The visible-light photocatalyticE/Zisomerization of olefins can be mediated by a wide spectrum of triplet sensitizers (photocatalysts). However, the search for the most efficient photocatalysts through screenings in photo batch reactors is material and time consuming. Capillary and microchip flow reactors can accelerate this screening process. Combined with a fast analytical technique for isomer differentiation, these reactors can enable high-throughput analyses. Ion mobility (IM) spectrometry is a cost-effective technique that allows simple isomer separation and detection on the millisecond timescale. This work introduces a hyphenation method consisting of a microchip reactor and an infrared matrix-assisted laser desorption ionization (IR-MALDI) ion mobility spectrometer that has the potential for high-throughput analysis. The photocatalyzedE/Zisomerization of ethyl-3-(pyridine-3-yl)but-2-enoate (E-1) as a model substrate was chosen to demonstrate the capability of this device. Classic organic triplet sensitizers as well as Ru-, Ir-, and Cu-based complexes were tested as catalysts. The ionization efficiency of theZ-isomer is much higher at atmospheric pressure which is due to a higher proton affinity. In order to suppress proton transfer reactions by limiting the number of collisions, an IM spectrometer working at reduced pressure (max. 100 mbar) was employed. This design reduced charge transfer reactions and allowed the quantitative determination of the reaction yield in real time. Among 14 catalysts tested, four catalysts could be determined as efficient sensitizers for theE/Zisomerization of ethyl cinnamate derivativeE-1. Conversion rates of up to 80% were achieved in irradiation time sequences of 10 up to 180 s. With respect to current studies found in the literature, this reduces the acquisition times from several hours to only a few minutes per scan.

Infrared matrix-assisted laser dispersion and ionization (IR-MALDI) in combination with ion mobility (IM) spectrometry enables the direct analysis of biomolecules in aqueous solution. The release of ions directly from an aqueous solution is based on a phase explosion, induced by the absorption of an IR laser pulse, which disperses the liquid as vapor, nano-and micro-droplets. The ionization process is characterized initially by a broad spatial distribution of the ions, which is a result of complex fluid dynamics and desolvation kinetics. These processes have a profound effect on the shape and width of the peaks in the IM spectra. In this work, the transport of ions by the phase explosion-induced shockwave could be studied independently from the transport by the electric field. The shockwave-induced mean velocities of the ions at different time scales were determined through IM spectrometry and shadowgraphy. The results show a deceleration of the ions from 118 m.s(-1) at a distance of 400 mu m from the liquid surface to 7.1 m.s(-1) at a distance of 10 mm, which is caused by a pile-up effect. Furthermore, the desolvation kinetics were investigated and a first-order desolvation constant of 325 +/- 50 s(-1) was obtained. In the second part, the IR-MALDI-IM spectrometer is used as an HPLC detector for the two-dimensional separation of a pesticide mixture.

Mold fungi on malting barley grains cause major economic loss in malting and brewery facilities. Possible proxies for their detection are volatile and semivolatile metabolites. Among those substances, characteristic marker compounds have to be identified for a confident detection of mold fungi in varying surroundings. The analytical determination is usually performed through passive sampling with solid phase microextraction, gas chromatographic separation, and detection by electron ionization mass spectrometry (EI-MS), which often does not allow a confident determination due to the absence of molecular ions. An alternative is GC-APCI-MS, generally, allowing the determination of protonated molecular ions. Commercial atmospheric pressure chemical ionization (APCI) sources are based on corona discharges, which are often unspecific due to the occurrence of several side reactions and produce complex product ion spectra. To overcome this issue, an APCI source based on soft X-radiation is used here. This source facilitates a more specific ionization by proton transfer reactions only. In the first part, the APCI source is characterized with representative volatile fungus metabolites. Depending on the proton affinity of the metabolites, the limits of detection are up to 2 orders of magnitude below those of EI-MS. In the second part, the volatile metabolites of the mold fungus species Aspergillus, Alternaria, Fusarium, and Penicillium are investigated. In total, 86 compounds were found with GC-EI/APCI-MS. The metabolites identified belong to the substance classes of alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, terpenes, and sesquiterpenes. In addition to substances unspecific for the individual fungus species, characteristic patterns of metabolites, allowing their confident discrimination, were found for each of the 4 fungus species. Sixty-seven of the 86 metabolites are detected by X-ray-based APCI-MS alone. The discrimination of the fungus species based on these metabolites alone was possible. Therefore, APCI-MS in combination with collision induced dissociation alone could be used as a supervision method for the detection of mold fungi.

The lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.

The numerous applications of rare earth elements (REE) has lead to a growing global demand and to the search for new REE deposits. One promising technique for exploration of these deposits is laser-induced breakdown spectroscopy (LIBS). Among a number of advantages of the technique is the possibility to perform on-site measurements without sample preparation. Since the exploration of a deposit is based on the analysis of various geological compartments of the surrounding area, REE-bearing rock and soil samples were analyzed in this work. The field samples are from three European REE deposits in Sweden and Norway. The focus is on the REE cerium, lanthanum, neodymium and yttrium. Two different approaches of data analysis were used for the evaluation. The first approach is univariate regression (UVR). While this approach was successful for the analysis of synthetic REE samples, the quantitative analysis of field samples from different sites was influenced by matrix effects. Principal component analysis (PCA) can be used to determine the origin of the samples from the three deposits. The second approach is based on multivariate regression methods, in particular interval PLS (iPLS) regression. In comparison to UVR, this method is better suited for the determination of REE contents in heterogeneous field samples. View Full-Text