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Unbiased backward Monte Carlo method for addressing stiff chemical kinetics
No full textInternational audienceIn this research, a new Monte Carlo-based approach is introduced to rethink kinetic models for reaction schemes involving coupled first-order reactions. The study examines two specific schemes: one with exclusively irreversible reactions and another with a reversible reaction, all within an isothermal regime. The kinetic model is reformulated under a system of coupled expectation-based formulations and subsequently solved via Monte Carlo simulation. The solutions are visualized in path space to demonstrate the double randomization technique, also known as the walk-on equation. This approach allows for a qualitative analysis of the algorithm’s behavior under varying conditions. Two scenarios are analyzed: a non-stiff scenario and a stiff scenario, considering timescales related to quasi-steady-state (QSS) and partial equilibrium (PE). The new method provides results consistent with the analytical solution for the non-stiff case and the QSS stiff case. For the PE case, zero variance and classical approximations are employed to derive the optimal probability density functions, capturing the dynamics of fast reactions and eliminating rare events, which leads to good convergence of the method and comparable results to analytical solutions
AI-driven approach for creating and evaluating a synthetic dataset for Medication Errors
No full textInternational audienceObjective:This study aims to create a complete Medication Error (ME) dataset. This will help to address the challenge of limited access to real-world data for developing machine learning models in healthcare applications.Methods:We use transformer-based models (GPT-4, LLAMA3, and Mistral) to create our synthetic dataset in French. These models generate a diverse range of descriptions that capture the variability of ME types. We assess the effectiveness of our synthetic dataset through expert evaluations by healthcare professionals and an AI-driven analysis, to test its realism and its utility in training machine learning models for ME classification.Results:The synthetic dataset demonstrates high accuracy and realism in representing diverse ME scenarios. Expert evaluation confirms that the dataset is similar to real-world ME data. The AI-driven evaluation also shows that models trained on synthetic data achieved robust classification performance, validating the dataset’s utility for the development of effective ME classification tools.Conclusion:The proposed approach demonstrates the potential of large language models to generate realistic synthetic ME reports in French. Out of 200 evaluated reports, 70% of zero-shot outputs were deemed below expectations, while 80% of one-shot and few-shot outputs were considered valid or valid with minor revisions by clinical experts. Furthermore, classifiers trained on 800 synthetic reports attained an F1-score of up to 0.78 when tested on real data. These results confirm that synthetic data can effectively support AI-driven ME analysis in contexts where real-world data is limited or unavailable
Rational engineering of hydroxyapatites for sustainable chemicals, H 2 , biofuels and CO 2 conversion: review
No full textInternational audienceChemical and fuel production through conventional catalytic processes requires significant improvement to reduce CO2 emission levels. Sustainable, potentially direct, chemical production is in critical need, such as methane coupling to olefins, propane dehydrogenation to propylene, biodiesel production, and greenhouse gas emissions modulating reactions, including CO2-utilizing dry reforming of methane, partial oxidative of methane, CO oxidation, CO2 methanation are some of the emerging technologies to address sustainability challenges. These technologies remained constrained due to the lack of stable and efficient catalysts. Hydroxyapatite (HAP), a highly functional versatile material, offers great potential due to its flexible tunability, and several studies have outlined the synthesis protocol of HAP and design modifications for catalytic applications. However, a comprehensive understanding of connecting reaction-specific demands to tailor HAP catalyst designs is limited. In this review, we bridge that gap, highlight key challenges in catalytic reactions, and propose the necessary HAP catalyst modifications, including acid-base tuning, defects-induced lattice oxygen or vacancies, mesoporosity modulation, and catalyst active metal species dispersion, to improve catalytic performance by limiting catalyst deactivation from absorbates surface poisoning, sintering, and coking. Finally, future research areas for improvement for HAP catalysts are suggested to advance the maturity of catalytic technologies
Thermochemical conversion modelling of multilayer packaging waste using Aspen Plus®
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Co-combustion of anthracite coal and biomass-derived biocarbons: reaction kinetics and combustion performance indicators
