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In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. For this purpose, the research project I4S (intelligence for soil) has the goal to develop an integrated system. This system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS.
LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm) was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated.[1] When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
As part of the BonaRes research initiative funded by the German Federal Ministry of Education and Research (BMBF), strategies are being developed to use soil as a sustainable resource in the bioeconomy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarized in models and decision-making algorithms will be used to control fertilization and accordingly improve soil functions. This would allow investigations in close meshed dynamic grid and fast analysis of large areas to generate higher yields. This is important given that the distribution of minor and trace elements varies widely. Aim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-analysis of arable land. The non-destructive and contactless XRF is suitable for rapid in-situ analysis on the field due to minimal sample preparation and simultaneous multi-element analysis.
Soils are already considered as a complex matrix due to their wide range of elements in different contents, especially light elements with low atomic numbers (Z<19). Problems by measuring soil samples also arise from heterogeneity of the sample and matrix effects. Large grain size distribution causes strong inhomogeneity and matrix effects occur through physical properties related to high concentration of main components. Matrix-specific calibration strategies for determination of total major and minor plant essential nutrients are particularly important regarding these difficulties. For accurate calibration, data treatment and evaluation must also be considered. Univariate and multivariate data analysis were compared regarding their analytical figures of merit. Using principal component analysis (PCA) it was possible to classify German soils in different groups as sand, clay and silt. Calibration models were obtained by partial least squares regression (PLSR) and the content of macro- and micronutrients in German soils was predicted. Elemental distribution maps for different German arable lands were created and the results compared to reference measurements. The correlation between predicted values and reference values were in good agreement for most major and minor nutrients.
In respect of an efficient cultivation of agricultural cropland, a site-specific fertility management is necessary. Therefore, affordable and extensive mapping methods are needed. The research projects I4S (intelligence for soil) has the goal to develop a system for this purpose. I4S is one of ten interdisciplinary research project associations of the innovation programme called BonaRes, which is funded by the German Federal Ministry of Education and Research (BMBF).
The system includes a sensor platform, which contains different sensors, like XRF, VIS-NIR, Gamma and LIBS. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of major and minor nutrients in soils, like calcium, magnesium, potassium. LIBS (laser-induced breakdown spectroscopy) is known as a fast and simultaneous multi-element analysis with little or no sample preparation. The main task of LIBS measurements in this project is the real-time determination of the elemental contents of nutrients in soils, like calcium, magnesium, potassium. For this purpose, a special setup has been designed. The sample uptake operates with the help of a rotatable sample plate which circulates with different velocities to simulate the application on the field. To provide a higher intensity and a better reproducibility of the obtained signal, a double-pulse Nd:YAG laser (1064 nm)was used. In order to minimize dust formation from the soil during the operation of the laser, a dust removal by suction has been integrated. When using relative methods such as LIBS, a suitable calibration curve is needed for absolute quantification. The complex matrix of soils, as well as the influence of moisture and grain size in soils makes the absolute quantification by LIBS challenging. To overcome these influences, chemometric methods were used. With the principal component analysis (PCA) a classification of soils into different soil types was performed and a calibration curve based on partial least squares regression (PLSR) was generated. With this calibration model’s elemental distribution maps for different German agricultural fields were created.
Aim of the Federal Institute for Materials Research and Testing (BAM) in the frame of I4S - “Intelligence for Soil” is the characterization of an X-ray fluorescence (XRF) based sensor for robust online-application of arable land.Fast soil mapping for agricultural purpose allows the site-specific optimized introduction of plant essential nutrients like S, K, Ca, and Fe. This is important given that the distribution of minor and trace elements varies widely. The non-destructive and contactless XRF is suitable for rapid in-situ analysis on the field due to minimal sample preparation and simultaneous multi-element analysis.
Soils are already considered as a complex matrix due to their wide range of elements in different contents, especially light elements with low atomic numbers (Z<19). Problems by measuring soil samples also arise from heterogeneity of the sample and matrix effects. Large grain size distribution causes strong inhomogeneity and matrix effects occur through physical properties related to high concentration of main components. Matrix-specific calibration strategies for determination of total major and minor plant essential nutrients are particularly important regarding these difficulties. For accurate calibration, data treatment and evaluation must also be considered. Empirical univariate and multivariate data analysis were compared regarding their analytical figures of merit. Using principal component analysis (PCA) it was possible to classify German soils in different groups as sand, clay and silt. A calibration curve was obtained by partial least squares regression (PLSR) and the elemental content of German soils was predicted. Elemental distribution maps for different German arable lands were created and the results compared to reference measurements. The correlation between predicted values and reference values were in good agreement for most major and minor nutrients.
