Publications

The advent of van der Waals (vdW) heterostructures has enabled formation of bespoke materials with atomic precision, where numerous quantum and topological phenomena have already been discovered. This atomic-layer tunability, however, comes at a cost: individual 2D layers must be picked up, moved, and placed in a deterministic manner while keeping their interfaces atomically clean. Recent advances in machine learning and robotics place even stronger emphasis on the deterministic aspect of vdW assembly. Current polymer-based transfer methods satisfy neither the determinism nor cleanliness requirements. To this end, solutions are needed where adhesion can be dynamically and deterministically controlled without leaving organic contamination. Here, we present a polymer-free transfer technique employing thin muscovite (mica) crystals. Temperature control over mica adhesion enables deterministic pick-up, stacking, and release of 2D materials, while their crystalline, inorganic nature ensures pristine interfaces and suppresses strain. Fully compatible with existing fabrication workflows, this approach enables the assembly of demanding vdW heterostructures, including those with exposed conductive layers, moiré superlattices and suspended membranes. Our method represents a promising strategy for vdW heterostructure fabrication toward its automatisation.

No abstract is provided for this article.

Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions-self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here, we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides a different view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications.

Plasmonic biosensing has emerged as the most sensitive label-free technique to detect various molecular species in solutions and has already proved crucial in drug discovery, food safety and studies of bio-reactions. This technique relies on surface plasmon resonances in ~50 nm metallic films and the possibility to functionalize the surface of the metal in order to achieve selectivity. At the same time, most metals corrode in bio-solutions, which reduces the quality factor and darkness of plasmonic resonances and thus the sensitivity. Furthermore, functionalization itself might have a detrimental effect on the quality of the surface, also reducing sensitivity. Here we demonstrate that the use of graphene and other layered materials for passivation and functionalization broadens the range of metals which can be used for plasmonic biosensing and increases the sensitivity by 3-4 orders of magnitude, as it guarantees stability of a metal in liquid and preserves the plasmonic resonances under biofunctionalization. We use this approach to detect low molecular weight HT-2 toxins (crucial for food safety), achieving phase sensitivity~0.5 fg/mL, three orders of magnitude higher than previously reported. This proves that layered materials provide a new platform for surface plasmon resonance biosensing, paving the way for compact biosensors for point of care testing.

Data for manuscript "Wandering principal optical axes in van der Waals triclinic materials"

We study the ultrafast dynamics of non-thermal electron relaxation in graphene upon impulsive excitation. The 10-fs resolution two color pump-probe allows us to unveil the non-equilibrium electron gas decay at early times.

It is widely assumed that the dominant source of scattering in graphene is charged impurities in a substrate. We have tested this conjecture by studying graphene placed on various substrates and in high-k media. Unexpectedly, we have found no significant changes in carrier mobility either for different substrates or by using glycerol, ethanol and water as a top dielectric layer. This suggests that Coulomb impurities are not the scattering mechanism that limits the mean free path currently attainable for graphene on a substrate.

Bottom-up electrochemical synthesis of atomically thin materials is desirable yet challenging, especially for non-van der Waals (non-vdW) materials. Thicknesses below a few nanometers have not been reported yet, posing the question how thin can non-vdW materials be electrochemically synthesized. This is important as materials with (sub-)unit-cell thickness often show remarkably different properties compared to their bulk form or thin films of several nanometers thickness. Here, a straightforward electrochemical method utilizing the angstrom-confinement of laminar reduced graphene oxide (rGO) nanochannels is introduced to obtain a centimeter-scale network of atomically thin (<4.3 Å) 2D-transition metal oxides (2D-TMO). The angstrom-confinement provides a thickness limitation, forcing sub-unit-cell growth of 2D-TMO with oxygen and metal vacancies. It is showcased that Cr2 O3 , a material without significant catalytic activity for the oxygen evolution reaction (OER) in bulk form, can be activated as a high-performing catalyst if synthesized in the 2D sub-unit-cell form. This method displays the high activity of sub-unit-cell form while retaining the stability of bulk form, promising to yield unexplored fundamental science and applications. It is shown that while retaining the advantages of bottom-up electrochemical synthesis, like simplicity, high yield, and mild conditions, the thickness of TMO can be limited to sub-unit-cell dimensions.

ABSTRACT Inverse design of functional materials—using target performance to guide optimal parameters—provides a powerful alternative to traditional forward methods, especially for complex, high‐dimensional problems. Advances in machine learning (ML) enhance its feasibility through fast surrogate modeling, efficient design‐space exploration, and direct mapping from desired properties to material solutions. This review presents a unified overview of ML‐driven inverse design methodologies, covering topology optimization, direct inverse mapping, and hybrid frameworks. We analyze key ML models, optimization algorithms, and adaptive schemes that tackle challenges including data scarcity and coupled physical constraints. Focusing on diverse functional materials, we highlight and illustrate how ML‐based inverse design is accelerating innovation across diverse classes of materials by rapid generation of microstructures and geometries tailored to specific functionalities, including mechanical and architected materials, acoustic and thermal metamaterials, optical materials, energy functional materials, biomedical and chemical materials. Finally, we outline key challenges and future directions toward autonomous, physics‐integrated, and generative pipelines for advanced functional materials. This review aims to provide a unified foundation for ML‐based inverse design and to guide the development of intelligent discovery pipelines for advanced materials.

No abstract is provided for this article.

In this paper, we want to outline the principles of how scanning probe lithography on graphene is performed. We will show several examples of structures etched in graphene using this technique, including an example of a nanoelectronic device. In the last part, we present data regarding the oxidation kinetics when performing scanning probe lithography on graphite (HOPG). magnified image Two lines oxidized in graphene using scanning probe lithography. The left line (indicated by the arrow) is only 30 nm wide, indicating that this technique has good enough resolution to create nanoelectronic device structures.