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Its properties make copper one of the world’s most important functional metals. Numerous megatrends are increasing the demand for copper. This requires the prospection and exploration of new deposits, as well as the monitoring of copper quality in the various production steps. A promising technique to perform these tasks is Laser Induced Breakdown Spectroscopy (LIBS). Its unique feature, among others, is the ability to measure on site without sample collection and preparation. In this work, copper-bearing minerals from two different deposits are studied. The first set of field samples come from a volcanogenic massive sulfide (VMS) deposit, the second part from a stratiform sedimentary copper (SSC) deposit. Different approaches are used to analyze the data. First, univariate regression (UVR) is used. However, due to the strong influence of matrix effects, this is not suitable for the quantitative analysis of copper grades. Second, the multivariate method of partial least squares regression (PLSR) is used, which is more suitable for quantification. In addition, the effects of the surrounding matrices on the LIBS data are characterized by principal component analysis (PCA), alternative regression methods to PLSR are tested and the PLSR calibration is validated using field samples.
Its properties make copper one of the world’s most important functional metals. Numerous megatrends are increasing the demand for copper. This requires the prospection and exploration of new deposits, as well as the monitoring of copper quality in the various production steps. A promising technique to perform these tasks is Laser Induced Breakdown Spectroscopy (LIBS). Its unique feature, among others, is the ability to measure on site without sample collection and preparation. In this work, copper-bearing minerals from two different deposits are studied. The first set of field samples come from a volcanogenic massive sulfide (VMS) deposit, the second part from a stratiform sedimentary copper (SSC) deposit. Different approaches are used to analyze the data. First, univariate regression (UVR) is used. However, due to the strong influence of matrix effects, this is not suitable for the quantitative analysis of copper grades. Second, the multivariate method of partial least squares regression (PLSR) is used, which is more suitable for quantification. In addition, the effects of the surrounding matrices on the LIBS data are characterized by principal component analysis (PCA), alternative regression methods to PLSR are tested and the PLSR calibration is validated using field samples.
Infrared matrix-assisted desorption and ionization (IR-MALDI) enables the transfer of sub-micron particles (sMP) directly from suspensions into the gas phase and their characterization with differential mobility (DM) analysis. A nanosecond laser pulse at 2940 nm induces a phase explosion of the aqueous phase, dispersing the sample into nano- and microdroplets. The particles are ejected from the aqueous phase and become charged. Using IR-MALDI on sMP of up to 500 nm in diameter made it possible to surpass the 100 nm size barrier often encountered when using nano-electrospray for ionizing supramolecular structures. Thus, the charge distribution produced by IR-MALDI could be characterized systematically in the 50-500 nm size range. Well-resolved signals for up to octuply charged particles were obtained in both polarities for different particle sizes, materials, and surface modifications spanning over four orders of magnitude in concentrations. The physicochemical characterization of the IR-MALDI process was done via a detailed analysis of the charge distribution of the emerging particles, qualitatively as well as quantitatively. The Wiedensohler charge distribution, which describes the evolution of particle charging events in the gas phase, and a Poisson-derived charge distribution, which describes the evolution of charging events in the liquid phase, were compared with one another with respect to how well they describe the experimental data. Although deviations were found in both models, the IR-MALDI charging process seems to resemble a Poisson-like charge distribution mechanism, rather than a bipolar gas phase charging one.
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
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
The increasing development of antibiotic resistance in bacteria has been a major problem for years, both in human and veterinary medicine. Prophylactic measures, such as the use of vaccines, are of great importance in reducing the use of antibiotics in livestock. These vaccines are mainly produced based on formaldehyde inactivation. However, the latter damages the recognition elements of the bacterial proteins and thus could reduce the immune response in the animal. An alternative inactivation method developed in this work is based on gentle photodynamic inactivation using carbon nanodots (CNDs) at excitation wavelengths λex > 290 nm. The photodynamic inactivation was characterized on the nonvirulent laboratory strain Escherichia coli K12 using synthesized CNDs. For a gentle inactivation, the CNDs must be absorbed into the cytoplasm of the E. coli cell. Thus, the inactivation through photoinduced formation of reactive oxygen species only takes place inside the bacterium, which means that the outer membrane is neither damaged nor altered. The loading of the CNDs into E. coli was examined using fluorescence microscopy. Complete loading of the bacterial cells could be achieved in less than 10 min. These studies revealed a reversible uptake process allowing the recovery and reuse of the CNDs after irradiation and before the administration of the vaccine. The success of photodynamic inactivation was verified by viability assays on agar. In a homemade flow photoreactor, the fastest successful irradiation of the bacteria could be carried out in 34 s. Therefore, the photodynamic inactivation based on CNDs is very effective. The membrane integrity of the bacteria after irradiation was verified by slide agglutination and atomic force microscopy. The method developed for the laboratory strain E. coli K12 could then be successfully applied to the important avian pathogens Bordetella avium and Ornithobacterium rhinotracheale to aid the development of novel vaccines.
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.
The contamination of barley by molds on the field or in storage leads to the spoilage of grain and the production of mycotoxins, which causes major economic losses in malting facilities and breweries. Therefore, on-site detection of hidden fungus contaminations in grain storages based on the detection of volatile marker compounds is of high interest. In this work, the volatile metabolites of 10 different fungus species are identified by gas chromatography (GC) combined with two complementary mass spectrometric methods, namely, electron impact (EI) and chemical ionization at atmospheric pressure (APCI)-mass spectrometry (MS). The APCI source utilizes soft X-radiation, which enables the selective protonation of the volatile metabolites largely without side reactions. Nearly 80 volatile or semivolatile compounds from different substance classes, namely, alcohols, aldehydes, ketones, carboxylic acids, esters, substituted aromatic compounds, alkenes, terpenes, oxidized terpenes, sesquiterpenes, and oxidized sesquiterpenes, could be identified. The profiles of volatile and semivolatile metabolites of the different fungus species are characteristic of them and allow their safe differentiation. The application of the same GC parameters and APCI source allows a simple method transfer from MS to ion mobility spectrometry (IMS), which permits on-site analyses of grain stores. Characterization of IMS yields limits of detection very similar to those of APCI-MS. Accordingly, more than 90% of the volatile metabolites found by APCI-MS were also detected in IMS. In addition to different fungus genera, different species of one fungus genus could also be differentiated by GC-IMS.
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.