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The study, dedicated to Beautiful Pietàs conserved in South Tyrol (Northern Italy), aims to establish, for the first time, a connection between Austroalpine raw materials and the high-fired gypsum mortars constituting the Gothic figure groups in question. The origin and chronology of this stylistically and qualitatively differing ensemble have been subject of art historical debate for nearly a century. The discourse is dominated by three main hypotheses: itinerary of an Austrian artist versus itinerary of the work of art created in an artist’s workshop in Austria versus itinerary of the stylistic vocabulary via graphical or three-dimensional models. The comparison of the δ34S values and the 87Sr/86Sr ratios of the gypsum mortars and Austroalpine sulphate deposits (in a compilation of own reference samples and literature data) points to the exploitation of sediments in the Salzkammergut and possibly also in the evaporite district of the Eastern Calcareous Alps, thus evidencing the import of the sculptures and not the activities of local South Tyrolean or itinerant artists. Two geochronological units are distinguishable: The Pietà in the Church St. Martin in Göflan can be assigned to Upper Permian raw material, whereas the metrologically consistent sculptures in the Church of Our Lady of the Benedictine Abbey Marienberg and in the Chapel St. Ann in Mölten correlate with deposits of the Early Triassic (or the Lower-Middle Triassic transition). The medieval gypsum mortars also differ in their mineralogical characteristics, i.e. in their geologically related minor components, as in the first case, characterised by a significant proportion of primary anhydrite, natural carbonate impurities mainly consist of calcite (partly converted to lime-lump-like aggregates), whereas in the second group dolomite (or rather its hydration products after pyrometamorphic decomposition) predominates, accompanied by celestine, quartz and potassium feldspar. The Pietà in the Cathedral Maria Himmelfahrt in Bozen turned out to be made of Breitenbrunn calcareous sandstone (Leitha Mountains, Burgenland, Austria), which is why the sample is not considered in the geochemical analysis.
Accreditation of analytical methods, either according to GxP or ISO regulations, requires a comprehensive quality management system. General quality documents are often already in place, which need to be extended by method-specific documentation. In this presentation we like to show an idea of a modular set of standard operating procedures (SOP) specifically developed meeting the requirements for quantitative NMR. The future goal is to collaborate with different accreditated NMR laboratories to compile a universal set of SOP and other quality documents that can be modified and used as a starting point for developing your own quality system for applications of qNMR in a regulated environment.
Back Deposition of Titanium Oxides under Laser Ablation of Titanium: Simulation and Experiment
(2022)
Titanium is widely used in medicine for implants and prostheses, thanks to its high biocompatibility, good mechanical properties, and high corrosion resistance. Pure titanium, however, has low wear resistance and may release metallic titanium into surrounding tissues. Structuring and coating its surface with oxide layers are necessary for high wear resistance and improved biocompatibility. In this work, a combination of theoretical and experimental methods was used to study processes responsible for deposition of titanium oxides during ablation of titanium in air.
The deposition process was modeled via the Navier-Stokes equations that accounted for the material removal and accumulation of the deposit on the ablation surface. The chemical part was based on the equilibrium model embedded into the hydrodynamic code. Simulations showed that the most active zone of production of condensed titanium oxides were at plasma periphery whereas a zone of strong condensation of titanium metal was above the molten pool.
In experiment, a pulsed Yb fiber laser was scanned across a titanium surface. The temperature and composition of the plasma were inferred from plasma emission spectra. The post-ablation surface was analyzed by SEM, TEM, STEM, AFM, and XRD.
The developed model well reproduced the main features of experimental data. It was concluded that the deposition of condensed metal oxides from the plasma is a principal mechanism of formation of nanoporous oxide layer on the metal surface. The method of surface structuring and modification by nanosecond laser ablation can be developed into a useful technology that may find applications in medicine, photonics, and other areas.
We present a workflow for obtaining fully trained artificial neural networks that can perform automatic particle segmentations of agglomerated, non-spherical nanoparticles from electron microscopy images “from scratch”, without the need for large training data sets of manually annotated images. This is achieved by using unsupervised learning for most of the training dataset generation, making heavy use of generative adversarial networks and especially unpaired image-to-image translation via cycle-consistent adversarial networks. The whole process only requires about 15 minutes of hands-on time by a user and can typically be finished within less than 12 hours when training on a single graphics card (GPU). After training, SEM image analysis can be carried out by the artificial neural network within seconds, and the segmented images can be used for automatically extracting and calculating various other particle size and shape descriptors.
The development of an automated miniaturized analytical system that allows for the rapid monitoring of carbamazepine (CBZ) levels in serum and wastewater is proposed. Molecular recognition of CBZ was achieved through its selective interaction with microbeads carrying anti-CBZ antibodies. The proposed method combines the advantages of the micro-bead injection spectroscopy and of the flow-based platform lab-on-valve for implementation of automatic immunosorbent renewal, rendering a new recognition surface for each sample. The sequential (or simultaneous) perfusion of CBZ and the horseradish peroxidase-labelled CBZ through the microbeads is followed by real-time on-column Monitoring of substrate (3,30,5,50-tetramethylbenzidine) oxidation by colorimetry. The evaluation of the initial oxidation rate and also the absorbance value at a fixed time point provided a linear response versus the logarithm of the CBZ concentration. Under the selected assay conditions, a single analysis was completed after only 11 min, with a quantification range between 1.0 and 50 µg L⁻¹. Detection of CBZ levels in undiluted wastewater samples was feasible after a simple filtration step while good recoveries were attained for spiked certified human serum, analyzed without sample clean-up.
