@article{PrzymusRykaczewskiMartinSeguraetal.2025, author = {Przymus, Piotr and Rykaczewski, Krzysztof and Mart{\´i}n-Segura, Adri{\´a}n and Truu, Jaak and De Santa Pau, Enrique Carrillo and Kolev, Mikhail and Naskinova, Irina and Gruca, Aleksandra and Sampri, Alexia and Frohme, Marcus and Nechyporenko, Alina}, title = {Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models}, series = {Frontiers in Microbiology}, volume = {15}, journal = {Frontiers in Microbiology}, publisher = {Frontiers}, issn = {1664-302X}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19889}, year = {2025}, abstract = {Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward.}, language = {en} } @article{DemskiSeidlitzKukeetal.2025, author = {Demski, Nils Maximilian and Seidlitz, Holger and Kuke, Felix and Dorn, Oliver Niklas and Zoglauer, Janina and H{\"u}ckstaedt, Tobias and Kamm, Paul Hans and Garc{\´i}a-Moreno, Francisco and Kremp, Noah and Dreyer, Christian and Oberschmidt, Dirk}, title = {Flow-Line-Reducing Tetrahedral Metal Effect Pigments for Injection Molding: A Yield-Rate-Improved Particle Manufacturing Method Based on Soft UVImprint Lithography}, series = {Polymers}, volume = {17}, journal = {Polymers}, number = {19}, publisher = {MDPI}, issn = {2073-4360}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-20850}, year = {2025}, abstract = {This publication presents an improved manufacturing method for tetrahedral metal effect pigment particles that demonstrates reduced flowlines in injection-molded polymer components compared with conventional platelet-shaped pigment particles. The previously published cold forming process for tetrahedral particles, made entirely from aluminum, faced manufacturing challenges, resulting in a high reject rate due to particle adhesion to the micro-structured mold roller. In contrast, this study introduces a new manufacturing method for tetrahedral particles, now consisting of metallized UV-cured thermoset polymer. These particles, dispersed in amorphous matrix thermoplastics, have shown to maintain their shape during the injection molding process. The manufacturing technique for these novel particles is based on UV imprint lithography, omitting the reject rates compared with the previously presented cold rolling process of tetrahedral full aluminum particles. Thus, the novel manufacturing technique for tetrahedral pigment particles shows increased potential for automation through roll-to-roll manufacturing in the future.}, language = {en} } @article{KsianzouVillringerGrytsenkoetal.2025, author = {Ksianzou, Viachaslau and Villringer, Claus and Grytsenko, Kostyantyn and Pekur, Demyd and Lytvyn, Peter and Sopinskyy, Mykola and Lebedyeva, Iryna and Niemczyk, Agata and Baranowska, Jolanta}, title = {Structural and Optical Anomalies in Thin Films Grown in a Magnetic Field by Electron-Assisted Vacuum Deposition of PTFE}, series = {Macromolecular Materials and Engineering}, volume = {310}, journal = {Macromolecular Materials and Engineering}, number = {3}, publisher = {Wiley}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-19712}, year = {2025}, abstract = {Polytetrafluoroethylene (PTFE) films are deposited in parallel and perpendicular magnetic fields (MF) by electron-enhanced vacuum deposition (EVD) and EVD + low-temperature plasma (LTP) methods. The structure, morphology, and nanomechanical properties of the films are studied by infrared spectroscopy (IRS), atomic force microscopy (AFM), and spectroscopic ellipsometry. The structure of the thicker films is closer to that of bulk PTFE than that of thin films. The films' crystallinity and surface roughness are higher than those deposited without MF. The birefringence of the refractive index (n) of the films deposited in the MF is inverse to the anisotropy of the n of the films deposited without MF. The hardness of the films is close to that of bulk PTFE.}, language = {en} } @article{BauerHeinrichVillasmuntaetal.2025, author = {Bauer, Joachim and Heinrich, Friedhelm and Villasmunta, Francesco and Villringer, Claus and Reck, Johanna and Peters, Sven and Treffer, Alexander and Kuhnt, Christian and Marschmeyer, Steffen and Fursenko, Oksana and Stolarek, David and Mai, Andreas and Regehly, Martin}, title = {Reflectometric method for measuring residual oxides in through-silicon vias for 3D chip integration}, series = {Optics Express}, volume = {33}, journal = {Optics Express}, number = {15}, publisher = {Optica}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:526-opus4-20633}, pages = {32175 -- 32189}, year = {2025}, abstract = {A significant aspect of fabricating 3D chip architectures is ensuring proper contact between the different layers of the chip, which often requires removing the underside of isolation layers before filling vias with conductive material. Currently, scanning electron microscopy is the established method for investigating such structures. In this paper, we propose a rapid, non-destructive optical analysis technique for the simultaneous measurement of through-silicon vias (TSV) depths, silicon wafer thickness, and residual oxide thickness. The proposed method utilizes Fourier peak shift analysis (FPSA) of reflectance measurements in the near-infrared (1200 nm—2200 nm) spectral regions. The application of FPSA to representative samples taken from a commercial TSV integration process for MEMS and CMOS fabrication demonstrated good agreement with reference scanning electron microscopy measurements, confirming the feasibility of the method for in-line and in-situ metrology. The results indicate that FPSA has great potential for real-time process monitoring and control during 3D chip manufacturing.}, language = {en} }