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Coupling of shrinking core and Eulerian-Eulerian models for chemical looping combustion
No full textOne of the main barriers to the implementation of chemical looping combustion (CLC) on an industrial scale is the lack of knowledge about its operation at such scales. As a first step towards modeling industrial CLC devices, a consistent coupling between the shrinking core model (SCM) and the Eulerian-Eulerian multiphase framework with multicomponent phases was derived. This coupling enforces the constant volume assumption of a solid particle and allows for simple and consistent control of the oxygen transfer capacity. The derived approach was then applied to the chemical kinetic model of ilmenite redox reactions, which was sourced from the literature and was based on thermogravimetric analysis (TGA) data. Important corrections were made to the kinetic model parameters to ensure consistency with the present methodology, and the result was verified using a zero-dimensional TGA simulation setup. The methodology was further validated using experimental data from the literature for a laboratory-scale batch fluidized bed reactor. Three-dimensional simulations and an analytical one-dimensional quasi-steady-state model, derived on the basis of the coupling, have shown very close results to each other. However, as also previously observed in the literature, the conversion rates of H2 were severely underestimated by the TGA-based kinetic model. Finally, the developed approach was applied to a 300 W CLC reactor, previously experimentally studied at Chalmers University. The agreement with the experimental data was reasonably good, but the reactivity of H2 was higher than that reported in the experiments. The chemistry model source code and simulation configurations are made openly available.</p
Evaluating Net-Zero Energy Buildings and Their Grid Interaction: A Comprehensive Framework for Operational Phase and A Nordic Case Study
No full textThis paper presents a synthesized evaluation framework for assessing Net-Zero Energy Buildings (NZEBs) during their operational phase, with a specific focus on grid interaction under real market conditions. The framework integrates four established Key Performance Indicators (KPIs): Load/Generation Balance, Self-Consumption Rate, Emission Reduction Rate and Cost Reduction Rate – using high-resolution operational data, hourly grid emission factors, and spot prices. Demonstrated through a case study of a large Finnish residential NZEB equipped with a ground-source heat pump and off-site solar PV, the analysis reveals both the potential and limitations of PV-based solutions in cold climates. While the system achieved an 89% annual load/generation balance, hourly analysis showed a 31% self-consumption rate, with most PV production exported during periods of low prices and low emissions. Operational emissions and electricity costs were reduced by 56% and 41%, respectively, compared to a baseline without PV. However, sensitivity analysis indicates that economic outcomes are highly dependent on prevailing market conditions, highlighting the importance of multi-year evaluation. The framework’s parallel KPIs, when used collectively, enable stakeholders to assess trade-offs and guide practical decisions regarding demand-side management, energy storage, and operational strategies. The economic analysis focuses on market exposure, including O&M costs for PV, but excluding investment costs. The framework is flexible and can be applied to NZEBs with various configurations, supporting robust, data-driven decision-making for improved cost-effectiveness and decarbonization
High-Resolution Synchrotron µXRD and µXRF for Local Phase and Elemental Analysis in Suspension Plasma Sprayed Environmental Barrier Coatings
No full textSuspension plasma spraying (SPS) enables the fabrication of environmental barrier coatings (EBCs) with complex multilayer architectures; however, degradation in such systems often initiates locally at buried interfaces, making it difficult to resolve using conventional laboratory-scale characterization techniques. In this work, the applicability of synchrotron-based micro-x-ray diffraction (µXRD), combined with micro-x-ray fluorescence (µXRF), is evaluated for the characterization of SPS-deposited ytterbium disilicate (YbDS) EBCs. An as-sprayed YbDS coating was investigated as a baseline case to examine differences between conventional XRD and spatially resolved µXRD, while an annealed and CMAS-exposed YbDS coating was studied as a service-relevant case to probe localized phase evolution. The samples were selected from previously optimized SPS process conditions and are not intended for direct comparison. Laboratory-scale XRD provided global phase information, whereas µXRD enabled layer-specific phase identification and resolved localized interfacial features. In the as-sprayed condition, µXRD confirmed phase-pure YbDS, resolved the crystallinity of individual coating layers, and verified the absence of unintended interfacial reaction phases that are not accessible by conventional XRD. In the annealed + CMAS-exposed coating, µXRD and µXRF revealed the formation of a calcium–ytterbium–silicate oxyapatite phase confined to the YbDS/Si interface, highlighting the localized nature of CMAS-induced degradation. These results demonstrate that synchrotron microanalysis provides valuable complementary insight for probing localized phase evolution in thermally sprayed EBC systems.</p