The overall interest in nanotoxicity, triggered by the increasing use of nanomaterials in the material and life sciences, and the synthesis of an ever increasing number of new functional nanoparticles calls for standardized test procedures1,2 and for efficient approaches to screen the potential genotoxicity of these materials. Aiming at the development of fast and easy to use, automated microscopic methods for the determination of the genotoxicity of different types of nanoparticles, we assess the potential of the fluorometric γH2AX assay for this purpose. This assay, which can be run on an automated microscopic detection system, relies on the detection of DNA double strand breaks as a sign for genotoxicity3. Here, we provide first results obtained with broadly used nanomaterials like CdSe/CdS and InP/ZnS quantum dots as well as iron oxide, gold, and polymer particles of different surface chemistry with previously tested colloidal stability and different cell lines like Hep-2 and 8E11 cells, which reveal a dependence of the genotoxicity on the chemical composition as well as the surface chemistry of these nanomaterials. These studies will be also used to establish nanomaterials as positive and negative genotoxicity controls or standards for assay performance validation for users of this fluorometric genotoxicity assay. In the future, after proper validation, this microscopic platform technology will be expanded to other typical toxicity assays.
The isotopic analysis of lithium is also relevant to the study of geological phenomena.1 In this work we propose improvements to the method for the isotopic analysis of lithium using a high-resolution continuum source atomic absorption spectrometer (HR-CS-AAS) coupled to a double echelle modular spectrometer (DEMON). 2 This tool for isotopic ratio determination is based on monitoring the isotopic components of lithium by their spin-orbit coupling and its isotopic shift of about 15 pm for the 22P←2 2S electronic transition around 670.788 nm. The data analysis was carried out by using a decision-tree-based ensemble machine learning (ML) algorithm. For the training of the algorithm (XGBoost), a set of samples with 6Li isotope amount fractions ranging from 0.06 to 0.99 mol-1 was used. Subsequently, the procedure was validated of a set of stock chemicals (Li2CO3, LiNO3, LiCl and LiOH) and a BAM candidate reference material, the cathode material LiNi1/3Mn1/3Co1/3O2 (NMC111). Finally, the ML model was applied to the set of geological samples, previously digested, for the determination of their isotope ratio. The optical resolution was improved from 140,000 to 790,000 to better deconvolution the lithium isotopic components in the atomic spectrum. And the method was compared with multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). The results are metrologically comparable.
Asymmetrically bridged aroyl-S,N-ketene acetals and aroyl-S,N-ketene acetal multichromophores can be readily synthesized in consecutive three-, four-, or five-component syntheses in good to excellent yields by several successive Suzuki-couplings of aroyl-S,N-ketene acetals and bis(boronic)acid esters. Different aroyl-S,N-ketene acetals as well as linker molecules yield a library of 23 multichromophores with substitution and linker pattern-tunable emission properties. This allows control of different communication pathways between the chromophores and of aggregation-induced emission (AIE) and energy transfer (ET) properties, providing elaborate aggregation-based fluorescence switches.
To investigate the imaging performance of an elastin-specific molecular magnetic resonance imaging (MRI) probe with respect to the extracellular matrix (ECM) in an experimental hepatic cancer model. Twelve rabbits with hepatic VX2 tumors were examined using 3 T MRI 14, 21, and 28 days after tumor implantation for two subsequent days (gadobutrol, day 1; elastin-specific probe, day 2). The relative enhancement (RE) of segmented tumor regions (central and margin) and the peritumoral matrix was calculated using pre-contrast and delayed-phase T1w sequences. MRI measurements were correlated to histopathology and element-specific and spatially resolved mass spectrometry (MS). Mixed-model analysis was performed to assess the performance of the elastin-specific probe. In comparison to gadobutrol, the elastin probe showed significantly stronger RE, which was pronounced in the tumor margin (day 14–28: P ≤ 0.007). In addition, the elastin probe was superior in discriminating between tumor regions (χ2(4) = 65.87; P < 0.001). MRI-based measurements of the elastin probe significantly correlated with the ex vivo elastinstain (R = .84; P <0 .001) and absolute gadolinium concentrations (ICP-MS: R = .73, P <0 .01). LA-ICP-MS imaging confirmed the colocalization of the elastin-specific probe with elastic fibers. Elastin-specific molecular MRI is superior to non-specific gadolinium-based contrast agents in imaging the ECM of hepatic tumors and the peritumoral tissue.
Atherosclerosis is a progressive inflammatory vascular disease characterized by endothelial dysfunction and plaque burden. Extracellular matrix (ECM)-associated plasma proteins play an important role in disease development. Our magnetic resonance imaging (MRI) study investigates the feasibility of using two different molecular MRI probes for the simultaneous assessment of ECM-associated intraplaque albumin deposits caused by endothelial damage and progressive inflammation in atherosclerosis. Male apolipoprotein E-deficient (ApoE-/-)-mice were fed a high-fat diet (HFD) for 2 or 4 months. Another ApoE-/--group was treated with pravastatin and received a HFD for 4 months. T1- and T2*-weighted MRI was performed before and after albumin-specific MRI probe (gadofosveset) administration and a macrophage-specific contrast agent (ferumoxytol). Thereafter, laser ablation inductively coupled plasma mass spectrometry and histology were performed. With advancing atherosclerosis, albumin-based MRI signal enhancement and ferumoxytol-induced signal loss areas in T2*-weighted MRI increased. Significant correlations between contrast-to-noise-ratio (CNR) post-gadofosveset and albumin stain (R2 = 0.78, p < 0.05), and signal loss areas in T2*-weighted MRI with Perls’ Prussian blue stain (R2 = 0.83, p < 0.05) were observed. No interference of ferumoxytol with gadofosveset enhancement was detectable. Pravastatin led to decreased inflammation and intraplaque albumin. Multi-target MRI combining ferumoxytol and gadofosveset is a promising method to improve diagnosis and treatment monitoring in atherosclerosis.
