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Electrochemical Debromination of Brominated Aromatic Flame Retardants Using Activated Carbon-Based Cathodes
No full textInternational audienceBoth legacy (e.g., polybrominated diphenyl ethers; PBDEs) and emerging (e.g., hexabromobenzene) flame retardants frequently feature brominated aromatic motifs, which are associated with persistent and bioaccumulative properties. While activated carbon has been used to treat brominated aromatics, it is a passive sorbent and does not degrade them. Using model bromobenzenes, this study illustrates debromination of brominated aromatics sorbed to activated carbon when the carbon is fashioned into cathodes and treated at a −1.3 V/SHE applied potential. Debromination rates fit a quantitative structure–activity relationship (QSAR), increasing with increasing free energy changes (ΔG) for a two-electron transfer to the brominated aromatic. Half-lives ranged from ∼4 min for hexabromobenzene to ∼15 d for bromobenzene, although the debromination half-life for bromobenzene decreased to ∼50 h at −1.8 V/SHE. The QSAR for bromobenzene debromination was also predictive for debromination of two PBDEs (BDE-99 and BDE-47), indicating that the QSAR was broadly applicable across brominated aromatic structures. Debromination released bromide to the catholyte, while lower-order brominated aromatic intermediates remained sorbed to the cathode. Debromination rates roughly correlated with the conductivity of the black carbon. The strong sorption capacity of carbon-based cathodes permits sequestration of brominated aromatics from contaminated waters within short hydraulic residence times, while an electric potential can be periodically applied to debrominate contaminants sequestered on the carbon
In Situ Investigation of Plasticity Mechanisms of the Phase in (Ni, Pt)Al Bond Coats During Thermal Cycling by High-Energy X-Ray Diffraction
No full textEuroSuperalloys 2026 - 5th European Symposium on Superalloys and their applications, 03 au 07 mai 2026, GiensInternational audienceThe durability of thermal barrier coating (TBC) systems is strongly influenced by the interaction between oxidation of the metallic bond coat and its mechanical behavior. While the response of bond coats under isothermal monotonic loading has been widely studied, the effect of thermal cycling remains poorly understood, even though cyclic loading naturally arises from the mismatch in coefficients of thermal expansion between the ceramic top coat, thermally grown oxide, metallic bond coat, and the superalloy substrate. In this work, high-energy X-ray diffraction was used to investigate the strain and stress evolution in the -(Ni, Pt)Al bond coat of a standard TBC deposited on a nickel-based single-crystal superalloy during thermal cycling. Before in situ cycling, some of the studied specimens were aged through long furnace cycles. Strains and stresses in the phase were quantified in situ using the method combined with micromechanical modeling. The results reveal that plastic deformation in is strongly controlled by evolving interfacial effects and by the cyclic β ⇌ γ ′ phase transformation during thermal cycling. These mechanisms govern the accumulation of plastic strain in and may promote rumpling, spallation, and ultimately TBC degradation. This study provides new mechanistic insight into bond-coat plasticity under thermal cycling
An integrated fuzzy AHP-fuzzy TOPSIS approach for multi-criteria decision-making framework in sustainable manufacturing process selection
No full textInternational audienceThe design of a sustainable manufacturing process is complex, as it requires balancing technological, economic, environmental, and social factors while dealing with uncertainties and conflicting criteria. This research is focused on the uncertainties in the preferences of decision-makers. This study introduces an integrated fuzzy AHP-fuzzy TOPSIS framework, offering a reliable and systematic approach to multi-criteria decision-making. Fuzzy AHP is used to assign accurate weights to criteria by incorporating expert input and addressing the vagueness of linguistic terms, while fuzzy TOPSIS helps rank the alternatives based on how close they are to an ideal solution. In addition, CRITIC method is implemented to find the objective weights of each criterion to identify which criteria require careful consideration and to ensure that the decisions made are best against uncertainties. A case study was conducted on selecting a manufacturing process for hydraulic manifolds, considering 15 distinct criteria such as cost, energy efficiency, material utilization, and functional performance. Three Alternatives were evaluated, these are: conventional manufacturing with 316 Stainless Steel, additive manufacturing with AlSi10Mg, and additive manufacturing with 316 Stainless Steel. The results demonstrated that additive manufacturing with 316 Stainless Steel emerged as the optimal solution, exceeding the other alternatives in terms of sustainability and functional performance. Sensitivity analysis using one at a time weight variations, confirmed the stability and reliability of the proposed methodology. The results highlight the framework’s adaptability to diverse scenarios and its capacity to provide useful insights for decision-makers. This study provides a practical, reliable and effective tool for promoting sustainable practices in manufacturing process selection by integrating sustainability principles and addressing the complexities of modern manufacturing
