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Functional hydrochar/biochar through thermochemical conversion of millet Bran from Senegal: physicochemical, morphological and electrochemical properties
No full textInternational audienceThere is an ever-growing interest in the thermochemical conversion of biomass into functional porous chars. This is driven by their efficiency in timely applications in agriculture, medicine, water treatment, and energy storage, therefore addressing numerous sustainable development goals. This study concerns the valorization of millet bran into functional chars. This biomass constitutes the fibrous outer layer enveloping the millet seed, a cereal widely cultivated in Senegal. It was separated from the flour by sieving or sifting before conversion into chars. The latter was pyrolyzed (P) in the 400–900 °C range to provide biochar samples, or hydrothermally treated at 300 °C to yield hydrochar (H300). This material was further post-pyrolyzed (300–900 °C) to yield pyro-hydrochars. The chars were characterized by Raman spectroscopy, X-ray diffraction, X-ray electron spectroscopy, and cyclic voltammetry. Raman studies showed that the biochars obtained at 400 and 500 °C (P400 and P500), hydrochar (H300), and the pyro-hydrochar (H300-P300) are the most graphitized, as judged from the D/G peak intensity ratio. These results were confirmed by cyclic voltammetry demonstrating that these products have the best redox properties, i.e. high current (10 µA for 0.1 mg) and lowest peak potential difference ΔE of 0.18 V for P400. The remarkable electroactive properties of P400 correlate with its lowest Raman ID/IG peak intensity, and surface atomic O/C ratios, respectively. As the most electroactive char, P400 was further as electrode material for detecting heavy metal ions, in phosphate buffer solution (pH 4), by differential pulse voltammetry (DPV). The detection limits were 0.100 and 0.108 µΜ, for Pb2+ and Cu2+, respectively. This work demonstrates the possibility of developing electroactive chars for environmental monitoring of pollutants, provided the carbonization conditions of the initial biomass are tuned
PREFIGS : Préfigurer une transition écologique juste
No full textLe projet Prefigs (PRécarité Écologie Futur Imaginaires orGanisations Savoirs) est un programme de recherche-action participative visant à intégrer les expériences de vie précaires dans la construction, par le bas, d’une transition écologique juste, entendue comme une transformation radicale, démocratique et socialement émancipatrice de la société. Pendant deux ans, quatre collectifs socialement mixtes ont enquêté sur des problématiques concrètes (mobilité rurale bas carbone, alimentation durable accessible, habitabilité des quartiers populaires) et conçu des solutions locales (covoiturage, caisse alimentaire, jardins partagés, animations et œuvre collective), soutenues par des partenariats institutionnels. Ces expérimentations ont permis d’explorer les tensions sociales relatives à l’écologie et les manières de les contourner en vue de faire de la transition écologique juste un référentiel d’action publique
Use of Natural Magnetosome Crystals from Magnetotactic Bacteria for Local Therapy Versus Magnetic Nanoparticles of Similar Compositions, Sizes and Shapes
No full textInternational audienceCapable of transforming iron into magnetic nano-treasures, these microorganisms form a diverse group of prokaryotes known as magnetotactic bacteria (MTB), recognized for their unique ability to biomineralize magnetic particles (called magnetosomes) inside the cell. These membrane-bound mag-netic crystals, typically arranged in chain-like structures, enable passive align-ment along geomagnetic fields, a phenomenon known as magnetotaxis [1, 2]. In recent years, magnetic nanoparticles (MNPs) have gained significant attention due to their applicability in various biotechnological and medical domains, including drug delivery, cancer theranostics, imaging, biosensing, catalysis, and biosepara-tion [2–4]. However, synthetic MNPs face limitations in clinical use, particularly due to concerns about biocompatibility, toxicity, and functional instability [5]. This review investigates the potential of magnetotactic bacteria-derived nanopar-ticles (MTB-NPs) as superior alternatives to synthetic MNPs. Due to their natural origin, MTB-NPs exhibit enhanced biocompatibility, reduced immunogenicity, greater functional stability, and sustainability, making them highly attractive for targeted biomedical applications. We conducted a comparative analysis of recent literature, focusing on the mechanisms through which MTB contribute as person-alized tools in biomedical applications. Furthermore, we propose an integrative conceptual framework that bridges existing empirical evidence with future direc-tions for clinical translation. The novelty of our approach lies in the synthesis of the most recent data, the identification of current research gaps, and the structured presentation of MTB in direct opposition to their synthetic counterparts (magnetic nanoparticles) in terms of quality, sustainability, biocompatibility, and efficiency in biomedical and biotechnological applications
Hea thin films as protective barrier against carbon diffusion during sps
No full textInternational audienceThe production of metal parts by powder metallurgy using the Spark Plasma Sintering (SPS) process, results in a fine and homogeneous microstructure with a chemical composition close to that of the initial powder. In this process, the sintering is performed by the simultaneous application of a pulsed current, making it possible to heat the powder, and of a uniaxial pressure
Using Gaussian Mixture Model to predict bead geometry of WAAM-CMT beads
