The development of cold-rolled textures in aluminum alloys affects their formability and surface quality. Accurate prediction of texture evolution, particularly at intermediate rolling reductions, is essential for optimizing industrial rolling schedules. This study establishes a simulation workflow based on the GF-VPSC (Grain-Fragmentation ViscoPlastic Self-Consistent) framework to predict the texture evolution of a 6014 aluminum alloy from the hot-band condition to a final thickness reduction of 90% in cold rolling. A genetic-algorithm-based optimization scheme is employed to identify the hardening parameters. Texture predictions are benchmarked against full-field VPFFT (ViscoPlastic Fast Fourier Transform) simulations and experimental measurements at multiple deformation stages. GF-VPSC matches and, in some cases, outperforms VPFFT in reproducing the volume fraction of β-fiber and Cube components while requiring significantly lower computational cost. Based on the stability of each orientation predicted by GF-VPSC, main texture components can be categorized into three groups: stable orientations, orientations with stable reorientation paths, and orientations with unstable reorientation paths. These results provide practical guidance for industrial texture design and process optimization.

The breaking of time reversal symmetry (TRS) is commonly discussed in relation to the chiral-induced spin selectivity (CISS) effect. Here, we define the conditions under which TRS breaks and discuss two complementary mechanisms: the delocalization of the electron's wave function and dissipation. The delocalization of the electron wave function generates non-local interactions that change the angular momentum of the electron while preserving the total angular momentum in the system at room temperature. This process is associated with the interaction of the electron spin with chiral phonons as well as dissipation to an open system. Frequency-dependent conduction through chiral and achiral gold indicates that at frequencies up to about 100 MHz, there is enhancement of the second harmonic in the chiral gold, indicating a dominant non-linear dissipative behavior in the chiral case. The compelling conclusion is that in most of the past experiments in which the CISS effect was measured, indeed, TRS broke.

Jumbo phages protect their genomes from DNA-sensing bacterial defense systems by enclosing them within vesicles and nucleus-like compartments. Very little is known about defense systems specialized to counter these phages. Here, we show that AVAST Type 5 (Avs5) systems, part of the STAND superfamily and spanning three phylogenetic Avs5 clades, confer conserved immunity against jumbo phages. Using localization microscopy and biotin proximity labeling we demonstrate that Avs5 localizes to early infection vesicles, where it senses an essential, early expressed phage protein named JADA—Jumbophage AVAST5 Defense Activator. Recognition of phage infection triggers the Sir2-like effector domain of Avs5 across all three Avs5 clades, resulting in rapid NAD+ hydrolysis, disruption of phage nucleus formation, and arrest of infection. These findings reveal a spatially coordinated bacterial immune strategy that targets an early vulnerability in jumbo phage infection.

The interface of calcium silicate hydrate (C–S–H) plays a a crucial role in the mobility of ions through hardened cement paste and thus in the long term durability of concrete structures. Here, we combine experimental zeta potential measurements with non-equilibrium molecular dynamics simulations to elucidate the surface state of C–S–H under cement-relevant conditions. Our findings reveal that the co-adsorption of Ca2+ and OH− ions overcompensates the negative surface charge of C–S–H, originating from deprotonated silanol groups, thereby forming a surface adsorption network. This mechanism is governed by the interplay between solution pH and calcium speciation, where higher pH values favor the formation of calcium–hydroxide solution complexes. Consequently, an increase in pH leads to a higher Ca/Si ratio in C–S–H and a greater adsorption of these complexes, further increasing the effective surface charge density. Conversely, a reduction in pH promotes ion desorption, leading to a progressive decrease in the Ca/Si ratio and decrease in the effective surface charge. These findings provide new insights into the long-term compositional evolution of C–S–H in cement paste, with implications for phase stability, mechanical performance, and concrete durability.