A Model-Based System Engineering approach for Business Continuity Management in Hospitals
No full textInternational audienceIn hospitals, the information system is essentialfor coordinating actors and operations, as well as achievingobjectives. Its loss would have grave consequences, whetherdirectly affecting patients, finances, or logistics, thus creatinga need to preserve the health service’s critical functions and toenhance cooperation and communication among stakeholders.Business Continuity Management (BCM) is used to mitigatethese consequences. Still, traditional approaches often remaindescriptive and lack a formal method for representing the interdependenceamong governance, medical, and logistical processesin complex sociotechnical systems, which is essential for understandingthe extent of the problem and for designing appropriatesolutions. In contrast, Model-Based System Engineering (MBSE)provides a holistic view of the system, representing the problemas a whole. It has been used in healthcare to address complexitybefore, but applications that support resilience and continuity inorganizational systems remain underexplored.This paper proposes a conceptual framework, based on adomain-specific metamodel for hospital continuity management,which combines MBSE and BCM to illustrate dependenciesbetween technical and organizational elements and the collaborativedynamics among stakeholders. By showing how tasksand responsibilities are shared among actors and linking thesemodels to simulation, the framework enables the anticipationof disruptions’ impacts and the assessment of recovery options.The relevance of the framework is illustrated through a geriatricsurgical health service use case, in which critical dependenciesbetween IT, medical, and logistics processes are represented andevaluated.This paper argues that MBSE can be expanded to serve as themethodological basis for BCM in healthcare by drawing parallelsbetween MBSE and BCM processes. We illustrate how MBSEcan help predict the impact of disruptions and verify models ofdegraded hospital operations, highlighting MBSE’s potential as abasis for a decision-support tool for hospital business continuity
Background Issues in X-Ray Diffraction and Raman Spectroscopy of Carbon Materials
No full textInternational audienceRemoving background signals is a common preprocessing step, but it is not without drawbacks. In X-ray diffraction data, background correction can artificially symmetrize diffraction peaks, which becomes a critical issue for lamellar materials such as graphenic carbon when the Laue indices lie in the plane (e.g., the 10 and 11 peaks). We discuss several approaches to background correction and their implications for the resulting data. In Raman spectroscopy, defects activate the phonon density of states, leading to higher intensity below the D band than above the G band, with respect to the Raman shift. After discussing the linear and circular polarization on the Raman selection rules, we show how flattening the background-a widely used measure of disorder-alters the ID/IG ratio. Finally, principal component analysis (PCA) provides a useful preliminary exploration of data structure; however, because its components may include negative contributions, it cannot be directly applied to spectral decomposition. In contrast, non-negative component decomposition offers an optimal way to preserve the Raman background, even in the presence of luminescence. We confirm our analysis with ANOVA p-values
Sustainable utilization of citrus peel waste: Biochar production, modification, and applications
No full textInternational audienceCitrus peel waste (CPW) is generated in substantial quantities worldwide and poses an environmental burden through improper disposal; however, its high lignocellulose content makes it a promising feedstock for producing value-added biochar. This review aims to critically synthesize recent advances in CPW-derived biochar production, modification, and applications within a circular bioeconomic context. Key findings show that pyrolysis conditions, feedstock variability, and activation strategies strongly influence biochar yield, pore development, and functional chemistry. Physical, chemical, and hybrid surface modification techniques substantially increase the adsorption selectivity and efficiency. CPW-derived biochars effectively remove heavy metals, organic pollutants, and nutrient contaminants, and offer additional benefits in terms of soil improvement, carbon sequestration, and emerging biomedical applications. Despite these advantages, challenges remain regarding feedstock heterogeneity, process scalability, regeneration performance, and environmental safety. Overall, the valorization of CPW into biochar represents a sustainable pathway for waste reduction and environmental remediation, with future opportunities in advanced material engineering and alignment with global sustainability goals