No full textInternational audienceMastering the prediction of geometry of a part is vital during designing fabrication protocols for a manufacturing process. But for Additive Manufacturing (AM) techniques like Wire Arc Additive Manufacturing (WAAM), the complex nature of the process produces parts with irregular surfaces. This adversely affects the design of experiments and makes the development of bead geometry prediction models more complex. The existing models often uses several hypothesis which simplifies the geometry making predictions which are different from that of reality. Therefore, in the present study several single beads are fabricated using different input parameters and their surface is measured comprising of irregularities. This data is then used to develop a geometry prediction model using Gaussian Mixture Model (GMM) with curve-fitted polynomial functions as input. GMM is then used to generate random variables using which beads are simulated using Monte-Carlo Simulations. The model is then validated by comparing the simulated beads with the fabricated beads. The proposed model is found to be capable of simulating beads with surface irregularities similar to that of experiments. This methodology can now serve as base to simulate more complex geometries of parts fabricated using WAAM
Design and optimization of Al2O3-based Strip-Loaded optical waveguides for ring resonator
No full textInternational audienc
Study of all-pass optical micro-ring resonators using titanium- and zinc oxides on an insulating platform via atomic layer deposition
No full textInternational audienceAtomic layer deposition (ALD) is a versatile technique to grow thin films for a wide range of applications including energy conversion and electronics. Materials deposited on insulating platforms through ALD can expand their use in optics and photonics. In this work, we present the design of an integrated optics all-pass micro-ring resonators based on measured optical properties of ALD materials, particularly, titanium- and zinc oxides (TiO2 and ZnO on insulator). For optical communication applications, zinc oxide on an insulator (ZOI) provides mode confinement of 46%, an evanescent decay of 855 nm, and a quality factor of up to 104 at 1550 nm. Atomic layer deposited core materials on an insulator provide an effective alternative for optics and photonics
A Study on the Prediction of Long-Term Carbon Absorption by Applying the Renewal Scenario of Forest in Korea
No full textInternational audienceAs global warming has emerged as an essential global solution, the role of carbon neutrality is required to respond to rapidly changing environmental policies. Forests are an important means for achieving carbon neutrality as they act as a key carbon sink, and, among them, forest management called afforestation is emerging as a decisive factor. However, although various studies are being conducted to enhance carbon absorption capacity, there are not many long-term research cases on afforestation. In this study, the cumulative carbon absorption for a total of 90 years from 1 January 2020 to 31 December 2100 was set as the baseline. Various changes were made according to the cyclical trend of the species and age classes planted nationwide, and various results were derived through the regeneration scenario. As a result of the study, the difference between the maximum value and the baseline CO2 absorption was approximately 130 million t CO2 when compared with the 90-year cumulative value. When converted into an annual unit, it increased by more than 14 million t CO2. Based on the highest figures, compared with statistics from the Ministry of Environment’s Greenhouse Gas Information Center, it was confirmed that the forest absorption source, which was offset by 6.26 percent in 2019, could be changed by up to 8.74 percent. When analyzing the maximum figures from this study, depending on the method of afforestation, the greenhouse gases emitted by approximately 9.32 million passenger cars per year could be offset. In conclusion, among the carbon neutrality tasks that must be addressed at the national level, it is very important to establish long-term direction decisions and detailed plans for the forest sector, which is the core of carbon sinks, and a strategic approach is essential. Based on this study, it is expected that a more systematic direction can be presented for planning and implementing future afforestation
Stochastic modeling of movement for Helium particles in a graphite channel
No full textInternational audienceIn this article, we present a stochastic model for the movement of Helium particles within a graphite channel, focusing on Knudsen diffusion. We develop a semi-Markov model to describe the movement of the particle, derive the stationary distribution of its mean position, and analyze the model's asymptotic properties. To validate the model, we compare its theoretical outcomes with Monte Carlo simulations. As temperature significantly influences on the movement of particles, two situations are studied for high and low temperature. In both cases, theoretical and simulation results by Monte Carlo coincide. Furthermore, we propose estimation methods for the local parameters of the model and demonstrate its application using data from Molecular Dynamics simulations.</div
Unsupervised Anomaly Detection via Brownian Feature Trajectories and Stochastic Geometry
No full textEmbedded sensor systems operating in heterogeneous and evolving environments face critical challenges in detecting rare or novel anomalies under strict power, communication, and supervision constraints. To address these limitations, we propose a dual model framework that combines stochastic modeling and geometric analysis for unsupervised anomaly detection. First, we model the evolution of sensor-derived feature vectors as multivariate Brownian motion trajectories, capturing both the temporal and statistical dynamics of nominal actions. This parametric representation provides interpretable descriptors, such as drift and covariance, which characterize the average direction and variability of feature evolution over time, respectively. Second, we introduce a nonparametric decision layer based on kernel density estimation and convex hulls, enabling the identification of anomalies as trajectories that traverse low-density regions or exit the geometric envelope of previously observed behaviors. This dual statistical–geometric perspective allows for real-time, unsupervised detection of anomalies without requiring labeled data or static assumptions. The proposed framework is lightweight, adaptable to multiple sensing modalities, and suitable for embedded deployment