Frustrated Lewis pairs (FLPs), composed of reactive combinations of Lewis acids (LAs) and bases (LBs) offer a metal-free platform for catalyzing a wide range of chemical transformations. Designing the optimal FLP active site for a particular chemical reaction is a challenging task due to the lack of rigorous principles and countless chemical possibilities. We recently designed principles, which outline the relative disposition (i.e., distance and angle) and chemical composition of the LA and LB centers that maximize activity in B- and N-based FLPs. These criteria were already used to screen 25,000 FLP active sites built on N-containing linkers extracted from the CoRE MOF dataset, but in such an enormous multifunctional catalyst space, inverse design approaches provide a more efficient mean to explore all possible combinations. Here, we use the NaviCatGA genetic algorithm to simultaneously optimize the chemical and geometrical characteristics of intramolecular FLPs while considering synthetic complexity and catalyst quenching constraints. By integrating activity maps and non-linear regression models, our workflow explores a vast chemical space of 1.7 billion FLP candidates built from organic fragments curated from the literature — released as the open-source FragFLP25 dataset— to identify optimal compositions suitable for catalytic CO2 hydrogenation. Analyzing the top candidates extracted from various Pareto fronts in the catalyst space, we not only uncover active FLP motifs for hydrogenation that have not been previously reported but also refine and extend the design principles previously established from our high-throughput screening study.

This thesis explores the role of carbon in climate-neutral energy systems. Climate mitigation is often framed as the decarbonization of the energy system, but in reality, in a climate-neutral system, carbon would still be present and circulate through remaining fossil fuels, biomass, CO2, synthetic fuels, chemicals, and negative emission technologies. The interactions between these components lead to complex mechanisms, calling for a systemic vision of carbon flows in the energy system.

To address this challenge, this work develops the Energy-Carbon Cycle Framework, which formalises the coupling between carbon sources and sinks under a neutrality constraint. It shows that residual fossil use is a possible system choice only with sufficient availability of carbon sinks. This concept is then translated into practice within the EnergyScope TD energy system model through a method that tracks the carbon flows between the different energy system technologies.

The proposed method is applied to the French energy system under climate neutrality constraints. This future climate-neutral energy system is compared with the national French strategy and other reference prospective documents. The results propose a clear merit order of lignocellulosic biomass use, in which sacrificing biomass to store carbon to reach climate neutrality is prioritised.

Building on this foundation, the thesis explores the specific case of the aviation sector. It is shown that e-bio-fuels are the most optimal pathway to produce sustainable aviation fuels (SAF), although this optimality is subject to uncertainty, which can favour the e-fuel pathway. The production cost of SAF, the total system cost, and the energy required for their production all depend strongly on the selectivity of the Fischer-Tropsch process towards jet fuel.

This work highlights that domestic production of SAF can reduce the need for flexible gas-based electricity production. These results are evaluated for different uncertainties surrounding the energy system, but the impact of interconnection and the recent downward trend in renewable energy CAPEX significantly reduce the initial values, bringing them to almost no effect.

Energy and carbon exchange between countries forces the expansion of the scope of this work to a European energy system, via the EnergyScope Multi-Cells model. To reduce the computational load of such an expansion, spatial and temporal aggregation methods are proposed and validated, leading to a reduction of the model's solve time by approximately 98 % without major change in energy system behaviour.

This new version of the model allows for uncertainty analysis of the European energy system. The robust decision-making method is used to identify carbon management strategies that are robust to uncertainties across multiple metrics, including total cost and life cycle impact assessment. This method particularly underlines that achieving European climate neutrality requires 339â 1018 MtCO2/yr of geological sequestration.

Overall, this work demonstrates that carbon remains a central component of climate-neutral energy systems. Rather than being eliminated, carbon resources must be understood from a systemic perspective, balancing two main roles: fulfilling the demand for carbonaceous sustainable fuels and enabling the system to reach climate neutrality by offsetting carbon sources with carbon sinks.

Understanding turbulence in magnetically confined fusion plasmas is a major element in the reliable design and operation of fusion power plants. Turbulence produces interacting fluctuations ranging from the Larmor radius to the size of the machine, which are notoriously difficult to model. In particular, despite recent advances, the interaction of microturbulence, i.e. turbulence on the scale of the Larmor radius, with macroturbulence, on the scale of the machine size, is not well understood. In order to describe the microturbulence, usually a gyrokinetic ordering is imposed, where the amplitude of the turbulent fluctuations is assumed to be small and separated from the background ($\delta$-f approach). On the other hand, in order to describe the macroturbulence, a drift-kinetic ordering is usually imposed, where the turbulent fluctuations are assumed to have a large spatial extent and large amplitude, with no separation between equilibrium and fluctuating quantities (full-f approach). In this thesis, we combine both orderings and self-consistently derive a comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model. The standard two-fluid drift-reduced Braginskii model and the $\delta$-f gyrokinetic model are retrieved in the appropriate limits. Then, we use an orthogonal Hermite-Laguerre basis expansion to efficiently include collisions and obtain a numerically efficient model.

