Spectroscopic techniques such as Surface-Enhanced Raman Scattering (SERS), Surface-Enhanced Infrared Absorption (SEIRA), and Surface-Enhanced Fluorescence (SEF) are essential analytical techniques used to study the composition of materials by analyzing the way materials scatter light, absorb infrared radiation or emit fluorescence signals. This provides information about their molecular structure and properties. However, traditional SERS, SEIRA, and SEF techniques can be limited in sensitivity, resolution, and reproducibility, hindering their ability to detect and analyze trace amounts of substances or complex molecular structures. Metasurfaces, a class of engineered two-dimensional metamaterials with unique optical properties, have emerged as a promising tool to overcome these limitations and enhance spectroscopic techniques. This article provides a state-of-the-art overview of metasurfaces for enhanced SERS, SEIRA and SEF, covering their theoretical background, different types, advantages, disadvantages, and potential applications.

Cyanobacterial blooms pose significant environmental and public health risks due to the production of toxins that contaminate water sources and disrupt aquatic ecosystems. Rapid and accurate identification of cyanobacterial species is crucial for effective monitoring and management strategies. In this study, we combined Raman spectroscopy with deep learning techniques to classify four toxic cyanobacterial species: Dolichospermum crassum, Aphanizomenon sp., Planktothrix agardhii and Microcystis aeruginosa. Spectral data were acquired using a confocal Raman microscope with a 532 nm excitation wavelength and subjected to preprocessing and filtering to enhance signal quality. We evaluated a multichannel one-dimensional convolutional neural network (1D-CNN) approach that incorporates raw spectra, baseline estimations, and preprocessed spectra. This multichannel approach improved overall classification accuracy, achieving 86% compared to 74% with a traditional single-channel 1D-CNN using only preprocessed spectra while maintaining low overfitting. Shapley Additive exPlanations (SHAP) were applied to identify critical spectral regions for classification to enhance interpretability. These findings highlight the potential of combining Raman spectroscopy with explainable deep learning methods as a powerful tool for water quality monitoring and the early detection of Harmful Algal Blooms (HABs).

Phytoplankton are essential to aquatic ecosystems but can cause harmful algal blooms (HABs) that threaten water quality, aquatic life, and human health. Developing new devices based on spectroscopic techniques offers a promising alternative for rapid and accurate monitoring of aquatic environments. However, phytoplankton undergo various physiological changes throughout their life cycle, leading to alterations in their optical properties, such as autofluorescence. In this study, we present a modification of a low-cost photobioreactor designed to implement fluorescence spectroscopy to analyze the evolution of spectral signals during phytoplankton growth cycles. This device primarily facilitates the characterization of changes in autofluorescence, providing valuable information for the development of future spectroscopic techniques for detecting and monitoring phytoplankton. Additionally, real-time testing was performed on cyanobacterial cultures, where changes in autofluorescence were observed under different conditions. This work demonstrates a cost-effective implementation of spectroscopic techniques within a photobioreactor, offering a preliminary analysis for the future development of functional field devices for monitoring aquatic ecosystems.

Patella vulgata shells preserve geochemical and structural variations that can provide insights into past environmental conditions. Their composition, primarily calcium carbonate with organic residues from the biomineralization process, is influenced by external factors, such as sea surface temperature. Raman spectroscopy has emerged as a rapid, non-destructive tool for studying biogenic carbonates, enabling the identification of crystalline phases, organic components, and ion distribution. In this study, Raman imaging was applied to six shell sections of P. vulgata live-collected from Langre Beach in Cantabria, Spain. Spectral data were acquired using a Raman probe with a 532 nm excitation laser, providing high-resolution mapping of structural and compositional features. The analysis revealed spatial variations in mineralogy, organic matrix distribution, and ion incorporation in the calcium carbonate lattice, suggesting patterns originating during shell formation. Notably, the results suggest a consistent relationship between the organic and mineral components of the shells, with carotenoid distribution and carbonate ion substitution in the calcium carbonate lattice following similar growth patterns. These findings highlight the potential of Raman spectroscopy for studying biomineralization processes and the environmental records preserved in marine mollusk shells.

Water testing is becoming increasingly important due to dangerous phenomena such as Harmful Algal Blooms (HABs). Commonly, the content of a water sample is measured for the detection, monitoring and control of these events. Raman spectroscopy is a technique for the molecular characterization of materials in solid, liquid or gaseous form, which makes it an attractive method for analysing materials’ components. However, Raman scattering is a weak optical process and requires an accurate system for detection. In our work, we present, from design to fabrication, a microfluidic device on fused silica adapted to optimise the Raman spectrum of liquid samples when using a Raman probe. The device features a portable design for rapid on-site continuous flow measurements avoiding the use of large, costly and complex laboratory equipment. The main manufacturing technique used was ultrafast laser-assisted etching (ULAE). Finally, the effectiveness of the microfluidic device was demonstrated by comparing the Raman spectra of a known species of cyanobacteria with those obtained using other conventional substrates in laboratory analysis. The results demonstrate that the microfluidic device, under continuous flow conditions, exhibited a lower standard deviation of the Raman signal, reduced background noise and avoided signal variations caused by sample drying in static measurements.