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Lecturer(s)
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Course content
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Week 1: Quantum technologies in industry - Overview of applications from sensors to new materials and their reliability in practice. Week 2: Magnetic field sensors - Introduction to NV center and SQUID-based sensors and comparison with classical inductive defectoscopy. Week 3: Microcrack and corrosion detection - Application of magnetometry for detecting corrosion under insulation (CUI) and fatigue cracks. Week 4: Li-ion battery diagnostics - Non-destructive current density mapping and internal short circuit detection. Week 5: Applications in aviation and energy - Inspection of composites and welds in critical applications using highly sensitive sensors. Week 6: Signal processing - Laboratory exercises focused on defect visualization and data interpretation. Week 7: Superconductor manufacturing - Technological aspects of forming and heat treatment of composite conductors. Week 8: Metallurgy for chips - Production and refining of ultra-pure materials and the effect of impurities on device function. Week 9: Production scaling and thin films - Transfer of 2D materials to substrates and adhesion solutions on an industrial scale. Week 10: Additive manufacturing for cryogenics - Specifics of 3D printing and materials for components operating at absolute zero. Week 11: Nanotechnology in manufacturing - Concepts of precision assembly of nanomaterials and the use of scanning probes. Week 12: Semester project - Design of an NDT system for inspecting a specific part, including a technical and economic assessment. Week 13: Defense and trends - Presentation of projects and discussion of future industry standards.
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Learning activities and teaching methods
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- Contact hours
- 52 hours per semester
- Undergraduate study programme term essay (20-40)
- 38 hours per semester
- Preparation for an examination (30-60)
- 30 hours per semester
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| prerequisite |
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| Knowledge |
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| Successful completion of QTM1 course is an advantage but not a strict requirement. Knowledge of basic types of material defects (cracks, corrosion, inclusions). |
| Skills |
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| Ability to process experimental data (Python/Matlab). |
| Competences |
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| Ability to plan and manage your own learning and project work. |
| learning outcomes |
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| Knowledge |
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| Describes the application potential of modern diagnostic methods (based on magnetometry) for detecting hidden defects. Explains the technological chain of composite conductor and special material production, including the impact of forming and heat treatment on quality. Is familiar with methods for inspecting thin layers and their adhesion. Knows the specifics of additive manufacturing of components for demanding operating conditions. |
| Skills |
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| Select a suitable NDT method for a specific industrial problem based on defined parameters (sensitivity, defect type). Process measured data from sensors and interpret it for decision-making on the condition of the material (OK/NOK). Propose a framework technological procedure for the production or processing of a specific material with an emphasis on minimizing defects. Prepare a technical and economic assessment of the practical application of the given technology. |
| Competences |
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| N/A |
| Independently formulate an engineering problem in the field of diagnostics and find a suitable solution. Combine knowledge of production technology with material quality requirements. Present and defend technical solutions before an expert audience. |
| teaching methods |
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| Knowledge |
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| Lectures linking physical principles with engineering practice. Analysis of case studies from industry (e.g., battery diagnostics in the automotive industry). |
| Skills |
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| Exercises focused on the analysis of real experimental data. Project-oriented teaching (system design). |
| Competences |
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| Guidance towards independent work. Development of presentation skills when defending projects. |
| assessment methods |
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| Knowledge |
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| Final exam (oral/written part focused on understanding the principles of quantum diagnostics and manufacturing technologies). |
| Skills |
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| Final project. Presentation and defense of the project. Evaluation of practical tasks (data analysis). |
| Competences |
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| Ability to work independently and as part of a team. Critical thinking. Presentation skills. |
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Recommended literature
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John F. Barry, Jennifer M. Schloss, Erik Bauch, Matthew J. Turner, Connor A. Hart, Linh M. Pham, Ronald L. Walsworth. Sensitivity Optimization for NV-Diamond Magnetometry. 2020.
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Kihwan Kim, Jong Sung Moon, Dongkwon Lee, Jin Hee Lee, Yuhan Lee, Chanhu Park, Jugyeong Chung, Donghun Lee, and Je-Hyung Kim. Quantum sensing with spin defects: principles, progress, and prospects for use cases. 2025.
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N. Banerjee, C. Bell, C. Ciccarelli, T. Hesjedal, F. Johnson, H. Kurebayashi, T. A. Moore, C. Moutafis, H. L. Stern, I. J. Vera-Marun, J. Wade, C. Barton, M. R. Connolly, N. J. Curson, K. Fallon, A. J. Fisher, D. A. Gangloff, W. Griggs, E. Linfield, C. H. Marrows, A. Rossi, F. Schindler, J. Smith, T. Thomson, O. Kazakova. Materials for Quantum Technologies: a Roadmap for Spin and Topology. 2025.
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