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Physics Scanning Devices and Nanoscale Techniques: An Historical Perspective
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Recette pour un polar de succès
No full textInternational audienceIn the course of my research on Steeman, it is evident that the author has an interest in the well-known English writer Agatha Christie. This poster wants to be not only a "recipe" to discover the codes of the crime genre, but also a reference to the writer of detective novels who most used poisons to kill her victims in her novels.Dans le cadre de mes recherches sur Steeman, un intérêt de l’auteur pour la célèbre écrivain anglaise Agatha Christie est évident. Ce poster veut être non seulement une "recette" pour découvrir les codes du genre policier, mais aussi une référence à l’autrice de romans policiers qui a le plus utilisé des poisons pour tuer ses victimes dans ses romans
Acoustic tweezers for targeted drug delivery
No full textNo data was used for the research described in the article.International audienceAcoustic tweezers are a highly promising technology for targeted drug delivery thanks to their unique capabilities: (i) they can effectively operate in both in vitro and in vivo environments, (ii) they can manipulate a wide range of particle sizes and materials, and (iii) they can exert forces several orders of magnitude larger than competing techniques while remaining safe for biological tissues. In particular, tweezers capable of selectively capturing and manipulating objects in 3D with a single beam, known as 'single beam tweezers', open new perspectives for delivering drug carriers to precise locations. In this review, we first introduce the fundamental physical principles underlying the manipulation of particles using acoustic tweezers and highlight the latest advancements in the field. We then discuss essential considerations for the design of drug delivery carriers suitable for use with acoustic tweezers. Finally, we summarise recent promising studies that explore the use of acoustic tweezers for in vitro, ex vivo, and in vivo drug delivery
Numerical Simulations of Methylammonium Tin Iodide-Based Perovskite Solar Cells
No full textInternational audiencePerovskite solar cells have attracted significant attention within the scientific community due to their rapidly advancing performance. In particular, inorganic perovskite devices are renowned for their remarkable performance and enduring stability.This study introduces a device optimization process guided by modeling to fabricate high-efficiency perovskite solar cells using lead-free methyl ammonium tin iodide (MASnI3) materials. The studied device follows the general architecture: glass/FTO/WS2/ FASnI3/Cu2o/Au. For these simulations, we employed the SCAPS-1D cell ca-pacity simulator. In addition to temperature, the thickness of different layers and doping concen-tration significantly affects the device’s efficiency. We varied the temperature in the range of 300 K to 400 K. Furthermore, we adjusted the thickness of the HTL layer (from 1 to 3 µm), the absorber layer (from 0.2 to 2 µm), and the ETL layer (from 0.01 to 0.1 µm). Regarding doping concentration, we explored levels ranging from 1013 cm−3 to 1018 cm−3 for the HTL layer, from 1014 cm−3 to 1018 cm−3 for the absorber layer, and from 1015 cm−3 to 1018 cm−3 for the ETl layer. Moreover, the MASnI3-based device showed the highest power conversion efficiency ((PCE) = 20%, fill factor (FF) = 65.03%, short-circuit current density (Jsc) = 27.67 mA/cm2, and open-circuit voltage (Voc) = 1.07 V. The previously reported results were given for 1014 cm−3, 0.1 µm, and 380 K. The findings of this study suggest (MASnI3-based absorber materials can play an important role in high efficiency perovskite solar cells with excellent stability
Transcutaneous delivery of disease-specific PI3K/Akt/mTOR inhibitor-based hybrid nanoparticles in hydrogel system for the management of psoriasis: Insights from in vivo studies
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A multi-perspective exploration of challenges for advancing human-autonomy teaming within industrial ecosystems
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Human Digital Twin: from a Skillful Manipulator to a Trusted Cooperative Partner
No full textInternational audienceIn current Cyber-Physical Systems, especially in Human-AI teams, the integration of Human Digital Twins raises several ethical concerns. To ensure fruitful completeness between human and technology, the design of the system must include representations of the human needs, abilities and acceptance. We propose a framework for a shared and common digital representation accessible to both human and artificial agents. This framework, based on the Human-Machine Cooperation principles, is illustrated by the example of a human-robot team in the context of a production system
Comparative Study of Real-Time Beamforming Techniques for Railway Communication Systems in LoS and NLoS Scenarios
No full textInternational audienceThis paper presents a comparative study of real-time beamforming techniques for railway communication systems in Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) scenarios. Using the 3GPP TR 38.901 channel model, initial machine learning (ML)-based beam prediction solutions are evaluated, and advanced models are proposed to address their limitations. The results demonstrate significant improvements in beam prediction accuracy and robustness, emphasizing the importance of diverse data collection to capture channel variability
Realistic EMF Exposure Estimation from Low Density Sensor Network by Finite & Infinite Neural Networks
No full textInternational audienceUnderstanding the spatial and temporal patterns of environmental exposure to radio-frequency electromagnetic fields (RF - EMF) is essential for conducting risk assessments. These assessments aim to explore potential connections between RF-EMF exposure and its effects on human health, as well as on wildlife and plant life. Existing research has used different machine learning tools for EMF exposure estimation; however, a comparative analysis of these techniques is required to better understand their performance for real-world datasets. In this work, we present both finite and infinite-width convolutional network-based methods to estimate and assess EMF exposure levels from 70 real-world sensors in Lille, France. A comparative analysis has been conducted to analyze the performance of the methods' execution time and estimation accuracy. To improve estimation accuracy for higher-resolution grids, we utilized a preconditioned gradient descent method for kernel estimation. Root Mean Square Error (RMSE) is used as the evaluation criterion for comparing the performance of these deep learning models
All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfO<sub>x</sub> ReRAM Devices
No full textInternational audienceAnalog in-memory computing is an emerging paradigm designed to efficiently accelerate deep neural network workloads. Recent advancements have focused on either inference or training acceleration. However, a unified analog in-memory technology platform-capable of on-chip training, weight retention, and long-term inference acceleration-has yet to be reported. This work presents an all-in-one analog AI accelerator, combining these capabilities to enable energy-efficient, continuously adaptable AI systems. The platform leverages an array of analog filamentary conductive-metal-oxide (CMO)/HfOx resistive switching memory cells (ReRAM) integrated into the back-end-of-line (BEOL). The array demonstrates reliable resistive switching with voltage amplitudes below 1.5 V, compatible with advanced technology nodes. The array's multi-bit capability (over 32 stable states) and low programming noise (down to 10 nS) enable a nearly ideal weight transfer process, more than an order of magnitude better than other memristive technologies. Inference performance is validated through matrix-vector multiplication simulations on a 64 × 64 array, achieving a root-mean-square error improvement by a factor of 20 at 1 s and 3 at 10 years after programming, compared to state-of-the-art. Training accuracy closely matching the software equivalent is achieved across different datasets. The CMO/HfOx ReRAM technology lays the foundation for efficient analog systems accelerating both inference and training in deep neural networks