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Eingeladener Vortrag
- nein (1008)
Raw data from metabolomics experiments are initially subjected to peak identification and signal deconvolution to generate raw data matrices m × n, where m are samples and n are metabolites. We describe here simple statistical procedures on such multivariate data matrices, all provided as functions in the programming environment R, useful to normalize data, detect biomarkers, and perform sample classification.
Das Ziel der Arbeit war die Entwicklung einer magnetischen Anreicherungsmethode für eine Bead-basierte Substanzbibliothek zur Identifizierung von Proteinbindern. Im Ramen des Forschungsbelegs wurde bereits eine Anreicherungsmethode für ein Modellsystem einer Peptidbibliothek entwickelt. Das Modellsystem bestand dabei aus zwei verschiedenen Peptiden. Als Positivkontrolle wurde das FLAG-Peptid gewählt, welches selektiv gegen den Anti-FLAG-Antikörper bindet, und ein weiteres Peptid wurde als Negativkontrolle gewählt. Anhand dieses Modellsystems konnte die zuvor entwickelte Anreicherungsmethode von 20.000 auf 1.000.000 Beads vergrößert werden. Hierbei wurde ein Anreicherungsfaktor von 818 und eine gute Wiederfindungsrate von 82% erreicht.
In dieser Arbeit wurden drei verschiedene Nanomaterialien auf ihre Bindungsfähigkeit zu Proteinen untersucht. Zu Beginn standen dabei die Herstellung stabiler Dispersionen der einzelnen Nanopartikel und die Stabilität der gebildeten Konjugate im Vordergrund. Der Nachweis einer erfolgreichen Konjugatbildung, sprich der Beschichtung von Nanopartikel mit Proteinen, wurde sowohl qualitativ mittels DLS-Messungen als auch über quantitative Protein-Bestimmungen erbracht. Für die Quantifizierung konnten verschiedene Methoden eingesetzt werden. Neben der klassischen Vorgehensweise, welche indirekt über die Quantifizierung von ungebundenem Protein im Überstand erfolgt, konnten ihm Rahmen dieser Arbeit verschiedene direkte Bestimmungsmethoden entwickelt werden. So wurden mittels kolorimetrischer Tests, wie dem BCA-Assay und dem Bradford-Assay, Nanodiamantdispersionen mit Hilfe einer Korrekturwellenlänge vermessen und quantifiziert. Ebenso zum Einsatz kam die Methode der Aminosäureanalytik, welche aufgrund ihrer guten Rückführbarkeit auf Aminosäurestandards Ergebnisse mit hoher Richtigkeit generieren kann und ebenso die Detektion kleiner Proteinmengen möglich macht.
Nach den erfolgten quantitativen Betrachtungen wurden die Protein-beschichteten Nanopartikel auf ihre Anwendbarkeit als Analoga von Virus-like Particles (VLP) bei einer Immunisierung zur Gewinnung von polyklonalen Antikörpern gegen humanes Ceruloplasmin in Kaninchen überprüft. Es konnte mittels ELISA gezeigt werden, dass die Konjugate erfolgreich für die Herstellung von Antikörpern eingesetzt werden können und im zeitlichen Verlauf einer Immunisierung eine Steigerung des Antikörper-Titers zu erreichen ist.
This Special Issue of Fire and Materials brings together a Collection of papers that were presented at the 2016 Interflam conference in the Fire & Facades session along with recent submissions to the Fire and Materials Journal that are very relevant to the Fire and Facades subject. Having attended the Interflam conference, all Fire and Materials Journal editors who attended realised the importance of this topic. We felt it important that this session content would both be reviewed and made available to a wider audience and that it was timely that we featured this increasing important topic in our journal. Accordingly, authors of selected papers were invited to update and extend their papers for journal submission, and other appropriate peer review papers subsequently submitted to the journal were included in the collection.
We propose a method based on artificial neural networks to extract strain information from wavelength-scanning coherent optical time domain reflectometry (C-OTDR) data. Our neural network algorithm performs more than two orders of magnitude faster than the conventional approach.
