Conference Agenda

Despite significant progress in the detection of small microplastics, the detection of such particles still faces problems caused by the limitations of current detection methods. We introduce optical methods for the analysis of individual microplastics and the fabrication of a substrate using plasmonic particles to detect plastic nanoparticles. We summarize recent experimental activities involving the construction of portable Raman tweezers that can be used for analysis of microsplastics. Optical trapping is complemented by nanoimprinting of plasmonics nanoparticles that enables create the "active" aggregates that can be used for Surface Enhanced Raman Spectroscopy (SERS) detection and as plasmon-enhanced thermoplasmonic concentrators for nanoscale plastics. The principle of nanoimprinting is based on the dominance of the scattering force (compared to the gradient force) for plasmonic particles, this force pushes particles in the direction of propagation of the light beam. In both cases, enhanced sensitivity is demonstrated, allowing the detection of nanoplastics of size orders of magnitude lower than what can be achieved by Raman spectroscopy. This study demonstrates that the combination of two optical manipulation techniques are capable of filling the technological gap in the detection of plastic particles ranging in size from a few tens of nm to 20 µm.

Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, represents a global health challenge due to its complexity and the limitations of current diagnostic techniques. By combining Raman spectroscopy and Artificial Intelligence (AI), we have succeeded in classifying tumor cells. In fact, we have performed a first Raman spectral analysis based on the characterization and differentiation between uncultured primary human liver cells derived from resected HCC tumor tissue and the adjacent non-tumor counterpart. Biochemical analysis of the collected Raman spectra revealed that there is more DNA in the nuclei of the tumor cells than in non-tumor cells. We then develop three machine learning approaches, including multivariate models and neural networks, to rapidly automate the recognition and classification of the Raman spectra of both cells. To evaluate the performance of the developed AI models, we prepared and analyzed two additional cell samples with a ratio of 4:1 and 3:1 between tumor and non-tumor cells and compared the obtained results with the nominal percentages (accuracy of 80 and 60%, respectively). These results confirm that the models are able to make classifications at the level of a single spectrum, indicating the possibility of rapidly analysing and classifying a primary HCC cell.

Metastasis stands as the leading cause of mortality among colorectal cancer (CRC) patients. Galunisertib (LY2157299, LY) is a small molecule demonstrating promising anti-cancer effects by targeting the Transforming Growth Factor-beta (TGF-β) pathway. This route plays a pivotal role in initiating the epithelial-to-mesenchymal transition (EMT), a critical process for metastatic spread. Unfortunately, LY chronic treatment causes undesired effects. To mitigate these side effects, nanoscale drug delivery systems have emerged as a transformative approach in cancer treatment, enhancing drug effectiveness while minimizing toxicity. In this study, we introduce a hybrid nanosystem (DNP-AuNPs-LY@Gel) comprising porous diatomite nanoparticles decorated with plasmonic gold nanoparticles (AuNPs), encapsulating LY within a gelatin shell. This multifunctional nanosystem demonstrates efficient LY delivery, EMT reversal in CRC 2D and 3D cultures, and anti-cancer effects in vivo. Moreover, the nanosystem allowed the quantification with sub-femtogram resolution of the drug intracellularly released using surface-enhanced Raman spectroscopy (SERS). The release of LY is triggered by CRC cell acidic microenvironment. Real-time monitoring of drug release at the single-cell level is achieved by analyzing SERS signals of LY within CRC cells. The heightened efficacy of LY delivery through the DNP-AuNPs-LY@Gel complex offers a promising alternative strategy for reducing drug dosages and subsequent undesired effects.

The presence of microplastics in various food products has raised significant concerns regarding potential health risks for consumers. Among these products, milk, being a staple in many diets, has attracted attention for its widespread consumption and nutritional significance. In this work, a metrological method was developed to accurately quantify and characterize small microplastics (100-5 μm) in milk powder (infant formula) using micro-Raman (μRaman) technology, combining enzymatic digestion, organic matter removal under alkaline conditions, chemical analysis, microwave digestion and a final filtration step through a silicon (Si) filter. The present methodology was developed and validated for several polymers using both commercially available reference materials with defined dimensions and morphology, and more representative polydisperse materials.

Regarding PET microplastics, size, number and numerical distribution were previously evaluated in an intervalidation study involving two different laboratories with different micro-Raman instrumentation to provide a reference number for this material. The analytical procedure was further validated in terms of microplastic recovery rate and quantification sensitivity with the calculation of LOD (limit of detection) and LOQ (limit of quantification)

Additives are excessively used in agriculture for the purposes of crop protection, and enhancement of the yield quality and quantity of the products. Although the use of these chemicals is necessary for the food industry, they are associated with short- and long-term effects on human health. Thus, their use should be regulated, and their detection is critical not only for human health but also for the environment and wildlife. In this study, the detection of additives by surface-enhanced Raman scattering (SERS) substrates is proposed. For this purpose, flexible substrates are prepared from poly(ethylene glycol) diacrylate (PEGDA) and gold Nanoparticles (AuNPs). The detection performance of the designed substrates was tested against sulfur dioxide (SO2). It was found that designed substrates can provide homogenous signal distribution and significant signal enhancement. Moreover, they can allow detection of SO2 in wine down to 0.4 ppm which is lower than the regulatory limits.