In silica optical fiber Surface Plasmon Resonance (SPR)-based sensors, an increase in fiber core diameter produces a corresponding increase in the sensitivity and Signal to Noise Ratio (SNR). In Plastic Optical Fiber (POF) realized in PMMA there are different influences of design parameters on the performance, as both sensitivity and SNR are concerned. In particular, the SNR, for different refractive index values of the analyte, in a 250 μm diameter POF is greater than the one in 1,000 μm diameter POF. On the other hand, the sensitivity, for the same refractive index values of the analyte, in a 1,000 μm diameter POF is greater than the one in a 250 μm diameter POF. The results of an experimental analysis demonstrating the above behavior are reported.

Widespread use of antibiotics has led to pollution of waterways, potentially creating resistance among freshwater bacterial communities. Microorganisms resistant to commonly prescribed antibiotics (superbug) have dramatically increased over the last decades. The presence of antibiotics in waters, in food and beverages in both their un-metabolized and metabolized forms are of interest for humans. This is due to daily exposure in small quantities, that, when accumulated, could lead to development of drug resistance to antibiotics, or multiply the risk of allergic reaction. Conventional analytical methods used to quantify antibiotics are relatively expensive and generally require long analysis time associated with the difficulties to perform field analyses. In this context, electrochemical and optical based sensing devices are of interest, offering great potentials for a broad range of analytical applications. This review will focus on the application of magnetic nanoparticles in the design of different analytical methods, mainly sensors, used for the detection of antibiotics in different matrices (human fluids, the environmental, food and beverages samples).

Faced with numerous challenges raised by the requirements of the modern industries for higher power and higher energy, supercapacitors study started playing an important role in offering viable solutions for some of these requirements. This paper presents the surface redox reactions based modeling in order to study the origin of high capacity of EDLC (electrical double-layer capacitor) for better understanding the working principles of supercapacitors. Some application-dependent sizing methods are also presented since proper sizing can increase the efficiency and the life cycle of the supercapacitor based systems

Food additives are utilized in countless food products available for sale. They enhance or obtain a specific flavor, extend the storage time, or obtain a desired texture. This paper presents an automatic classification system for five food additives based on their absorbance in the ultraviolet domain. Solutions with different concentrations were created by dissolving a measured additive mass into distilled water. The analyzed samples were either simple (one additive solution) or mixed (two additive solutions). The substances presented absorbance peaks between 190 nm and 360 nm. Each substance presents a certain number of absorbance peaks at specific wavelengths (e.g., acesulfame potassium presents an absorbance peak at 226 nm, whereas the peak associated with potassium sorbate is at 254 nm). Therefore, each additive has a distinctive spectrum that can be used for classification. The sample classification was performed using deep learning techniques. The samples were associated with numerical labels and divided into three datasets (training, validation, and testing). The best classification results were obtained using CNN (convolutional neural network) models. The classification of the 404 spectra with a CNN model with three convolutional layers obtained a mean testing accuracy of 92.38% ± 1.48%, whereas the mean validation accuracy was 93.43% ± 2.01%.