Portail des publications scientifiques IMT Mines Alès
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Dynamic wetting of polymers on fibers: effect of temperature
No full textInternational audienceComposite materials future relies on achieving lighter parts through integrating sustainable materials, with bio-based and/or circular materials. To meet the requirements of industrial applications, different thermoplastic matrix, like Poly Ether Ether Ketone (PEEK), Polypropylene (PP) and biodegradable PolyLactic Acid (PLA), can be reinforced by reclaimed carbon fibers, basalt fibers and biodegradable flax fibers [1]. Automated Processes (AP) like Automated Fiber Placement (AFP) and Automated Tape Laying (ATL), using fiber-reinforced tapes, are performant processes for the manufacturing of this kind of composites. Some studies are focused on the quality of the interface and the reduction of defects (voids) in composites manufactured by AP [1], but few studies are focused on the quality of interface in the tape during semi-product manufacturing. The main objective of this study is to investigate the dynamic wetting of molten polymer on fibers to control fiber/matrix interface formation at the micro and mesoscales of the thermoplastic tape. Furthermore, the understanding of dynamic wetting mechanisms as a function of temperature is also relevant for advanced control in Liquid Composite Molding (LCM) processes. Wetting dynamic depends on different properties of the constituents, and it has been usually related to the capillary number (Ca) and the definition of a dynamic contact angle θd [2]. The dimensionless capillary number, considering capillary and viscous effects, is defined by the ratio between the liquid viscosity (η) multiplied the liquid speed (v) and the liquid surface tension (γL).The current work focuses, firstly, on the surface tension determination of polymers as a function of temperature, using Pendant drop and Wilhelmy plate methods. Surface tension tests for several liquids were performed using the two methods at room temperature, showing similar results to the ones reported in literature [3]. Moreover, a reliable procedure to determine this surface tension variation along with temperature was applied using the Wilhelmy plate method. Some results are shown in Figure 1(a), proving that the liquid surface tension decreases linearly as the temperature increases, in accordance with Eötvös law [4], also for a polyethylene glycol (PEG 300) and an epoxy resin. Comparable results are obtained by the Pendant drop method (Figure 1(b)). In this case, implementing an external heating chamber allows to measure the surface tension of polymers at elevated temperatures. The density of the tested liquid at each studied temperature is also needed and determined. In parallel to the surface tension determination, other experimental procedures are developed for determining the interfacial tension, the polar and dispersive components of polymers as a function of temperature, followed by dynamic wetting analysis on single fibers
Identification of specific smoldering fire tracers for construction systems incorporating bio-based materials
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An approach for modular environmental life cycle assessment of effluent treatment: Configuration of effluent treatment modules based on decision tree tailored to best available techniques
No full textInternational audienceAn approach was developed to configure treatment scenarios for a given industrial effluent based on pollutant composition, intended end use, available technologies, as well as environmental impact assessments of the scenarios. To overcome the complexity of configuring the industrial effluent treatment chain due to the variety of contaminants and diverse available treatment technologies, a decision tree was developed based on the best available technology tailored to pollutant types. A parametric life cycle inventory was developed for the operation phase of fifteen conventional and advanced treatment technology modules to facilitate a comparative environmental impact assessment, including parametric sensitivity and uncertainty analysis. The comparative modular life cycle assessment revealed the hotspots and contributions of fifteen treatment modules to the environmental impacts of treating of 1m3 effluent, with nanofiltration, reverse osmosis, and ion-exchange having the highest overall impacts, whereas cartilage, sand filtration, and UV have the lowest environmental impacts. Sensitivity analysis unveiled high sensitivity of midpoint and endpoint environmental impacts to energy, resin and chemical consumptions. This approach offers a foundational framework for further decision tree developments as a supporting tool for treatment configuration in the effluent treatment industry, as well as sustainability assessment of treatment scenarios derived from the decision trees. Modular treatment configuration integrated into a decision tree promises more flexibility in setting up fit-for-purpose treatment scenarios and conducting modular life cycle assessments for more sustainable effluent treatment
Earthworm Mucus as a Natural Stabilizer for TiO₂ Nanoparticles: A bio-inspired Approach for Improved Photocatalysis and Biofilm Control in Water Treatment
No full textInternational audienceNanostructured titanium dioxide (TiO₂) is a well-known photocatalyst for wastewater treatment, yet its aggregation in aqueous media often limits its efficiency specially for microorganism’s inactivation [1]. In this study, we explore the potential of earthworm mucus, a natural, protein-rich exudate [2] as a biocompatible stabilizing agent for TiO₂ nanoparticles. Our results demonstrate that earthworm mucus not only improves the colloidal stability and dispersion of nanoparticles, but also enhances its photo-nano-activity against pathogenic bacteria and biofilm formation and stabilization
Smoldering in biobased concretes
