@phdthesis{Rieger2019,
  author    = {Rieger, Max},
  title     = {Preconcentration with Metal-Organic Frameworks as adsorbents for airborne Explosives and Hazardous Materials - A study using inverse gas chromatography},
  doi       = {10.25972/OPUS-17775},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-177750},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2019},
  abstract  = {Sensitivity and selectivity remain the central technical requirement for analytical devices, detectors and sensors. Especially in the gas phase, concentrations of threat substances can be very low (e.g. explosives) or have severe effects on health even at low concentrations (e.g. benzene) while it contains many potential interferents. Preconcentration, facilitated by active or passive sampling of air by an adsorbent, followed by thermal desorption, results in these substances being released in a smaller volume, effectively increasing their concentration. Traditionally, a wide range of adsorbents, such as active carbons or porous polymers, are used for preconcentration. However, many adsorbents either show chemical reactions due to active surfaces, serious water retention or high background emission due to thermal instability. Metal-organic frameworks (MOFs) are a hybrid substance class, composed inorganic and organic building blocks, being a special case of coordination polymers containing pores. They can be tailored for specific applications such as gas storage, separation, catalysis, sensors or drug delivery. This thesis is focused on investigating MOFs for their use in thermal preconcentration for airborne detection systems. A pre-screening method for MOF-adsorbate interactions was developed and applied, namely inverse gas chromatography (iGC). Using this pulse chromatographic method, the interaction of MOFs and molecules from the class of explosives and volatile organic compounds was studied at different temperatures and compared to thermal desorption results. In the first part, it is shown that archetype MOFs (HKUST-1, MIL-53 and Fe-BTC) outperformed the state-of-the-art polymeric adsorbent Tenax® TA in nitromethane preconcentration for a 1000 (later 1) ppm nitromethane source. For HKUST-1, a factor of more than 2000 per g of adsorbent was achieved, about 100 times higher than for Tenax. Thereby, a nitromethane concentration of 1 ppb could be increased to 2 ppm. High enrichment is addressed to the specific interaction of the nitro group as by iGC, which was determined by comparing nitromethane's free enthalpy of adsorption with the respective saturated alkane. Also, HKUST-1 shows a similar mode of sorption (enthalpy-entropy compensation) for nitro and saturated alkanes. In the second part, benzene of 1 ppm of concentration was enriched with a similar setup, using 2nd generation MOFs, primarily UiO-66 and UiO-67, under dry and humid (50 \%rH) conditions using constant sampling times. Not any MOF within the study did surpass the polymeric Tenax in benzene preconcentration. This is most certainly due to low sampling times - while Tenax may be highly saturated after 600 s, MOFs are not. For regular UiO-66, four differently synthesized samples showed a strongly varying behavior for dry and humid enrichment which cannot be completely explained. iGC investigations with regular alkanes and BTEX compounds revealed that confinement factors and dispersive surface energy were different for all UiO-66 samples. Using physicochemical parameters from iGC, no unified hypothesis explaining all variances could be developed. Altogether, it was shown that MOFs can replace or add to state-of-the-art adsorbents for the enrichment of specific analytes with preconcentration being a universal sensitivity-boosting concept for detectors and sensors. Especially with iGC as a powerful screening tool, most suitable MOFs for the respective target analyte can be evaluated. iGC can be used for determining "single point" retention volumes, which translate into partition coefficients for a specific MOF × analyte × temperature combination.},
  subject      = {Metallorganisches Netzwerk},
  language  = {en}
}