Human exposure to Volatile Organic Compounds (VOCs) and their presence in indoor and working environments is recognized as a serious health risk, causing impairments of varying severities. Different detecting systems able to monitor VOCs are available in the market; however, they have significant limitations for both sensitivity and chemical discrimination capability. During the last years we studied systematically the use of Fourier Transform Infrared (FTIR) spectroscopy as an alternative, powerful tool for quantifying VOCs in air. We calibrated the method for a set of compounds (styrene, acetone, ethanol and isopropanol) by using both laboratory and portable infrared spectrometers. The aim was to develop a new, and highly sensitive sensor system for VOCs monitoring. In this paper, we improved the setup performance, testing the feasibility of using a multipass cell with the aim of extending the sensitivity of our system down to the part per million (ppm) level. Considering that multipass cells are now also available for portable instruments, this study opens the road for the design of new high-resolution devices for environmental monitoring.

Superconducting split-ring resonator arrays allow to overcome two main limitations affecting metallic metamaterial resonating in the terahertz (THz) range: ohmic losses and tunability of their optical response. In this work, we design and experimentally realize direct and complementary square arrays of superconducting YBa₂Cu₃O₇ (YBCO) split-ring resonators working in the THz spectral range. The main purpose of this paper is to show how the metamaterial resonances can be tuned by temperature (T) when crossing the superconducting transition temperature T_c of YBCO. The tuning property can be quantified by describing the THz transmittance of the patterned YBCO films vs. T through a model of coupled resonators. This model allows us to estimate the THz resonances of split-ring arrays and their interaction, showing how the kinetic inductance L_k in the superconducting state is the main parameter affecting the metamaterial properties.

Optical properties of Si ₃ N ₄ membranes are modified via patterning of holes to excite surface phonon polaritons and increase transmittance.

Methylammonium lead iodide exhibits dual valence bands. A pump at 400 nm excites the transition between VB2 and CB1, creating holes in VB2 (lighter and more conductive) that relax to VB1 rather than directly recombine with the electrons in CB1.

Volatile Organic Compounds (VOCs) are organic molecules that have low boiling points and therefore easily evaporate into the air. They pose significant risks to human health, making their accurate detection the crux of efforts to monitor and minimize exposure. Infrared (IR) spectroscopy enables the ultrasensitive detection at low-concentrations of VOCs in the atmosphere by measuring their IR absorption spectra. However, the complexity of the IR spectra limits the possibility to implement VOC recognition and quantification in real-time. While deep neural networks (NNs) are increasingly used for the recognition of complex data structures, they typically require massive datasets for the training phase. Here, we create an experimental VOC dataset for nine different classes of compounds at various concentrations, using their IR absorption spectra. To further increase the amount of spectra and their diversity in term of VOC concentration, we augment the experimental dataset with synthetic spectra created via conditional generative NNs. This allows us to train robust discriminative NNs, able to reliably identify the nine VOCs, as well as to precisely predict their concentrations. The trained NN is suitable to be incorporated into sensing devices for VOCs recognition and analysis.