Tunable multicomponent emission from carbon nanomaterials for neuromorphic optical synaptic functions
Multi-component luminescent materials provide an effective route to engineer complex optical responses and temporal emission dynamics for neuromorphic photonics. In this work, we present a neuromorphic photonic layer based on a multi-component carbon-based nanomaterial (THR@BA) composed of a rigid boron oxide matrix derived from the calcination of boric acid (BA), which embeds three organic molecules, terephthalic acid (TPA), 1-hydroxy-2-naphthoic acid (HNA) and rhodamine B (RhB), forming a confined host-guest system with enhanced intermolecular interactions. The rigid matrix promotes energy transfer and excited-state coupling between the molecular components. The multi-component nanomaterial exhibits dual emission of fluorescence and long-lived phosphorescence and simultaneously provides complex responses upon multi-wavelength excitation. These responses result from the superposition of the optical properties of each single-component nanomaterial functionalized with the respective organic molecule, namely TPA@BA, HNA@BA, and RhB@BA. By tuning the excitation parameters, THR@BA reproduces key synaptic functions, including short- and long-term memory as well as potentiation involved in synaptic plasticity, deriving from the dynamic balance between fluorescence and phosphorescence emission with lifetimes ranging from nanoseconds to seconds. This work demonstrates that multi-component molecular systems confined within simple inorganic matrices can serve as versatile platforms for engineering programmable luminescence dynamics, making them ideal candidates for artificial optical synapses and neuromorphic computing.
Quantum Sensors: A Comprehensive Review and Future Perspectives
Quantum sensing is an emerging field that exploits the principles of quantum mechanics to achieve unprecedented levels of precision, sensitivity, and accuracy in measuring physical quantities. From navigation and artificial intelligence to environmental monitoring and medical diagnostics, these technologies have the potential to transform a wide range of applications completely. This review provides an exploration of the fundamental principles, state-of-the-art developments, and future perspectives of quantum sensors and perception. We discuss the underlying quantum phenomena, such as superposition, entanglement, and quantum coherence, and their role in enabling advanced sensing capabilities. We also look at how quantum sensors can be incorporated into several applications. Lastly, we address the field’s challenges and outline future research directions.
Microstrip T-Resonator: Review, Design, and Error Assessment for Dielectric Characterization
This paper presents a comprehensive study of the Microstrip T-Resonator (MTR) for dielectric characterization of PCB substrates. Due to conflicting assumptions in existing models, we evaluate two MTR configurations: MTR-S, corresponding to the short-substrate configuration, and MTR-E, corresponding to the extended-substrate configuration. Microstrip discontinuities, particularly the open end, significantly affect dielectric characterization. Specifically, the effective length used for relative permittivity extraction relies on an open-end correction derived for extended substrates, making MTR-E the appropriate choice for this parameter. Loss tangent estimation, in turn, depends on radiation loss models that assume the substrate terminates at the open end of the resonator, as in the MTR-S configuration. Thus, this work investigates the MTR methodology and the effects of substrate extension on dielectric characterization accuracy. Relative permittivity and loss tangent are estimated using measured and simulated data, and the results are employed to assess the error and deviation of each configuration. The analysis highlights that aligning device geometry with model assumptions significantly improves estimation accuracy, particularly for loss tangent, which exhibits greater variation with substrate termination.
Parametric vertical photovoltaic system: Fibonacci methodology applied to solar trees design
This study presents a novel Fibonacci-inspired solar tree design to optimize energy efficiency in urban environments while addressing the growing demand for sustainable energy solutions. The objective was to propose a project methodology for Solar Trees, one that can adapt to diverse contexts and enhance the viability of these structures as source of renewable energy within urban landscapes. The research used Rhinoceros 7 and Grasshopper to create a base model, parametrically adjust key design parameters, and simulate solar irradiation through the Ladybug plug-in. The optimized solar tree design achieved an annual solar energy incidence of 84,017 kWh and an irradiation of 3,222 kWh/m2/year, surpassing traditional flat PVS, which would generate only 68,084 kWh/year with an irradiation of 2,611 kWh/m2/year. Comprising 21 modular photovoltaic leaves made from mono-crystalline silicon, the design meets technical energy requirements and offers aesthetic and landscaping benefits in urban settings. This work underscores the importance of a multidisciplinary approach, merging architecture, energy efficiency, and environmental science to propose feasible solutions for contemporary urban challenges. The findings indicate that the proposed solar tree design fulfills energy demands and enhances public spaces, challenging conventional perceptions of photovoltaic systems as fragmented and visually unappealing.
High-Resolution Distributed Optical Fiber Sensor for Multiparameter Sensing, Local Inhomogeneity Detection, and Poisson’s Ratio Estimation in Concrete Structures
The structural health monitoring (SHM) concept emerges as an important approach for the continuous assessment of civil infrastructure, directly aligning with the demands for sustainability, safety, and smart devices in modern urbanization and smart cities. Addressing these demands, this article presents the development and validation of a multiparameter distributed optical fiber sensor (DOFS) for the simultaneous measurement of temperature, longitudinal strain, and transverse strain with subcentimeter-scale spatial resolution. Based on optical frequency domain reflectometry (OFDR), the system utilizes a geometrically configured commercial fiber to act as a continuous spatial rosette. The sensor was characterized, achieving linear sensitivities of 0.1253 GHz/ μϵ and 1.251 GHz/°C, with a limit of detection of 0.1 μϵ and 0.1 ∘ C and a spatial resolution of 2.5 mm. Application tests on concrete cylinders under compressive loading demonstrated the system’s capability to decouple multiaxial strain components while inherently compensating for thermal cross-sensitivity, maintaining a stable temperature variation of 0.3 ∘ C during testing. The decomposed experimental strain profiles were validated against a numerical finite element analysis (FEA). In contrast with the idealized numerical model, the distributed sensor successfully mapped real-world localized strain concentrations and inherent material inhomogeneities, registering spatial strain variations of up to 619.22 μϵ (longitudinal) and 113.75 μϵ (transverse) near the elastic limit (160 kN). By aggregating the synchronized multitest data, the Poisson’s ratio of the concrete was estimated at 0.16. Furthermore, the system captured the complete spatiotemporal evolution of damage, precisely identifying the macroscopic rupture region. Finally, the results demonstrate that the proposed device provides a robust, single-fiber solution for 2-D strain field mapping and multiparameter SHM, filling key technological gaps for future smart infrastructure applications.
POF-Based Sensor Device for Assessment of Human-Robot Interaction Forces
This article presents the development and validation of a polymer optical fiber (POF) sensor system for quantifying human–robot interaction (HRI) torque in knee exoskeletons. The device consists of a four-channel POF array embedded in a shank cuff that provides online torque signals during gait tasks. The channels were calibrated against a reference load cell, yielding dynamic force RMSE in the range 3.93–5.14 N and correlation between 0.88 and 0.94. In quasi-static interface tests with six participants, the reconstructed POF torque tracked full torque transfer through the cuff, with RMSE of 1.4–1.8 N ⋅ m for sinusoidal and triangular actuator profiles. In treadmill trials, the POF-based torque was used as a reference to assess disturbance observer (DOB) techniques. All observers reproduced the main interaction-torque patterns but showed attenuation and temporal shifts with respect to the POF reference, with RMSE between 0.8 and 1.3 N ⋅ m. These results indicate that the POF cuff delivers a consistent interaction-torque signal at the interface and can serve as a practical reference when analyzing bandwidth and robustness limitations of observer-based estimators.