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Connecting Porosity to Storage Capacity: Core-Shell Carbon Material As High-Capacity Negative Electrodes for Sodium Ion Batteries
No full textDue to abundant raw materials, low costs and promising high reversible specific capacities, hard carbons (HCs) are a common choice for commercially manufactured anodes in sodium-ion batteries (SIBs). Despite their potential and extensive use, the storage mechanism is still under debate. The non-stoichiometric adsorption mechanism also means that the search for an upper limit for the reversible capacity is ongoing. There is a strong requirement for synthetic anodes that enable a better understanding of the theoretical capacity associated with HC-anodes. We have developed core-shell carbon anodes consisting of a highly porous carbon core and (almost) non-porous shell. Thus, the reversible capacity can be deconvoluted from irreversible capacity losses, arising from the formation of the solid electrolyte interphase (SEI). Moreover, the porosity of the carbon-core can be linked to the reversible capacities gained.
A range of microporous activated carbons were coated via an optimized chemical vapour deposition technique.[1][2] These materials were characterised using a range of techniques including powder x-ray diffraction, small angle x-ray diffraction (SAXS), gas physisorption (N2, CO2) measurements and electrochemical characterisation at coin cell level.
After coating, the material showed a significant reduction in detectable surface area (up to a factor of 192x) by N2-physisorption. The tailor-made shell allows the stable cycling of a carbon anode vs metallic Na-electrode in coin cells at room temperature. For the best performing material, the reversible capacity increased from 139 ± 2 mAhg-1 to 396 ± 2 mAhg-1 while irreversible capacity is decreased from 636 ± 3 mAhg-1 to 89 ± 3 mAhg-1 (see Figure 1). After initial stabilization, a CE of 99% was achieved. The coating technique was usefully applied to a range of materials. [3]
The successful formation of core-shell structures with high capacities enables separation of the storage mechanism from SEI-formation. In turn a proposed calculation to rank the contributions of surface adsorption and pore filling capacity can be confirmed. The materials also create the opportunity to conduct a range of operando experiments (e.g., SAXS) that can shed further light on the sodium storage mechanism
Element analytical approaches for emerging contaminant analysis-materials meet environment
No full textIm Rahmen des Vortrags wurden Elementanalytische Methoden auf Basis der ICP-MS und HR-CS-GFMAS im Kontext der Material-Umweltanalytik in Form eines Übersichtsvortrags vorgestellt. Neben den Techniken wurden diverse Applikationsbeispiele für unterschiedliche Umweltmatrizes genannt
Depth-Resolved Lithium Isotope Fractionation as a Diagnostic of Interphase Evolution and Degradation in Lithium Ion Batteries
No full textLithium isotopic fractionation is well-established in dynamic geochemical systems; however, its role in lithium-ion batteries (LIBs) remains uninvestigated. Herein, we report the first depth-resolved demonstration that isotopic separation occurs during Li-ion cell operation whose magnitude depends on the cycling history. Using depth-resolved glow discharge mass spectrometry, we monitored the 7Li/6Li ratio in LiNi0.333Mn0.333Co0.333O2 (NMC111)||graphite coin cell electrodes at defined life-cycle stages. Different charging rates were examined to get mechanistic insight into kinetic and thermodynamic control in the fractionation process. Although pristine electrodes exhibit a uniform isotopic ratio, cycled electrodes show a distinct 7Li enrichment in the positive electrode and a corresponding accumulation of 6Li at the surface of the negative electrode. The degree of isotopic separation varies with the charging rate. Isotopic signatures correlate with capacity fading, indicating lithium isotope mapping as a sensitive diagnostic tool for tracking electrode degradation and the evolution of the electrode–electrolyte interphases in LIBs
MR4SafeOperations: Mixed reality system for training and supporting industrial plant personnel.