Recent advancements in artificial intelligence - driven breast cancer molecular subtypes classification using multi-omics: A comprehensive review
Full text linkBreast cancer is one of the heterogeneous diseases comprising various molecular subtypes. All molecular subtypes have different characteristics and behave differently to treatment response, prognosis and therapy. Accurate and precise classification of breast cancer molecular subtypes is crucial to know how breast cancer behaves, grows and responds to treatment and prognosis on the molecular level. There are a few existing studies conducted on breast cancer molecular subtypes classification, either using mono-omics or multi-omics, while lacking systematic comparisons of both. However, there is a need to know the performance and differences of mono-omics and multi-omics high-throughput technologies for breast cancer molecular subtypes classification, including the taxonomy, heterogeneity, causes, risk factors and unique molecular characterization. Therefore, to overcome these issues, this comprehensive review provides a structured synthesis of the current state of research on breast cancer molecular subtypes classification. Artificial Intelligence (AI)-driven Machine Learning (ML) and Deep Learning (DL) models, are employed for breast cancer molecular subtypes classification, mainly focusing on mono-omics and multi-omics. The analysis of this review shows that multi-omics technologies have great potential for the accurate and precise classification of breast cancer compared to mono-omics. It not only provides a detailed structure of the breast cancer molecular subtypes but also provides a comprehensive view of tumor progression, growth dynamics, aggressiveness, and underlying biological mechanisms. The correct integration of multi-omics data types and variants plays a significant role in classifying breast cancer molecular subtypes. Based on the extensive analysis of the existing studies, some of the main challenges that still exists remain in the classification of breast cancer molecular subtypes, include high dimensionality of multi-omics data, overfitting, data imbalance, models overperformance on minority classes, high correlation and overlapping, computational complexity, accurate integration of multi-omics data types and variants, analysis of the misclassification patterns and accurate classification of breast cancer molecular subtypes
Towards Real-Time Design Collaboration:LiveCol Final Report
Full text linkLiveCol set out to develop and validate methods and tools that enable realtime,data-driven collaboration in construction design. The project aimedto move from differentiated, semi-coordinated workflows toward parallel,open, and up-to-date information management supported by 3D tools andintegrated communication services
Investigation of the reaction kinetics of 3-chloro-2-hydroxypropyl-N,N,N-trimethylammonium chloride (CHPTAC) with cellulose fibres
No full textIn the reaction system consisting of 3-chloro-2-hydroxypropyl- N , N , N -trimethylammonium chloride (CHPTAC), sodium hydroxide, cellulose and H2O, it is widely accepted that the low reaction yield is the result of fast alkaline hydrolysis of CHPTAC. Some inconsistencies remain unexplained by rapid hydrolysis alone, indicating the need to understand the role of the cellulose-NaOH interaction to advance beyond the current state of the art. This raises two key questions: is NaOH uptake on cellulose decisive for cationisation yield, and is all epoxide consumed by end of the cationisation? Investigations into the reactions rates were conducted in the absence and the presence of cellulose fibres by applying a novel ion-exchange high-performance liquid chromatography method and nitrogen analysis to quantify both reactant in solution and product formation. It was found that hydrolysis rates are slower in the presence of the fibre, which was attributed to sorption of reactants, particularly sodium hydroxide, onto the fibre. The bonding of CHPTAC to cellulose shows initially high reaction rates but approaches a plateau, even though 40% of the cationisation agent is still available in solution. This phenomenon is attributed to the consumption of “active” (deprotonated) cellulose sites, highlighting the need for improved understanding of the cellulose-NaOH interaction, and its influence on derivatisation reactions.</p
Optimizing an iron- and manganese-based electrocatalyst for the oxygen evolution reaction in a proton exchange membrane electrolyzer
