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Quelles conditions sociotechniques et d’activité pour concevoir et intégrer des IA explicatives de confiance en situations professionnelles et socio-domestiques ?
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Water-induced Spinodal Decomposition of Mixed Halide Perovskite captured by Real-Time Liquid TEM Imaging
No full textInternational audienceHybrid halide perovskite materials face significant challenges in reaching long-term stability, particularly due to their vulnerability to humidity. While encapsulation can provide some protection by delaying the water-induced degradation, understanding the precise reaction pathways is crucial for developing more robust compositions and advanced device architectures. Traditional ex situ methods offer only snapshots of degradation states, failing to capture the initiation and full evolution of the degradation process in real time. In this study, we utilize cutting-edge real-time electron imaging and in situ liquid-cell transmission electron microscopy to dynamically observe the degradation processes of the Cs0.05(MA0.17FA0.83)0.95Pb(Br0.17I0.83)3 (CsMAFA) perovskite in contact with water molecules. Using in situ live TEM imaging, combined to SAED and 4D-STEM ACOM techniques, we capture the subsequent degradation steps at both nanoscopic and mesoscopic levels from inception to full degradation. Our results particularly support the spinodal nature of the early-stage decomposition of the perovskite, where a spontaneous coexistence of CsMAFA, PbI2 and CsPb2Br5 is observed within a single grain upon contact with water molecules. This step either competes with, or, is followed by a dissolution/recrystallization mechanism from CsMAFA to PbI2 grains, while the solid-state spinodal decomposition of CsMAFA to CsPb2Br5 continues. From live image segmentations, two different growth rates of PbI2 are highlighted, i.e. in t1/2 and t1/3, controlling the final particles morphology into fine polygons and needles. Longer exposure times leads to two stages, firstly the complete degradation of the pristine perovskite, resulting in the two end members, namely PbI2 and CsPb2Br5 as a single-phase particles, secondly the dissolution of the CsPb2Br5 particles and disentangling of the remaining PbI2 particles
Synthesis and Comprehensive Characterization of Amino Acid-Derived Vinyl Monomer Gels
No full textInternational audienceIn this study, we report an easy synthetic pathway to vinyl monomers derivatized with amino acids. Tyrosine-, phenylalanine-, tryptophan-, leucine-, and methionine-based monomers were synthesized, and their polymerization in the presence of cross-linking agents led to the formation of amino acid-based gels. The nature of cross-linker, the time of polymerization, and the type of initiation (photopolymerization or thermopolymerization) were investigated. The obtained gels were characterized using a combination of thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), rheology, scanning electron microscopy (SEM), and solid-state nuclear magnetic resonance (NMR) spectroscopy. These novel amino acid-based gels could find applications in various areas such as drug delivery, biosensing, and biotechnology
Bridging classical data assimilation and optimal transport: the 3D-Var case
No full textInternational audienceBecause optimal transport (OT) acts as displacement interpolation in physical space rather than as interpolation in value space, it can avoid double-penalty errors generated by mislocations of geophysical fields. As such, it provides a very attractive metric for non-negative, sharp field comparison -the Wasserstein distance -which could further be used in data assimilation (DA) for the geosciences. However, the algorithmic and numerical implementations of such a distance are not straightforward. Moreover, its theoretical formulation within typical DA problems faces conceptual challenges, resulting in scarce contributions on the topic in the literature.We formulate the problem in a way that offers a unified view with respect to both classical DA and OT. The resulting OTDA framework accounts for both the classical source of prior errors, background and observation, and a Wasserstein barycentre in between states which are pre-images of the background state and observation vector. We show that the hybrid OTDA analysis can be decomposed as a simpler OTDA problem involving a single Wasserstein distance, followed by a Wasserstein barycentre problem that ignores the prior errors and can be seen as a McCann interpolant. We also propose a less enlightening but straightforward solution to the full OTDA problem, which includes the derivation of its analysis error covariance matrix. Thanks to these theoretical developments, we are able to extend the classical 3D-Var/BLUE (best linear unbiased estimator) paradigm at the core of most classical DA schemes. The resulting formalism is very flexible and can account for sparse, noisy observations and non-Gaussian error statistics. It is illustrated by simple one-and two-dimensional examples that show the richness of the new types of analysis offered by this unification.</div
Sufficiency, consumption patterns and limits: a survey of French households
