Using Artificial Neural Networks to Determine Ontologies Most Relevant to Scientific Texts

This paper provides an insight into the possibility of how to find ontologies most relevant to scientific texts using artificial neural networks. The basic idea of the presented approach is to select a representative paragraph from a source text file, embed it to a vector space by a pre-trained fine-tuned transformer, and classify the embedded vector according to its relevance to a target ontology. We have considered different classifiers to categorize the output from the transformer, in particular random forest, support vector machine, multilayer perceptron, k-nearest neighbors, and Gaussian process classifiers. Their suitability has been evaluated in a use case with ontologies and scientific texts concerning catalysis research. From results we can say the worst results have random forest. The best results in this task brought support vector machine classifier

From coiled flow inverter to stirred tank reactor – bioprocess development and ontology design

Miniaturized bioreactors, such as the coiled flow inverter (CFI), offer several benefits within process development such as lower time and cost factors. In this study, we demonstrate continuous flow experiments in a CFI and transferred them to experiments in a batch reactor by using the oxygen transfer coefficient kLa as a key parameter. In order to simplify the parameter transfer and at the same time develop a basis for future data handling according to the FAIR data principles, an equipment and process ontology was developed for these examples

Impact of nanoparticle surface modification on the mechanical properties of polystyrene-based nanocomposites

Nanocomposites consisting of metal oxide nanoparticles in a polymeric matrix enable the improvement of material properties and have become highly relevant for numerous applications, such as in lightweight structures with an enhanced Young's modulus for automotive and aircraft applications. The mechanical properties can be adjusted by controlling the amount of particles, their degree of agglomeration and their direct interaction with the matrix. Whilst the latter aspect is particularly promising to achieve high reinforcement at low filler contents, the mechanisms behind this effect are still not fully understood, preventing the rational design of a particle–polymer system with customized properties. In this work, a two-step modification strategy is used to tailor the particle–matrix interface via chemical groups bound to the surface of zirconia nanoparticles. Two modifications featuring terminal vinyl functions as potentially polymerizable groups are compared. Moreover, an inert reference modification is used to determine the influence of the terminal vinylic groups. In contrast to previous studies, all groups are covalently linked to the particle surface, thereby excluding effects such as detachment or weak coordination and ensuring that changes in the mechanical properties can be correlated to chemical groups on the particle surface. After embedding modified particles in polystyrene, the mechanical properties as well as the cross-linkage between the particles and the matrix are characterized, clearly showing the significant impact of a covalent particle–matrix linkage, with an increase of the Young's modulus by up to 28% with only 3 wt% filler content

From Lab to Pilot Scale: Commissioning of an Integrated Device for the Generation of Crystals

Fast time-to-market, increased efficiency, and flexibility of production processes are major motivators for the development of integrated, continuous apparatuses with short changeover times. Following this trend, the modular belt crystallizer was developed and characterized in lab scale with the model system sucrose-water. Based on the promising results, the plant concept was upscaled and commissioned in industrial environment. The results are presented within the scope of this work. Starting from small seed crystals in solution, it was possible to grow, separate, and dry product particles. Further, the conducted experiments demonstrated that it is feasible to transfer the results from laboratory to pilot scale, which in turn enables accelerated process design as well as development

Absorption and Chemisorption of Small Levitated Single Bubbles in Aqueous Solutions

The absorption and chemisorption of small bubbles with N2 or CO2 were investigated experimentally in aqueous and alkaline solutions. Different bubble sizes were studied ranging from 0.1 to 2.5 mm in alkaline concentrations of 0.1 mM to 1 M NaOH. The experiments were conducted in a device consisting of a converging microchannel with a down flowing liquid. Levitation positions of single bubbles were optically characterized. A correlation was developed for the drag force coefficient, CD, including wall effects based on the force equilibrium. A linear decrease of bubble diameters was identified with and without chemical reaction, which is referred to as a rigid bubble surface area. Measured Sherwood numbers agree well with the literature values for the investigated Reynolds number range

Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management

Abstract As scientific digitization advances it is imperative ensuring data is Findable, Accessible, Interoperable, and Reusable (FAIR) for machine-processable data. Ontologies play a vital role in enhancing data FAIRness by explicitly representing knowledge in a machine-understandable format. Research data in catalysis research often exhibits complexity and diversity, necessitating a respectively broad collection of ontologies. While ontology portals such as EBI OLS and BioPortal aid in ontology discovery, they lack deep classification, while quality metrics for ontology reusability and domains are absent for the domain of catalysis research. Thus, this work provides an approach for systematic collection of ontology metadata with focus on the catalysis research data value chain. By classifying ontologies by subdomains of catalysis research, the approach is offering efficient comparison across ontologies. Furthermore, a workflow and codebase is presented, facilitating representation of the metadata on GitHub. Finally, a method is presented to automatically map the classes contained in the ontologies of the metadata collection against each other, providing further insights on relatedness of the ontologies listed. The presented methodology is designed for its reusability, enabling its adaptation to other ontology collections or domains of knowledge. The ontology metadata taken up for this work and the code developed and described in this work are available in a GitHub repository at: https://github.com/nfdi4cat/Ontology-Overview-of-NFDI4Cat