Luminescent semiconductor quantum dots (QDs) are frequently used in the life and material sciences as reporter for bioimaging studies and as active components in devices such as displays, light-emitting diodes, solar cells, and sensors. Increasing concerns regarding the use of toxic elements like cadmium and lead, and hazardous organic solvents during QD synthesis have meanwhile triggered the search for heavy-metal free QDs using green chemistry syntheses methods. Interesting candidates are ternary AgInS2 (AIS) QDs that exhibit broad photoluminescence (PL) bands, large effective Stokes shifts, high PL quantum yields (PL QYs), and long PL lifetimes, which are particularly beneficial for applications such as bioimaging, white light-emitting diodes, and solar concentrators. In addition, these nanomaterials can be prepared in high quality with a microwave-assisted (MW) synthesis in aqueous solution. The homogeneous heat diffusion and instant temperature rise of the MW synthesis enables a better control of QD nucleation and growth and thus increases the batch-to-batch reproducibility. In this study, we systematically explored the MW synthesis of AIS/ZnS QDs by varying parameters such as the order of reagent addition, precursor concentration, and type of stabilizing thiol ligand, and assessed their influence on the optical properties of the resulting AIS/ZnS QDs. Under optimized synthesis conditions, water-soluble AIS/ZnS QDs with a PL QY of 65% and excellent colloidal and long-term stability could be reproducible prepared.
To establish the SI traceability of the element content w(Hg) of a gravimetrically prepared 1 g/kg mercury solution, the latter was compared to a primary reference solution by means of high accuracy inductively coupled plasma optical emission spectrometry (ICP OES) measurements. For this purpose, the ICP OES bracketing method previously described [1] was improved to yield a relative expanded uncertainty of the mercury element content of Urel(w(Hg)) = 0.5 % (with k = 2) associated with the comparison of two monoelemental solutions. In case of mercury, such a small uncertainty was achieved for the first time. Before, in general, such small uncertainties were only reported applying isotope dilution methods performed with MC-ICP-MS. However, for mercury solutions at this concentration range, several dilution steps are necessary to prepare the samples for ID-MC-ICP-MS. The sum of the uncertainty contributions stemming from the dilution steps, as well as the intrinsic difficulties of measuring mercury at low concentrations, result in an overall uncertainty of the ICP-MS measurements, which is comparable to or even larger than those, achieved with the ICP OES method applied. We will present details of the sample preparation as well as of the dedicated ICP OES measurement approach, which were crucial to achieve such a small measurement uncertainty.
The newly developed method was successfully applied in the context of the development and production of elemental solution chemical reference standards (CRS), which are distributed by the EDQM. The CRS are intended to support measurements required by the European Pharmacopoeia, which has recently incorporated a new international guideline for the control of elemental impurities in medicinal products.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Arraying of single cells for mass spectrometric analysis is a considerable bioanalytical challenge. In this study, we employ a novel single cell arraying technology for quantitative analysis and isotopic fingerprinting by laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS). The single cell arraying approach is based on a piezo-acoustic microarrayer with software for automated optical detection of cells within the piezo dispense capillary (PDC) prior to arraying. Using optimized parameters, single cell occupancy of >99%, high throughput (up to 550 cells per hour), and a high cell recovery of >66% is achieved. LA-ICP-TOF-MS is employed to detect naturally occurring isotopes in the whole mass range as fingerprints of individual cells. Moreover, precise quantitative determination of metal-containing cell dyes is possible down to contents of ∼100 ag using calibration standards which were produced using the same arrayer.
The visualization of index-of-refraction (IoR) distribution is one of the common methods to investigate fluid flow or pressure fields. While schlieren and shadowgraphy imaging techniques are widely accepted, their inherent limitations often lead to difficulties in elucidating the IoR distribution and extracting the true IoR information from the resulting images. While sophisticated solutions exist, the IoR-gradient-to-image was achieved by purposely introducing a commonly avoided “defect” into the optical path of a conventional coincident schlieren/shadowgraphy setup; the defect is a combination of slight defocusing and the use of non-conjugate optical components. As such, the method presented in this work is referred to as defocusing shadowgraphy, or DF-shadowgraphy. While retaining the ease of a conventional schlieren/shadowgraphy geometry, this DF approach allows direct visualization of complicated resonant acoustic fields even without any data processing. For instance, the transient acoustic fields of a common linear acoustic resonator and a two-dimensional one were directly visualized without inversion. Moreover, the optical process involved in DF-shadowgraphy was investigated from a theoretical perspective. A numerical solution of the sophisticated impulse response function was obtained, which converts the phase distortion into intensity distributions. Based on this solution, the IoRs of various gas streams (e.g., CO2 and isopropanol vapor) were determined from single images.
Bioanalytical, diagnostic, and security applications require the fast and sensitive determination of a steadily increasing number of analytes or events in parallel in a broad variety of detection formats and increased sensitivities. This – flanked by recent technical advancements and the availability of simple to use, commercial time-resolved photoluminescence measuring devices at reasonable costs - calls for the exploitation of the species- and environment-specific photoluminescence parameter luminescence lifetime. In this context, time-resolved photoluminescence measurements of different classes of molecular and nanocrystalline emitter and luminescent particles in different time windows are presented and examples for applications such as lifetime multiplexing and barcoding in conjunction with fluorescence lifetime imaging microscopy (FLIM) and flow cytometry are given.
An alternative method for lithium isotope analysis by using high-resolution atomic absorption spectrometry (HR-CS-AAS) is proposed herein. This method is based on monitoring the isotope shift of approximately 15 pm for the electronic transition 22P←22S at around the wavelength of 670.8 nm, which can be measured by state-of-the-art HR-CS-AAS. Isotope analysis can be used for (i) the traceable determination of Li concentration and (ii) isotope amount ratio analysis based on a combination of HR-CS-AAS and spectral data analysis by machine learning (ML).
In the first case, the Li spectra are described as the linear superposition of the contributions of the respective isotopes, each consisting of a spin-orbit doublet, which can be expressed as Gaussian components with constant spectral position and width and different relative intensity, reflecting the isotope ratio in the sample. Precision was further improved by using lanthanum as internal spectral standard. The procedure has been validated using human serum-certified reference materials. The results are metrologically comparable and compatible with the certified values.
