Time-Resolved Analysis of a Highly Sensitive Förster Resonance Energy Transfer Immunoassay Using Terbium Complexes as Donors and Quantum Dots as Acceptors

CdSe/ZnS core/shell quantum dots (QDs) are used as efficient Förster Resonance Energy Transfer (FRET) acceptors in a time-resolved immunoassays with Tb complexes as donors providing a long-lived luminescence decay. A detailed decay time analysis of the FRET process is presented. QD FRET sensitization is evidenced by a more than 1000-fold increase of the QD luminescence decay time reaching ca. 0.5 milliseconds, the same value to which the Tb donor decay time is quenched due to FRET to the QD acceptors. The FRET system has an extremely large Förster radius of approx. 100 Å and more than 70% FRET efficiency with a mean donor-acceptor distance of ca. 84 Å, confirming the applied biotin-streptavidin binding system. Time-resolved measurement allows for suppression of short-lived emission due to background fluorescence and directly excited QDs. By this means a detection limit of 18 attomol QDs within the immunoassay is accomplished, an improvement of more than two orders of magnitude compared to commercial systems

Soil Nutrient Detection for Precision Agriculture Using Handheld Laser-Induced Breakdown Spectroscopy (LIBS) and Multivariate Regression Methods (PLSR, Lasso and GPR)

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

Continuous synthesis of pyridocarbazoles and initial photophysical and bioprobe characterization

Pyridocarbazoles when ligated to transition metals yield high affinity kinase inhibitors. While batch photocyclizations enable the synthesis of these heterocycles, the non-oxidative Mallory reaction only provides modest yields and difficult to purify mixtures. We demonstrate here that a flow-based Mallory cyclization provides superior results and enables observation of a clear isobestic point. The flow method allowed us to rapidly synthesize ten pyridocarbazoles and for the first time to document their interesting photophysical attributes. Preliminary characterization reveals that these molecules might be a new class of fluorescent bioprobe

Characterization of volatile metabolites formed by molds on barley by mass and ion mobility spectrometry

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.BMEL, 2814801811, Verbundprojekt: Rohstoffscreening mit spektral-optischen Verfahren bei der Getreidelagerung (OptiScreen) - Teilprojekt

Distributed Fiber Optical Sensing of Oxygen with Optical Time Domain Reflectometry

sensor

Optical Oxygen Micro- and Nanosensors for Plant Applications

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Comparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture

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

Comparison of Handheld and Echelle Spectrometer to Assess Copper in Ores by Means of Laser-Induced Breakdown Spectroscopy (LIBS)

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

Detection of Rare Earth Elements in Minerals and Soils by Laser-Induced Breakdown Spectroscopy (LIBS) Using Interval PLS

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