Solution-processable semiconductors such as halide perovskites and certain molecules are promising for next-generation spin-optoelectronic applications [1]. Yet, we don’t fully understand what mechanisms govern charge, spin and light polarization in such emerging energy materials, and even less how these are affected by chirality [2].

In this talk, I will give an overview of our recent efforts to understand the spin-optoelectronic performance of these materials through time-, space- and polarization-resolved spectroscopy and microscopy, and how these insights may enhance the performance of solar cells and other energy conversion applications.

For investigating halide perovskite films, we pushed broadband circular dichroism to diffraction-limited spatial and 15 fs time resolution for creating a spin cinematography technique to witness the ultrafast formation of spin domains due to local symmetry breaking and spin-momentum locking [3].

I will then introduce our most recent development of a transient sensitive broadband full Stokes-vector spectroscopy with unprecedented time- and polarization resolution to track the emergence of chiral light emission [4].

To conclude, I will apply some of the above tools to the study of new halide perovskite materials [5].

[1] Nature Reviews Materials 8, 365 (2023).

[2] Nature Reviews Chemistry 9, 208 (2025).

[3] Nature Materials 22, 977 (2023).

[4] Nature 643, 675 (2025).

[5] Unpublished (2026).

Classifiers for antisemitism detection inherit a shortcut from keyword-sampled training data: predictions flip when a Jewish identity term is rewritten to another religion, even though the gold label is preserved, conflating antisemitism with legitimate criticism of Israel.We audit this fragility jointly along three axes (positive-class performance, calibration, counterfactual stability) and report a previously unquantified tension.While four group-conditional post-hoc calibrators all improve global calibration (ECE), they inflate the soft counterfactual flip rate by 15-80%.A formal proposition shows interceptfree rescaling cannot change hard flips.Orbitconstrained calibration resolves it: it penalises calibrated probability differences on development-set identity-swap pairs, recovering the calibration gain (ECE -46%) with the hard rate unchanged; it is the only calibrator we test that lowers ECE without inflating soft flips.Separately, we attack invariance at training time: CCI (Counterfactual Consistency Invariance) adds a Jensen-Shannon consistency penalty between each tweet and its religiously rewritten counterfactuals.This cuts the hard flip rate by 49-73% across three encoder backbones and two model families, while preserving or improving F + 1 (up to 0.72).Plain counterfactual augmentation reaches a lower flip rate at 3× the training-set size; raising CCI's multiplier closes that gap at a matched predicted-positive rate.Three prompted 7-8B LLMs cap at F + 1 0.45 and flip an order of magnitude more often.Finally, Integrated Gradients show identity-token attribution nearly unchanged: the gain comes from coupling pair scores, not from suppressing identity salience.Content warning: this paper contains masked antisemitic slurs and offensive content.

Jacques Dubochet is the winner of the Nobel Prize in Chemistry “for developing cryo-electron microscopy for the high-resolution structure determination of biomolecules in solution.” The cryo-electron microscopy (also known as cryo-EM) is a technique that utilizes electrons to determine a high-resolution 3D structure of biomolecules by creating a glass-type sample of those molecules. The IEEE Sensors Magazine has had the honor to interview this great scientist, who has revolutionized the field of sensing biological molecules. Here is reported his interview.

Halide perovskite nanocrystals are excellent materials for light-emitting devices and hold promise for quantum information technologies as well. Isovalent transition metal doping has been shown to further enhance luminescence yields, yet the underlying mechanism had been previously unclear.

Using transient optical spectroscopy, I will first show that such luminescence efficiency gains result from reduced nonradiative losses but also, surprisingly, from enhanced radiative rates upon doping [1]. I will explain these observations in the context of local lattice periodicity breaking and subsequent localization effects, and generalize these mechanisms for Mn, Zn, and Ni doping [2].

Then, I will introduce a new ambient room temperature synthesis route, giving easy access to controlled doping of Mn, Ni, or Zn into CsPbX3nanocrystals (with X = Cl, Br, I) across various sizes, including for the first time the doping of small, truly confined nanocrystals, which had been inaccessible using hot-injection approaches so far – with profound consequences for the light-emitting properties of the resulting materials [3].

Time permitting, I will conclude with the (spin-)photophysics of new nanocrystal materials investigated in our lab [4].

[1] J. Am. Chem. Soc. 2021, 143, 23, 8647–8653

[2] J. Am. Chem. Soc. 2022, 144, 34, 15862–15870

[3] J. Am. Chem. Soc. 2025, 147, 19, 16536–16544

[4] Unpublished results 2026

Photoelectrochemical (PEC) cells offer a direct route to solar-driven fuels and chemicals, yet practical devices still operate far below their thermodynamic limits owing to coupled losses spanning light absorption, carrier recombination and transport, interfacial charge transfer, and semiconductor–electrolyte matching. These losses are intrinsically entangled, and a general framework for quantifying and comparing these losses across real devices remains lacking, leaving PEC optimization largely empirical.