In the second case, for isotope amount ratio analysis, a scalable tree boosting ML algorithm (XGBoost) was employed and calibrated using a set of samples with 6Li isotope amount fractions ranging from 0.06 to 0.99 mol mol−1. The training ML model was validated with certified reference materials. The procedure was applied to the isotope amount ratio determination of a set of stock chemicals and a BAM candidate reference material NMC111 (LiNi1/3Mn1/3Co1/3O2), a Li-battery cathode material. These determinations were compared with those obtained by MC-ICP-MS and found to be metrologically comparable and compatible. The residual bias was −1.8‰, and the precision obtained ranged from 1.9‰ to 6.2‰. This precision was sufficient to resolve naturally occurring variations. The NMC111 cathode candidate reference material was analyzed using high-resolution continuum source atomic absorption spectrometry with and without matrix purification to assess its suitability for technical applications. The results obtained were metrologically compatible with each other.
Two calibration-free LIBS techniques are used for the quantitative analysis of synthetic cement samples: the CF-LIBS based on the Boltzmann plot method and the Monte Carlo (MC) LIBS based on the iterative spectrum fitting. In CF-LIBS, the inverse problem is solved, i.e. the elemental concentrations are determined by the reconstruction of plasma parameters from spectra. The MC-LIBS technique solves the direct problem by finding the highest correlation between the model-generated and experimental spectrum. The accuracy of both calibration-free LIBS methods suffers from factors such as inaccurately determined instrumental function, the deviation of experimental plasma from the mathematical model used, not taking into account the collection geometry and from the uncertainty of spectroscopic data. Therefore, the both calibration-free LIBS approaches are applied to synthetic spectra which perfectly suit the mathematical model of the method. This test yields the accuracy of both the approaches for the ideal case. In addition, the accuracy of both methods is investigated for non-isothermal plasma, because real laser-induced plasma often has high gradients in temperature. Both methods assume an isothermal plasma.
Two calibration-free (CF) LIBS approaches are used for the quantitative analysis of cement samples: the CF-LIBS based on the Boltzmann plot method and the Monte Carlo (MC) LIBS based on the iterative spectrum fitting. In CF-LIBS, the inverse problem is solved, i.e. the elemental concentrations are determined by the reconstruction of plasma parameters from spectra. The MC-LIBS technique solves the direct problem by finding the highest correlation between the model-generated and experimental spectrum. The accuracy of both calibration-free LIBS methods suffers from factors such as inaccurately determined instrumental function, the deviation of experimental plasma from the mathematical model used, not taking into account the collection geometry, and from the uncertainty of spectroscopic data. The both calibration-free LIBS approaches are first applied to synthetic spectra which perfectly suit the mathematical model of the method, i.e. the model of the uniform, isothermal, and stationary plasma. This test yields the accuracy of both the approaches for the ideal case. In addition, the accuracy of both the methods is investigated for non-uniform and non-isothermal plasma, because real laser-induced plasma often has high gradients in temperature and particle number densities. Finally, both calibration-free LIBS approaches are applied to experimental spectra obtained from cement samples. The figures of merits of two approaches are compared when working with both synthetic and experimental spectra.
Spatial Heterodyne Spectroscopy (SHS) is a spectrometric technique that combines both dispersive and interferometric features into a customizable instrument. The Basis of SHS is a Michelson interferometer with its mirrors replaced by diffraction gratings and with no moving parts. The output signal from SHS is the interferogram, which is recorded with a 1D or 2D pixel array detector. The spatial periodicity of the fringes on the interferogram is a function of the wavelength of the diffracted light. Using the Fast Fourier Transform, the original optical spectrum that enters SHS is retrieved. The light that is analyzed by SHS can come from a variety of sources. In our work, we used Raman scattering and Laser-Induced Plasma to perform quantitative and qualitative analyses. Figure 1 compares the performance of the SHS with that of high Resolution echelle and portable low-resolution asymmetrically crossed Czerny-Turner spectrometers (OO in Fig.1). The analyzed light came from the plasma induced on a stainless-steel reference material. The SHS exhibits the resolution comparable to that of the echelle spectrometer used, about 8000. Due to a high throughput of the SHS (theoretically, ~200 times higher than that of grating instruments), the number of spectra needed to be accumulated for comparable signal-to-noise ratios is much smaller than in the case of the echelle and comparable to OO spectrometers.
Examples of Raman SHS applied to several pure liquids are given in Fig. 2. Raman SHS was used in three different settings: (i) for classification of six types of oils, (ii) for univariate/multivariate analysis of binary mixture cyclohexane-isopropanol, and (iii) for multivariate analysis of glycerol solution in water. For the last two settings, chemometric analysis of the spectra yielded linear calibration plots over the range 1-90% of concentrations of isopropanol in cyclohexane, and 0.5-10% of glycerol in water.
The antimicrobial photodynamic approach has been demonstrated as an efficient and sustainable process for the eradication of microbial pathogens. In this work, silica-coated Magnetite nanoparticles (NPs) were used as carriers of glycosylated porphyrins and phthalocyanines. Their subsequent cationization
resulted in the production of stable antimicrobial photosensitizing materials, effective against E. coli. Suspensions of the photocatalysts in water present bimodal size distributions formed by big clusters and small NPs with hydrodynamic diameters between 8 and 38 nm. The presence of small NPs in the suspensions is related to an effective photodynamic inactivation (PDI) of E. coli cells. Glycosylation of the PS showed a positive effect on the PDI performance, which could be related to a higher accumulation of the photocatalyst over the bacterial cell membrane. In addition, these biocidal agents proved to be photostable and their photoactive performance decreased only between 23% and 28% upon 5 PDI cycles, mostly because of the loss of material between cycles, which makes them promising materials for water disinfection purposes.
