensed matter physics, offers transformative potential in the design of robust optical systems. This study investigates the integration of topologically protected edge states into plasmonic nanostructures for enhanced optical sensing. We propose a toy model comprising two chains of metallic filaments forming a one-dimensional plasmonic crystal with diatomic-like unit cells, positioned on a waveguide. The system exhibits edge states localized at the boundaries and a central defect, supported by the Su-Schrieffer-Heeger (SSH) model. These edge states, characterized by significant electric field enhancement and topological robustness, are shown to overcome key limitations in traditional plasmonic sensors, including sensitivity to noise and fabrication inconsistencies. Through coupled mode theory, we demonstrate the potential for strong coupling between plasmonic and guided optical modes, offering pathways for improved interferometric sensing schemes. This work highlights the applicability of topological photonics in advancing optical sensors.

Abstract
This study explores the application of machine learning algorithms to optimize the geometry of the plasmonic layer in a surface plasmon resonance photonic crystal fiber sensor. By leveraging the simplicity of linear regression ( LR) alongside the advanced predictive capabilities of the gradient boosted regression (GBR) algorithm, the proposed approach enables accurate prediction and optimization of the plasmonic layer's configuration to achieve a desired spectral response. The integration of LR and GBR with computational simulations yielded impressive results, with an R-2 exceeding 0.97 across all analyzed variables. Moreover, the predictive accuracy demonstrated a remarkably low margin of error, epsilon < 10(-15). This combination of methods provides a robust and efficient pathway for optimizing sensor design, ensuring enhanced performance and reliability in practical applications.

Abstract
In our study, we conducted a thorough analysis of the spectral characteristics of a D-shaped surface plasmon resonance (SPR) photonic crystal fiber (PCF) refractive index sensor, incorporating a full width at half maximum (FWHM) analysis. We explored four distinct plasmonic materials—silver (Ag), gold (Au), Ga-doped zinc oxide (GZO), and an Ag-nanowire metamaterial—to understand their impact on sensor performance. Our investigation encompassed a comprehensive theoretical modeling and analysis, aiming to unravel the intricate relationship between material composition, sensor geometry, and spectral response. By scrutinizing the sensing properties offered by each material, we laid the groundwork for designing multiplasmonic resonance sensors. Our findings provide valuable insights into how different materials can be harnessed to tailor SPR sensing platforms for diverse applications and environmental conditions, fostering the development of advanced and adaptable detection systems. This research not only advances our understanding of the fundamental principles governing SPR sensor performance but also underscores the potential for leveraging varied plasmonic materials to engineer bespoke sensing solutions optimized for specific requirements and performance metrics. © 2025 SBMO/SBMag.

Abstract
This theoretical study presents a D-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) based sensor designed for humidity detection in transformer oil. Humidity refers to the presence of water dissolved or suspended in the oil, which can affect its dielectric properties and, consequently, the efficiency and safety of the transformer's operation, failures in the sealing system and the phenomenon of condensation can be the main sources of this humidity. This sensor leverages the unique properties of the coupling between surface plasmons and fiber guided mode at the Au-PCF interface to enhance the sensitivity to humidity changes in the external environment. The research demonstrated the sensor's efficacy in monitoring humidity levels ranging from 0% to 100% with an average sensitivity of measured at 1106.1 nm/RIU. This high sensitivity indicates a substantial shift in the resonance wavelength corresponding to minor changes in the refractive index caused by varying humidity levels, which is critically important in the context of transformer maintenance and safety. Transformer oil serves as both an insulator and a coolant, and its humidity level is a key parameter influencing the performance and longevity of transformers. Excessive humidity can lead to insulation failure and reduced efficiency and, therefore, the ability to accurately detect and monitor humidity levels in transformer oil can significantly enhance preventive maintenance strategies, reduce downtime, and prevent potential failures, ensuring the reliable operation of electrical power systems. © The Authors.

Abstract
Easily accessible through tabletop experiments, paraxial fluids of light are emerging as promising platforms for the simulation and exploration of quantumlike phenomena. In particular, the analogy builds on a formal equivalence between the governing model for a Bose-Einstein condensate under the mean-field approximation and the model of laser propagation inside nonlinear optical media under the paraxial approximation. Yet, the fact that the role of time is played by the propagation distance in the analog system imposes strong bounds on the range of accessible phenomena due to the limited length of the nonlinear medium. In this Letter, we present an experimental approach to solve this limitation in the form of a digital feedback loop, which consists of the reconstruction of the optical states at the end of the system followed by their subsequent reinjection exploiting wavefront shaping techniques. The results enclosed demonstrate the potential of this approach to access unprecedented dynamics, paving the way for the observation of novel phenomena in these systems.