No full textInternational audienceHow can the concept of consumption corridors be operationalised? This research provides socio-demographic knowledge of the setting of the upper limit. Four distinct ‘modes of consumption’ are identified, based on material consumption levels and openness to consumption limits. A survey of French households (n = 2452) reveals people are generally reluctant to accept strict consumption caps, especially binding ones. Both high and low material consumption groups strongly oppose consumption limits, suggesting that wealth does not correlate with a sense of having ‘enough’. Individuals with fewer possessions support the idea of limits to consumption, though not outright bans. Despite the cultural value placed on limitless consumption and political aversion to restrictions, actual consumption modes are inherently limited. Individuals operate within certain boundaries, whether or not acknowledged. Since perceptions of ‘enough’ are shaped by economic, social and technical contexts, urban settings and buildings could play a critical role in establishing these de facto limits. By facilitating frugal-yet-comfortable lifestyles, cities and buildings could help to restrain consumption without invoking a sense of deprivation. This approach suggests a pathway for fostering sustainable consumption corridors that feel normal rather than imposed. Practice relevance This research identifies four main modes of consumption in relation to sufficiency in mainland France. It shows that what constitutes enough is socially and economically situated and is not an external reality that would mechanically satisfy consumption needs. It also shows a general reluctance of individuals towards setting limits to individual consumption levels. To the extent that urban planning and dwelling types are already important forces in the shaping of sustainable modes of consumption, cities and buildings may prove instrumental in providing the condition of de facto upper limits to consumption. By understanding consumption modes and their relationship with sufficiency, policymakers, urban planners and architects could implement the means to conduct frugal lifestyles that do not evoke feelings of deprivation
Slot‐Die Deposition of CuSCN Using Asymmetric Alkyl Sulfides as Cosolvent for Low‐Cost and Fully Scalable Perovskite Solar Cell Fabrication
No full textInternational audienceThe development of industrially relevant deposition processes for efficient, stable, and inexpensive charge extracting layers is crucial for the commercialization of perovskite solar cells (PSCs). This work demonstrates, for the first time, the deposition of copper thiocyanate (CuSCN) as a low‐cost and reliable hole‐transport layer using slot‐die coating. Methyl ethyl sulfide is thereby used as an asymmetric cosolvent to significantly increase the solubility of CuSCN in the utilized slot‐die ink compared to traditional pure diethyl sulfide‐based solutions. Optimized CuSCN inks allow for the deposition of CuSCN layers on 5 × 10 cm 2 substrates with a wide range of thicknesses. Multidimensional imaging photoluminescence techniques are used to investigate the uniformity of the CuSCN thin films deposition as well as the influence of the solvent on charge losses. Finally, the CuSCN slot‐die deposition is integrated into a fully upscalable PSC fabrication process showing 19.1% power conversion efficiency for small laboratory cells and 14.7% for 9 cm 2 minimodules. Furthermore, semitransparent minimodules retain 80% of their initial efficiency after 500 h of constant illumination
Detecting outlying simulations in BEPU appraoches
No full textInternational audienceNuclear safety studies, based on the so-called BEPU (Best Estimate Plus Uncertainty) approaches, aim to calculate not only the possible values of a physical variable of interest, but also to quantify its associated uncertainty. From the results of a BEPU study, statistical analysis tools aim to improve the understanding of the physical phenomena simulated by the computer codes. The data outputs generated by these codes typically possess a functional nature, i.e. they represent the temporal evolution of a physical parameter throughout a transient. However, this functional nature is not always taken into account, in spite of the fact that it may provide relevant information regarding nuclear safety. On top of that, the functional analysisof data is even more relevant for transients where the safety criteria is directly associated to the dynamic behavior of a physical parameter, as it is in the case of the pressurized thermal shock. This work addresses the automatic identification of atypical transients (called “outliers”) in sets of time-dependent simulations that can help to better detect the physical phenomena that influence the safety margins, to find penalizing scenarios, or to verify the physical consistency of industrial simulators. A new functional outlier detection technique is then presented, as well as the eventual statistical link between the outlying simulations and the inputs of the computer code. The relevance of this methodology is illustrated on pressurized thermal shock simulations
Savoir ce que nous ne savons pas: L'analyse des incertitudes en modélisation
No full textDes dynamiques économiques à l’évolution du vivant, la modélisation joue un rôle essentiel dans de multiples domaines pour connaître le monde qui nous entoure et en anticiper les évolutions. Dans cet article, nous verrons comment les scientifiques tentent de répondre à la question suivante : « quelle confiance accorder à une modélisation ?