The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example.
Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations).
Based on concentration measurements of reagents and products by the NMR analyzer a continuous production and direct loop process control were successfully realized for several validation runs in a modular industrial pilot plant and compared to conventional analytical methods (HPLC, near infrared spectroscopy). The NMR analyser was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (“Integrated CONtrol and SENsing”, www.consens-spire.eu).
Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example.Based on concentration measurements of reagents and products by the NMR analyzer a continuous production and direct loop process control were successfully realized for several validation runs in a modular industrial pilot plant and compared to conventional analytical methods (HPLC, near infrared spectroscopy). The NMR analyser was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme.
The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example.
Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs. We present a range of approaches for the automated spectra analysis moving from statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations).
Based on concentration measurements of reagents and products by the NMR analyzer a continuous production and direct loop process control were successfully realized for several validation runs in a modular industrial pilot plant and compared to conventional analytical methods (HPLC, near infrared spectroscopy). The NMR analyser was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (“Integrated CONtrol and SENsing”, www.consens-spire.eu).
The comparison required the assignment of the mass fraction of folic acid present as the main component in the comparison sample. Performance in the comparison is representative of a laboratory's measurement capability for the purity assignment of organic compounds of medium structural complexity [molecular weight range 300–500] and high polarity (pKOW < −2).
Methods used by the eighteen participating NMIs or DIs were based on a mass balance (summation of impurities) or qNMR approach, or the combination of data obtained using both methods. The qNMR results tended to give slightly lower values for the content of folic acid, albeit with larger associated uncertainties, compared with the results obtained by mass balance procedures. Possible reasons for this divergence are discussed in the report, without reaching a definitive conclusion as to their origin.
The comparison demonstrates that for a structurally complex polar organic compound containing a high water content and presenting a number of additional analytical challenges, the assignment of the mass fraction content property value of the main component can reasonably be achieved with an associated relative standard uncertainty in the assigned value of 0.5%
Many applications of nanomaterials in the life sciences require the controlled functionalization of these materials with ligands like polyethylene glycol (PEG) and/or biomolecules such as peptides, proteins, and DNA. This enables to tune their hydrophilicity and biocompatibility, minimize unspecific interactions, improve biofunction-nalization efficiencies, and enhance blood circulation times and is the ultimate prerequisite for their use as reporters in assays or the design of targeted optial probes for bioimaging.
At the core of these functionalization strategies are reliable and validated methods for surface group and ligand quantification that can be preferably performed with routine laboratory instrumentation, require only small amounts of substances, and are suitable for many different types of nanomaterials.
We present here versatile and simple concepts for the quantification of common functional groups, ligands, and biomolecules on different types of organic and inorganic nano-materials, using different types of optical reporters and method validation with the aid of multimodal reporters and mass balances.
Gold-shell coated NaYF4:Er3+, Yb3+ nanoparticles for the enhancement of fluorescence emission
(2018)
In the present work, we aim to explore how far the UCNP emission intensity can be enhanced by the aid of plasmonic interactions using a gold shell. The distance between the UCNP core and the gold shell is varied by adding a silica spacer of different thicknesses.
The synthetic conditions for obtaining UCNP@SiO2@Au core-shell nanoparticles with precisely tuneable silica shell thicknesses were investigated. A gold shell on the UCNP@SiO2 nanoparticles is expected to give rise to a noticeable enhancement of particle brightness and fluorescence, given that the thicknesses of the silica shell and the gold coating can be controlled and fine-tuned. First single particle studies revealing shortening of the Er3+ lifetimes suggest that plasmonic enhancement occurs.
Gold-shell coated NaYF4:Er3+, Yb3+ nanoparticles for the enhancement of fluorescence emission
(2018)
In the present work, we aim to explore how far the UCNP emission intensity can be enhanced by the aid of plasmonic interactions using a gold shell. The distance between the UCNP core and the gold shell is varied by adding a silica spacer of different thicknesses.
The synthetic conditions for obtaining UCNP@SiO2@Au core-shell nanoparticles with precisely tuneable silica shell thicknesses were investigated. A gold shell on the UCNP@SiO2 nanoparticles is expected to give rise to a noticeable enhancement of particle brightness and fluorescence, given that the thicknesses of the silica shell and the gold coating can be controlled and fine-tuned. First single particle studies revealing shortening of the Er3+ lifetimes suggest that plasmonic enhancement occurs.
Lanthanide-based upconversion nanoparticles (UCNPs)like hexagonal beta-NaYF4 UCNPs doped with Yb3+ and Er3+, which efficiently convert 976 nm light to ultraviolet, visible, and near infrared photons, offer new strategies for luminescence-based sensing and imaging. The properties of their upconversion (UC) luminescence (UCL) are, however, strongly influenced by particle size, concentration and spatial arrangement of dopant ions, surface chemistry, microenvironment, and excitation power density (P).
