Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

Glyphosate is one of the most widely used herbicides in the world. Its safety for both human health and aquatic biomes is a subject of wide debate. There are limits to glyphosate’s presence in bodies of water, and it is usually detected through complex analytical procedures. In this work, the presence of glyphosate is detected directly through optical interrogation of aqueous solution. For this purpose, silver nanoparticles were produced by pulsed laser ablation in liquids. Limits of detection of 0.9 mg/L and 3.2 mg/L were obtained with UV-Vis extinction and Surface Enhanced Raman spectroscopies, respectively. The sensing mechanism was evaluated in the presence of potential interferents as well as with commercial glyphosate-based herbicides.

This work proposes a fiber Bragg gratings (FBG) sensor for simultaneous measurements of water level, velocity, and temperature in open channels. The optical fibers are placed on a rectangular aluminum rod attached to the bottom of the channel. The water flow causes a distributed load and a characteristic bend in the rod, depending on the water level and velocity. A set of four FBGs are used to monitor the rod’s bending profile. Taking the wavelength difference of two FBGs makes it possible to eliminate the temperature effect and obtain a system of nonlinear equations dependent on velocity and water level. Velocities from 0.1 ms to 0.9 m/s and water levels from 0.4 m to 0.7 m were experimentally evaluated. A semi-empirical model was obtained to describe the wavelength shift, and finally the water level and velocity are calculated. The results show that the proposed system can determine the water level, mean velocity and temperature with satisfactory accuracy. Artificial neural networks are used as a second and more straightforward approach to determine the water level and velocity from the measured wavelengths, considering mean values and standard deviation. With this method, the maximum absolute deviations were 0.02 m/s and 3.1 cm, for the mean velocity and water level, respectively.

Background/Objectives: Beta-lactam antibiotics, derived from penicillin, are the most used class of antimicrobials used for treating bacterial infections. Over the years, microorganisms have developed resistance mechanisms capable of preventing the effect of these drugs. This condition has been a significant public health concern for the 21st century, especially after predictions that antimicrobial resistance could lead to 10 million deaths annually by 2050. The challenge of developing new antimicrobials brings with it the need to ensure the efficacy of existing ones, hence the importance of developing fast and low-cost monitoring techniques. Methods: In this study, we present an alternative based on nanophotonics using Surface-Enhanced Raman Spectroscopy (SERS) mediated by nanoparticles for the detection of antimicrobials, with emphasis on some beta-lactam antibiotics commonly prescribed in cases of critically ill patients. It is a sensitive and accurate technique for drug monitoring, allowing for rapid and specific detection of its molecular signatures. This approach is crucial to address the challenge of antimicrobial resistance and ensure the therapeutic efficacy of existing treatments. Results: Our experiments demonstrate the possibility of identifying spectra with characteristic vibrations (fingerprints) of these antimicrobials via SERS. Conclusions: Our results point to new strategies for molecular monitoring of drugs by optical techniques using unconventional nanoparticles.

Amoxicillin is still one of the most common antibiotics to treat bacterial infections. Although conventional drug monitoring techniques often exhibit significant limitations in terms of accuracy and analysis speed, optical-based methods can be introduced as an appealing option for this purpose. Fourier transform infrared spectroscopy (FTIR) has been long established and implemented across various fields, including biomedical, clinical, and pharmaceutical. This work presents an alternative approach to the usual amoxicillin detection and monitoring procedures using vibrational spectroscopy. In this preliminary investigation, our results show several characteristics for quantification, with limit of detection of 93 µM, point to the high potential of the technique for drug monitoring.