Conference Agenda

Optical sensors based on a plasmonic multilayer stack, such as metal-insulator-metal (MIM), have attracted considerable attention over the past decades owing to their high resolution and high performance compared to conventional surface plasmon resonance (CSPR) sensors for bulk sensing (BS) applications. In this paper we show that CSPR is better than MIM sensors for thin film sensing, i.e. when a dielectric sensing layer (SL) is deposited on the outermost metal layer of the structure. We demonstrate that the deposition of a thin film SL on the top of the outermost-layer of an optimized multilayer structure, i.e. MIM, strongly decreases the evanescent electric field and the field enhancement at metal-SL interface and decreases the sensor’s sensitivity for MIM versus CSPR. By considering the theoretical and experimental results we demontrated that CSPR is more suitable than MIM for thin films sensing applications.

Nucleic acids are essential biomolecules for the functioning of cells. In past years, nucleic acids have been assessing their role in prognostics and diagnostics. The progress of nanotechnology has allowed the fabrication of various type of nanostructured biosensors able to detect them with high sensitivity and specificity. Among the available sensing mechanisms, the sensor technology based on Surface-enhanced Raman Spectroscopy (SERS) is frequently preferred for identifying nucleic acids. In these sensors, natural or synthetic oligonucleotide sequences, acting as probes to hybridize the target molecules, are immobilized on a plasmonic sensing platform. In particular, aptamers, short DNA/RNA sequences, are emerging as new recognition elements for their chemical stability and specificity. Here, we focus on the combination of a specific type of aptamer, a molecular aptamer beacon, and nanostructured SERS biosensors for a sensitive detection of nucleic acids.

We report the experimental and theoretical terahertz absorption characteristics of unprocessed crude oils from Kuwait oil wells. Using frequency domain THz spectroscopy technique between the frequencies from 2 – 18 THz (66 – 600 cm-1) and semi-empirical computational chemistry calculation, five (SA121T, SA-151TS, SA108T, SA-120T, SA159T) of the 30 crude oils revealed characteristic absorption peaks. Experimental data showed absorption peaks at 6.0 THz, 7.7 THz, 13 THz, and 16 THz. On the other hand, the calculated spectral bands of 10 nonane molecules were found at around 2.8 THz, 7.7 THz, 10 THz, and 16 THz. Although only two bands were predicted by the calculation, adding alkane molecules of different lengths (pentane to decane) resulted in the formation of new peaks. These preliminary results suggest that there is a mixture of different alkanes present in the investigated samples, a typical characteristic of unprocessed crude oil.

The localized surface plasmon resonance (LSPR) is a phenomenon which consists in a collective oscillation of free electrons in metal nanoparticles (NPs), it is very sensitive to any changing of the optical properties of the surrounding medium, for instance, provoked by the adsorption or desorption of molecules over metal surface. In our work we investigated the LSPR response of silver NPs chemically grafted onto transparent substrates and exposed to increasing quantities of water vapor inside a vacuum chamber. Extinction spectra are obtained by using an “in situ” UV-Vis spectrophotometer as a function of the vapor pressure inside the chamber. We studied the adsorption and desorption mechanism of vapor over plasmonic substrates. The huge sensitivity and the accessible and cost-effective equipment make these effects promising candidates for various sensing applications, including the environmental monitoring

A scattering snapshot hold an enormous potential for cell class and state classification, allowing to avoid costly fluorescence labelling. Beside convolutional neural networks show outstanding image classification performance compared to other state-of-the-art methods, regarding accuracy and speed. Therefore, we combined the two techniques (Light Scattering and Deep Learning) to identify living cells with high precision. Neural Networks show high prediction performance for known classes but struggles when unknown classes need to be identified. In such a scenario no prior knowledge of the unknown cell class can be used for the model training, which inevitably results in a misclassification. To overcome the hurdle, of identifying unknown cell classes, we must first define an in-distribution of known snapshots to afterwards detect out of distribution snapshots as unknowns. Ones, such a new cell class is identified, we can retrain our cell classifier with the obtained knowledge, so we dynamically update the cell class database. We applied this measurement approach to scattering pattern snapshots of different classes of living cells. Our outcome shows a precise cell classification, which can be applied to a wide range of single cell classification approaches.