We present here comprehensive studies of the influence of UCNP size and particle architecture for Yb3+ and Er3+ co-doped NaYF4 nanoparticles and energy transfer processes in these materials, which underline the importance of quantitative luminescence measurements for mechanistic insights and the determination of application-relevant matrix- and P-dependent optimum dopand concentrations.
The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control.
The talk introduces a smart online NMR sensor module provided in an explosion proof housing as example. This sensor was developed for an intensified industrial process (pharmaceutical lithiation reaction step) funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs.
Industry 4.0, IIoT, or Lab 4.0 will enable us to handle more complex processes in shorter time. Intensified production concepts require for adaptive analytical instruments and control technology to realize short set-up times, modular control strategies. They are based on a digitized Laboratory 4.0.
Comparing the photoluminescence (PL) properties of ensembles of nanocrystals like semiconductor quantum dots (QDs) with single particle studies is of increasing interest for many applications of These materials as reporters in bioimaging studies performed under very dilute conditions or even at the single particle level. Particularly relevant is here the PL quantum yield (ΦF), which determines the signal size together with the reporter’s molar extinction coefficient and is a direct measure for nanocrystal quality, especially for the inorganic surface passivation shell and its tightness, which can be correlated also with nanocrystal stability and the possible release of heavy metal ions. Exemplarily for red and green emitting CdTe nanocrystals, we present a method for the determination of ΦF of nanoparticle dispersions at ultralow concentration compared to cuvette measurements using fluorescence correlation spectroscopy (FCS), a single molecule method, and compared to molecular dyes with closely matching spectral properties and known ΦF. Our results underline the potential of this approach, provided that material-inherent limitations like ligand- and QD-specific aggregation affecting particle diffusion and QD drawbacks such as their complex and power-dependent blinking behavior are properly considered as shown here.
The increasing request for hair ethyl glucuronide (HEtG) in alcohol consumption monitoring according to cut-off levels set by the Society of Hair Testing (SoHT) has triggered a proficiency testing program based on interlaboratory comparisons (ILC). Here, the outcome of nine consecutive ILC rounds organised by the SoHT on the determination of HEtG between 2011 and 2017 is summarised regarding interlaboratory reproducibility and the influence of procedural variants. Test samples prepared from cut hair (1 mm) with authentic (in-vivo incorporated) and soaked (in-vitro incorporated) HEtG concentrations up to 80 pg/mg were provided for 27–35 participating laboratories. Laboratory results were evaluated according to ISO 5725-5 and provided robust averages and relative reproducibility standard deviations typically between 20 and 35% in reasonable accordance with the prediction of the Horwitz model. Evaluation of results regarding the analytical techniques revealed no significant differences between gas and liquid chromatographic methods In contrast, a detailed evaluation of different sample preparations revealed significantly higher average values in case when pulverised hair is tested compared to cut hair. This observation was reinforced over the different ILC rounds and can be attributed to the increased acceptance and routine of hair pulverisation among laboratories. Further, the reproducibility standard deviations among laboratories performing pulverisation were on average in very good agreement with the prediction of the Horwitz model. Use of sonication showed no effect on the HEtG extraction yield.
An overview about different analytical approaches will be presented of how to detect metals in individual biological cells by use of ICP-MS. For this purpose, we are using different sample introduction systems for ICP-MS for detection, imaging and quantification of metals at cellular levels.
By use of laser ablation, we have studied the up-take by and distribution of nanoparticles in single cells. Recently we have developed staining techniques to measure protein and DNA content of cells and identifying the cell status by immunoassays using metal-tagging of antibodies. New research based on cell arrays will be shortly discussed.
Using pneumatic nebulization and microdroplet generation, we have also studied the up-take of nanoparticles and toxic metals as well as essential elements in single cells using different ICP-MS mass spectrometric concepts (sector field instrument, triple-quad instrument, time of flight (CyTOF) instrument).
The different ICP-MS based methods will be compared concerning their analytical figures of merit and their strengths and weaknesses will be evaluated.
Carboxy, amino, and thiol groups play a critical role in a variety of physiological and biological processes and are frequently used for bioconjugation reactions. Moreover, they enable size control and tuning of the surface during the synthesis of particle systems. Especially, thiols have a high binding affinity to noble metals and semiconductors (SC). Thus, simple, inexpensive, robust, and fast methods for the quantification of surface groups and the monitoring of reactions involving ligands are of considerable importance for the characterization of modified or stabilized nanomaterials including polymers.
