Portail HAL UHA (Université de Haute-Alsace)
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Risques technologiques et risques naturels : retour sur l'année 2024-2025
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Electrospinning for Biomedical Applications: An Overview of Material Fabrication Techniques
No full textInternational audienceThis review examines recent methodologies for fabricating nonwoven polymer materials through electrospinning, focusing on the underlying physical principles, including the effects of external parameters, experimental conditions, material selection, and primary operational mechanisms. Potential applications of electrospun polymer matrices in tissue engineering are analyzed, with particular emphasis on their utility in biomedical contexts. Key challenges in incorporating new materials into biomedical devices are discussed, along with recent advances in electrospinning techniques driving innovation in this field
Gold nanoparticles combined with ultrafine TiO 2 layer: a reliable probe for Raman thermometry
No full textInternational audienceTemperature determination methods in metal nanoparticles are essential for providing information on energy dissipation dynamics in such systems and for temperature-sensitive applications, hence the need for high-performance thermometry techniques is evident. In this study, we propose new efficient probes for Raman antiStokes-Stokes thermometry based on gold nanoparticles (AuNPs), prepared by thermal dewetting and controllably functionalized with a 2 nm TiO2 layer by the sol–gel method. AuNPs@TiO2 demonstrated good stability and a usable response over a temperature range from 25 °C to 240 °C generated by external thermal and thermoplasmonic heating of the sample. We validate the methodology by taking into account the spectral efficiency of the Raman spectrometer as well as the extinction properties of AuNPs@TiO2 in the calculations. This approach enables us to propose a reliable temperature measurement over two hundred degrees range
Honeycomb germanene flakes on Ag nano-islands
No full textInternational audienceSince germanene cannot be exfoliated from a bulk material, and since Ge-based surface alloys are more stable than pure Ge monolayers (MLs) on metal surfaces, the synthesis of germanene flakes remains challenging. Here we report on the synthesis of germanene nanoflakes on Ag ML islands grown on Ag(111). The maximum achievable size is of the order of 30 nm, and germanene growth occurs due to the heavily oversaturated chemical potential for the Ge atoms confined on top of the Ag islands during growth. The nucleation of three-dimensional clusters, however, prevents the formation of larger germanene flakes.</div
CoqFib : approches de coques spécifiques pour la mise en forme des renforts composites à fibres continues
No full textInternational audienceThe aim of this work is to develop original and complementary numerical tools, specific to fibrous reinforcements, forsimulating the draping of composite reinforcements and prepregs. The following three directions of development areproposed to address this issue : (i) analysis of the deformation of fibrous yarns at the mesoscopic scale, development ofsolid-beam Finite Element (FE) ; (ii) analysis of shape forming process based on shell and fibrous shell solid FE’s includingtransverse normal effect ; (iii) mechanical behavior, characterization and validation experimental tests.L’objectif de ce travail est de développer des outils numériques originaux et complémentaires, spécifiques aux renfortsfibreux, pour la simulation du drapage des renforts de composites et des préimprégnés. Les trois axes de développementsuivants sont proposés pour répondre à cette problématique : (i) analyse de la déformation des mèches fibreuses à l’échellemésoscopique, développement d’Elément Fini (EF) solide-poutre ; (ii) analyse de la mise en forme des composites baséesur des EF de coque et de solide coque fibreuses avec prise en compte de l’écrasement transverse ; (iii) comportementmécanique, essais de caractérisation et de validation
Characterizing Particulate and Condensable Emissions from a Wood-Burning Insert
No full textInternational audienceWood combustion in domestic appliances is a key contributor to renewable energy in France, accounting for 31% of primary renewable energy production in 2023. However, it remains a significant source of particulate and gaseous emissions, including total suspended particles (TSP), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs). This study evaluates particulate and condensable organic emissions from a modern wood-burning insert under nominal and degraded combustion conditions. The experimental setup accords to NF EN 16510-1 standards, using gravimetric methods for TSP quantification and gas chromatography coupled to mass spectrometry (GCMS) for chemical characterization of organic compounds. TSP concentrations mainly remained low under both combustion conditions. Higher filter temperatures (180 °C) lead to lower TSP collected mass by inhibiting the condensation of volatile species. Condensable compounds were captured using a series of impingers. Chemical analysis revealed the presence of heavy alkanes, PAHs, and oxygenated PAHs (O-PAHs), especially during degraded combustion. Phenolic compounds, indicative of lignin degradation, were also identified. Temperature and combustion phase significantly influence the partitioning of organic molecules between particle and gas phases. This study highlights the importance of characterizing condensable organic compounds to better understand their role in air quality and health impacts. The findings emphasize the need for further studies on the chemical composition of emissions from wood-burning appliances, especially under real-world conditions, to optimize their environmental performance and compliance with evolving regulations
Validation empirique d’une méthodologie de Learning Design centrée sur la brique et son implémentation sous la forme du kit de scénarisation Eduscript Doctor
