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    Pokročilé technologie 3D tisku kovových dílů a jejich obrábění
    (Vysoké učení technické v Brně. Fakulta strojního inženýrství) Malý, Martin; Zouhar, Jan; Koutný, Daniel; Pagáč, Marek
    Tato disertační práce se zabývá pokročilou technologií 3D tisku kovových dílů metodou extruze materiálu (MEX) s využitím nástrojové oceli H13 a hodnotí vliv následného zpracování a obrábění na výsledné vlastnosti vyrobených dílů. Hlavním cílem studie bylo stanovit vztah mezi parametry tisku, pórovitostí, mikrostrukturou, mechanickými vlastnostmi a obrobitelností materiálu. Vytisknuté vzorky vykazovaly průměrnou pórovitost 4,76 %, což výrazně omezuje jejich pevnost v tahu (UTS 1200 MPa) a tažnost (
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    Analýza dynamiky hybridních pohonných jednotek s vypínáním válců
    (Vysoké učení technické v Brně. Fakulta strojního inženýrství) Fridrichová, Kateřina; Píštěk, Václav; Bauer, František; Pavlov, Michal
    V posledních letech se automobilový průmysl zaměřuje zejména na snižování produkce emisí CO2, což prakticky znamená také snižování spotřeby paliva. Toho je možné dosáhnout zvyšováním účinnosti spalovacích motorů pomocí vypínání válců. Díky vypínání válců dochází k zážehům jen ve vybraných válcích, což umožňuje úspornější jízdu při nižších zatíženích motoru. Aktuálním trendem v automobilovém odvětví je hybridizace. Hybridní pohonná jednotka díky přidanému elektromotoru/generátoru také umožňuje snížit spotřebu paliva. Vypínání válců navíc způsobuje zvýšení vibrací, které je možné kompenzovat pomocí variabilního průběhu točivého momentu elektrického točivého stroje. Cílem této dizertační práce je vyvinout metodiku dynamických simulací mild-hybridní pohonné jednotky s vypínáním válců a posoudit přínos vyhlazování točivého momentu u tohoto typu hnacího ústrojí. Výsledky jsou získány ze simulace dynamiky hybridní pohonné jednotky a ověřeny dostupnými experimenty.
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    Liquid Metal Microrobots for Magnetically Guided Transvascular Navigation
    (Wiley, 2026-01-13) Ju, Xiaohui; Velluvakandy, Roshan Sreenivasan; Wu, Xianghua; Merlos Rodrigo, Miguel Angel; Heger, Zbyněk; Bendíčková, Kamila; Frič, Jan; Pumera, Martin
    Soft microrobots, compared with their rigid counterparts, offer superior adaptability in dynamic and confined biological environments. Here, magnetically-guided liquid metal microrobots composed of gallium-indium alloys embedded with Fe nanoparticles are introduced. The unique combination of magnetic maneuverability, high surface tension, intrinsic radiopacity, and deformability allows liquid metal-based microbots to overcome limitations of both hard microrobots and fragile droplet-based systems. Under magnetic actuation, liquid metal-based magnetic microrobots exhibit controllable rolling and upstream locomotion resembling neutrophil-like navigation, enabling precise maneuvering even against physiological flow. Bridging in vitro with in vivo experiments, quail egg chorioallantoic membrane models are used to demonstrate guided transport of these microrobots through blood vessels, accumulation at tumor xenografts, and migration within subcutaneous tissues. Moreover, their strong X-ray visibility enables real-time fluoroscopic tracking, validated in porcine heart vasculature. Importantly, liquid metal-based magnetic microbots can cross endothelial barriers in a vascular flow-on-a-chip platform, while maintaining endothelial biocompatibility. By integrating deformability, magnetic steerability, and imaging visibility, liquid metal-based microrobots establish a powerful platform for minimally invasive transvascular navigation. This work highlights the potential of liquid metal-based magnetic microrobots for targeted drug delivery, image-guided therapy, and intelligent biomedical interventions.
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    Laser-Induced Microfabrication of Carbon Nanostructure: Processing Mechanism and Application for Next-Generation Battery Technology
    (Wiley, 2026-03-01) Deshmukh, Sujit; Nouseen, Shaista; Pumera, Martin
    Direct laser writing with a single beam of multiple parallel optical configurations facilitate the production of conductive carbon-based nanomaterials, carbon-based catalysts, micropatterned carbon surface, hierarchical 3D porous carbon structure, numerous carbon-based composites, and so on. This chemical-free, cost-effective, binder-free, maskless patterning technology minimizes the environmental impact and enables more sustainable production of diverse carbon-based materials with a wide range of applications. In this review, 1st a comprehensive overview of the state-of-the-art advancements and formation mechanism of laser processed carbon (LPC) is provided from various carbon-rich precursors (polyimide/PI, polytetrafluoroethylene, poly (ether sulfone)), as well as natural resources like wood, lignin, clothes, paper, and even food are explored. Additionally, the article reports a comprehensive overview of how different laser types, processing conditions, and environmental factors influence the resulting structure and surface chemistry of LPC. Next, specifically, the emerging applications of these LPC in battery technology are focused on. This includes the carbon anode of Li-ion/Na-ion battery, the current collector (CC) for Li-metal battery, electrode for Li-sulfur battery, the catalyst for Li-air batteries, and electrodes for Zn-ion and Zn-air battery. The article concludes with the insights and future perspectives on the advancement of this processing technology for next-generation smart and sustainable battery materials.
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    Fully differentiable Lagrangian convolutional neural network for physics-informed precipitation nowcasting
    (Elsevier, 2025-12-01) Pavlík, Peter; Výboh, Martin; Bou Ezzeddine, Anna; Rozinajová, Věra
    This paper presents a convolutional neural network model for precipitation nowcasting that combines data-driven learning with physics-informed domain knowledge. We propose LUPIN, a Lagrangian Double U-Net for Physics-Informed Nowcasting, that draws from existing extrapolation-based nowcasting methods. It consists of a U-Net that dynamically produces mesoscale advection motion fields, a differentiable semi-Lagrangian extrapolation operator, and an advection-free U-Net capturing the growth and decay of precipitation over time. Using our approach, we successfully implement the Lagrangian convolutional neural network for precipitation nowcasting in a fully differentiable and GPU-accelerated manner. This allows for end-to-end training and inference, including the data-driven Lagrangian coordinate system transformation of the data at runtime. We evaluate the model and compare it with other related AI-based models both quantitatively and qualitatively in an extreme event case study. Based on our evaluation, LUPIN matches and even exceeds the performance of the chosen benchmarks, opening the door for other Lagrangian machine learning models.