This is due to the highly parallel evaluation of the neural networks on a GPU accelerated computer and the fact that conventional correlation and interpolation analysis needs many Iteration steps. This opens the way for real-time C-OTDR strain sensing because the neural Network strain predictions require less time than the measurements themselves. Real-time data Analysis enables long-term sensing e.g. in structural health monitoring, because the large amount of raw data does not have to be stored but can immediately be reduced to the strain data of interest.
Monitoring chemical reactions is the key to chemical process control. Today, mainly optical online methods are applied, which require excessive calibration effort. NMR spectroscopy has a high potential for direct loop process control while exhibiting short set-up times. Compact NMR instruments make NMR spectroscopy accessible in industrial and harsh environ¬ments for advanced process monitoring and control, as demonstrated within the European Union’s Horizon 2020 project CONSENS.
We present a range of approaches for the automated spectra analysis moving from conventional multivariate statistical approach, (i.e., Partial Least Squares Regression) to physically motivated spectral models (i.e., Indirect Hard Modelling and Quantum Mechanical calculations). By using the benefits of traditional qNMR experiments data analysis models can meet the demands of the PAT community (Process Analytical Technology) regarding low calibration effort/calibration free methods, fast adaptions for new reactants or derivatives and robust automation schemes.
Der Mangel an Anwendungsrichtlinien und Validierungsverfahren für struktur-eingebettete faseroptische Dehnungssensoren, insbesondere auf Faser-Bragg-Gitter (FBG)-Basis, führte bislang dazu, dass diese Sensoren trotz ihrer hervorragenden Eigenschaften im Bereich der kommerziellen Material- und Strukturüberwachung nur in geringem Umfange eingesetzt wurden. Fragen zur Degradation der Sensoren unter Beanspruchung, Alterung durch klimatische Einflüsse und Enthaftung infolge Belastung der Struktur konnten bisher nur theoretisch simuliert oder anhand exemplarischer Proben für einzelne einflussnehmende Parameter im Labor untersucht werden. Die Erfassung des Sensorverhaltens im Bauteil unter komplexen Umwelteinflüssen während des Bauteilbetriebes, um damit eine Aussage zur Zuverlässigkeit der Sensormessdaten zu gewinnen, war bisher nicht möglich.
Im Forschungsprojekt FAMOS² wurde deshalb von 2014 bis 2018 die Realisierung eines Autodiagnoseverfahrens für faseroptische Dehnungssensoren erarbeitet, um die Funktionszuverlässigkeit und Langzeitstabilität der Sensoren während des Bauteilbetriebes innerhalb einer Bauteilstruktur bewerten zu können. Im Rahmen des Forschungsprojektes wurde gezeigt, dass ein FBG-Sensor mit einer speziell angepassten magnetostriktiven Schicht magnetisch so angeregt werden kann, dass mit einer zugeschnittenen Messmethodik zu jedem Zeitpunkt eine Bewertung der Sensorzuverlässigkeit unter Betriebsbedingungen möglich ist. Die auf den Sensor aufgebrachte magnetostriktive Schicht erzeugt bei Anregung mit einem passenden Magnetfeld eine gepulste, synthetische Dehnung. Diese Dehnung steht in einem definierten Verhältnis zum anregenden Magnetfeld und wird optisch über die Verschiebung der Bragg-Wellenlänge ausgelesen. Die Konstanz dieses Verhältnisses stellt dann ein Maß für die korrekte Funktionsfähigkeit des Sensors dar. Das Beschichtungs-verfahren, das magnetische Anregungs- und optische Ausleseverfahren, wie auch die Validierung des Sensors unter Berücksichtigung faseroptischer Effekte wurden während des Projekts konzeptionell entwickelt, aufgebaut und charakterisiert. Qualitativ kann mit diesem Verfahren zunächst eine Gut-/Schlecht-Beurteilung des Sensors vorgenommen werden. Perspektivisch bietet das Verfahren jedoch auch die Möglichkeit, verschiedene Versagensmechanismen bestimmen und klassifizieren zu können. Es zeichnete sich schon während des Projektverlaufs ab, dass sich aus den erzielten Ergebnissen über das Autodiagnoseverfahren hinaus weitere Anwendungsmöglichkeiten für den Einsatz des kombinierten magnetostriktiv-faseroptischen Sensorprinzips, beispielsweise als reiner Magnetfeldsensor, ergeben.
