Métallopeptides comme électrocatalyseurs pour la synthèse de carburants
Hydrogen (H2) is a promising clean energy vector, but its industrial production is primarily based on fossil fuels, leading to high CO2 emissions. Only a small fraction is "green hydrogen," produced through water electrolysis using renewable energy. Similarly, ammonia (NH3), crucial for fertilizers and industrial applications, has potential as a carbon-free energy carrier but relies on the energy-intensive Haber-Bosch process. The electrochemical reduction of nitrite (NO2?) to ammonium (NH4?) offers a sustainable alternative but faces challenges in achieving high selectivity and efficiency. Metal complexes based on the ATCUN (Amino-Terminal Copper and Nickel binding) motif have shown potential for H2 and NH3 production. In this context, our project aims at investigate a series of ATCUN-based metal complexes (M-ATCUN) to design homogeneous electrocatalysts for H2 and NH3 production, active in aqueous solutions with optimal performance. Our strategy is based on the use of water-soluble peptide or pseudopeptide scaffolds that can be adequately modulated to isolate series of well-defined metal complexes. From a structure/activity relationship study combined with a mechanistic investigation, we attempt to rationalize the key parameters for optimal activity.
Computational quantum transport for extremely large systems
Quantum transport is the study of how electrons propagate through conductors while retaining their wave-like coherence. It explains phenomena related to the wave nature of electrons such as conductance quantization, Aharonov-Bohm effect, weak localization, universal conductance fluctuations, and many others. Computational quantum transport is almost as old as the associated theory, however the existing approaches struggle with system sizes large enough to describe relevant experiments, especially in three dimensions. In this PhD project, we will build on a recent breakthrough in quantum-inspired approaches (https://scipost.org/SciPostPhys.18.3.104) to develop quantum transport methods that scale well beyond existing ones. Our working hypothesis is that the scattering wave function at the core of quantum transport theory can be strongly compressed using a tensor network representation. This approach is analogous to that taken for the quantum many-body problem in the density matrix renormalization group framework. In a second stage, we will apply this method in 2D to various difficult problems related to graphene-based electronic interferometers and in 3D to topological materials. This project requires good mathematical skills and experience with scientific programming. The work will involve theoretical as well as numeric aspects.
Multiplex microscopy with lanthanide-based luminescent probes
Lanthanide complexes with push-pull antennas for luminescence sensitisation have demonstrated their strong potential for cellular imaging, thanks in particular to their two-photon absorption properties, which enable excitation in the near infrared. As part of the MAGELLAN project, we are seeking to develop luminescent probes for multiplex biological imaging of cells, with two objectives: (1) to develop Yb3+-based probes for NIR-NIR imaging and (2) to develop probes capable of performing spatio-temporal imaging of O2 consumption and the production of reactive oxygen species (ROS = HOCl, H2O2) in macrophages, using Tb3+, Eu3+, Sm3+ and/or Yb3+ probes. To this end, we will design conjugates between lanthanide complexes and peptides or biological macromolecules (polysaccharides, polylysine, zymosan) in which the peptide or macromolecule will enable the lanthanide complex to be localised in the cytosol or phagosome of the macrophage or will constrain it in the extracellular environment. The PhD student recruited will have three objectives: (1) the synthesis and characterisation of lanthanide-based probes, (2) the evaluation in solution of their ability to detect ROS, and (2) their evaluation for two-photon microscopy and monitoring of ROS production or O2 consumption with macrophages.
Electrochemical energy storage: Increasing the storage capacity of organic redox flow batteries
This thesis focuses on increasing the storage capacity of organic redox flow batteries using redox polymer-based boosters. To this end, the student will work in a motivating and international environment on the synthesis of redox polymers and their electrochemical study. The student will also participate in the integration of the developed boosters into demonstrators. This work will be carried out at the interface between the fundamental and applied research laboratories of the CEA in Grenoble, and the student will have access to both laboratories and their characterization facilities. This project will be developed in partnership with laboratories at the University of Rennes and Amiens, and the student will be able to exchange ideas with other students working on the project.
Quantum inspired algorithms meet artificial intelligence
Quantum computers are expected to change computations as we know it. How are they supposed to do that? Essentially they allow us to perform a subpart of linear algebra (certain matrix-vector multiplications) on exponentially large vectors. A natural mathematical framework to understand what they do is the tensor network formalism. Conversely, tensor networks are becoming popular as tools that can take the place of quantum computers, yet run on perfectly classical hardware. To do so, they rely on a hidden underlying structure of some mathematical problems (a form of entanglement) that can be harvested to compress exponentially large vectors into small tensor networks. An increasing number of, apparently exponentially difficult, problems are getting solved this way. Tensor networks are also intimately linked to artificial intelligence. For instance, automatic differentiation – the core algorithm at the center of all neural network optimizations – amounts to the contraction of a tensor network.
This PhD lies at the intersection between theoretical quantum physics and applied mathematics. The goal will be to develop and apply new algorithms to “beat the curse of dimensionality”, i.e. to push the frontier of problems that we are able to access computationally. More specifically, we will develop an extension of the tensor cross interpolation (TCI) algorithm to tensor trees (aka loopless tensor networks). In its current form, TCI is an active learning algorithm that can map an input high dimensional function onto a tensor train (linear tensor network) [1]. Its extension to trees will significantly enhance the expressivity of the network. In a second step, we will apply this algorithm to compute a class of high dimensional integrals that arise in the context of Feynman diagram calculations [2]. The envisioned algorithms combine the normalization flow approach (from neural networks) with the tensor cross interpolation (from tensor networks). The goal is to be able to calculate the out-of-equilibrium phase diagram of various correlated models starting from double quantum dots (of high current interest due to their applications to qubits) in the Kondo regime to the propagation of voltage pulses in electronic interferometers.
