Development of a low-cost method for quantifying microplastics in soils and compost using near-infrared spectroscopy

Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and high-throughput mass quantification of microplastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermoanalytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99% of the matrix at recovery rates of 83%–104%. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg. The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common polyethylene-, polypropylene-, and polystyrene-based packaging materials with a particle size 10% determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g−1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most compact NIR spectrometers available.BMBFPeer Reviewe

Standardization and control of Grignard reactions in a universal chemical synthesis machine using online NMR

A big problem with the chemistry literature is that it is not standardized with respect to precise operational parameters, and real time corrections are hard to make without expert knowledge. This lack of context means difficult reproducibility because many steps are ambiguous, and hence depend on tacit knowledge. Here we present the integration of online NMR into an automated chemical synthesis machine (CSM aka. ‘Chemputer’ which is capable of small-molecule synthesis using a universal programming language) to allow automated analysis and adjustment of reactions on the fly. The system was validated and benchmarked by using Grignard reactions which were chosen due to their importance in synthesis. The system was monitored in real time using online-NMR, and spectra were measured continuously during the reactions. This shows that the synthesis being done in the Chemputer can be dynamically controlled in response to feedback optimizing the reaction conditions according to the user requirements

2D to 3D crossover of the magnetic properties in ordered arrays of iron oxide nanocrystals

The magnetic 2D to 3D crossover behavior of well-ordered arrays of monodomain gamma-Fe2O3 spherical nanoparticles with different thicknesses has been investigated by magnetometry and Monte Carlo (MC) simulations. Using the structural information of the arrays obtained from grazing incidence small-angle X-ray scattering and scanning electron microscopy together with the experimentally determined values for the saturation magnetization and magnetic anisotropy of the nanoparticles, we show that MC simulations can reproduce the thickness-dependent magnetic behavior. The magnetic dipolar particle interactions induce a ferromagnetic coupling that increases in strength with decreasing thickness of the array. The 2D to 3D transition in the magnetic properties is mainly driven by a change in the orientation of the magnetic vortex states with increasing thickness, becoming more isotropic as the thickness of the array increases. Magnetic anisotropy prevents long-range ferromagnetic order from being established at low temperature and the nanoparticle magnetic moments instead freeze along directions defined by the distribution of easy magnetization directions

Machine learning techniques to characterize functional traits of plankton from image data

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms

Interpretative and predictive modelling of Joint European Torus collisionality scans

Transport modelling of Joint European Torus (JET) dimensionless collisionality scaling experiments in various operational scenarios is presented. Interpretative simulations at a fixed radial position are combined with predictive JETTO simulations of temperatures and densities, using the TGLF transport model. The model includes electromagnetic effects and collisions as well as □(→┬E ) X □(→┬B ) shear in Miller geometry. Focus is on particle transport and the role of the neutral beam injection (NBI) particle source for the density peaking. The experimental 3-point collisionality scans include L-mode, and H-mode (D and H and higher beta D plasma) plasmas in a total of 12 discharges. Experimental results presented in (Tala et al 2017 44th EPS Conf.) indicate that for the H-mode scans, the NBI particle source plays an important role for the density peaking, whereas for the L-mode scan, the influence of the particle source is small. In general, both the interpretative and predictive transport simulations support the experimental conclusions on the role of the NBI particle source for the 12 JET discharges

Insight into the Structure and Properties of Novel Imidazole-Based Salts of Salicylic Acid

The preparation of new active pharmaceutical ingredient (API) multicomponent crystal forms, especially co-crystals and salts, is being considered as a reliable strategy to improve API solubility and bioavailability. In this study, three novel imidazole-based salts of the poorly water-soluble salicylic acid (SA) are reported exhibiting a remarkable improvement in solubility and dissolution rate properties. All structures were solved by powder X-ray diffraction. Multiple complementary techniques were used to solve co-crystal/salt ambiguities: density functional theory calculations, Raman and 1H/13C solid-state NMR spectroscopies. In all molecular salts, the crystal packing interactions are based on a common charged assisted +N-H(SA) ⋯ O−(co-former) hydrogen bond interaction. The presence of an extra methyl group in different positions of the co-former, induced different supramolecular arrangements, yielding salts with different physicochemical properties. All salts present much higher solubility and dissolution rate than pure SA. The most promising results were obtained for the salts with imidazole and 1-methylimidazole co-formers.Peer Reviewe

Quantitative ¹H Nuclear Magnetic Resonance (qNMR) of Aromatic Amino Acids for Protein Quantification

Hydrolysis of protein samples into amino acids facilitates the use of NMR spectroscopy for protein and peptide quantification. Different conditions have been tested for quantifying aromatic amino acids and proteins. The pH-dependent signal shifts in the aromatic region of amino acid samples were examined. A pH of 12 was found to minimize signal overlap of the four aromatic amino acids. Several aromatic compounds, such as terephthalic acid, sulfoisophthalic acid, and benzene tricarboxylic acid, were applied as internal standards. The quantification of amino acids from an amino acid standard was performed. Using the first two suggested internal standards, recovery was ~97% for histidine, phenylalanine, and tyrosine at a concentration of approximately 1 mM in solution. Acidic hydrolysis of a certified reference material (CRM) of bovine serum albumin (BSA) and subsequent quantification of Phe and Tyr yielded recoveries of 98% ± 2% and 88% ± 4%, respectively, at a protein concentration of 16 g/L or 250 µM

Calibration‐Free Chemical Process and Quality Control Units as Enablers for Modular Production

Modular chemical production is a tangible translation of the digital transformation of the process industry for specialty chemicals. In particular, it enables the speeding‐up of process development and, thus, a quicker time to market by flexibly connecting and orchestrating standardized physical modules and bringing them to life (i.e., parameterizing them) with digitally accumulated process knowledge. We focus on the specific challenges of chemical process and quality control, which in its current form is not well suited for modular production and provide possible approaches and examples of the change towards direct analytical methods, analytical model transfer or machine‐supported processes

Cleaving Ergot Alkaloids by Hydrazinolysis—A Promising Approach for a Sum Parameter Screening Method

Ergot alkaloids are mycotoxins formed by fungi of the Claviceps genus, which are some of the most common contaminants of food and feed worldwide. These toxins are a structurally heterogeneous group of compounds, sharing an ergoline backbone. Six structures and their corresponding stereoisomers are typically quantified by either HPLC-FLD or HPLC-MS/MS and the values subsequently summed up to determine the total ergot alkaloid content. For the development of a screening method targeting all ergot alkaloids simultaneously, the alkaloids need to be transferred to one homogeneous structure: a lysergic acid derivative. In this study, two promising cleaving methods—acidic esterification and hydrazinolysis—are compared, using dihydroergocristine as a model compound. While the acidic esterification proved to be unsuitable, due to long reaction times and oxidation sensitivity, hydrazinolysis reached a quantitative yield in 40‒60 min. Parallel workup of several samples is possible. An increasing effect on the reaction rate by the addition of ammonium iodide was demonstrated. Application of hydrazinolysis to a major ergot alkaloid mix solution showed that all ergopeptines were cleaved, but ergometrine/-inine was barely affected. Still, hydrazinolysis is a suitable tool for the development of a sum parameter screening method for ergot alkaloids in food and feed