arXiv subjects

Petr Novak

Publications and source records attributed to Petr Novak.

6 recordsLinked to original sources

arXiv2026

Crystalline b-Ga2O3 thin films deposited via reactive magnetron sputtering of a liquid Ga target

Ga2O3 thin films were deposited by reactive magnetron sputtering from a liquid gallium target. The influence of deposition temperature, substrate type, and discharge parameters on the structural and electrical properties was systematically investigated. Films deposited on silicon and quartz glass exhibit polycrystalline growth, whereas sapphire substrates enable highly oriented growth of b-Ga2O3 with a preferred (-201) orientation. The lowest electrical resistivity of 7x10_3 ohm.cm was obtained for films deposited on sapphire at a temperature of 585C. At this temperature, the films reach sufficient crystalline quality to enable efficient charge carrier transport and thus the manifestation of unintentional conductivity. At higher deposition temperatures, pronounced crystallization occurs; however, it is not homogeneous throughout the entire film thickness, which leads to a deterioration of the electrical properties. These results demonstrate that, despite intrinsic limitations, reactive magnetron sputtering can be successfully employed for the preparation of Ga2O3 thin films with optimized electrical properties when appropriate substrates and deposition temperatures are selected.

cond-mat.mtrl-sci↗

arXiv2025

Product Digital Twin Supporting End-of-life Phase of Electric Vehicle Batteries Utilizing Product-Process-Resource Asset Network

In a circular economy, products in their end-of-life phase should be either remanufactured or recycled. Both of these processes are crucial for sustainability and environmental conservation. However, manufacturers frequently do not support these processes enough in terms of not sharing relevant data about the products nor their (re-)manufacturing processes. This paper proposes to accompany each product with a digital twin technology, specifically the Product Digital Twin (PDT), which can carry information for facilitating and optimizing production and remanufacturing processes. This paper introduces a knowledge representation called Bi-Flow Product-Process-Resource Asset Network (Bi-PAN). Bi-PAN extends a well-proven Product-Process-Resource Asset Network (PAN) paradigm by integrating both assembly and disassembly workflows into a single information model. Such networks enable capturing relevant relationships across products, production resources, manufacturing processes, and specific production operations that have to be done in the manufacturing phase of a product. The proposed approach is demonstrated in a use-case of disassembling electric vehicle (EV) batteries. By utilizing PDTs with Bi-PAN knowledge models, challenges associated with disassembling of EV batteries can be solved flexibly and efficiently for various battery types, enhancing the sustainability of the EV battery life-cycle management.

cs.RO↗

arXiv2025

Product-oriented Product-Process-Resource Asset Network and its Representation in AutomationML for Asset Administration Shell

Current products, especially in the automotive sector, pose complex technical systems having a multi-disciplinary mechatronic nature. Industrial standards supporting system engineering and production typically (i) address the production phase only, but do not cover the complete product life cycle, and (ii) focus on production processes and resources rather than the products themselves. The presented approach is motivated by incorporating the impacts of the end-of-life phase of the product life cycle into the engineering phase. This paper proposes a modeling approach coming up from the Product-Process-Resource (PPR) modeling paradigm. It combines requirements on (i) respecting the product structure as a basis for the model, and (ii) incorporates repairing, remanufacturing, or upcycling within cyber-physical production systems. The proposed model called PoPAN should accompany the product during the entire life cycle as a digital shadow encapsulated within the Asset Administration Shell of a product. To facilitate the adoption of the proposed paradigm, the paper also proposes serialization of the model in the AutomationML data format. The model is demonstrated on a use-case for disassembling electric vehicle batteries to support their remanufacturing for stationary battery applications.

cs.RO↗

arXiv2025

Mitigating Undesired Conditions in Flexible Production with Product-Process-Resource Asset Knowledge Graphs

Contemporary industrial cyber-physical production systems (CPPS) composed of robotic workcells face significant challenges in the analysis of undesired conditions due to the flexibility of Industry 4.0 that disrupts traditional quality assurance mechanisms. This paper presents a novel industry-oriented semantic model called Product-Process-Resource Asset Knowledge Graph (PPR-AKG), which is designed to analyze and mitigate undesired conditions in flexible CPPS. Built on top of the well-proven Product-Process-Resource (PPR) model originating from ISA-95 and VDI-3682, a comprehensive OWL ontology addresses shortcomings of conventional model-driven engineering for CPPS, particularly inadequate undesired condition and error handling representation. The integration of semantic technologies with large language models (LLMs) provides intuitive interfaces for factory operators, production planners, and engineers to interact with the entire model using natural language. Evaluation with the use case addressing electric vehicle battery remanufacturing demonstrates that the PPR-AKG approach efficiently supports resource allocation based on explicitly represented capabilities as well as identification and mitigation of undesired conditions in production. The key contributions include (1) a holistic PPR-AKG model capturing multi-dimensional production knowledge, and (2) the useful combination of the PPR-AKG with LLM-based chatbots for human interaction.

cs.RO↗

arXiv2025

A Roadmap for Improving Data Reliability and Sharing in Crosslinking Mass Spectrometry

Crosslinking Mass Spectrometry (MS) can uncover protein-protein interactions and provide structural information on proteins in their native cellular environments. Despite its promise, the field remains hampered by inconsistent data formats, variable approaches to error control, and insufficient interoperability with global data repositories. Recent advances, especially in false discovery rate (FDR) models and pipeline benchmarking, show that Crosslinking MS data can reach a reliability that matches the demand of integrative structural biology. To drive meaningful progress, however, the community must agree on error estimation, open data formats, and streamlined repository submissions. This perspective highlights these challenges, clarifies remaining barriers, and frames practical next steps. Successful field harmonisation will enhance the acceptance of Crosslinking MS in the broader biological community and is critical for the dependability of the data, no matter where it is produced.

q-bio.OT↗

arXiv2020

Towards Increased Reliability, Transparency and Accessibility in Crosslinking Mass Spectrometry

Crosslinking mass spectrometry (Crosslinking MS) has substantially matured as a method over the last two decades through parallel development in multiple labs, demonstrating its applicability for protein structure determination, conformation analysis and mapping protein interactions in complex mixtures. Crosslinking MS has become a much-appreciated and routinely applied tool especially in structural biology. Therefore, it is timely that the community commits to the development of methodological and reporting standards. This white paper builds on an open process comprising a number of events at community conferences since 2015 and identifies aspects of Crosslinking MS for which guidelines should be developed as part of a Crosslinking MS standards initiative.

q-bio.OT↗