No full textOperation and maintenance tasks in industrial process plants are complex and safety-critical, often relying on heterogeneous documentation and limited contextual support for field personnel. This work presents MR4SafeOperations, a mixed reality–based system designed to assist operators during operation and maintenance activities through context-aware, hands-free guidance. Using a vacuum distillation sampling procedure as a case study, operational workflows are formalized with BPMN and structured according to ISA-88.1 principles. Process data, 3D plant models, P&IDs, and procedural information are integrated via standardized interfaces into a mixed reality application. The system enhances situational awareness, reduces cognitive load, and supports safer decision-making during plant operation. The results demonstrate the potential of mixed reality to improve safety, efficiency, and usability in industrial process environments
Simulation-assisted multimodal deep learning (Sim-MDL) fusion models for the evaluation of thermal barrier coatings using infrared thermography and Terahertz imaging
No full textThermal Barrier Coatings (TBCs) are critical for high-temperature applications, such as gas turbines and aerospace engines, protecting metallic substrates from extreme thermal stress and degradation. Accurate evaluation of TBCs is essential to improve operational efficiency, optimize predictive maintenance strategies, and extend component life. Conventional non-destructive evaluation (NDE) techniques such as infrared thermography (IRT) and terahertz (THz) imaging have been widely used for TBC inspection with limitations when used independently, including sensitivity to surface conditions, limited penetration depth mainly in multi-layer coatings. This study proposes a novel framework called simulation-assisted multimodal deep learning (Sim-MDL) that combines IRT and THz data for a comprehensive evaluation of TBCs. To generalize the study to varying thermophysical properties of TBCs, the study uses simulation-generated data along with experimental data for training deep learning models. Two deep learning frameworks based on a 1D convolutional neural networks (CNN) and a long short-term memory (LSTM) with attention were developed for the multimodal feature fusion. The IR-THz fused frameworks enable simultaneous prediction of key TBC topcoat properties including thermal conductivity, heat capacity, topcoat thickness and refractive index. Experiments were conducted on four newly coated samples topcoat thicknesses ranging from 24 to 120 μm. An attention-based LSTM model trained on both simulation and experimental data shows high prediction accuracy with MAPE values ranging from 2.06% to 4.43% for thermal conductivity, 2.05% to 3.57% for heat capacity, 11.53% to 1.75% for topcoat thickness, and 0.27% to 1.05% for refractive index, respectively, for the topcoat layers of four samples. The proposed Sim-MDL framework outperformed single-modality and conventional parameter estimation methods in accuracy and robustness, highlighting the potential of multimodal data for automated analysis of TBC in industrial settings
Machine Learning Approach for Robust Acoustic Emission-Based Damage Classification in Pressure Vessels
No full textAccurate damage classification of Composite Pressure Vessels (CPVs) is crucial for understanding failure behaviour of hydrogen storage systems. Acoustic Emission (AE) monitoring is a non-destructive testing technique capable of detecting signals from different failure mechanisms such as fiber breakage and matrix cracking, supporting durability assessment of CPVs. Therefore, the main objective of this study is to combine AE and advanced deep learning techniques to develop a robust framework for automatic and accurate identification and classification of damage mechanisms across various CPVs.
The evolutionary Genetic Algorithms (GA) was used for feature selection, followed by unsupervised clustering to generate automatic labels for model training. Two different FCNN and CNN-LSTM architectures were used to train individual models based on different AE datasets. Later, Adaptive Transfer Learning (ATL) and Meta Ensemble Learning (MEL) techniques were applied to handle data variability and train predictive generalized model over varied AE datasets. The ATL fine-tunes a pre-trained models to leverage their knowledge, while MEL uses pre-trained models' predictions as meta features to train a meta model.
Experimental results demonstrate that while both generalized ATL and MEL trained models perform well across different AE datasets, the MEL framework outperforms ATL method in terms of evaluation metrics. The Mean-Accuracy score reaches 0.9026, and 0.9900 for ATL, and MEL, respectively. The most accurate multi-class classification results was achieved using MEL method in terms of the Mean-Accuracy and Recall metrics. The proposed framework provides a scalable, adaptive approach for automated damage classification using AE signals across diverse CPVs in real-world settings
Luminescence Thermometry in Pure TeO₂ Glasses Doped with Er3+/Yb3+ and Eu3+: Remote Sensing Capability Across the Biological Temperature Range
No full textThe development of non-contact and remote temperature sensors based on rare-earth (RE3+)-doped glasses is crucial for emerging applications in biomedical diagnostics and microscale thermal monitoring. In this study, we explore the fundamental thermometric properties of pure tellurite (TeO2) glasses doped with either Er3+/Yb3+ or Eu3+ ions in a wide range of temperature. The Er3+/Yb3+-co-doped system was analyzed through upconversion emission thermometry, particularly involving thermally coupled levels 2H11/2 and 4S3/2. The intensity ratio between these levels exhibits a clear temperature dependence from 100 K to 530 K, with an inversion near 160 K and optimal sensitivity above 250 K. At 300 K, the relative sensitivity (SR) reached 1.1% K−1, and the absolute sensitivity peaked at 6.5 × 10−3 K−1 at 460 K. Figure on the left is the Er3+ upconversion luminescence dependency on temperature, in the center is the excitation spectra of Eu3+ with temperature, and on the right is the comparison between calculated and measured temperature for Eu3+ samples. In parallel, the excitation spectra of Eu3+-doped TeO2 glasses were acquired from 100 K to 520 K, revealing three thermally responsive spectral regions. These transitions exhibited temperature-dependent intensity inversions, enabling the use of excitation-based thermometry. Notably, one of the regions provided the most accurate temperature predictions, with a relative sensitivity of 0.5% K−1 at 300 K. Both systems benefit from the high optical transparency, chemical stability, and low phonon energy of pure TeO2 glass, making them ideal candidates for remote sensing platforms, such as fiber-optic tips or implantable probes. While the Er3+/Yb3+ system provides robust upconversion emission for conventional thermometry, the Eu3+ system introduces an innovative excitation-based strategy, broadening the applicability of RE3+-doped tellurite glasses for optical thermometry.
