This topic covers load-bearing structural materials designed to withstand demanding environments relevant to security technologies. Contributions are expected to address strength, toughness, damage tolerance, fatigue, creep, and thermal stability, including performance under impact, high temperatures, chemical exposure, and irradiation. Relevant material classes include metals, ceramics, polymers, and composites, spanning bulk structures and structural elements within systems. Submissions that clarify degradation pathways, quantify safety margins, and connect microstructural design to predictable failure resistance are particularly encouraged. Application-oriented studies are welcome when they remain grounded in mechanistic insight and demonstrate clear relevance to resilient infrastructures and protective technologies.

This topic addresses functional materials and engineered surfaces that enhance protection and performance in security applications. Contributions may involve multifunctional coatings, barrier layers, tribological and anti-corrosion systems, electromagnetic shielding and radar-absorbing materials, radiation-shielding concepts, and surface designs that improve durability, reliability, and environmental resistance in demanding operational environments. Relevant work also includes functional integration at the device and system level, for example in trusted electronics, protective textiles, and sensor-enabled structures. Submissions are expected to link composition, processing, and microstructure to functional response, with a clear view toward deployment constraints such as robustness, aging, and compatibility within complex assemblies.

This topic focuses on processing and synthesis routes that enable reliable and scalable production of security-relevant materials, coatings, and system solutions. Contributions may cover additive and subtractive manufacturing, forming, joining, heat treatment, surface engineering, and synthesis pathways for metals, ceramics, polymers, and composites. Particular interest lies in process-structure-property relationships under extreme-service requirements, as well as strategies for defect control, monitoring, quality assurance, and reproducibility across batches and supply chains. Work that demonstrates transferability from laboratory to industrial manufacturing, including robust process window definition and qualification strategies, fits especially well within the conference scope.

This topic focuses on advanced experimental methods that reveal how materials behave under security-critical conditions. Contributions are expected to address microstructure and property characterization across length scales, including mechanical response to impact and fatigue, thermal stability at elevated temperatures, resistance to chemical exposure and corrosion, and performance under irradiation. Emphasis is placed on methods that connect measurable signals to damage mechanisms and failure modes, enabling robust qualification, safety margins, and lifetime assessment. Work that integrates in situ and operando approaches, high-throughput testing, and quantitative uncertainty analysis is particularly relevant for translating laboratory insight into reliable security technologies and protection concepts.

This topic covers computational, data-centric, and digital engineering methods that support predictive design and risk mitigation for materials in security technologies. Submissions may include multiscale modeling, physics-based simulation, data-driven approaches, and hybrid strategies that combine mechanistic understanding with machine learning. Contributions on constitutive material modeling, especially for impact and high-strain-rate loading, are also welcome. Relevant work spans digital twins for components and systems, model-assisted materials development, and computational workflows for accelerated qualification under extreme loads such as impact, high temperature, chemical exposure, and irradiation. Topics relating to experimental characterisation and validation, parameter identification, uncertainty quantification and inverse engineering are likewise invited. Contributions that demonstrate validation against experiments, transparent assumptions, and reproducible data pipelines are particularly encouraged, especially where they enable scalable decision-making from material selection to system-level performance.