Simulations are carried out, first, for a higher-order drift-kinetic model in a linear plasma device showing clear limitations of this model. Simulations using the comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model are then carried out in the same geometry. A hybrid spectral and finite difference numerical scheme is implemented and the simulations show that the limitations seen in the extended drift-kinetic model are removed. Microturbulence is not present at the large ion-ion collision frequency, however, when collisions are suppressed, the gyrokinetic fields are seen to affect the turbulence.

Stepping up in complexity, we carry out simulations of the comprehensive full-f drift-kinetic and $\delta$-f gyrokinetic model in a tokamak geometry. We use two different levels of resistivity corresponding to increasing the input power. At low power, the simulations share several similarities with the simulations in the linear plasma device. At high power, the formation of a transport barrier at the plasma edge through an increase in the radial electric field is observed while the gyrokinetic fields are dominating the turbulence in the core of the device through universal modes.

Finally, we focus on the boundary conditions of the model. In the vicinity of solid walls, a Debye sheath forms where the quasi-neutrality assumption is violated. Therefore, any global simulation must impose boundary conditions at the sheath entrance. We consistently derive sheath boundary conditions in the case of perpendicular incidence of the magnetic field with the wall. Simulations with these boundary conditions are carried out in a linear plasma device geometry. The sheath boundary conditions are shown to have an impact on the particle balance and equilibrium state of the plasma.

Low-density lipoproteins (LDL) are naturally occurring, monodisperse nanoparticles composed of proteins and lipids with an average diameter in the range of 20 nm. Their biological functions such as cholesterol trafficking and their roles in various diseases have been extensively studied. However, their properties as nanoparticles are seldom discussed. In this thesis, we investigate the formation of a protein corona on LDL, establish methods for its characterization, and explore the use of LDL-protein complex in cancer detection, biomarker discovery and disease monitoring. First, we show that LDL isolated from human serum or plasma carry a protein corona. Polyethylene glycol (PEG)-based precipitation followed by size-exclusion chromatography (SEC) preserves exchangeable proteins associated with LDL, while density-gradient ultracentrifugation largely removes the soft corona and yields LDL whose protein composition is dominated by ApoB-100. The two isolation strategies produce particles with distinct hydrodynamic size, homogeneity, and protein composition. Furthermore, incubation studies with purified proteins show negligible interaction of LDL with human serum albumin and apotransferrin, while immunoglobulin G exhibits only weak association, indicating that corona formation is selective rather than a consequence of indiscriminate adsorption of abundant plasma proteins. Building on this discovery, we focus on the study of LDL together with their protein corona, that we call protein-rich lipoproteins (ProLips), specifically on their potential in cancer detection. The tool we use is a proteomic comparison between samples from cancer patients and sample from healthy donors. Compared with whole-serum proteomics, ProLips proteomics consistently reveals substantially more differentially expressed proteins and provides clearer separation between healthy individuals and patients with melanoma, colon carcinoma, and liver cancer. The reduced contribution of highly abundant plasma proteins improves the detection of disease-associated proteins. Finally, six proteins as a melanoma-associated biomarker set are identified and validated. At last, we evaluate whether ProLips proteomics can monitor disease progression and treatment response. In a melanoma rat model, ProLips protein profiles distinguish different stages of tumor development. We show that in colorectal cancer patients sampled before, during, and after surgery, temporal changes in ProLips composition reflect surgery-associated remodeling, while persistent deviations after surgery are associated with clinical relapse. In liver cancer patients receiving immunotherapy, ProLips proteomics captures individualized treatment-associated changes, and both TMT- and DIA-based analyses reveal consistent biological trends despite differences in individual proteins identified.