We studied the potential of the Ellman’s assay, recently used for the quantification of thiol ligands on SC nanocrystals by us1 and the 4-aldrithiol assay for the determination of thiol groups in molecular systems and on polymeric, noble and SC nanomaterials. The results were validated with ICP-OES and reaction mechanisms of both methods were studied photometrically and with ESI-TOF-MS.
The investigation of the reaction mechanisms of both methods revealed the influence of different thiols on the stoichiometry of the reactions2, yielding different mixed disulfides and the thiol-specific products spectroscopically detected. The used methods can quantify freely accessible surface groups on nanoparticles, e.g., modified polystyrene nanoparticles. For thiol ligands coordinatively bound to surface atoms of, e.g., noble or SC nanomaterials, depending on the strength of the thiol-surface bonds, particle dissolution prior to assay performance can be necessary.
We could demonstrate the reliability of the Ellman’s and aldrithiol assay for the quantification of surface groups on nanomaterials by ICP-OES and derived assay-specific requirements and limitations. Generally, it is strongly recommended to carefully control assay performance for new samples, components, and sample ingredients to timely identify possible interferences distorting quantification.
Monitoring specific chemical properties is the key to chemical process control. Today, mainly optical online methods are applied, which require time- and cost-intensive calibration effort. NMR spectroscopy, with its advantage being a direct comparison method without need for calibration, has a high potential for closed-loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and rough environments for process monitoring and advanced process control strategies.
We present a fully automated data analysis approach which is completely based on physically motivated spectral models as first principles information (Indirect Hard Modelling – IHM) and applied it to a given pharmaceutical lithiation reaction in the framework of the European Union’s Horizon 2020 project CONSENS. Online low-field NMR (LF NMR) data was analysed by IHM with low calibration effort, compared to a multivariate PLS-R (Partial Least Squares Regression) approach, and both validated using online high-field NMR (HF NMR) spectroscopy.
Polymeric nanoparticles (NP) are of increasing importance for a wide range of applications in the material and life sciences, including their use as drug carriers, fluorescent sensors and multimodal reporters in a large variety of bioassays and bioimaging studies. Application-relevant properties of NP include their size (and size distribution), shape, optical properties, and ease of subsequent functionalization, e.g. with linkers, sensor molecules, and bioligands. In this respect, knowledge of the chemical nature, the total number of surface groups and the number of groups accessible for subsequent coupling reactions is mandatory.1 Commercially available polystyrene NP often contain different additives like stabilizers, radical starters and crosslinkers, which can influence the quantification of surface functionalities. Moreover, they often have unknown surface group densities that may vary from batch to batch, which complicates or even hampers their reliable use in many (bio)applications. To circumvent these issues, we synthesized amino- and carboxy-functionalized, monodisperse 100 nm polystyrene NP with three different, well-defined surface group densities. Using a recently developed approach for the quantification of functional groups on nano- and microparticles with cleavable reporters,2 we quantified the assessible functional groups on the self-synthesised PS particles via optical spectroscopy (photometry) and inductively coupled optical emission spectrometry (32S ICP-OES). In addition, we developed a fluorimetric approach for the quantification of surface functional groups on nanoparticles based on the labelling with luminescent lanthanide complexes (LLC). In contrast to common organic dyes, LLC are not prone to photo¬luminescence quenching arising from reabsorption or dye aggregation, and thus, enable a reliable fluorometric quantification of the assessible functional groups on NP surfaces. Moreover, lanthanide tags can be detected with high specificity and sensitivity with analytical techniques such as XPS and ICP-MS, which allow for the multimodal validation of the fluorometric quantification approach.
Many applications of nanometer- and micrometer-sized particles include their surface functionalization with linkers, sensor molecules, and analyte recognition moieties like (bio)ligands. This requires knowledge of the chemical nature and number of surface groups accessible for subsequent coupling reactions. Particularly attractive for the quantification of these groups are spectrophotometric and fluorometric assays, which can be read out with simple instrumentation. In this respect, we present here a novel Family of cleavable spectrophotometric and multimodal reporters for conjugatable amino and carboxyl surface groups on nano- and microparticles. This allows determination of particle-bound labels, unbound reporters in the supernatant, and reporters cleaved off from the particle surface, as well as the remaining thiol groups on particle, by spectrophotometry and inductively coupled optical emission spectrometry (32S ICP-OES). Comparison of the performance of these cleavable reporters with conductometry and conventional labels, utilizing changes in intensity or Color of absorption or emission, underlines the analytical potential of this versatile concept which elegantly circumvents Signal distortions by scattering and Encoding dyes and enables straightforward validation by method comparison.