THE USE OF THE THERMOPHORETIC FORCE FOR AEROSOL PARTICLE SEPARATION
No full textInternational audienceThe conception of a micro-separator for aerosol particles based on the thermophoretic effect is proposed. The first step in designing of such a micro-device is the development of a mathematical model. A threedimensional model of a mini-channel (millimetric size) is proposed, and several key parameters are tested, including the length and width of the mini-channel, the intensity of the temperature gradient, and the velocity of the carrier gas. The next steps will be the fabrication of a mini-prototype followed by experimental testing of its efficiency. Ultimately, the final micro-device will be manufactured and tested.</div
Performance study on a Knudsen pump prototype fabricated viatwo-photon-polymerization
No full textInternational audienceThermal transpiration flow is characterized by the displacement of a rarefied gas along a temperature gradient applied to the bounding wall. The exploitation of thermal transpiration flow for gas pumping was initially proposed in the work by Knudsen [1], who presented a prototype of a multistage Knudsen pump. Knudsen pumps displace gas without any moving parts. Enabling vibration-free operation and robust performance over long periods. These key features make Knudsen pumps attractive for integration with often mechanically delicate sensing devices [2]. Sensing applications typically require low but precise flow rates, which can be provided by a Knudsen pump. Knudsen pumps have also been proposed as compressors for refrigeration [3] systems, an application demanding higher performance in terms of pressure head and flow rate. Performance enhancement of Knudsen pumps is a key factor in their wider application. The main driver of performance is the temperature gradient, which must be applied along the pumping channels of the device, typically micrometer in size. The creation and maintenance of considerable temperature gradients on such a small scale are challenging. One of the main strategies to increase the temperature gradient is to</div
Influence of tow-preg composition on microstructure and mechanical behaviour of oxide/oxide CMCs
No full textInternational audienceCeramic matrix composites could soon be used in an ever-increasing number of high-temperature applications. This study explores the potential of a new tow-preg process for the manufacturing of oxide-based ceramic matrix composites. With these techniques, the choice of impregnation slurries is crucial for the quality of the materials. This study focuses on the development of several grades of alumina matrices CMCs. The impact of the slurry composition on the CMCs microstructure and mechanical behaviour is evaluated. The benefits of a minimum amount of boehmite, a gel-forming agent which is a precursor of alumina, on inter-ply cohesion have been demonstrated
Automatic demand forecast model selection in supply chains: a forecast value-added analysis of selection strategies, machine learning, and hyperparameter optimisation
No full textInternational audienceDemand forecasting plays a critical role in supply chain management, enabling suppliers, manufacturers, and retailers to synchronise operations and enhance overall efficiency. Despite extensive research on time series forecast model selection, choosing the most appropriate forecasting model for a given time series remains a complex challenge, particularly in volatile and uncertain environments. The increasing availability of data and the emergence of new forecasting methods have introduced greater complexity, making automated model selection essential for improving forecasting accuracy and decision-making in supply chain operations. This study proposes an automated demand forecast model selection framework that integrates a broad range of statistical and machine learning models. A key feature of the framework is the optimisation of hyperparameters across all models, ensuring each method is fine-tuned for optimal performance. The approach is validated on the M3 monthly dataset, where it outperforms all previously submitted methods, demonstrating significant improvements in forecast accuracy. Additionally, the methodology is tested in a real-world supply chain setting, further showcasing its effectiveness in handling complex and dynamic demand patterns. By enhancing forecast accuracy and reducing the reliance on manual model selection, this research provides an efficient decision support system for supply chain demand forecasting in fast-changing supply chain environments