The primary screening of hybridoma cells is a time-critical and laborious step during the development of monoclonal antibodies. Often critical errors occur in this phase, which supports the notion that the generation of monoclonal antibodies with hybridoma technology is difficult to control and hence a risky venture. We think that it is crucial to improve the screening process to eliminate most of the immanent deficits of the conventional approach. With this new microarray-based procedure, several advances could be achieved: Selectivity for excellent binders, high throughput, reproducible signals, avoidance of misleading avidity (multivalency) effects, and simultaneous performance of competition experiments. The latter can directly be used to select clones of desired cross-reactivity properties. In this paper, a model system with two excellent clones against carbamazepine, two weak clones and blank supernatant has been designed to examine the effectiveness of the new system. The excellent clones could be detected largely independent of the IgG concentration, which is unknown during the clone screening since the determination and subsequent adjustment of the antibody concentration is not possible in most cases. Furthermore, in this approach, the enrichment, isolation, and purification of IgG for characterization is not necessary. Raw cell culture supernatant can be used directly, even when fetal calf serum (FCS) or other complex media had been used. In addition, an improved method for the oriented antibody-immobilization on epoxy-silanized slides is presented. Based on the results of this model system, we conclude that this approach should be preferable to most other protocols leading to many of false positives, causing expensive and lengthy confirmation steps to weed out the poor clones.
Probe 1, which contains an anilinopyridine chromophore and an azaoxa macrocyclic subunit, presented an absorption band centered at 340 nm in acetonitrile. Addition of Fe(III), Cr(III) and Hg(II) induced the growth of a new absorption band at 430 nm (with color change from colorless to yellow), whereas in the presence of Cu(II), Zn(II) and Pb(II), less marked changes were observed. The color changes observed upon addition of Fe(III), Cr(III) and Hg(II) were ascribed to the formation of 1:1 stoichiometry complexes with probe 1. Coordination of Fe(III), Cr(III) and Hg(II) with the pyridine fragment of 1 induced an enhancement of the charge transfer character accompanied with a marked bathochromic shift that was reflected in a color change from colorless to yellow. The strength of the interaction between probe 1 and Fe(III) cation was modulated upon interaction with anions. Of all the anions tested, only cyanide was able to induce the bleaching of the yellow 1·Fe(III) complex solution. This bleaching was ascribed to
the formation of 1·Fe(III)-CN complex that restored, to some extent, the optical features of the free probe allowing the chromogenic sensing of cyanide. Besides, 1·Fe(III) complex was used to detect
cyanide in acetonitrile-water 90:10 v/v mixtures with good recoveries.
Functional nanomaterials (NM) of different size, shape, chemical composition, and surface chemistry are of increasing relevance for many key technologies of the twenty-first century. This includes polymer and silica or silica-coated nanoparticles (NP) with covalently bound surface groups, semiconductor quantum dots (QD), metal and metal oxide NP, and lanthanide-based NP with coordinatively or electrostatically bound ligands, as well as surface-coated nanostructures like micellar encapsulated NP. The surface chemistry can significantly affect the physicochemical properties of NM, their charge, their processability and performance, as well as their impact on human health and the environment. Thus, analytical methods for the characterization of NM surface chemistry regarding chemical identification, quantification, and accessibility of functional groups (FG) and surface ligands bearing such FG are of increasing importance for quality control of NM synthesis up to nanosafety. Here, we provide an overview of analytical methods for FG analysis and quantification with special emphasis on bioanalytically relevant FG broadly utilized for the covalent attachment of biomolecules like proteins, peptides, and oligonucleotides and address methodand material-related challenges and limitations. Analytical techniques reviewed include electrochemical titration methods, optical assays, nuclear magnetic resonance and vibrational spectroscopy, as well as X-ray based and thermal analysis methods, covering the last 5–10 years. Criteria for method classification and evaluation include the need for a signal-generating label, provision of either the total or derivatizable number of FG, need for expensive instrumentation, and suitability for process and production control during NM synthesis and functionalization.
Working towards a comprehensive understanding of introduction pathways, number, and fate of micro¬plastics in the environment, suitable analytical methods are a precondition. Micro-spectroscopic methods are probably the most widely used techniques. Besides their ability to measure single spectra of a particle or fiber, most modern FTIR- and Raman microscopes are also capable of two-dimensional imaging. This is very appealing to microplastics research because it allows to simultaneously characterize the analytes chemically as well as their size (distribution) and shape.
Two-dimensional imaging on extensive sample areas with FTIR-micros¬copes is facilitated by focal plane array (FPA) detectors resulting in large data sets comprised of up to several million spectra. With numbers too large for manual inspection of each individual spectrum, automated data evaluation is inevitable. Identifying different polymers based on the comparison with known reference spectra (library search) has proven to be a suitable approach. For that purpose, FTIR-spectra of common plastics can be collected to create an individual reference library.
To Supplement this ‘targeted analysis’, looking for known substances via library search, an exploratory approach was tested. Principal component analysis (PCA) proved to be a helpful tool to drastically reduce the size of the data set while maintaining the significant information. Subsequently, cluster analysis was used to find groups of similar spectra. Spectra found in different clusters could be assigned to different polymer types. The variation observed within clusters gives a hint on chemical variability of microplastics of the same polymer found in the sample. Spectra labeled according to the respective cluster/polymer type were used to build a classification model which allowed to quickly predict the polymer type based on the FTIR spectrum. Classification was tested on a second, independent data set and results were compared to the spectral library search procedure.
A particle roughness analysis tool, based on electron microscopy (EM) images. The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
A particle roughness analysis tool, based on electron microscopy images (SEM and TEM). The influence of various parameters on the calculated roughness was also investigated: the setting of the proper threshold, accelerating voltage, etc. The samples were gradually tilted to extend imaging information of more than only one projection. Furthermore, the measurement uncertainty of the profile roughness of particles associated to various orientations was estimated.
Analysis of electrochemical and liver microsomal transformation products of lasalocid by LC/HRMS
(2022)
Rationale: Lasalocid (LAS), an ionophore, is used in cattle and poultry farming as feed additive for its antibiotic and growth-promoting properties. Literature on transformation products (TP) resulting from LAS degradation is limited. So far, only hydroxylation is found to occur as the metabolic reaction during the LAS degradation. To investigate potential TPs of LAS, we used electrochemistry (EC) and liver microsome (LM) assays to synthesize TPs, which were identified using liquid chromatography high-resolution mass spectrometry (LC/HRMS).