Here we introduce, to our knowledge, the first unified analytical model that treats both built-in junction (BIJ) and semiconductor–electrolyte junction (SEJ) photoelectrodes within a single, physically meaningful set of parameters while retaining the distinct interfacial physics of each architecture. By fitting experimental current–voltage data, the model decomposes device performance into thermodynamic, optical, recombination/transport, interfacial charge-transfer, and parasitic loss channels, and maps each directly onto concrete optimization strategies such as surface passivation, co-catalyst integration, contact optimization, and nanostructuring. Energy flows are visualized through Sankey diagrams, giving an intuitive picture of how incident solar energy is absorbed, dissipated, or converted into chemical output.

We validate the model against state-of-the-art devices spanning solar water splitting, CO₂ reduction, NH₃ synthesis, and solar redox flow batteries, achieving high-quality fits and physically consistent loss attributions. The analysis reveals a systematic divergence between material classes: photovoltaic-grade absorbers such as Si and perovskite in BIJ devices are dominated by bulk recombination and transport losses, whereas non-photovoltaic materials such as BiVO₄ and Ta₃N₅ in SEJ devices are limited primarily by interfacial reaction kinetics. We further propose a semiconductor–electrolyte matching criterion beyond simple energy-level alignment and construct efficiency maps that define material-selection windows, identifying a moderate reaction-potential window of approximately 0.7–1.1 V as a universal operating regime for high-efficiency PEC systems.

Together, these capabilities help researchers see where energy is lost and turn that insight directly into better device designs, supporting a shift from empirical optimization toward mechanism-informed rational design of viable solar fuels technologies. The model is accompanied by an open, ready-to-use toolkit for efficiency-map generation, J–V fitting with loss decomposition, and interface-matching analysis.

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.

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.

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.

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.

Barrel cortex has a characteristic somatotopic map, where each whisker maps to a single barrel. Each cortical column therefore has a known principal whisker input, so a functional response can be studied against structure identified in advance. Individual barrels are visible in the CCFv3 average template, but the parcellation stops at area and layer. Anatomically detailed modelling of the full region had been done in rat, and no model of mouse barrel cortex reached this level of detail. In this thesis, I identified the features that distinguish barrel cortex, and supplied them to a model built in atlas space with the barrel columns annotated.

Firstly, I segmented 33 barrels in CCFv3, and used an area-preserving flatmap to annotate the barrel columns representing the posteromedial barrel subfield. The resulting columns follow the curvature of the cortex, span full depth and never overlap. The annotations are released with the flatmap as a tool for data integration and analysis.

Then, I used this enhanced atlas space and a pipeline validated in rat non-barrel somatosensory cortex to build a detailed model. The model contains 33 barrel columns, approximately 230,000 neurons and around 357 million synapses, drawn from 60 morphological and 15 electrical types. Every inhibitory neuron additionally carries a molecular class, so the model can be compared against mouse measurements directly. The connectome is built from anatomy alone, with no rule imposing how likely one cell type is to connect to another. I extended the pipeline with morphological synthesis, generalised electrical models and a new set of thalamocortical projections, with VPM confined to individual barrels and POm spread across the field.

In barrel cortex, layer 4 dendritic trees grow preferentially towards, and remain within, the barrel that carries their principal whisker input. Dendritic polarity has been described in individual cell types, but not measured across them. To address this, I curated dendritic reconstructions from mouse barrel cortex and applied the same descriptors to every cell, so classes can be ranked against one another. Polarity is class-dependent and concentrated in layer 4, in spiny stellate cells, non-Martinotti somatostatin neurons and parvalbumin neurons. I used the result to build two models differing in a single property of basal dendritic synthesis: one polarised, one symmetric. Finally, I ran in silico experiments on a 3x3 grid of barrel columns and the associated septum, with C2 as the principal column throughout. I simulated three experimental contexts: the quiet and whisking brain states, and active touch of the C2 whisker. Membrane potential and spikes were recorded from every neuron, with the afferent connectivity of each known. The model reproduces patterns reported in mouse: the laminar sequence of the touch response, the recruitment order of the molecular classes, and their dependence on brain state.

We decomposed the spiking response using cyclicity analysis and retained two components. The two modes have distinct spatial profiles: the first is focused on the principal column, while the second crosses the septum and the neighbouring barrels. The same decomposition applied across the two circuits divides the signal into a common and a contrast plane. The contrast plane isolates the unique response contributions of each circuit. Layer 4 dendritic polarisation sharpens the touch response in space and advances it in time.