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Paper des Monats
- ja (2)
Ring-opening polymerizations (ROPs) of l-lactide (LA) were performed with ethyl l-lactate or 11-bromoundecanol as initiators (In) and tin(II) ethyl hexanoate (SnOct2) as catalyst (Cat) using four different LA/In ratios (20/1, 40/1, 60/1, and 100/1). One series of ROPs was conducted in bulk at 120 °C, yielding PLAs with low dispersities (Ð ~ 1.2–1.4), and a second series was conducted in bulk at 160 °C, yielding higher dispersities (Ð ~ 1.3–1.9). Samples from both series were annealed for 1 or 14 days at 140 °C in the presence of SnOct2. Both polycondensation and disproportionation reactions occurred, so that all four samples tended to form the same type of molar mass distribution below 10,000 Da, regardless of their initially different number average molar masses (Mn). Both initiators gave nearly identical results. The thermodynamic control of all reversible transesterification processes favored the formation of crystallites composed of chains with a Mn around 3500–3700, corresponding to a crystal thickness of 10–13 nm.
Durch Auslagerungs- und elektrochemische Tests im Labor kann das Korrosionsverhalten verschiedener metallener Werkstoffe beurteilt werden. Aus den Versuchsergebnissen, die in verschiedenen synthetischen Geothermalwässern im Labor erzielt wurden, lassen sich die folgenden Schlussfolgerungen ziehen:
• Die Anfälligkeit für Spaltkorrosion wurde als wichtigster Aspekt für die Werkstoffqualifizierung ermittelt.
• Der niedriglegierte Stahl 25CrMo4 zeigte bei niedrigem Salzgehalt in MB-Geothermalwasser eine gleichmäßige Korrosion unterhalb der akzeptierten Schwelle von 0,3 mm/Jahr. Daher kann er als geeignet für geothermische Bedingungen mit niedrigem Salzgehalt angesehen werden, wie sie für MB getestet wurden. Es besteht keine Notwendigkeit, auf höherlegierte (teurere) Werkstoffe auszuweichen. Ein niedrigerer pH-Wert (wie bei LHD) führt zu Korrosionsraten oberhalb der akzeptablen Grenzwerte, so dass die niedriglegierten Werkstoffe nicht für Flüssigkeiten mit niedrigem pH-Wert geeignet sind.
• Stark salzhaltige Geothermalwässer erfordern höherlegierte Werkstoffe, da die Korrosionsrate von niedriglegiertem Stahl zu hoch ist.
• Der Duplexstahl X2CrNiMo22-5-3 und der Superduplexstahl X2CrNiMoCuWN25-7-4 wurden aufgrund ihrer kritischen Anfälligkeit für örtliche Korrosion in Form von Loch- und Spaltkorrosion unter Betriebsbedingungen nicht als geeignet für geothermische Anwendungen in Geothermalwässern mit einer mit NDB und ORG vergleichbaren Zusammensetzung angesehen.
• Der superaustenitische Stahl X1CrNiMoCu32-28-7 eignet sich für ORG und salzarme Wässer. In NDB-Geothermalwasser wurde er bei 100 °C als geeignet angesehen. Jedoch schränkt seine Anfälligkeit für Spaltkorrosion seine Anwendbarkeit ein. Neben seiner guten Korrosionsbeständigkeit ist sein Repassivierungsverhalten für seine begrenzte Anwendbarkeit in Geothermalwässern mit niedrigem pH-Wert verantwortlich.
• Die Nickelbasislegierung NiCr23Mo16Al wurde als geeignet erachtet und stellt eine sichere Option für den Einsatz in geothermischen Anlagen dar, selbst wenn mit stark salzhaltigem Geothermalwasser gearbeitet wird.
Durch Auslagerungs- und elektrochemische Tests im Labor kann das Korrosionsverhalten verschiedener metallener Werkstoffe beurteilt werden. Aus den Versuchsergebnissen, die in verschiedenen synthetischen Geothermalwässern im Labor erzielt wurden, lassen sich die folgenden Schlussfolgerungen ziehen:
• Die Anfälligkeit für Spaltkorrosion wurde als wichtigster Aspekt für die Werkstoffqualifizierung ermittelt.
• Der niedriglegierte Stahl 25CrMo4 zeigte bei niedrigem Salzgehalt in MB-Geothermalwasser eine gleichmäßige Korrosion unterhalb der akzeptierten Schwelle von 0,3 mm/Jahr. Daher kann er als geeignet für geothermische Bedingungen mit niedrigem Salzgehalt angesehen werden, wie sie für MB getestet wurden. Es besteht keine Notwendigkeit, auf höherlegierte (teurere) Werkstoffe auszuweichen. Ein niedrigerer pH-Wert (wie bei LHD) führt zu Korrosionsraten oberhalb der akzeptablen Grenzwerte, so dass die niedriglegierten Werkstoffe nicht für Flüssigkeiten mit niedrigem pH-Wert geeignet sind.
• Stark salzhaltige Geothermalwässer erfordern höherlegierte Werkstoffe, da die Korrosionsrate von niedriglegiertem Stahl zu hoch ist.
