Micro-electro-mechanical membranes having nanoscale holes were developed, to be used as a nanofluidic sample inlet in novel analytical applications. Nanoscopic holes can be used as sampling points to enable a molecular flow regime, enhancing the performance and simplifying the layout of mass spectrometers and other analytical systems. To do this, the holes must be placed on membranes capable of consistently withstanding a pressure gradient of 1 bar. To achieve this goal, a membrane-in-membrane structure was adopted, where a larger and thicker membrane is microfabricated, and smaller sub-membranes are then realized in it. The nanoscopic holes are opened in the sub-membranes. Prototype devices were fabricated, having hole diameters from 300 to 600 nm, a membrane side of 80 μm, and a simulated maximum displacement of less than 150 nm under a 1 bar pressure gradient. The obtained prototypes were tested in a dedicated vacuum system, and a method to calculate the effective orifice diameter using gas flow measurements at different pressure gradients was implemented. The calculated diameters were in good agreement with the target diameter sizes. Micro-electro-mechanical technology was successfully used to develop a novel micromembrane with nanoscopic holes, and the fabricated prototypes were successfully used as a gas inlet in a vacuum system for mass spectrometry and other analytical systems.

Early and significant results for a real-time, column-free miniaturized gas mass spectrometer in detecting target species with partial overlapping spectra are reported. The achievements have been made using both nanoscale holes as a nanofluidic sampling inlet system and a robust statistical technique. Even if the presented physical implementation could be used with gas chromatography columns, the aim of high miniaturization requires investigating its detection performance with no aid. As a study case, in the first experiment, dichloromethane (CH2Cl2) and cyclohexane (C6H12) with concentrations in the 6–93 ppm range in single and compound mixtures were used. The nano-orifice column-free approach acquired raw spectra in 60 s with correlation coefficients of 0.525 and 0.578 to the NIST reference database, respectively. Then, we built a calibration dataset on 320 raw spectra of 10 known different blends of these two compounds using partial least square regression (PLSR) for statistical data inference. The model showed a normalized full-scale root-mean-square deviation (NRMSD) accuracy of $$10.9\mathrm{\%}$$ 10.9 % and $$18.4\mathrm{\%}$$ 18.4 % for each species, respectively, even in combined mixtures. A second experiment was conducted on mixes containing two other gasses, Xylene and Limonene, acting as interferents. Further 256 spectra were acquired on 8 new mixes, from which two models were developed to predict CH2Cl2 and C6H12, obtaining NRMSD values of 6.4% and 13.9%, respectively.

Early and significant results for a real-time, column-free miniaturized gas mass spectrometer (MS) in detecting target species with partial overlapping spectra are reported. The achievements have been possible using both nanoscale holes to be used as a nanofluidic sampling inlet system and a robust statistical technique based on multivariate analysis to build predictive models. Even if the presented physical implementation could be used with gas chromatography (GC) columns, the aim of high miniaturization requires investigating its detection performance with no GC aid. For this reason, suitable analytical models were studied to get a semi-quantitative evaluation with very low computational resources. As a study case, dichloromethane (CH2Cl2) and cyclohexane (C6H12) with concentrations in the 6-93ppm range in single and compound mixtures were used. The nano-orifice approach was able to acquire raw spectra in 60 seconds with correlation coefficients of 0.525 and 0.578 with respect to the NIST reference database, respectively. Then, we built a calibration dataset on 2277 raw spectra of 10 known different mixtures using partial least square regression (PLSR) for statistical data inference. The model showed a normalized full-scale root-mean square deviation (NRMSD) accuracy of \(10.9\text{\%}\) and \(18.4\text{\%}\) for each species, respectively, even in combined mixtures.