No full textInternational audienceTo design and script courses, practitioners often collaboratively use simple and tangible tools such as Post-it notes. In light of this, research and development were conducted to develop Eduscript Doctor, an analogic tool that would retain the inductive potential of Post-it notes while structuring the pedagogical scripting process. This Design-Based Research was carried out in three stages: the initial design of the scripting methodology and the tool (3 researchers), their improvement with the participation of practitioners (11 centers), and then an external evaluation (3 teams). The latter stage took the form of a qualitative empirical study on the tool's utility and usability by examining three MOOCs. The results of the qualitative study showed that the tool was generally useful and usable, facilitating an in-depth analysis of the scripting of the three MOOCs. However, some negative aspects emerged from the interviews, such as the tool's apparent complexity at first glance, the long time required to store the pieces after use, and the lack of digital backup for the produced models. Among the results of this study, the foundations of a new Learning Design theory centered around the concept of "bricks" also emerged. Although it still requires further research to be stabilized, improved, and validated, a high level of abstraction carried by this new theory will be necessary to consider the tool's future developments. In conclusion, the results of this initial study on the kit seem promising, but much more research is needed to better understand its uses, methodology, and potential audiences.Pour concevoir et scénariser des formations, les praticiens recourent souvent de manière collaborative à des outils simples et tangibles tels que les Post-it.Face à ce constat, une recherche et développement a été menée pour développer un outil analogique (Eduscript Doctor) qui conserverait le potentiel inductif du Post-it tout en structurant le processus de scénarisation pédagogique.Cette recherche de type Design Based Research, s'est déroulée en trois étapes : la conception initiale de la méthodologie de scénarisation et de l’outil (3 chercheurs), leur amélioration avec la participation de praticiens (11 centres), puis une évaluation externe (3 équipes de concepteurs). Cette dernière étape a pris la forme d’une étude empirique qualitative de l’utilité et de l’utilisabilité de l’outil, en examinant trois modèles distincts de MOOC.Les résultats de l’étude qualitative ont montré que l'outil était globalement utile et acceptable et qu’il a facilité une analyse approfondie de la scénarisation des trois MOOC. Néanmoins, certains aspects négatifs sont ressortis des entretiens comme une apparente complexité du kit à première vue, un temps de rangement long des pièces après utilisation et une absence de sauvegarde numérique des modélisations produites.Parmi les résultats de cette étude, sont aussi apparus les prémisses d’une nouvelle théorie de Learning Design centrée sur le concept de « brique ». Bien qu’elle nécessite encore d’autres recherches pour être stabilisée, améliorée et validée, un niveau d’abstraction élevé porté par cette nouvelle théorie sera nécessaire pour envisager les prochaines évolutions de l’outil.En conclusion, les résultats de cette première étude sur le kit semblent prometteurs mais de nombreuses autres recherches devront être réalisées pour mieux cerner ses usages, sa méthodologie et ses publics potentiels
Nitroxide-Mediated Photopolymerization: When Surface Grafting on Cross-Linked Networks Goes up to Several Hundred Microns
No full textInternational audienceChain-extension with multiple blocks is one of the major advantages of reversible deactivation radical polymerisation (RDRP) and as so, it is widely applied to the synthesis of (block)copolymers. Mainly exploited for the synthesis of linear and branched structures, this characteristic is less recognized on highly cross-linked networks by RDRP, possibly due to the limited thickness usually achieved which generally sits below few tens nanometers. In this study, we demonstrated how, under optimized conditions, chain extension on highly cross-linked networks is possible up to several hundreds of microns, with obvious opportunities for microfabrication and structuring of complex polymer surfaces. By using nitroxide mediated photopolymerisation (NMP2) and patterning by digital light processing (DLP) photolithography we successfully grew multiples layers on model glass substrates which extended up to more than 300 microns by simply depositing bare monomer on top of every polymerized layer. A systematic study on irradiation conditions, UV-vis, FT-IR and profiling measurements revealed that the surface grafting process was more efficient when the supporting underlayer was not fully grown. We speculated that its “incomplete polymerisation” grants the polymer a gel phase allowing easy diffusion of both fresh monomer and reactive nitroxide-ends. A similar behaviour, although much weaker, was also observed on reference, free-radical photopolymerized (FRPP) resins, which possibly relates to the slow diffusion and activation of 2 unreacted photoinitiator. Thus, besides the obvious advantage of using NMP2 in DLP photolithography, this study also illustrates how interlayer properties critically affect the efficiency of surface grafting by surface-(re)-initiated radical polymerisation, opening unique possibilities for highly cross-linked networks by NMP
Self‐Supervised Learning Based Clustering Workflow for Exploring Seismological Data From Dense Networks
No full textInternational audienceA key challenge in environmental seismology is processing seismic data to study source physics, natural and human-induced forcings, and geological structures such as landslides, glaciers, and volcanoes. Seismic arrays with dozens of stations have expanded data set sizes, and this, combined with signal complexity and high noise levels, makes it difficult to analyze using traditional event detection and labeling methods especially for low-energy or rare events. Clustering continuous data offer a comprehensive method for exploring the data sets and detecting all relevant events. In this study, we present a clustering workflow based on self-supervised learning (SSL) designed to handle data sets ranging in size from thousands to millions of events. This approach enables automated clustering of continuous data from seismic arrays containing dozens of stations. When applied to the "Marie-sur-Tinée" landslide data set, our workflow processed 10 millions 30-s windows and identified four main families: Potential Endogenous Landslide Seismic Events, Potential Regional Earthquakes, Potential Rainfall-Induced Signals, and Noise. Despite the overall consistency, some noise remained in the event-related clusters highlighting areas for further improvement in clustering methods. Nevertheless, the proposed SSL-based clustering workflow shows great potential for an efficient exploration of seismic data sets of millions of events and could be a solution for the blind exploration of similarly large data sets.Plain Language Summary Analyzing seismic activity helps scientists better understand natural events such as landslides and earthquakes by studying both micro and macro-seismicity. In this study, we developed a method to automatically group large amounts of seismic data into categories without requiring prior knowledge of the types of seismic sources and of the seismotectonic context of the region. This method uses self-supervised learning to process continuous seismological data flows from a data set consisting of 69 stations. When applied to data from the "Marie-sur-Tinée" landslide, it grouped over 10 million short time windows into four categories: microseismic events, regional earthquakes, rainfall signals, and other events. Although some grouping errors remained in the final groups, this new approach shows promise for quickly analyzing large amounts of seismic data and could lead to improved models for documenting natural hazards