No full textThe development of electrocatalysts based on earth-abundant elements has gained significant attention due to the scarcity and high cost of Ir- and Ru-based materials typically used in proton exchange membrane electrolyzers (PEMELs). This study focuses on Fe-Mn-based catalysts for promoting the oxygen evolution reaction (OER), the sluggish four-electron process at the anode of PEMELs, where acidic conditions and high anodic potentials (1.6–2.0 V RHE) often accelerate corrosion. The catalysts were synthesized via a hydrothermal method and optimized using a response surface design of experiments (DOE), followed by detailed physicochemical characterization. The optimized composition ( [Figure presented] ) demonstrates good electrochemical activity and stability, maintaining performance over 10,000 potential cycles between 1.2 and 2.0 V RHE, with a moderate shift in the overpotential required to reach 10 mAcm −2 (from 1.78 V iR−corr vs RHE to 1.84 V iR−corr vs RHE). Inductively coupled plasma mass spectrometry of flow cells scanning (SFC-ICP-MS) confirms high stability at elevated potentials, showing reduced [Figure presented] to [Figure presented] oxidation. At lower potentials (≤1.4V RHE), dissolution signals indicate reductive leaching of Fe and Mn. Integration of the catalyst into a laboratory PEMEL demonstrate operational stability, sustaining 10 mAcm −2 at 50 °C over 80 h, with a 50 mV increase in iR-corrected potential (1.82 to 1.87 V).</p
Solvent-Mediated Dewetting Principles for Cell-Sized Liposome Formation
No full textReconstitution of synthetic cells holds potential to advance synthetic biology, biomanufacturing, and therapeutics. Microfluidic generation of cell-sized liposomes via double emulsion templating offers precise control over composition and formation process, yet the principles underlying solvent-mediated dewetting remain poorly understood. Using a solvent combination of hexanol and paraffin oil, we demonstrate that solvent-mediated dewetting liposome generation entails both solvent removal and the application of mechanical stimuli. Solvent removal suffices to induce the morphological transition from double emulsions to partially dewetted liposomes exhibiting low and high budding angles of the residual oil pockets. This transition is driven by relaxation of monolayer and membrane tensions, arising from the increased lipid packing density at the liposome interfaces during solvent depletion. While dewetting kinetics and intermediate stages are governed by solvent removal rate, complete dewetting is not spontaneous. Using optical tweezers, we identify tethering between the liposome and oil pocket and characterize the mechanical force required for liposome detachment. By integrating these principles, a predictive, high-throughput approach for generating biocompatible, surfactant-free liposomes is provided. These findings establish a mechanistic framework for liposome dewetting and, through similarities to lipid droplet morphogenesis, offer a protocell platform that could further the understanding of biological budding processes.</p
AI-Powered Orchestration-as-a-Service for 6G Networks:The 6G-CLOUD View
No full textAs 6G networks grow more complex, managing resources and orchestrating services across diverse, dynamic, and energy-efficient environments becomes challenging. This paper presents a programmable Orchestration-as-a-Service (OaaS) Framework from the 6G-Cloud project that enables dynamic, scalable, and intelligent orchestration. The framework separates service orchestration from resource orchestration, supports network service–agnostic management, and integrates AI-driven optimization, digital twins (DT), and Cloud Continuum technologies. Finally, we demostrate the functionality and feasibility of the proposed architecture through an early-stage implementation, where an AI-powered use case for dynamic resource orchestration is validated, showing significant improvements in power efficiency and proactive resource scaling compared to baseline methods.</p
Large Language Model hallucination mitigation in three industrial use cases
No full textLarge-language models (LLMs) can produce factually false or ungrounded information. This occurs when the language model, being fundamentally a statistical model, generates inaccurate or non-existent information with high confidence in response to a given query. This phenomenon is particularly dangerous in safety-critical systems and involves risks when LLMs are used to access or control hardware such as robots, sensors, and other internet-of-things (IoT) applications. Additionally, in automation-rich industrial environments, effective human-machine cooperation depends on maintaining a shared and adaptive understanding of complex situations. Distributed Situation Awareness (DSA) provides a framework for how awareness emerges collectively across networks of operators, robots, sensors, digital interfaces, and LLM based artificially intelligent systems. LLMs can fuse fragmented data streams into coherent, actionable context, enabling Extended Reality (XR) technologies to strengthen DSA by embedding digital cues into physical workflows. While this process improves coordination and adaptability, it also makes reliable and verifiable model outputs essential, as hallucinations can erode operator trust and compromise distributed decision-making. Therefore, mitigation of hallucinations becomes essential for sustaining stable human–AI teaming. We present three industrial projects where LLMs are at the forefront, highlighting practical approaches for hallucination mitigation and demonstrating a transition from online to offline model. Scope is to demonstrate through architectural means how established mitigation mechanisms can be used in industrial systems.</p