The emerging technology of wearable robots is gradually transforming our daily life. Up to 2026, many commericallized exoskeleton products have flourished, including those from companies ReWalk, HAL, ULS Robotics, Kenqing Technologies, ready on market to improve wearer's mobility and endurance. Nearly all the market-available exoskeletons are motor-driven, based on bulky external components, rigid transmission elements, or complex control architectures, which decreases wearing willingness. Developing soft wearable robots can well address some of the challenges of hard exoskeletons, improving wearing comfort, unobtrusiveness, and social ac-ceptance. However, to realize soft wearable robots critically depends on soft, thin, compact, and lightweight actuators to deliver sufficient torque (10 ~ 100 Nm) and displacement (5 ~ 30 cm) while maintaining energy-efficiency and reliability. This thesis investigates soft actuators in textile format (textile actuators) to enable unobtrusive actuation on body. This thesis explores the design, modeling, fabrication, experimental characterization, and application scenarios of fabric actuators based on shape memory alloys (SMAs) and electrostatic clutches (ESClutches). This thesis has three contributions to soft wearable robotics. Firstly, by encapsulating ESClutches and commercially available SMA springs into woven fabrics, we developed an energy-efficient fabric actuator (weighing 55 grams) that can lift 40 N mass and consumes milliwatts power for statically holding the mass. Secondly, using mechanics-informed principles, we developed X-Crossing architecture to enable SMA fabrics of 4.5 grams lifting 1 kg mass with 50% contraction, outperforming published SMA fabrics, including knitted and knotted SMA fabric actuators. Thirdly, we developed variable-stiffness mechanics model for X-Crossing SMA fabric actuator. This model predicts the actuator performances and add understandings to the actuator characteristics, including the peak in its performance curves and fundamental limits on actuator contraction. In this thesis, we also demonstrate the applications of developed fabric actuators. The clutchable fabric actuator mounted on the upper limb of a mannequin lifted 2.6 kilograms of mass, reducing 70 % energy consumption by using ESClutches compared to that without using ESClutches. Using this actuator, we demonstrate a waistband with controllable tightness as exoskeleton anchoring for force transmission to body, and using the ESClutches reduces 80% of energy consumption in defined tasks. We also demonstrate the X-Crossing architectured SMA fabric actuator can be well controlled to bend a mannequin elbow to target angles, and this fabric actuator can be fabricated as wrist sleeve to generate compression up to 13 kPa, and then maintain a residual compression pressure of 5 kPa with zero energy cost.

Previous studies in mouse and human melanoma indicated that the TGF-β superfamily member Activin-A (encoded by INHBA) is a major driver of metastatic progression and primary resistance to immune checkpoint blockade (ICB). The goal of this thesis was to test if activin-induced tumor immune evasion involves the type I activin receptor ALK4, and to identify relevant direct target cells. Systemic Alk4 depletion in the tumor microenvironment (TME) of YUMM3.3 mouse melanoma grafts using Rosa26-CreERT2 shows that this receptor is required to mediate CD8 T cell exclusion and tumor growth by paracrine Activin-A signaling. Blocking monocyte/macrophage recruitment to the tumor in Ccr2 knockout mice, or eliminating them by pharmacological depletion, is shown to not impair Activin-A oncogenic function. By contrast, Alk4 deletion in DCs using Zbtb46-Cre or the conventional DC1-specific Xcr1-Cre identifies cDC1 cells as essential mediators. Blocking cDC1 maturation in Batf3-/- host mice further corroborated this conclusion. Furthermore, analysis of human TCGA-SKCM and single cell transcriptomes reveals that a tumor cell-intrinsic Activin-A signaling signature score correlates with poor overall survival and with an immune-excluded phenotype characterized by impaired DC cross-presentation and profound CD8+ T cell exhaustion also in human melanoma. Mechanistically, Alk4 signaling in cDC1 cells is shown to promote ER stress, paralyzing the antigen-processing machinery. The cDC1 malfunction correlated with entrapment of CD8+ T cells in a CD44 CD62L double-negative state in tumor-draining lymph nodes, indicating incomplete T cell priming. In line with this interpretation, deletion of Alk4 specifically in cDC1s restored antigen presentation, T cell polyfunctionality, and tumor immune surveillance.

Contrasting the novel oncogenic function in cDC1 cells, ALK4 signaling in melanoma cells themselves can be tumor-suppressive. This was first shown in B16-F1 mouse melanoma expressing constitutively active mutant Alk4 (caAlk4) as a doxycycline-inducible transgene. Here, I show that caALK4 similarly inhibits melanoma growth in a Tet-OFF setting and in YUMM3.3 melanoma, and that it involves stimulation of anti-tumor immunity by tumor cell-intrinsic Smad2/3 signaling. Finally, yet another novel protective function of Activin-A signaling is shown to rely on ALK4 signaling in endothelial cells, which limited tumor infiltration by Tregs and neutrophils. Altogether, these findings highlight Activin-A/ALK4 signaling as a critical bottleneck for melanoma immunogenicity. Therapeutic targeting of ALK4-induced ER stress specifically in cDC1 cells, ideally without blocking tumor-suppressive ALK4 functions in other cell types, may provide a potent new approach to overcome immune checkpoint therapy resistance.