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- 1 Analytische Chemie; Referenzmaterialien (18)
- 6 Materialchemie (10)
- 8 Zerstörungsfreie Prüfung (10)
- 1.1 Anorganische Spurenanalytik (9)
- 3 Gefahrgutumschließungen; Energiespeicher (9)
- 3.1 Sicherheit von Gefahrgutverpackungen und Batterien (8)
- 8.5 Röntgenbildgebung (7)
- 1.4 Prozessanalytik (6)
- 1.6 Anorganische Referenzmaterialien (6)
- 5 Werkstofftechnik (6)
Eingeladener Vortrag
- nein (16)
The co-crystallisation of [NiEn3](NO3)2 (En = ethylenediamine) with Na2MoO4 and Na2WO4 from a water solution results in the formation of [NiEn3](MoO4)0.5(WO4)0.5 co-crystals. According to the X-ray diffraction analysis of eight single crystals, the parameters of the hexagonal unit cell (space group P–31c, Z = 2) vary in the following intervals: a = 9.2332(3)–9.2566(6); c = 9.9512(12)–9.9753(7) Å with the Mo/W ratio changing from 0.513(3)/0.487(3) to 0.078(4)/0.895(9). The thermal decomposition of [NiEn3](MoO4)0.5(WO4)0.5 individual crystals obtained by co-crystallisation was performed in He and H2 atmospheres. The ex situ X-ray study of thermal decomposition products shows the formation of nanocrystalline refractory alloys and carbide composites containing ternary Ni–Mo–W phases. The formation of carbon–nitride phases at certain stages of heating up to 1000 °C were shown.
Climate change and the need to reduce greenhouse gas emissions pose tremendous challenges to policymakers, the economy, and society. In this context, the development of clean, low-emission technologies plays a crucial role in mitigating the negative impact of fossil fuels on the climate. Hydrogen is a promising energy carrier and fuel that, thanks to its versatility, can be used in many applications. However, the adoption of hydrogen technology requires sufficient trust in its safety. To proxy the development of hydrogen safety innovations, we provide an analysis along the three knowledge and technology transfer channels of publications, patents, and standards. Our results show that research on hydrogen safety has increased significantly in the last decades, with hydrogen safety patents experiencing a general upward trend between 1980 and 2020, just recently decreasing. However, an analysis of almost 100 international hydrogen and fuel cell standards shows only a small number of references to scientific publications. This apparently limited transfer of knowledge from publications points to the need to optimize the coordination of the three knowledge and technology transfer channels for the future development of hydrogen technology. Based on the exploration of this gap, we recommend that research on the three channels for hydrogen be intensified and that the impact of hydrogen safety technology research and development on their diffusion be investigated.
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.
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.
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.
BAM Inside #5/2021
(2021)
Liebe Leser*innen,
wir stehen an der Schwelle einer globalen Transformation, des Aufbruchs in ein neues Zeitalter. Deutschland hat sich verpflichtet, bis 2045 das Ziel der Klimaneutralität zu erreichen, und stellt sich damit der Verantwortung, die Erderwärmung in vertretbaren Grenzen zu halten.
Der Weg zu einer CO₂-neutralen Zukunft wird nur mit sicheren Technologien und Innovationen möglich sein. Der Koalitionsvertrag der neuen Bundesregierung betont in seiner Präambel, dass "dieser Fortschritt auch mit einem Sicherheitsversprechen einhergehen muss". Unsere Expertise als Ressortforschungseinrichtung des Bundes mit dem Auftrag, Sicherheit in Technik und Chemie zu gewährleisten, ist daher auch 150 Jahre nach unserer Gründung gefragt - vielleicht mehr denn je. Wir verbinden sie mit dem Anspruch, Wissenschaft mit Wirkung zu betreiben, und leisten so einen Beitrag für eine klimaneutrale Zukunft.
Dieser Wandel und seine Innovationen werden von neuen Ideen in der Chemie sowie den Material- und Werkstoffwissenschaften getragen, unsere Zukunft ist eine stoffliche. Neue und alte Materialsysteme, Komponenten und Infrastrukturen werden im Kontext der Nachhaltigkeit gedacht werden müssen. Ihre Sicherheit macht Märkte, trägt also zum gesellschaftlichen Wohlstand bei, wenn wir das Vertrauen in Wissenschaft und Technik stärken. Dieses Vertrauen wiederum erwächst aus der Expertise von Menschen und der Verlässlichkeit von Institutionen wie der BAM.
Im letzten Jahr haben wir unsere Anstrengungen insbesondere in Schlüsselbereichen der Energiewende ausgebaut: unter anderem mit unserem Kompetenzzentrum für die Sicherheit moderner Wasserstofftechnologien, umfassenden Testmöglichkeiten für elektrische Energiespeicher und der Suche nach effizienteren, umweltschonenden Alternativen zu Lithium-Ionen-Batterien sowie mit Projekten zur Standfestigkeit noch größerer, leistungsstärkerer Windenergieanlagen und zur CO₂-Einsparung.
Zusammen mit unseren Partner*innen haben wir die große Chance, den Wandel aktiv voranzubringen. Davon erfahren Sie mehr in unserem neuen BAM Report 2021/22. Viel Spaß beim Lesen und Eintauchen in unsere Arbeit!
BAM Update #1/2021
(2021)
BAM Update #3/2021
(2021)