No full textInternational audienceThis work investigates the feasibility of using biocarbon as an alternative fuel to substitute fossil fuels in industrial processes. Woody biocarbons (WBC1, WBC2) were used for this purpose. Anthracite was selected as the reference fossil fuel to be replaced and biocoke was also considered for comparison. Samples were characterized in terms of chemical composition, and physical and thermal properties. Fuel thermal stability and combustion performance were evaluated using thermogravimetric analysis. Ignition kinetics were modelled using differential scanning calorimetric-based model. The results showed that biocarbon with a lower volatile matter content presents a higher thermal stability while its behavior is close to that of anthracite. Biocoke and biocarbons showed lower ignition temperatures but a higher burning index compared to anthracite, which was shown as the most stable fuel. Kinetic parameters, namely activation energy (Ea), frequency factor (A), and oxidation rate (k), were compared for the samples of study. While oxidation rates of biocoke and biocarbons were comparable, different values were obtained for WBC samples with different volatile matter content. Synergistic effects were observed when biocarbon was mixed with anthracite, mainly due to AAEM present in the samples. To mitigate biocarbon reactivity, a 50/50 biocarbon/anthracite co-combustion was proposed, offering the advantages of higher ignition temperature and low reactivity
Non-destructive evaluation (NDE) via Acoustic Emission (AE) simulation: Response and modelling of AE transmitting sensor
No full textInternational audienceAcoustic Emission (AE) method is a monitoring technique that detects transient elastic waves generated by therapid release of energy from localised sources within materials. It is widely used for non-destructive evaluationand health monitoring of structures. However, interpreting AE signals is challenging, requiring a comprehensiveunderstanding of the AE measurement chain, including excitation sources, propagation mediums, sensors andtheir coupling conditions, acquisition systems and signal processing. To address these challenges, a combinationof simulation and experimental approaches is essential. In this study, the modelling of the AE measurementchain, with particular emphasis on excitation sources, was developed by using highly reliable and reproducibleemission waves from piezoelectric sensors. To achieve this, the velocity response across the entire active surfaceof typical AE sensors (R3α, R6α, R15α, and F15α, from Physical Acoustics, USA) was first identified experi-mentally, using a laser vibrometer. Then, 3D models incorporating these AE sources were established throughboth experiment and simulation, with a configuration using a metallic plate, to study the wave propagation andthe influence of the AE measurement chain. Several parameters were studied, including the relationship betweenthe sensor’s response and excitation pulse characteristics, aperture effect of AE sensors, and main parameters ofAE signals. The results demonstrated a very good agreement in both signal waveform and key characteristicsbetween simulations and experiments obtained using a laser vibrometer. These findings are essential for un-derstanding sensor characteristics, controlling artificial source generation, performing AE sensor calibrationtests, and advancing models for the entire AE measurement chain
CRSS evolution with changing grain size for a dual-phase Ti-6Al-2Sn-4Zr-2Mo-Si alloy having a martensite structure.
No full textInternational audienceDuplex martensitic microstructures have recently attracted significant attention due to their unique ability to combine high strength, excellent ductility, and superior work-hardening properties, making them ideal for structural applications. This study explores the micromechanisms underlying the tensile properties of a Ti-6Al-2Sn-4Zr-2Mo-Si alloy with hot-rolled T-split textures and various microstructures, tested along two tensile directions: TensileD//RD (referred to as 0°) and TensileD⊥RD (referred to as 90°). A comparative analysis highlights the superior work-hardening capacity and isotropic behavior of duplex (α+α') and (α+α") microstructures containing martensite, compared to equiaxed (α+β) microstructures. The enhanced work-hardening observed in the duplex microstructures is attributed to mechanisms such as variant reorientation in the martensitic phases, the pronounced mechanical contrast between the harder α phase and the softer α'/α" phases, and the interactions between α slipping and α'/α" twinning. Macroscopic Hall-Petch relations further clarify how microstructures influence strength and ductility. Specifically, duplex (α+α') microstructures exhibit improved ductility due to lower Hall-Petch constants and diminished grain boundary effects. Interestingly, reverse Hall-Petch behavior is observed in the duplex (α+α") microstructure at 90°, which is associated with the presence of α" martensite. Slip trace analysis is conducted to determine the experimental Critical Resolved Shear Stress (CRSS) ratios and qualitatively assess the impact of grain size and tensile direction on the activation of slip systems. Multiscale simulations are then utilized to calculate CRSS values and investigate the roles of deformation modes and crystallographic texture in shaping the macroscopic behavior of duplex (α+α") microstructures. At 0°, the T-split texture and the facilitation of prismatic slip between adjacent prismatic grains result in a low Hall-Petch constant and minimal grain boundary effects, acting as soft grains. In contrast, basal and pyramidal systems exhibit much higher Hall-Petch constants, behaving as hard grains and significantly contributing to work hardening. At 90°, basal</a
A Level Set Discrete Element Model for sintering with an optimization-based contact detection
No full textInternational audienceSintering is a high temperature process for the consolidation of ceramic, metal and polymer powders. The Discrete Element Method (DEM) has been effectively used to model the sintering process at the particle scale considering spherical particles. However, standard manufacturing processes rarely deal with spherical particles. As sintering is a curvature-controlled process, it is important to take into account the deviation from sphericity. This study presents a DEM sintering model for non-spherical particles. The description and dynamic evolution of arbitrary shape particles is achieved by using the Level Set discrete element method (LS-DEM). The original LS-DEM approach uses boundary nodes on the particles to detect contacts. We employ an optimization-based contact detection approach. This improves the capture of small contacts, which is important for a correct description of sintering evolution with reasonable CPU-time consumption. A Newton-Raphson scheme is employed for the optimization algorithm. The normal force and neck size evolution expressions of spherical particles are adapted for arbitrary shape particles by using the local curvature at the contact. The developed model is validated for elastic contacts on superquadric ellipsoids. It is compared with standard DEM on spheres for sintering. The model is applied to investigate the consolidation kinetics of a packing of ellipsoidal particles. It is shown, that a deviation from sphericity is beneficial for both prolate and oblate ellipsoids. An optimum aspect ratio is evaluated, demonstrating that particles that are too elongated slow down densification kinetics
Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow
No full textInternational audienceBackground In this study, we implemented an indoor positioning system to track the activities of healthcare professionals during their shifts in an emergency department, aiming to gain a better understanding of the emergency care production process. Methods An ultrawideband-based tracking system was used in an experiment at the emergency department of Le Corbusier Hospital in Firminy, France. Over a 46-day period, healthcare professionals, including assistant nurses, nurses, doctors, and managers, wore a sensor to record their location within the emergency department. We analyzed a substantial amount of quasi-real-time data to objectively assess physicians’ time allocation and movement patterns and their correlation with the emergency department’s occupancy. Additionally, we developed a user recognition algorithm (i.e., random forest classifier) capable of detecting the job category of the participant wearing the sensor. Results The proportion of time spent on care-related activities ranges from 26% to 39% for doctors. In contrast, this share reaches approximately half of the time for triage nurses and intensive care unit nurses. The burden of non-care-related activities appears to be largely induced by the time spent on administrative duties and transit. For doctors, the share of non-care-related activities is found to be correlated with the occupancy level. The hourly distance walked by nurses (except triage nurses) is found to increase with occupancy, while for doctors, the walking distance remains invariant to patient load. The random forest classifier predicts job categories with 96% accuracy. Conclusions Indoor tracking systems offer additional perspectives for enhancing the understanding of emergency department systems. The technology tested in this study demonstrates its potential to quantify physicians’ time allocation and movements
From conception to consumption: Applications of semi-solid extrusion 3D printing in oral drug delivery: Review
No full textInternational audienceSemi-Solid Extrusion 3D printing (SSE 3DP) has emerged as a promising technology for fabricating oral drug formulations, offering significant opportunities for personalized medicine and tailored therapeutic outcomes. SSE 3DP is particularly advantageous for producing soft and chewable drug products and is well-suited for formulations containing thermosensitive drugs due to its low-temperature printing process. Among various 3D printing techniques, SSE 3DP holds considerable potential for point-of-care applications, enabling the on-demand production of patient-specific dosage forms. Despite these advantages, SSE 3DP faces certain limitations that affect its overall development and widespread adoption. This review provides a comprehensive overview of SSE 3DP’s fundamental principles, current applications, and future prospects in oral drug delivery. It also addresses the challenges and limitations associated with SSE 3DP and examines the current outlook of this technique in oral drug delivery applications. An example of such a challenge is the lack of a harmonized method for evaluating rheological properties. To address this issue, the review describes a methodology for obtaining information related to extrudability and shape fidelity from rheological properties. Overall, this review aims to highlight the transformative potential of SSE 3DP in the pharmaceutical landscape, paving the way for tailored, and patient-centric therapies