Analysis of Electroluminescence Data Imaging using Physical Models and Machine Learning for Photovoltaic Applications
No full textInternational audienceElectroluminescence imaging, a widely utilized technique in solar cell analysis, is often employed for mapping optoelectronic parameters. Voltage-dependent Electroluminescence (ELV) measurements have been shown to mimic local diode current-voltage characteristics [1]. A corresponding physical model enables the derivation of two local parameters from ELV data measured on solar cells: a pseudo-recombination current J0∗ and a pseudo-series resistance Rs∗. Various local characteristics of the cells, namely the series resistance and dark saturation current, can be deduced from these parameters.ELV measurements performed on solar cells are stored in large data cubes, typically sized at a few hundred thousand pixels. Pixel-wise regression of this data is possible through Least Squares minimization; however, this method is time-consuming and requires a trade-off between data dimension, fitting accuracy, and computation duration. To mitigate the need for compromise, we propose using Machine Learning (ML) techniques, known for their efficiency in rapidly processing large datasets. Additionally, the knowledge of a physical model allows for generating a significant amount of ELV data numerically, enabling supervised training of ML models.We initially employ a Multilayer Perceptron (MLP) for pixel-wise analysis of the data cube, utilizing an ELV curve as input to predict either Rs∗ or J0∗. Secondly, we use a fully Convolutional Neural Network (CNN), known as U-NET [2], to process the entire cube as input, generating a parameter map. The MLP is almost as accurate as the Least Squares fitting, whereas the CNN prediction precision is lower. Compared to Least Squares fitting, the use of ML permits a significant reduction in analysis duration—by a factor of 240 (using the MLP) to 1200 (using the CNN) in this study; this paves the way for real-time analysis of solar cells.This technique could be applied to study other types of data cubes, such as those resulting from Hyperspectral imaging.[1] D. Ory, N. Paul, and L. Lombez, ‘Extended quantitative characterization of solar cell from calibrated voltage-dependent electroluminescence imaging’, Journal of Applied Physics, vol. 129, no. 4, p. 043106, Jan. 2021, doi: 10.1063/5.0021095.[2] O. Ronneberger, P. Fischer, and T. Brox, ‘U-Net: Convolutional Networks for Biomedical Image Segmentation’. arXiv, May 18, 2015. Accessed: Jan. 26, 2024. [Online]. Available: http://arxiv.org/abs/1505.0459
Proportional marginal effects for global sensitivity analysis
No full textInternational audiencePerforming (variance-based) global sensitivity analysis (GSA) with dependent inputs has recently benefited from cooperative game theory concepts, leading to meaningful sensitivity indices suitable with dependent inputs. The "Shapley effects", i.e., the Shapley values transposed to variance-based GSA problems, are an example of such indices. However, these indices exhibit a particular behavior that can be undesirable: an exogenous input (i.e., which is not explicitly included in the structural equations of the model) can be associated with a strictly positive index when it is correlated to endogenous inputs. This paper investigates using a different allocation, called the "proportional values" for GSA purposes. First, an extension of this allocation is proposed to make it suitable for variance-based GSA. A novel GSA index is then defined: the "proportional marginal effect" (PME). The notion of exogeneity is formally defined in the context of variance-based GSA. It is shown that the PMEs are more discriminant than the Shapley values and allow the distinction of exogenous variables, even when they are correlated to endogenous inputs. Moreover, their behavior is compared to the Shapley effects on analytical toy cases and more realistic use cases