Methods: Electrochemically produced TPs were analyzed online by direct coupling of the electrochemical cell to the electrospray ionization (ESI) source of a Sciex Triple-TOF high resolution mass spectrometer. Then, EC-treated LAS solution was collected and analyzed offline using LC/HRMS to confirm stable TPs and improve their annotation with a chemical structure due to informative MS/MS spectra. In a complementary approach, TPs formed by rat and human microsomal incubation were investigated using LC/HRMS. The resulting data were used to investigate LAS modification reactions and elucidate the chemical structure of obtained TPs.
Results: The online measurements identified a broad variety of TPs, resulting from modification reactions like (de-)hydrogenation, hydration, methylation, oxidation as well as adduct formation with methanol. We consistently observed different ion complexations of LAS and LAS-TPs (Na+; 2Na+ K+; NaNH4 +; KNH4 +). Two stable methylated EC-TPs were found, structurally annotated, and assigned to a likely modification reaction. Using LM incubation, seven TPs were formed, mostly by oxidation/hydroxylation. After the identification of LM-TPs as Na+-complexes, we identified LM-TPs as K+-complexes.
Conclusion: We identified and characterized TPs of LAS using EC- and LM-based methods. Moreover, we found different ion complexes of LAS-based TPs. This knowledge, especially the different ion complexes, may help elucidate the metabolic and environmental degradation pathways of LAS.
Analysis of dynamic systems
(2019)
Monitoring specific information (i.e., physico-chemical properties, chemical reactions, etc.) is the key to chemical process control when looking at dynamic systems, and quantitative online NMR spectroscopy is the method of choice for the investigation and understanding of dynamic multi-component systems. NMR provides rapid and non-invasive information, and due to the inherent linearity between sample concentration and signal intensity, peak areas can be directly used for quantification of multiple components in a mixture (without the need for any further calibration). This is one of the most attractive features of quantitative NMR spectroscopy. With the launch of devices covering magnetic field strengths from 40 to 90 MHz, so called compact or benchtop NMR systems, this analytical method is now reaching a sufficient degree of compactness and operability for an application outside of very specialized laboratories.
Whilst there are also many other tools available to examine various analytical parameters from dynamic processes, such as mass spectrometry, (near) infrared or Raman spectroscopy, each of these tools can only really be used independently. How can we examine and compare all data describing a particular chemical reaction? How can we visualize information rich, specific, or direct methods together with less specific but established analytical methods? And how can we transfer calibration information to the most appropriate process analytical method or method combination? Quantitative NMR spectroscopy (qNMR) has the potential to substitute offline laboratory analysis for calibration purposes by delivering quantitative reference data as an online method.
The workshop briefly presents the current state of the art of the analysis of dynamic systems by online NMR spectroscopy and analytical data fusion, with the remaining time being used for questions and open discussion with the attendees.
Spatial heterodyne spectroscopy (SHS) is used for quantitative analysis and classification of liquid samples. SHS is a version of a Michelson interferometer with no moving parts and with diffraction gratings in place of mirrors. The instrument converts frequency-resolved information into spatially resolved one and records it in the form of interferograms. The back-extraction of spectral information is done by the Fast Fourier transform. A SHS instrument is constructed with the resolving power 5000 and spectral range 522 - 593 nm. Two original technical solutions are used as compared to previous SHS instruments: the use of a high frequency diode pumped solid state (DPSS) laser for excitation of Raman spectra and a microscope-based collection system. Raman spectra are excited at 532 nm at the repetition rate 80 kHz. Raman shifts between 330 cm-1 and 1600 cm-1 are measured. A new application of SHS is demonstrated: for the first time it is used for quantitative Raman analysis to determine concentrations of cyclohexane in isopropanol and glycerol in water. Two calibration strategies are employed: univariate based on the construction of a calibration plot and multivariate based on partial least square regression (PLSR). The detection limits for both cyclohexane in isopropanol and glycerol in water are at a 0.5 mass% level. In addition to the Raman-SHS chemical analysis, classification of industrial oils (biodiesel, poly(1-decene), gasoline, heavy oil IFO380, polybutenes, and lubricant) is performed using their Raman-fluorescence spectra and principal component analysis (PCA). The oils are easily discriminated as they show distinct non-overlapping patterns in the space of principal components.
HR-CS-GFMAS turned out to be a sensitive, fast and highly selective tool for fluorine determination. The method is based on the in situ formation of diatomic gallium-mono fluoride (GaF) at a temperature of 1550°C. GaF can be detected at 211.248 nm providing limits of quantificationin the low μg/L range. However, several species specific effects where observed when analyzing PFASs.
Hepcidin-25 has attracted much attention ever since its discovery in 2001. It is widely recognized that this peptide hormone plays a major role in the regulation of iron levels in mammals and can reveal important clinical information about several iron-related disorders. However, the development of a reliable assay to quantify hepcidin proved to be problematic and serum hepcidin-25 concentrations determined by various assays differ substantially. Challenges arise in the MS analysis of hepcidin due to the “sticky” character of the peptide and the lack of suitable standards.
With the aim to tackle the current difficulties in hepcidin quantification and improve the status of this promising biomarker in the clinical field, we developed a rapid and robust analytical strategy for the quantification of hepcidin-25 in human samples based on HPLC-MS/MS (QqQ) as a reference method candidate to be implemented in routine laboratories. The novelty of the method is the use of amino- and fluoro-silanized autosampler vials to reduce hepcidin interaction to laboratory glassware surfaces. Furthermore, we have investigated two sample preparation strategies and two chromatographic separation conditions where the use of acidic mobile phases was compared with a novel approach involving solvents at high pH containing 0.1% of ammonia. Both methods were carefully validated and applied to clinical samples in an intra-laboratory comparison of two LC-MS/MS methods using the same hepcidin-25 calibrators with very good correlation of the results.