• Der Duplexstahl X2CrNiMo22-5-3 und der Superduplexstahl X2CrNiMoCuWN25-7-4 wurden aufgrund ihrer kritischen Anfälligkeit für örtliche Korrosion in Form von Loch- und Spaltkorrosion unter Betriebsbedingungen nicht als geeignet für geothermische Anwendungen in Geothermalwässern mit einer mit NDB und ORG vergleichbaren Zusammensetzung angesehen.
• Der superaustenitische Stahl X1CrNiMoCu32-28-7 eignet sich für ORG und salzarme Wässer. In NDB-Geothermalwasser wurde er bei 100 °C als geeignet angesehen. Jedoch schränkt seine Anfälligkeit für Spaltkorrosion seine Anwendbarkeit ein. Neben seiner guten Korrosionsbeständigkeit ist sein Repassivierungsverhalten für seine begrenzte Anwendbarkeit in Geothermalwässern mit niedrigem pH-Wert verantwortlich.
• Die Nickelbasislegierung NiCr23Mo16Al wurde als geeignet erachtet und stellt eine sichere Option für den Einsatz in geothermischen Anlagen dar, selbst wenn mit stark salzhaltigem Geothermalwasser gearbeitet wird.
This study explores the acceleration of material design in the concrete industry, focusing on improving carbonation resistance, a key factor in the durability of concrete structures. Traditional tests for carbonation resistance are lengthy, but with the construction industry aiming for sustainable production, finding a balance between carbonation resistance and CO2 footprint is crucial. Our research employs two innovative methods: 1. Applying the Sequential Learning App for Materials Discovery (SLAMD), an AI materials design framework, to an extensive dataset of real-world concrete compositions to selectively test materials that meet market demands: maximum durability, optimal eco-durability, and the best cost-durability trade-off. 2. Investigating 1H Nuclear Magnetic Resonance (NMR) relaxometry as a quick alternative for characterizing carbonation behavior, as it saves time compared to traditional tests and assesses the complete material's pore space. Specific NMR features are then integrated into the material design model, with the model's performance compared against traditional approaches. The results of our study are compelling, demonstrating that materials can be precisely tailored to meet specific requirements with minimal data points. This marks a significant stride in the concrete industry, indicating that NMR-based, low-fidelity surrogate characterizations, combined with a focused, data-driven design approach, can substantially accelerate the development of durable, sustainable concrete mixtures.
In recent years, many protocols in computational materials science have been automated and made available within software packages (primarily Python-based). This ranges from the automation of simple heuristics (oxidation states, coordination environments) to the automation of protocols, including multiple DFT and post-processing tools such as (an)harmonic phonon computations or bonding analysis. Such developments also shorten the time frames of projects after such developments have been made available and open new possibilities. For example, we can now easily make data-driven tests of well-known rules and heuristics or develop quantum chemistry-based materials descriptors for machine learning approaches. These tests and descriptors can have applications related to magnetic ground state predictions of materials relevant for spintronic applications or for predicting thermal properties relevant for thermal management in electronics. Combining high-throughput ab initio computations with fitting, fine-tuning machine learning models and predictions of such models within complex workflows is also possible and promises further acceleration in the field. In this talk, I will show our latest efforts to link automation with data-driven chemistry and materials science.
We introduce a novel adaptive Gaussian Process Regression (GPR) methodology for efficient construction of surrogate models for Bayesian inverse problems with expensive forward model evaluations. An adaptive design strategy focuses on optimizing both the positioning and simulation accuracy of training data in order to reduce the computational cost of simulating training data without compromising the fidelity of the posterior distributions of parameters. The method interleaves a goal-oriented active learning algorithm selecting evaluation points and tolerances based on the expected impact on the Kullback-Leibler divergence of surrogated and true posterior with a Markov Chain Monte Carlo sampling of the posterior. The performance benefit of the adaptive approach is demonstrated for two simple test problems.
This research deals with the development of the Vickers hardness knowledge graph, mapping the example dataset in them, and exporting the data-mapped knowledge graph as a machine-readable Resource Description Framework (RDF). Modeling the knowledge graph according to the standardized test procedure and using the appropriate upper-level ontologies were taken into consideration to develop the highly standardized, incorporable, and industrial applicable models. Furthermore, the Ontopanel approach was utilized for mapping the real experimental data in the developed knowledge graphs and the resulting RDF files were successfully evaluated through the SPARQL queries.
This study aims to represent an approach for transferring the materials testing datasets to the digital schema that meets the prerequisites of the semantic web. As a use case, the tensile stress relaxation testing method was evaluated and the testing datasets for several copper alloys were prepared. The tensile stress relaxation testing ontology (TSRTO) was modeled following the test standard requirements and by utilizing the appropriate upper-level ontologies. Eventually, mapping the testing datasets into the knowledge graph and converting the data-mapped graphs to the machine-readable Resource Description Framework (RDF) schema led to the preparation of the digital version of testing data which can be efficiently queried on the web.
AbstractThis study applies Semantic Web technologies to advance Materials Science and Engineering (MSE) through the integration of diverse datasets. Focusing on a 2000 series age-hardenable aluminum alloy, we correlate mechanical and microstructural properties derived from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities.