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This study shows remarkably different features between the oxidation of secondary and primary C₃-C₅ alcohols. The oxidation of primary alcohols is controlled by the oxidative removal of blocking adsorbates, such as CO, formed after the dissociative adsorption of alcohol molecules. Conversely, secondary alcohols do not undergo dissociative adsorption and therefore their oxidation is purely controlled by the energetics of the elementary reaction steps. In this respect, a different role of ruthenium is revealed for the electrooxidation of primary and secondary alcohols on bimetallic platinum-ruthenium catalysts. Ruthenium enhances the oxidation of primary alcohols via the established bifunctional mechanism, in which the adsorption of (hydr)oxide species that are necessary to remove the blocking adsorbates is favored. In contrast, the oxidation of secondary alcohols is enhanced by the Ru-assisted stabilization of an O-bound intermediate that is involved in the potential-limiting step. This alternative pathway enables the oxidation of secondary alcohols close to the equilibrium potential.
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.
Der AK 3.7 ist ein "erweiterter" AK und je zur Hälfte mit NAMUR-Vertretern und Vertretern der Geräte- und Softwarehersteller besetzt. Er wurde ins Leben gerufen, um Begrifflichkeiten der digitalen Transformation aufzugreifen, wie etwa Smarte Sensorik, Sensordatenfusion, Schwarmsensorik oder Softsensorik. Eine erste Aufgabe bestand darin, einige exemplarische Anwendungsfälle der Nutzung smarter Eigenschaften von Feldgeräten sowie deren zukünftige Kommunikationsmöglichkeiten sowohl mit Bezug auf Bestandsanlagen als auch mit Blick auf einen potentiellen Technologiewechsel zu betrachten. Neuer Scope des AK 3.7 ist eine "Next Generation Automation" um einen potentiellen Technologiewechsel rechtzeitig vorauszudenken. Dieses erfolgt unter vollständiger gedanklicher Trennung von heutiger Automatisierung und auch vom NOA-Konzept.
Ebenso wird ein Technologiewechsel in der Produktion der Prozessindustrie (wahrscheinlich modulbasiert) postuliert. Ziel des AK 3.7 wird es in Zukunft sein, diese Anforderungen an smarte Feldgeräte aufzugreifen und gemeinsam mit den thematisch überlappenden Interessenskreisen in Standards zu übersetzen.
The CCQM-K148.a comparison was coordinated by the BIPM on behalf of the CCQM Organic Analysis Working Group for NMIs and DIs which provide measurement services in organic analysis under the CIPM MRA. It was undertaken as a "Track A" comparison within the OAWG strategic plan. CCQM-K148.a demonstrates capabilities for assigning the mass fraction content of a solid organic compound having moderate molecular complexity, where the compound has a molar mass in the range (75 - 500) g/mol and is non-polar (pKow < −2), when present as the primary organic component in a neat organic solid and where the mass fraction content of the primary component in the material is in excess of 950 mg/g.
Participants were required to report the mass fraction of Bisphenol A present in one supplied unit of the comparison material. Participants using a mass balance method for the assignment were also required to report their assignments of the impurity components present in the material. Methods used by the seventeen participating NMIs or DIs were predominantly based on either stand-alone mass balance (summation of impurities) or qNMR approaches, or the combination of data obtained using both methods. The results obtained using thermal methods based on freezing-point depression methods were also reported by a limited number of participants. There was excellent agreement between assignments obtained using all three approaches to assign the BPA content.
The assignment of the values for the mass fraction content of BPA consistent with the KCRV was achieved by most of the comparison participants with an associated relative standard uncertainty in the assigned value in the range (0.1 - 0.5)%.
Intelligent sensor systems, certified reference materials and instrumental analytical-chemical methods contribute to safety and functionality in hydrogen technologies.
This article gives a brief overview of SensRef activities in the Competence Centre H2Safety@BAM on the issues: Analytical methods for the determination of hydrogen purity, certified reference materials as measurement standards with regard to gas quality (primary calibrators) of BAM, test methods for gas sensor systems to detect hydrogen in air as well as the application of fibre-optic sensor systems to monitor the expansion and ageing behaviour of composite containers in hydrogen technologies.
Der Vortrag zum Mess- und Prüfverfahren mit verschiedenen Sensortechnologien und Ultraschallwellen beinhaltet die Themen:
Metrologie zur Wasserstoffspeicherung - Euramet-Vorhaben "MefHySto",
Erkennung von freigesetztem Wasserstoff sowie die Bestimmung des Wasserstoff-Luftverhälntisses mit Gassensoren, zerstörungsfreie Fehlstellenerkennung mit integriertem Zustandsüberwachungssystem basierend auf geführten Ultraschallwellen zur Lebensdauerüberwachung von Composite-Behältern (Wasserstoffspeicher) sowie faseroptische Sensorik zur Schadenfrüherkennung von Wasserstoffspeichern aufgrund erkennbarer Dehnungsänderungen an Druckbehältern.
On the way to a low carbon economy mixtures containing carbon dioxide become increasingly important. Processing of such gas mixtures requires reliable thermodynamic models that can accurately describe the state of matter over an extended range. Mixtures with oxygen for example, are frequently encountered in carbon capture and storage (CCS) processes.
This work reports new experimental (p, rho, T) data at T = (250 to 375 K) and up to a maximum pressure pmax = 20 MPa for five binary (CO2 + O2) mixtures which cover the entire composition range.
The decarbonization of the energy sector is driving the interest in hydrogen as an energy-storage medium. A practical alternative to transport and distribute H2 is using the existing infrastructure for natural gas. The GERG-2008 equation of state currently serves as the ISO standard (ISO 20765-2) for the calculation of thermodynamic properties of natural gas, but H2 appears only as a secondary component.
The availability of consolidated data for mixtures with H2 available at the time of its constitution was very limited. The experimental characterization of the thermodynamic behavior of mixtures of H2 with the main components of natural gas is thus of great relevance to validate and improve the GERG-2008 equation of state for its use with H2-enriched natural gas.
Rückgebaute Mineralwolledämmstoffe und Baustellenverschnitte werden in der Regel deponiert und damit als Rohstoffe dem Markt entzogen. Ziel dieses Projektes ist es darzulegen, dass das Recycling von Glas- und Steinwolle im großmaßstäblich volumenrelevanten Umfang für das Schmelzwannenverfahren technisch umsetzbar und ökonomisch und ökologisch vorteilhaft ist. Neben verfahrenstechnischen Herausforderungen, gilt es die wirtschaftliche in-situ-Identifikation unbekannter Mineralwolle zu ermöglichen. Dazu wird auf erste erfolgversprechende Tastversuche mit spektroskopischen Methoden weiter aufgebaut.
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.