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Medium-resolution nuclear magnetic resonance spectroscopy (MR-NMR) currently develops to an important analytical tool for both quality control and processmonitoring. In contrast to high-resolution onlineNMR (HR-NMR),MR-NMRcan be operated under rough environmental conditions. A continuous re-circulating stream of reaction mixture fromthe reaction vessel to the NMR spectrometer enables a non-invasive, volume integrating online analysis of reactants and products. Here, we investigate the esterification of 2,2,2-trifluoroethanol with acetic acid to 2,2,2-trifluoroethyl acetate both by 1H HR-NMR (500MHz) and 1H and 19F MRNMR (43MHz) as amodel system. The parallel online measurement is realised by splitting the flow,which allows the adjustment of quantitative and independent flow rates, both in the HR-NMR probe as well as in the MR-NMR probe, in addition to a fast bypass line back to the reactor. One of the fundamental acceptance criteria for online MR-MNR spectroscopy is a robust data treatment and evaluation strategy with the potential for automation. The MR-NMR spectra are treated by an automated baseline and phase correction using the minimum entropy method. The evaluation strategies comprise (i) direct integration, (ii) automated line fitting, (iii) indirect hard modelling (IHM) and (iv) partial least squares regression (PLS-R). To assess the potential of these evaluation strategies for MR-NMR, prediction results are compared with the line fitting data derived from the quantitative HR-NMR spectroscopy. Although, superior results are obtained from both IHM and PLS-R for 1H MR-NMR, especially the latter demands for elaborate data pretreatment, whereas IHM models needed no previous alignment.
Inhalt des Vortrags sind die Einführung und Erläuterung der Ziele und Inhalte der Technologie-Roadmap „Prozesssensoren 4.0“, welche von der NAMUR im November 2015 veröffentlicht wurde. Dazu werden Beispiele von aktuellen und zukünftigen Anwendungen erläutert.
Derzeit finden gravierende Veränderungen im Umfeld der Informations- und Kommunikationstechnik statt, die eine große Chance für die optimierte Prozessführung und Wertschöpfung mit darauf abgestimmten vernetzt kommunizierenden Sensoren bieten. Diese Art „smarter“ Sensoren stellen Dienste innerhalb eines Netzwerks bereit und nutzen Informationen daraus. Dadurch ergibt sich aktuell die Notwendigkeit, die Anforderungen an Prozesssensoren sowie an deren Kommunikationsfähigkeiten detaillierter zu beschreiben.
Vernetzte Sensoren sind die Voraussetzung für die Realisierung von Cyberphysischen Produktionssystemen (CPPS) und zukünftiger Automatisierungskonzepte für die Prozess-industrie.
Wesentliche Ziele der Roadmap sind das Zusammenbringen von Technologie- und Marktsicht, Analyse und Priorisierung des (zukünftigen) Informationsbedarfs in verfahrenstechnischen Prozessen, das Erarbeiten von Entwicklungszielen für neue Sensorik sowie das Aufzeigen möglicher Lösungsansätze und ihres Realisierungszeitraums. Darüber hinaus liefert sie Perspektiven für Forschungs- und Entwicklungsförderung und gibt Ansätze für die Normungsarbeit.
We present Jobflow, a domain-agnostic Python package for writing computational workflows tailored for high-throughput computing applications. With its simple decorator-based approach, functions and class methods can be transformed into compute jobs that can be stitched together into complex workflows. Jobflow fully supports dynamic workflows where the full acyclic graph of compute jobs is not known until runtime, such as compute jobs that launch
other jobs based on the results of previous steps in the workflow. The results of all Jobflow compute jobs can be easily stored in a variety of filesystem- and cloud-based databases without the data storage process being part of the underlying workflow logic itself. Jobflow has been intentionally designed to be fully independent of the choice of workflow manager used to dispatch the calculations on remote computing resources. At the time of writing, Jobflow
workflows can be executed either locally or across distributed compute environments via an adapter to the FireWorks package, and Jobflow fully supports the integration of additional workflow execution adapters in the future.
Jobflow is a free, open-source library for writing and executing workflows. Complex workflows can be defined using simple python functions and executed locally or on arbitrary computing resources using the FireWorks workflow manager.
Some features that distinguish jobflow are dynamic workflows, easy compositing and connecting of workflows, and the ability to store workflow outputs across multiple databases.
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 lab-on-valve (LOV) is a mesofluidic platform that has been recently exploited for
the automation and miniaturization of bioanalytical assays, resorting namely to
molecular recognition schemes based on immunosensing. Due to its high versatility
for reagent accommodation, it is possible to establish immunoassays under several
formats (eg. direct competitive ELISA, sandwich ELISA or even label-free immunoaffinity
chromatography). For instance, the LOV has been used as a manifold for
UV-vis micro-Bead Injection Spectroscopy (μ-BIS), a technique that involves the
quantification of the target analyte by direct measurement on the surface of a solid
phase capable of retaining the target analyte by molecular recognition.
The μ-BIS-LOV strategy affords several analytical advantages, namely short time-toresult
intervals (3 to 15 min), low sample volume (1-20 μL), automated solution handling
and washing steps, downscaling of reagents’ consumption, low-cost analysis
and little generation of waste. Additionally, the solid support is renewed before each
determination, minimizing surface fouling, cross-contamination issues and functional
group deactivation. No sample clean-up steps are required because interferences
are separated from the target analyte upon quantification mediated by a molecular
recognition element attached to the micro-bead column. The portability of the LOV
device makes it compatible with point-of-care testing.
To our knowledge, this technique has been mainly employed for the evaluation and
optimization of bioaffinity processes, but its potential for clinical and environmental
analysis remains underexploited. Hence, in this communication, different immunosensing
strategies using the LOV platform will be addressed, namely the determination
of autoimmune IgG in human serum, and the assessment of drug (carbamazepine)
levels in wastewater samples.
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.