No full textInternational audienceThe building sector is actively seeking solutions to reduce its carbon footprint, as it plays a crucial role in the fight against global warming. Biobased concretes represent a promising class of insulation materials for more sustainable and environmentally friendly construction. However, little data is available on their smoldering reaction. In this work, 34 different biobased concrete formulations were tested using the protocol described in the EN 16733 standard to evaluate their tendency to smolder. The results highlighted two key influencing factors: material density and binder type. Lime-based formulations were found to be more susceptible to smoldering than those using earth or gypsum. To understand lime’s effect, several hypotheses were explored. Findings suggest that the basicity of calcareous binders promotes higher char yields during thermal decomposition, thereby increasing the risk of smoldering. Lastly, this study also assessed the influence of the burner exposure time of the EN 16733 standard for evaluating the smoldering behavior of biobased concretes
MVMIL: Multi-view Multiple Instance Learning for Whole Slide Image Classification of Bladder Cancer
No full textInternational audienceBladder cancer (BC) is a significant and prevalent malignant tumor of the urinary system, with accurate and efficient diagnosis remaining a critical challenge. Multiple Instance Learning (MIL) has shown promise in analyzing histopathological Whole Slide Images (WSIs) by enabling weakly supervised localization of critical regions for cancer diagnosis. However, BC’s benign-malignant classification poses challenges due to the high heterogeneity of histological subtypes within both categories, requiring precise mapping to binary classifications. To address this, we propose a multi-view attention framework to enhance WSI representation. This includes a multi-view masked attention mechanism to avoid redundant feature capture, a diversity learning constraint to ensure comprehensive representation, and a dual-granularity supervised contrastive learning strategy to improve inter-class discriminability. Experiments on a BC WSI dataset demonstrate that our method effectively distinguishes benign and malignant categories, significantly improving classification performance metrics. The dataset, code and model weights will be available to assist in clinical decision-making
Assessing physiological adaptations of professional soccer players using heart rate monitoring and data science
No full textInternational audienceProfessional soccer players face high physical demands across a season (Barnes et al., 2014). To ensure players' health (i.e., optimize physical performance and reduce injury risk), practitioners have developed monitoring strategies relying on external and/or internal indicators (Impellizeri et al., 2019). However, internal indicators have been neglected in favour of external indicators with the settlement of monitoring tools such as global positioning system units (GPS). Although the misuse of many internal indicators might be due to a technology not in adequacy with the environment of football, heart rate (HR), a surrogate measure of cardiorespiratory system, has raised some interest to monitor dose and response of training (Bellenger et al., 2016). Yet, there are several operational, technological and theoretical limitations hindering their daily use with elite athletes (Carling et al., 2018). To overcome these issues, recent sport science literature has shown interest in using machine learning models (Elstak et al., 2024) to create unobtrusive indicators, thus increasing the frequency of measurement alongside traditional metrics. A series of works were carried out with the aim of assessing physiological adaptations of professional soccer players using HR monitoring and data science. During multiple seasons, players' fitness was tracked using an indicator based on the difference between predicted and measured HR during specific football drills (ΔHR) (Diouron et al., 2025) as well as indicators regarding HR kinetics (i.e., acceleration and recovery of HR, Bellenger et al., 2016) and their difference with their predicted value to have a complete overview the player's cardiovascular system status. Data were collected between July 2022 and May 2025, covering 3 soccer seasons, on 40 professional soccer players in France. Player's activity and HR were recorded using a 10 Hz GPS unit linked with a designed 1 Hz HR vest during training and game sessions. Individual predictive models of HR responses and HR kinetics were built using traditional machine learning methods (e.g., Random Forest, eXtreme Gradient Boosting, Kernal Ridge) and more recent deep learning models. HR prediction models were trained on a dataset that containing drills' external load data, hourly weather data, Borg CR-10 scale scores and cumulative load. Robustness of models was assessed through a resampling procedure, and hyperparameters were tuned using a grid search cross-validation method (CV=5). Root mean squared error, absolute and relative mean absolute error (MAE) and coefficient of determination were used to assess the prediction performance of the models. HR kinetics prediction models were built using raw data of players' activity (i.e., time series) and followed the same validation protocol as ΔHR. On the first season, a significant difference in ΔHR between months was found (χ² = 20, P < .05). The face validity of ΔHR has been shown using the variation in the training load during a preseason. During the second season, preliminary results show better performance concerning HR prediction performance on the second season (MAE = 4.82). Additionally, preliminary results of HR kinetics predictive models showed good performance (i.e., <3bpm) over short prediction windows (i.e.
A delay-time model for inspector team assignment and non-periodic inspection intervals
No full textInternational audienceMaintenance models based on delay-time have been extensively used in industry. However, some models still impose strong assumptions, e.g., most models do not pay attention on determining who is responsible for performing maintenance actions. Even when models do consider this, decisions regarding the moment for these actions and who is responsible for them are typically separately optimized. This paper sets out to tackle the problem of optimizing the moments for maintenance actions and the assignment of inspection teams responsible for each task, such that both decisions are jointly optimized. Thus, we propose a hybrid policy that combines inspections and age-based replacement. Due to the complexity of the problem, we propose an Adaptive Simulated Annealing algorithm, which presents percentages of optimization of up to 4.4% when compared to a general “black-box” algorithm. Our numerical results indicate that neglecting to whom the inspection would be assigned could generate worse solutions. Finally, we developed an user-friendly online app for assessing the cost-rate of the policy
