06 Fakultät Luft- und Raumfahrttechnik und Geodäsie

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/7

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Institute: search.filters.UBSInstitut.Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)Subject: search.filters.subject.333.7

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    Hydroxyl-conductive 2D hexagonal boron nitrides for anion exchange membrane water electrolysis and sustainable hydrogen production
    (2025) Kaur, Jasneet; Schweinbenz, Matthew; Ho, Kane; Malekkhouyan, Adel; Ghotia, Kamal; Egert, Franz; Razmjooei, Fatemeh; Ansar, Syed Asif; Zarrin, Hadis
    In response to the urgent global call to transition from polluting fossil fuels to sustainable energy alternatives, hydrogen emerges as a promising and widely accessible energy source if it can be efficiently produced through water splitting and electrolysis. Anion exchange membrane (AEM) water electrolyzers (AEMWEs) have potential for large scale H2 production at a low cost. However, the development of alkaline membranes with high hydroxide conductivity, improved stability and better performance is a significant challenge for the commercial application of advanced AEMWEs. In this work, a novel structure for hydroxide-ion conductive membranes based on surface-engineered two-dimensional (2D) hexagonal boron nitrides (h-BN) is designed and validated in a highly active and durable AEMWE cell with non-precious metal Ni-based electrodes. Among two samples, the high-loaded 2D hBN nanocomposite membrane (M2) showed significantly high hydroxide-ion conductivity (190 mS cm-1) with improved electrochemical and mechanical stability. The AEMWE cell assembled with the M2 membrane exhibited superior cell performance, achieving 1.78 V at 0.5 A cm-2 compared to the cell utilizing the lower loading hBN nanocomposite membrane (M1). Additionally, its performance closely approached that of the cell employing a commercial membrane. During a long-term stability test conducted at a constant load of 0.5 A cm-2 for 250 hours, the M2 membrane maintained satisfactory electrolysis voltage without any notable failure. These findings demonstrate that 2D hBN nanocomposite membranes hold great promise for use in advanced AEMWEs.
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    Development of an uncertainty quantification predictive chemical reaction model for syngas combustion
    (2017) Slavinskaya, Nadezda; Abbasi, Mehdi; Starcke, Jan Hendrik; Whitside, Ryan; Mirzayeva, Aziza; Riedel, Uwe; Li, Wenyu; Oreluk, Jim; Hegde, Arun; Packard, Andrew; Frenklach, Michael; Gerasimov, G. Ya.; Shatalov, Oleg
    An automated data-centric infrastructure, Process Informatics Model (PrIMe), was applied to validation and optimization of a syngas combustion model. The Bound-to-Bound Data Collaboration (B2BDC) module of PrIMe was employed to discover the limits of parameter modifications based on uncertainty quantification (UQ) and consistency analysis of the model−data system and experimental data, including shock-tube ignition delay times and laminar flame speeds. Existing syngas reaction models are reviewed, and the selected kinetic data are described in detail. Empirical rules were developed and applied to evaluate the uncertainty bounds of the literature experimental data. The initial H2/CO reaction model, assembled from 73 reactions and 17 species, was subjected to a B2BDC analysis. For this purpose, a dataset was constructed that included a total of 167 experimental targets and 55 active model parameters. Consistency analysis of the composed dataset revealed disagreement between models and data. Further analysis suggested that removing 45 experimental targets, 8 of which were self-inconsistent, would lead to a consistent dataset. This dataset was subjected to a correlation analysis, which highlights possible directions for parameter modification and model improvement. Additionally, several methods of parameter optimization were applied, some of them unique to the B2BDC framework. The optimized models demonstrated improved agreement with experiments compared to the initially assembled model, and their predictions for experiments not included in the initial dataset (i.e., a blind prediction) were investigated. The results demonstrate benefits of applying the B2BDC methodology for developing predictive kinetic models.