Open Access

Ammonia is increasingly considered a promising hydrogen carrier due to its high hydrogen density and well-established infrastructure. Monitoring ammonia cracking efficiency requires robust, continuous gas analysis across a wide concentration range, with resistance to corrosive gases. Conventional methods such as gas chromatography or mass spectrometry meet these requirements, but are costly and operationally complex. To address this gap, a Thermal Conductivity Analyzer (TCA) was developed based on an OEM module and validated for continuous in situ monitoring of ammonia cracking. The system includes a pump-driven bypass extraction line and a three-stage calibration procedure: zero-point correction, look-up table generation, and span calibration. Measurement stability was assessed using binary H2/N2 mixtures and a quasi-binary surrogate of the ammonia cracking product gas, mixed via mass flow controllers (MFCs). The analyzer was then applied to characterize a monolithic ammonia cracking catalyst from 200– 650∘C. With daily zero-point and span calibration, all measured H2 concentrations fell within the mixing uncertainty of the MFCs across 0– 100vol.%. For the synthetic cracking gas, maximum conversion ratio deviations of +0.317 and −0.224 percentage points were achieved. These results demonstrate that the TCA offers a simple, ammonia-resistant alternative for monitoring NH3 cracking processes, with uncertainties competitive with MFC repeatability.

Additive manufacturing (AM), particularly laser powder bed fusion (L-PBF), provides unmatched design flexibility for creating intricate steel structures with minimal post-processing. However, adopting L-PBF for high-performance applications is difficult due to the challenge of predicting microstructure evolution. This is because the process is sensitive to many parameters and has a complex thermal history. Thin-walled geometries present an added challenge because their dimensions often approach the scale of individual grains. Thus, microstructure becomes a critical factor in the overall integrity of the component. This study focuses on applying cellular automata (CA) modeling to establish robust and efficient process–structure relationships in L-PBF of 316L stainless steel. The CA framework simulates solidification-driven grain evolution and texture development across various processing conditions. Model predictions are evaluated against experimental electron backscatter diffraction (EBSD) data, with additional quantitative comparisons based on texture and morphology metrics. The results demonstrate that CA simulations calibrated with relevant process parameters can effectively reproduce key microstructural features, including grain size distributions, aspect ratios, and texture components, observed in thin-walled L-PBF structures. This work highlights the strengths and limitations of CA-based modeling and supports its role in reliably designing and optimizing complex L-PBF components.

Between 51% and 72% of a bituminous roofing membrane used for structural water-proofing consists of organic material, predominantly bitumen - a derivative of crude oil refining - highlighting the strong dependence of this product on fossil resources. Considering that several tonnes of these membranes must be replaced every 30 to 50 years, substantial potential exists for emission reduction through the establishment of circular material systems. This study investigates this potential by analysing 26 Environmental Product Declarations (EPDs) and life cycle datasets from across Europe covering the period from 2007 to 2023. To ensure comparability, all data were normalised to a declared unit of 1 kg of roofing membrane. The reinforcement layers were categorised into glass and polyester & glass composites, and their differences were examined using Welch’s t-tests. Correlative analyses and linear as well as multiple regression models were then applied to explore relationships between environmental indicators and the shares of organic and mineral mass fractions. The findings reveal that renewable energy sources, although currently representing only a small share of total production energy, provide a major lever for reducing nearly all environmental impact categories. The type of reinforcement layer was also found to influence the demand for fossil resources, both materially and energetically. For most environmental indicators, only multiple regression models can explain at least 30% of the variance based on the proportions of organic and mineral inputs. Overall, the study underscores the crucial importance of high-quality, transparently documented product data for accurately assessing the sustainability of building products. It further demonstrates that substituting fossil energy carriers with renewable sources and optimising material efficiency can substantially reduce environmental burdens, provided that methodological consistency and clarity of indicator definitions are maintained.

Boundary spanning—the actions and roles individuals or organizations undertake to bridge differences and facilitate communication, collaboration, and understanding across diverse cultural, organizational, or geographical divisions—is an essential element of international cooperation. Set against the backdrop of the Russia-Ukraine war, The purpose of this study is to explore the type of boundary spanning in this uncertain context, the linguistic characteristics of boundary spanners, the necessary skills and competencies required to navigate the complex socio-political landscape, and the role of boundary spanners in humanitarian assistance. This study employs a qualitative research approach, using semi-structured interviews with informants actively engaged in relief efforts and cross-cultural collaboration. The findings emphasized the significance of English as the primary language of communication, the importance of cultural sensitivity and awareness, and the need for intercultural competence in effective boundary spanning. The study concludes with insights from the ongoing Ukrainian experience of bridging geodemographic boundaries and the role of boundary spanners in this process, highlighting key aspects of boundary spanning. Overall, the study contributes to the theoretical background of boundary spanning and enhances the understanding of how linguistic and cultural boundary spanning can drive positive change and improve outcomes in humanitarian aid. The implications of this study suggest that underscore the critical need for collaboration with experienced boundary spanners in educational settings.

Purpose – The purpose of this study is to introduce surrogate elements for static and transient finite element simulations. These elements are designed to replace regions of several conventional solid elements with a single artificial element that possesses a reduced number of degrees of freedoms (dofs). A notable advantage of our surrogate elements is their seamless integration into standard finite element meshes. Design/methodology/approach – The construction of the surrogate elements stiffness and mass matrices is achieved through an optimization process wherein displacements serve as the optimization objective. Moreover, the matrices are designed to possess properties analogous to those of standard finite elements. A particular focus is placed on ensuring that the artificial stiffness matrices are positive semi-definite. Furthermore, artificial degrees of freedom are introduced. Findings – The efficacy of the proposed technique is demonstrated through its application to two different use cases. It is demonstrated that, despite being trained on examples comprising a single surrogate element, the surrogate elements can be employed multiple times within complex and practical models. The degree of accuracy achieved in these applications is noteworthy. Moreover, the proposed method is considerably faster than the fully discretized models. Originality/value – The study expands the field of substructuring and model order reduction by incorporating artificial surrogate elements built by neural networks, which enables seamless integration with standard finite element analysis via positive semi-definite matrices. Furthermore, the introduction of artificial degrees of freedom, which are detached from the computational domain, is proposed. Once trained, the surrogate elements can be utilised in load and support independent scenarios.