Introduction
Per- and polyfluorinated alkyl substances (PFASs) are a group of over 4730 individual compounds. Several PFASs are extremely persistent, bioaccumulative and toxic. The analysis of PFASs is challenging because of their various chemical and physical properties as well as the high number of compounds. Target-based approaches (e.g., LC-MS/MS) are limited to the availability of analytical grade standards and are not suitable for the analysis of new/unknown PFASs and transformation products. Therefore, PFAS sum parameter methods become increasingly important to indicate realistic PFAS pollution levels.
Methods
For the instrumental analysis of such sum parameters, a fluorine selective detector is needed. In our study we used high resolution-continuum source-graphite furnace molecular absorption spectrometry (HR-CS-GFMAS) which is a sensitive and highly selective tool for fluorine determination. The method is based on the in situ formation of diatomic gallium-mono fluoride (GaF) in a graphite furnace at a temperature of 1550°C. The molecular absorption of GaF can be detected at its most sensitive wavelength at 211.248 nm providing limits of quantification of c(F) 2.7 µg/L.
Results
Here, we present an improved method for the determination of PFASs using HR-CS-GFMAS via GaF detection. The optimized method includes a modifier pretreatment step using a mixture of Mg, Pd and Zr and a correction measurement using perfluorooctanoic acid. The combination of both resulted in increased accuracy and precision as well as overall lower detection limits. Furthermore, during optimization the influence of species-specific responses during HR-CS-GFMAS analysis was reduced resulting in a more accurate determination of PFAS sum parameters. To test the applicability of the improved method, we analysed soil samples from a former fire-fighting training area combining the improved method for detection with our previously optimized extraction method for extractable organically bound fluorine (EOF) determination in soils.
Innovative aspects
• Highly sensitive and selective method for fluorine/PFASs analysis based on HR-CS-GFMAS
• Increased accuracy for the determination of EOF
• Reduction of PFAS species-specific responses by optimized modifier conditions
Per- and polyfluorinated alkyl substances (PFASs) are a group of over 4730 individual compounds. Several PFASs are extremely persistent, bioaccumulative and toxic. The analysis of PFASs is challenging because of their various chemical and physical properties as well as the high number of compounds. Target-based approaches (e.g., LC-MS/MS) are limited to the availability of analytical grade standards and are not suitable for the analysis of new/unknown PFASs and transformation products. Therefore, PFAS sum parameter methods become increasingly important to indicate realistic PFAS pollution levels.
PFAS sum parameters display the proportion of organically bound fluorine that can either be extracted (EOF) or adsorbed to activated carbon (AOF). For the instrumental analysis of such sum parameters, a fluorine selective detector is needed. High resolution-continuum source-graphite furnace molecular absorption spectrometry (HR-CS-GFMAS) is a sensitive and highly selective tool for fluorine determination. The method is based on the in situ formation of diatomic gallium-mono fluoride (GaF) in a graphite furnace at a temperature of 1550°C. The molecular absorption of GaF can be detected at its most sensitive wavelength at 211.248 nm providing limits of quantification of c(F) 2.7 µg/L.
Here, we present an improved method for the determination of PFASs using HR-CS-GFMAS via GaF detection. The optimized method includes a Ga pretreatment as described by Gawor et al. resulting in overall lower detection limits. Furthermore, during optimization the influence of species-specific responses during HR-CS-GFMAS analysis was reduced resulting in a more accurate determination of PFAS sum parameters. To test the applicability of the improved method, we analyzed soil samples from a former fire-fighting training area combining the improved method for detection with our previously optimized extraction method for EOF determination in soils.
Per- and polyfluorinated alkyl substances (PFASs) are a group of over 4730 individual compounds. Several PFASs are extremely persistent, bioaccumulative and toxic. The analysis of PFASs is challenging because of their various chemical and physical properties as well as the high number of compounds. Target-based approaches (e.g., LC-MS/MS) are limited to the availability of analytical grade standards and are not suitable for the analysis of new/unknown PFASs and transformation products. Therefore, PFAS sum parameter methods become increasingly important to indicate realistic PFAS pollution levels.
PFAS sum parameters display the proportion of organically bound fluorine that can either be extracted (EOF) or adsorbed to activated carbon (AOF). For the instrumental analysis of such sum parameters, a fluorine selective detector is needed. High resolution-continuum source-graphite furnace molecular absorption spectrometry (HR-CS-GFMAS) is a sensitive and highly selective tool for fluorine determination. The method is based on the in situ formation of diatomic gallium-mono fluoride (GaF) in a graphite furnace at a temperature of 1550°C. The molecular absorption of GaF can be detected at its most sensitive wavelength at 211.248 nm providing limits of quantification of c(F) 2.7 µg/L.
Here, we present an improved method for the determination of PFASs using HR-CS-GFMAS via GaF detection. The optimized method includes a Ga pretreatment as described by Gawor et al. resulting in overall lower detection limits. Furthermore, during optimization the influence of species-specific responses during HR-CS-GFMAS analysis was reduced resulting in a more accurate determination of PFAS sum parameters. To test the applicability of the improved method, we analyzed soil samples from a former fire-fighting training area combining the improved method for detection with our previously optimized extraction method for EOF determination in soils.
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been revealed as a convenient technique for trace elemental imaging in tissue sections, providing elemental 2D distribution at a quantitative level. For quantification purposes, in the last years several approaches have been proposed in the literature such as the use of CRMs or matrix matched standards. The use of Isotope Dilution (ID) for quantification by LA-ICP-MS has been also described, being mainly useful for bulk analysis but not feasible for spatial measurements so far. In this work, a quantification method based on ID analysis was developed by printing isotope-enriched inks onto kidney slices from rats treated with antitumoral Pt-based drugs using a commercial ink-jet device, in order to perform an elemental quantification in different areas from bio-images. For the ID experiments ¹⁹⁴Pt enriched platinum was used. The methodology was validated by deposition of natural Pt standard droplets with a known amount of Pt onto the surface of a control tissue, where could be quantified even 50 pg of Pt, with recoveries higher than 90%. The amount of Pt present in the whole kidney slices was quantified for cisplatin, carboplatin and oxaliplatin-treated rats. The results obtained were in accordance with those previously reported. The amount of Pt distributed between the medullar and cortical areas was also quantified, observing different behavior for the three drugs.
