Eldorado - Repository of the TU Dortmund
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Foldseek reveals a CBGA prenylating enzyme GlyMa_02G168000 from Glycine max
(2024-01-04) Jordan, Erin Noel; Schmidt, Christina; Kayser, Oliver
The present research provides an application for an aromatic prenyltransferase from Glycine max for use in heterologous microorganism expression to generate cannabinoids. The known cannabinoid prenyltransferase CsPT04 was queried in FoldSeek. An enzyme derived from Glycine max known as GLYMA_02G168000, which is a predicted homogentisate solanyltransferase, was identified and found to have affinity for the prenylation of geranyldiphosphate (GPP) and olivetolic acid (OA) to produce cannabigerolic acid (CBGA) and cannabigerol (CBG). The in vitro production of CBGA was accomplished through the heterologous expression of this prenyltransferase in Saccharomyces cerevisiae. After growing the yeast cells, a purified microsomal fraction was harvested, which was rich in the membrane-bound prenyltransferase GlyMa_02G168000. Addition of purified microsomal fraction to a reaction matrix facilitated the successful prenylation of externally supplied OA with GPP, culminating in the production of CBGA. Structural comparisons revealed a notably closer similarity between GLYMA_02G168000 and CsPT04, compared to the similarity of other cannabinoid prenyltransferases with CsPT04. Herein, a novel application for a homogentisate solanyltransferase has been established towards the production of cannabinoids.
Effect of the TiSiN interlayer properties on the adhesion and mechanical properties of multilayered TiSiCN thin films
(2024-01-26) Tillmann, Wolfgang; Urbanczyk, Julia; Thewes, Alexander; Bräuer, Günter; Lopes Dias, Nelson Filipe
The interlayer design is crucial in optimizing the adhesion of TiSiCN thin films on tool steel. Thus, Ti/TiN/TiSiN interlayer systems were deposited varying the TiSiN interlayer bias voltages (−100 V, −150 V, −200 V) in a magnetron sputtering process to investigate the influence on the mechanical properties and adhesion of the TiSiCN top layer with different thicknesses.
A higher bias voltage densifies the TiSiN interlayer structure, thereby increasing its residual stresses (0.11 GPa to 1.46 GPa) and hardness (28.1 GPa to 34.9 GPa). This occurs without affecting the columnar-like microstructure of the TiSiCN top layer and its hardness (25.9 GPa to 29.1 GPa for t = 1.0 μm, 22.6 GPa to 24.8 GPa for t = 2.3 μm). Adhesion classification by Rockwell C indentation of TiSiN declined from HF2 to HF4 due to increased residual stresses, impacting TiSiCN top layer adhesion with a similar deterioration. Scratch tests revealed reduced critical loads Lc2 and Lc3 for the TiSiN interlayer system and also for TiSiCN top layers. The highest critical loads were observed for TiSiCN (1.0 μm) and TiSiCN (2.3 μm) with TiSiN interlayer deposited at a bias voltage of −100 V, measuring (64.4 ± 4.5) N and (73.4 ± 8.3) N for Lc2, and (57.4 ± 5.3) N and (71.6 ± 4.5) N for Lc3, respectively. Increasing bias voltage decreases Lc2 and Lc3 to (23.2 ± 4.5) N and (50.20 ± 2.2) N for TiSiCN (1.0 μm), and (21.4 ± 4.5) N and (58.0 ± 3.6) N for TiSiCN (2.3 μm).
Achieving high adhesion strength of TiSiCN multilayered system requires minimizing the residual stress differences between the layers. Therefore, when designing a complex multilayer structure for TiSiCN thin films, careful consideration of the stress state among the layers is crucial, which is achieved by adjusting the bias voltage.
Expanding the boundaries of digital orientation research: scale development and validation
(2024-08-21) Kindermann, Bastian; Schmidt, Corinna Vera Hedwig; Fengel, Florian; Strese, Steffen
Firms keep struggling to realize the benefits of digital transformation processes. Research, therefore, proposes implementing a firm-wide organizational configuration with a digital orientation (DO) fostering digital innovation and transformation initiatives. However, exploration of DO remains somewhat constrained as existing measurement approaches are limited to secondary data. In this study, we develop a new scale to measure DO, specifically in survey-based research. Leveraging survey data from 1,488 top executives of German companies and drawing on the resource-based view, we empirically validate that our scale captures the performance-enhancing effects of DO.
Exploring the individual adoption of human resource analytics: behavioural beliefs and the role of machine learning characteristics
(2024-08-29) Hülter, Svenja M.; Ertel, Christian; Heidemann, Ansgar
The technological capabilities of Human Resource Analytics (HRA), enhanced by recent innovations in Machine Learning (ML), offer exciting opportunities. However, organisations often fail to realise these potentials because of a limited understanding of why individuals choose to adopt or disregard respective tools. Prior research on innovation adoption offers preliminary insights but fails to aggregate the determinants of individual adoption into actionable suggestions for decisions in the ML adoption process. Our study applies focused interviews to examine non-ML experts' reasoning for using a specific tool tailored to a public sector organisation, which corresponds to the usual end-user perspective of ML-based HRA adoption. By drawing from the HRA adoption framework, provided by Vargas et al. (2018), we contribute to the literature by identifying relevant beliefs and experiences influencing one's intention to adopt ML-based HRA and by qualitatively linking these beliefs to ML characteristics such as transparency, automation and fairness. For practitioners, we provide actionable guidance emphasising the need to ensure fairness proactively, as interviewees do not consider this aspect when deciding to adopt ML-based HRA.
Supplement to: The Football+ Manual
(2026) Asgari, Mojtaba; Terschlusse, Benedikt; Sueck, Maximilian; Schmidt, Marcus