The correlation between the copper-induced deep centers with Ev+0.1 eV and the appearance of luminescent copper centers, leading to a characteristic line spectrum with the most intense Cu00 no-phonon line at 1.014 eV, has been examined using deep level transient spectroscopy (DLTS) and photoluminescence (PL). Concentrations of the 0.1 eV copper centers ranging from 1011 to about 1014 cm−3 were obtained by a copper contamination treatment of floating zone p-type silicon samples without quenching. The dependence of the Cu00 line intensity on the excitation power in the transition region to intensity saturation was used to determine the saturation intensity ICusat, which represents the concentration of luminescent copper centers. The saturation intensities and therefore also the concentrations of luminescent copper centers, show a linear dependence on the concentrations of the 0.1 eV deep centers with good correlation, suggesting that the same Cu-induced centers are detected by DLTS and PL.

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Breath gas analysis is a promising tool for medical research and diagnosis. A particularly powerful technological approach is millimeter-wave/terahertz (mmW/THz) spectroscopy, because it is a very sensitive and highly selective technique. In addition, it offers the potential for compact and affordable sensing systems for wide use. In this work, we demonstrate the capability of a mmW/THz spectrometer for breath analysis. Samples from three volunteers and a sample from ambient air were analyzed with respect to 31 different molecular species. High-resolution absorption spectra were measured by scanning two absorption lines from each species. Out of the 31, a total of 21 species were detected. The results demonstrate the potential of mmW/THz spectroscopy for breath analysis.

We describe a spectrometer consisting of a vector network analyzer, a gas absorption cell, and a quasi-optical bench that acquires terahertz spectra of gaseous substances and mixtures. We tested volatile organic compounds that are medical biomarkers or chemicals which can be found on the US Environment Protection Agency list of harmful substances. Absorption spectra at gas pressures between 10 Pa and 5000 Pa were recorded. A subsequent multivariate data analysis demonstrated excellent qualitative and quantitative identification of pure substances and complex mixtures. The applied multivariate algorithms are principal components analysis, partial least square regression and soft independent modelling of class analogy.

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