The goal of this work is to identify the optimal operating input for a lithiation reaction that is performed in a highly innovative pilot scale continuous flow chemical plant in an industrial environment, taking into account the process and safety constraints. The main challenge is to identify the optimum operation in the absence of information about the reaction mechanism and the reaction kinetics. We employ an iterative real-time optimization scheme called modifier adaptation with quadratic approximation (MAWQA) to identify the plant optimum in the presence of plant-model mismatch and measurement noise. A novel NMR PAT-sensor is used to measure the concentration of the reactants and of the product at the reactor outlet. The experiment results demonstrate the capabilities of the iterative optimization using the MAWQA algorithm in driving a complex real plant to an economically optimal operating point in the presence of plant-model mismatch and of process and measurement uncertainties.
This is the first time that PE frits were used to quantify sulfur in copper and its alloys by isotope dilution combined with LA-ICP-MS: an alternative approach for sample preparation. The following properties of the PE frit meet the requirements for isotope dilution LA-ICPMS: porous material, thermo plastic (melting point >100°C), chemical resistance (nitric acid >70%) and high adsorption efficiency. The breakthrough, however, as a support material, especially when comparing the PE frit with other materials such gelatin or sodium silicate is the low blank, which is only two times of the gas flow blank (2.3-4.0 x 10⁴ cps). Additionally, the porosity of the frit was considered, as it directly affects the adsorption efficiency for the sample solution, which is present in the cavities of the frit.
Adsorption efficiency was studied by depositing sulfur standards with varying sulfur amounts (0, 2, 5, 10, 20, 40 and 80 µg S) on the frits. The remaining sulfur which was not absorbed by the frit was rinsed off and was measured by ICP-MS. This indirect method shows that more than 99.5 % of the loaded sulfur was absorbed by the frit. Such high absorption efficiency is completely sufficient for a support material to be used in LA-ICP-IDMS. The so prepared frits with increasing sulfur amount were measured by LA-ICP-MS showing a good linearity between 0 µg S and 40 µg S with a correlation coefficient r2 of 0.9987 and sensitivity of 3.4x10⁴ cps µgˉ¹ for 32S.
Three copper reference materials produced by BAM were selected to develop and validate the LA-ICP-IDMS procedure. The IDMS technique was applied to these samples as follows: the samples were spiked, dissolved, digested and then the digest was adsorbed on the frits.
This is the first time that PE frits were used to quantify sulfur in copper and its alloys by isotope dilution combined with LA-ICP-MS: an alternative approach for sample preparation. The following properties of the PE frit meet the requirements for isotope dilution LA-ICPMS:: porous material, thermo plastic (melting point >100oC), chemical resistance (nitric acid >70%) and high adsorption efficiency. The breakthrough, however, as a support material, especially when comparing the PE frit with other materials such gelatin or sodium silicate is the low blank, which is only two times of the gas flow blank (2.3-4.0 x 104 cps). Additionally, the porosity of the frit was considered, as it directly affects the adsorption efficiency for the sample solution, which is present in the cavities of the frit.
Adsorption efficiency was studied by depositing sulfur standards with varying sulfur amounts (0, 2, 5, 10, 20, 40 and 80 µg S) on the frits. The remaining sulfur which was not absorbed by the frit was rinsed off and was measured by ICP-MS. This indirect method shows that more than 99.5 % of the loaded sulfur was absorbed by the frit. Such high absorption efficiency is completely sufficient for a support material to be used in LA-ICP-IDMS. The so prepared frits whith increasing sulfur amount were measured by LA-ICP-MS showing a good linearity between 0 µg S and 40 µg S whit a correlation coefficient r2 of 0.9987 and sensitivy of 3.4x104 cpsµg-1 for 32S.
Three copper reference materials produced by BAM were selected to develop and validate the LA-ICP-IDMS procedure. The IDMS technique was applied to these samples as follows: the samples were spiked, dissolved, digested and then the digest was adsorbed on the frits.
Background signals from in situ-formed amorphous carbon, despite not being fully understood, are known to be a common issue in few-molecule surface-enhanced Raman scattering (SERS). Here, discrete gold and silver nanoparticle aggregates assembled by DNA origami were used to study the conditions for the formation of amorphous carbon during SERS measurements. Gold and silver dimers were exposed to laser light of varied power densities and wavelengths. Amorphous carbon prevalently formed on silver aggregates and at high power densities. Time-resolved measurements enabled us to follow the formation of amorphous carbon. Silver nanolenses consisting of three differently-sized silver nanoparticles were used to follow the generation of amorphous carbon at the single-nanostructure level. This allowed observation of the many sharp peaks that constitute the broad amorphous carbon signal found in ensemble measurements. In conclusion, we highlight strategies to prevent amorphous carbon formation, especially for DNA-assembled SERS substrates.
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 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.
The transition 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 comparison method”, independent of the matrix, it runs with very short set-up times in combination with “modular” spectral models. These are based on pure component NMR spectra without the need for tedious calibrations runs. We present approaches from statistical, (i.e., Partial Least Squares Regression) to physically motivated models (i.e., Indirect Hard Modelling).
Based on concentration measurements of reagents and products by the NMR analyser 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 CLEAN ENERGY Flagship is an initiative designed to utilize recent game changing developments in digital, materials and manufacturing technologies to catalyze a radical paradigm shift towards clean, reliable, efficient and cost-optimal energy.
Unifying and drastically accelerating radically new energy material design, processing and integration across the entire value chain addressing energy production, conversion, storage and systems.
CLEAN ENERGY participants are all distinguished research organisations that each benefit from their own industry networks and contacts with regions and state-level activities and have a long history of collaborating with each other (for 10 years now under the umbrella of EERA) within a European collaborative framework.
Through EERA, CLEAN ENERGY aims to become a crucial partner in the SET-Plan, supporting long-lasting approaches through its established networks and internal collaborations.