Acknowledgements
This work was supported by the São Paulo Research Foundation (FAPESP – N. 2020/11038-2, 2023/05994-6, 2024/04675-7) and National Council for Scientific and Technological Development (CNPq - 304718/2023-8)
Residual stress evaluation using the contour method of an additive manufactured high-strength steel solid cuboid
No full textDirect Energy Deposition with Arc (DED-Arc) enables the weight-optimized and near-net-shape manufacturing of complex structures. Lightweight construction principles allow a reduction of CO2 emissions by saving time, costs, and resources. Further optimisations can be achieved by using high-strength steel. This allows for a reduction in wall thickness and optimisation of weight. However, manufacturing intricate geometries using high-strength steels poses challenges in managing residual stresses (RS), which are essential for ensuring the structural integrity of welded components. High residual stresses can increase the risk of cold cracking, arising from the complex interactions between material properties, process conditions, and component design. Despite the availability of suitable filler metals, the lack of comprehensive knowledge and guidelines on residual stress formation limits the industrial application.
Therefore, in the present study, the contour method (CM) was used to analyse the full field longitudinal residual stresses in an solid cuboid component (dimensions: 120 × 50 × 35 mm³) manufactured by DED-Arc. The CM enables the analysis of the two-dimensional map of residual stresses normal to a cutting plane using a finite element model. For this purpose, a solid cuboid component was welded fully automatically with a high-strength solid wire specially adapted for DED-Arc (yield strength > 790 MPa) onto conventionally manufactured substrates made of S690QL. The residual stresses from CM in the volume are compared with residual stress analyses using X-Ray diffraction on the surface. Additionally, comparative data from previous studies on hollow cuboid structures was included in order to identify similarities and differences in the resulting stress state, and to complement and validate the CM results. These results demonstrate the significant influence of the geometry on the residual stress profiles within the solid cuboid in relation to the open hollow
Leitlinie für numerisch geführte Sicherheitsnachweise im Rahmen der Bauartprüfung von Behältern für den Transport von radioaktiven Stoffen
No full textDie Leitlinie dient der Unterstützung der Qualitätssicherung bei der Erstellung, Kontrolle und Beurteilung von Sicherheitsnachweisen, die auf einer numerischen Analyse von Problemstellungen basieren, welche zum Prüf- und Begutachtungsumfang von Behälterbauarten für den Transport radioaktiver Stoffe gehören. Sie soll insbesondere die Grundlage für die korrekte Durchführung der numerischen Analysen nach dem Stand der Technik bilden und die Prüfbarkeit des numerischen Sicherheitsnachweises unterstützen. Ihre Anwendung soll die Nachvollziehbarkeit des Berechnungsganges und der den Berechnungen zugrundeliegenden Voraussetzungen und Annahmen gewährleisten
Guideline for numerical safety analyses in the context of type approval of packages for the transport of radioactive material
No full textThe guideline serves to support quality assurance in preparation, control and assessment of safety cases based on a numerical analysis of problems that are part of the scope of testing and assessment of package designs for the transport of radioactive materials. In particular, it is intended to form the basis for the correct performance of numerical analyses in accordance with the state of the art and to support the verifiability of the numerical safety case. Its application should ensure the traceability of the calculation procedure and the preconditions and assumptions on which the calculations are based