[1] https://scipost.org/SciPostPhys.18.3.104
[2] https://journals.aps.org/prx/abstract/10.1103/PhysRevX.10.041038
Lanthanide complexes acting as supramolecular glue to induce protein crystallization
The primary objective of the project is to understand how dedicated lanthanide complexes induce protein crystallization and exploit them in new field of macromolecular crystallography. During the PhD project, we aim to (1) Explore the properties of the new molecules; (2) To develop a predictive tool on the propensity of the molecules to induce crystallization; (3) To develop the detection of sub-micron size crystals; (4) To study the possibility to generate crystals directly in cells.
Addressable transition metal complexes as models for quantum bits and logic gates
The project concerns the design, development and study of spin dynamics in binuclear transition metal complexes
as models for quantum logic gates. The first part focuses on Cu(II) complexes. The second part explores Fe(II)-
based complexes that can be optically addressed in the visible range. The complexes will first be characterized by
continuous-mode electron paramagnetic resonance (EPR) spectroscopy to highlight the quantum bit behavior of
the mononuclear complexes used to form the binuclear species. Detailed studies of spin-lattice relaxation time (T1)
and spin-spin relaxation time (coherence time, T2) will then be carried out using pulsed EPR. Studies on
addressable complexes (mononuclear and possibly binuclear) will determine the impact of the presence of one
paramagnetic center on the coherence time of another within the binuclear entity, enabling the robustness of
quantum logic gates manipulatable by visible light to be assessed.
Silver nanowires synthesized from end-of-life solar panels for CO2 reduction and transparent electrodes
Silver nanowires (AgNW) networks are remarkable materials with both the highest electrical and thermal conductivity at ambient temperature, and a good chemical stability. They are used in transparent electrodes, for instance in organic solar cells, heating films or electrochromic devices. Their synthesis has been upscaled at the industrial level with high yield and reproducibility. More recently, they also found promising applications in low-emissivity layers on windows, and in catalysis of CO2 reduction at ambient temperature as a selective electrocatalyst.
In this PhD project, we will turn to recycled sources of silver from dismantled end-of-life silicon solar panels for the synthesis of AgNWs, in a “green chemistry” approach. The quality of the nanomaterial will be checked directly in two relevant devices, namely IR-low-emissivity films for reduction of heat loss, and electroreduction of CO2 for the production of e-fuels. The project will focus on understanding the fundamental basis of the impact of impurities on the synthesis of AgNWs, the physical properties of the AgNW networks, their stability under electrical stress or chemical wear, and their performance as active material in the devices.
The work will take place in Grenoble, the second scientific hub in France. The PhD student will be hired by CEA, a major French research institution with a high focus on alternative energies. He/she will join the fundamental research lab SyMMES, expert in nanomaterial design and energy devices such as solar cells, batteries and electrolyzers. She/he will work in co-supervision in the partner lab LMGP expert in materials science, synthesis and implementation at Grenoble INP. SYMMES and LMGP belong to University Grenoble Alpes and host widely international teams. The project will be actively supported by a local industrial recycling company.
Applicants should hold a Master 2 degree in chemistry, physics or materials science with skills in nanomaterials, electrochemistry or physical chemistry and in basic science for energy. Good English proficiency and a strong interest for innovation and collaborative work are expected.
Optimizing cryogenic super-resolution microscopy for integrated structural biology
Super-resolution fluorescence microscopy (“nanoscopy”) enables biological imaging at the nanoscale. This technique has already revolutionized cell biology, and today it enters the field of structural biology. One major evolution concerns the development of nanoscopy at cryogenic temperature (“cryo-nanoscopy”). Cryo-nanoscopy offers several key advantages, notably the prospect of an extremely precise correlation with cryo-electron tomography (cryo-ET) data. However, cryo-nanoscopy has not provided super-resolved images of sufficiently high quality yet. This PhD project will focus on the optimization of cryo-nanoscopy using the Single Molecule Localization Microscopy (SMLM) method with fluorescent proteins (FPs) as markers. Our goal is to significantly improve the quality of achievable cryo-SMLM images by (i) engineering and better understanding the photophysical properties of various FPs at cryogenic temperature, (ii) modifying a cryo-SMLM microscope to collect better data and (iii) developing the nuclear pore complex (NPC) as a metrology tool to quantitatively evaluate cryo-SMLM performance. These developments will foster cryo- correlative (cryo-CLEM) studies linking cryo-nanoscopy and cryo-FIB-SEM-based electron tomography.
In vitro reconstitution of microtubule network polarization.
Microtubules, biological polymers present in all eukaryotic cells, serve as a support for intracellular transport via molecular motors, thus defining cellular polarity. Contrary to the dogma establishing the centrosome as the determinant of this polarity, research from the CytoMorpho Lab reveals that microtubules can self-organize without an organizing center. In vitro experiments have demonstrated that microtubules actively separate molecular motors of opposite polarities into distinct domains, creating a new mechanism of active phase separation. Such partitioning of space by microtubules and motors constitutes a new mechanism of morphogenesis. The doctoral project aims to encapsulate this system in lipid vesicles of controlled size to study how relative dimensions enable efficient polarization. This approach will require the development of a microfluidic device and optimization of biochemical conditions for anchoring motors in the lipid bilayer. The perspectives include the creation of "artificial cells" capable of polarization and the reevaluation of cellular polarization models, particularly for T lymphocytes and other differentiated cells.