The importance of biomarker quantification in technology cannot be overstated. It has numerous applications in medical diagnostics, drug delivery, and the timely implementation of prevention and control strategies for highly prevalent diseases worldwide. However, the discovery of new tools for detection has become increasingly necessary. One promising avenue is the use of perovskite-based materials, which exhibit excellent catalytic activity and redox properties. These make them ideal candidates for the development of electrochemical sensors. In this review, the advances of purely non-enzymatic electrochemical detection of bio-analytes, with ABO₃ perovskite form, are presented. The work allows the visualization of some of the modifications in the composition and crystal lattice of the perovskites and some variations in the assembly of the electrodes, which can result in systems with a better response to the detection of analytes of interest. These findings have significant implications for improving the accuracy and speed of biomarker detection, ultimately benefiting patients and healthcare professionals alike.

Lithium niobate perovskite and silver nanoparticle-based nanocomposites (LNB:AgNPs) were explored for developing an electrochemical glucose sensor. The perovskite to silver nanoparticle ratios investigated were 4:1, 1:1, 1:2, 2:1, and 1:4. Among these, the 4:1 ratio, with the lowest silver content, demonstrated the most stable performance during glucose quantification via amperometry. The sensor’s response was evaluated measuring the current at a fixed potential of 0.7 V following the injection of 1 mM glucose with each addition. The calibration curve obtained from the recorded data exhibited a linear response within the 1 to 15 mM glucose concentration range, achieving a sensitivity of 2 μA/mM, a high correlation coefficient (R² = 0.997), and a limit of detection (LOD) of 0.5 µM. The LNB4:1AgNP composite allowed taking advantage of the unique properties of both components in a balanced manner, maximizing the sensor performance in practical applications.

Cr₂Nb alloys are potential candidates for high-temperature structural materials. The influence of different mechanical alloying parameters (milling time) and sintering processes were studied. After mechanical alloying and observation by scanning electron microscope (SEM), nano powders were characterized and then sintered by spark plasma sintering (SPS). Electrochemical noise (EN) tests were also conducted in order to study the electrochemical behavior. From the current experimental results, it was revealed that ball milling times up to 20 h may explain the influence of Nb–Cr alloys and its association to the Laves phase and corrosion behavior. These insights aimed at improving the samples’ predicted behavior before spending time and resources at high-temperature industrial processes.

The growing accumulation of microplastics in marine environments demands fast and accurate analytical methods for polymer identification. This study presents a new canonical spectral transformation (CST) strategy designed to extract the most relevant information of Raman spectra and enhance the performance of artificial intelligence (AI) models in the classification of microplastics. Using the Marine Plastic Database (MPDB) as the source of Raman spectra, five supervised models—k-Nearest Neighbor (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and a one-dimensional Convolutional Neural Network (CNN-1D)—were trained and evaluated under both typical (conventional methodology) and CST workflows using 500 noisy samples per category. The CST consists of representing a Raman spectra in a vector where only the magnitude peaks of the most relevant frequency bands of the spectra are retained and the remaining values are null. This CST minimizes the inclusion of non-target data reaching the AI models. All models achieved higher accuracy with CST, where CNN-1D achieved the most significant performance, increasing accuracy to 0.90. In addition, CNN-1D identified Polystyrene (PS) and Poly(methyl methacrylate) (PMMA) with a score of 100% and 99%, respectively. The results demonstrate that CST effectively enhances spectral feature extraction and can be generalized to other spectroscopic techniques, providing a scalable framework for AI-assisted microplastic identification in seawater samples.