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Institute
Spot variation fluorescence correlation spectroscopy (SV-FCS) is a variant of the FCS techniques which may give useful information about the structural organisation of the medium in which the diffusion takes place. We show that the same results can be obtained by post-processing the photon count data from ordinary FCS measurements. By using this method, one obtains the fluorescence autocorrelation functions for sizes of confocal volume, which are effectively smaller than that of the initial FCS measurement. The photon counts of the initial experiment are first transformed into smooth intensity trace using kernel smoothing method or to a piecewise-continuous intensity trace using binning and then a non-linear transformation is applied to this trace. The result of this transformation mimics the photon count rate in an experiment performed with a smaller confocal volume. The applicability of the method is established in extensive numerical simulations and directly supported in in-vitro experiments. The procedure is then applied to the diffusion of AlexaFluor647-labeled streptavidin in living cells.
Total protein concentration (TPC) is a key parameter in many biochemical experiments and its quantification is often necessary for isolation, separation, and analysis of proteins. A sensitive and rapid nanobead-based TPC quantification assay based on Forster Resonance Energy Transfer (FRET) has been developed. A new, highly luminescent Tb(III) complex has been synthesised and applied as donor in this FRET assay with an organic dye (Cy5) as acceptor. FRET-induced changes in luminescence have been investigated both at donor and acceptor emission wavelength using time-resolved luminescence spectroscopy with time-gated detection. In the assay, the Tb(III) complex and fine-tuned polyglycidyl methacrylate (PGMA) nanobeads ensure that an improvement in sensitivity and background reduction is achieved. Using 40 nm large PGMA nanobeads loaded with the Tb(III) complex, it is possible to determine TPC down to 50 ng mL(-1) in just 10 minutes. Through specific assay components the sensitivity has been improved when compared to existing nanobead-based assays and to currently known commercial methods. Additionally, the assay is relatively insensitive to the presence of contaminants, such as non-ionic detergents commonly found in biological samples. Due to no need for any centrifugal steps, this mix-and-measure bioassay can easily be implemented into routine TPC quantification protocols in biochemical laboratories.
A promising replacement for the radioactive sources commonly encountered in ion mobility spectrometers is a miniaturized, energy-efficient photoionization source that produce the reactant ions via soft X-radiation (2.8 keV). In order to successfully apply the photoionization source, it is imperative to know the spectrum of reactant ions and the subsequent ionization reactions leading to the detection of analytes. To that end, an ionization chamber based on the photoionization source that reproduces the ionization processes in the ion mobility spectrometer and facilitates efficient transfer of the product ions into a mass spectrometer was developed. Photoionization of pure gasses and gas mixtures containing air, N-2, CO2 and N2O and the dopant CH2Cl2 is discussed. The main product ions of photoionization are identified and compared with the spectrum of reactant ions formed by radioactive and corona discharge sources on the basis of literature data. The results suggest that photoionization by soft X-radiation in the negative mode is more selective than the other sources. In air, adduct ions of O-2 - with H2O and CO2 were exclusively detected. Traces of CO2 impact the formation of adduct ions of O-2 - and Cl -(upon addition of dopant) and are capable of suppressing them almost completely at high CO2 concentrations. Additionally, the ionization products of four alkyl nitrates (ethylene glycol dinitrate, nitroglycerin, erythritol tetranitrate and pentaerythritol tetranitrate) formed by atmospheric pressure chemical ionization induced by X-ray photoionization in different gasses (air, N-2 and N2O) and dopants (CH2Cl2, C2H5Br and CH3I) are investigated. The experimental studies are complemented by density functional theory calculations of the most important adduct ions of the alkyl nitrates (M) used for their spectrometric identification. In addition to the adduct ions [M + NO3](-) and [M + Cl](-), adduct ions such as [M + N2O2](-), [M + Br](-) and [M+ I](-) were detected, and their gas-phase structures and energetics are investigated by density functional theory calculations. Copyright (C) 2016 John Wiley & Sons, Ltd.
The novel combination of infrared matrix-assisted laser dispersion and ionization (IR-MALDI) with ion mobility (IM) spectrometry makes it possible to investigate biomolecules in their natural environment, liquid water. As an alternative to an ESI source, the IR-MALDI source was implemented in an in-house-developed ion mobility (IM) spectrometer. The release of ions directly from an aqueous solution is based on a phase explosion, induced by the absorption of an IR laser pulse (lambda = 2.94 mu m, 6 ns pulse width), which disperses the liquid as nano- and micro-droplets. The prerequisites for the application of IR-MALDI-IM spectrometry as an analytical method are narrow analyte ion signal peaks for a high spectrometer resolution. This can only be achieved by improving the desolvation of ions. One way to full desolvation is to give the cluster ions sufficient time to desolvate. Two methods for achieving this are studied: the implementation of an additional drift tube, as in ESI-IM-spectrometry, and the delayed extraction of the ions. As a result of this optimization procedure, limits of detection between 5 nM and 2.5 mu M as well as linear dynamic ranges of 2-3 orders of magnitude were obtained for a number of substances. The ability of this method to analyze simple mixtures is illustrated by the separation of two different surfactant mixtures.
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.
The combination of high-performance liquid chromatography and electrospray ionization ion mobility spectrometry facilitates the two-dimensional separation of complex mixtures in the retention and drift time plane. The ion mobility spectrometer presented here was optimized for flow rates customarily used in high-performance liquid chromatography between 100 and 1500 mu L/min. The characterization of the system with respect to such parameters as the peak capacity of each time dimension and of the 2D spectrum was carried out based on a separation of a pesticide mixture containing 24 substances. While the total ion current chromatogram is coarsely resolved, exhibiting coelutions for a number of compounds, all substances can be separately detected in the 2D plane due to the orthogonality of the separations in retention and drift dimensions. Another major advantage of the ion mobility detector is the identification of substances based on their characteristic mobilities. Electrospray ionization allows the detection of substances lacking a chromophore. As an example, the separation of a mixture of 18 amino acids is presented. A software built upon the free mass spectrometry package OpenMS was developed for processing the extensive 2D data. The different processing steps are implemented as separate modules which can be arranged in a graphic workflow facilitating automated processing of data.
Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.
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.