Nach Abschluss dieses Forschungsprojekts besteht nun die Möglichkeit, den Funktionszustand faseroptischer Dehnungssensoren jederzeit unabhängig von Alter, Belastung oder Kenntnis eines Referenzzustands zu einem bestimmten Zeitpunkt bestimmen zu können. Aufbauend auf diesen Ergebnissen können weiterführende Forschungen die Messmethodik vertiefend charakterisieren und weitere Anwendungsfälle erschließen.
Degradation of AISI 630 exposed to CO2-saturated saline aquifer at ambient pressure and 100 bar
(2018)
In general high alloyed steels are suitable as pipe steels for carbon capture and storage technology (CCS), because they provide sufficient resistance against the corrosive environment of CO2-saturated saline aquifer which serves as potential CCS-site in Germany. High alloyed martensitic steel AISI 630 has been proven to be sufficient resistant in corrosive environments, e.g. regarding heat, pressure, salinity of the aquifer, CO2-partial pressure), but reveals a distinct corrosion pattern in CCS environment. Therefore coupons of AISI 630 heat treated using usual protocols were kept at T=60 °C and ambient pressure as well as p=100 bar up to 8000 h in an a) water saturated supercritical CO2 and b) CO2-saturated synthetic aquifer environment similar to on-shore CCS-sites in the Northern German Basin. AISI 630 precipitates a discontinuous ellipsoidal corrosion layer after being exposed for more than 4000 hours. Best corrosion resistance in the CO2-saturated synthetic aquifer environment phase is achieved via normalizing prior to exposure. In water saturated supercritical CO2 tempering at medium temperatures after hardening gives lowest corrosion rates. Corrosion fatigue via push-pull tests with a series of 30 specimens was evaluated at stress amplitudes between 150 MPa and 500 MPa (sinusoidal dynamic test loads, R=-1; resonant frequency ~ 30 Hz). The endurance limit of AISI 630 is reduced by more than 50% when exposed to CCS environment (maximum number of cycles (10 x 106) at a stress amplitude of 150 MPa).
In CCS environment (carbon capture and storage) pipes are loaded statically and/or cyclically and at the same time exposed constantly to the highly corrosive hot thermal water. Experimental procedures such as ambient pressure immersions tests, in-situ corrosion fatigue experiments using a flexibly designed corrosion chamber at ambient pressure and a specially designed corrosion chamber at high pressure. Experimental set-ups for push/pull and rotation bending load are introduced. The corrosion behavior and lifetime reduction of high alloyed steels (X46Cr13, 1.4043), (X5CrNiCuNb16-4, 1.4542) and (X2CrNiMoN22-5-3, 1.4462) is demonstrated (T=60 °C, geothermal brine: Stuttgart Aquifer flow rate: 9 l/h, CO2 ).
The commercial usage of latent thermal energy storages primarily depends on the development of a suitable phase change material (PCM). For industrial high temperature applications above 400 °C multicomponent chloride eutectics are promising and therefore discussed seriously. The profound thermodynamic investigation of such eutectics requires a much greater amount of specimen material than conventional calorimeter can handle. Therefore, a special adiabatic calorimeter was developed and designed. With a specimen mass of > 100 g the typical thermodynamic measurements with a commercial calorimeter can be extended by cycle stability measurements, which are often decisive for practical application of PCM. Furthermore, by implementing corrosion specimens inside the calorimeter high temperature corrosion experiments according to ISO 21608 can be performed inside the calorimeter. Adiabatic measuring conditions can be provided by using two separate heating systems. Therefore, the outer “protective system” follows the temperature curve of the inner “measuring system” minimizing the temperature difference between the heating systems and simultaneously preventing heat losses from the measuring systems.