Publications

A stoichiometric derivative of graphene with a fluorine atom attached to each carbon is reported. Raman, optical, structural, micromechanical, and transport studies show that the material is qualitatively different from the known graphene‐based nonstoichiometric derivatives. Fluorographene is a high‐quality insulator (resistivity >10 12 Ω ) with an optical gap of 3 eV. It inherits the mechanical strength of graphene, exhibiting a Young’s modulus of 100 N m −1 and sustaining strains of 15%. Fluorographene is inert and stable up to 400 °C even in air, similar to Teflon.

<title>Abstract</title> Machine learning has revolutionized the study of crystalline materials forenabling rapid predictions and discovery. However, most AI-for-Materialsresearch to date has focused on ideal crystals, whereas real-world materialsinevitably contain defects that play a critical role in modern functionaltechnologies. The defects and dopants break geometric symmetry andincrease interaction complexity, posing particular challenges for traditionalML models. Addressing these challenges requires models that areable to capture sparse defect-driven effects in crystals while maintainingadaptability and precision. Here, we introduce Defect-Informed EquivariantGraph Neural Network (DefiNet), a model specifically designedto accurately capture defect-related interactions and geometric configurationsin point-defect structures. Trained on 14,866 defect structures,DefiNet achieves highly accurate structural predictions in a single step,avoiding the time-consuming iterative processes in modern ML relaxationmodels and possible error accumulation from iteration. We furthervalidates DefiNet’s accuracy by using density functional theory (DFT) relaxation on DefiNet-predicted structures. For most defect structures,regardless of defect complexity or system size, only 3 ionic steps arerequired to reach the DFT-level ground state. Finally, comparisonswith scanning transmission electron microscopy (STEM) images confirmDefiNet’s scalability and extrapolation beyond point defects, positioningit as a groundbreaking tool for defect-focused materials research.

With the advance of on-chip nanophotonics, there is a high demand for high refractive index, low-loss materials. Currently, this technology is dominated by silicon, but van der Waals (vdW) materials with high refractive index can offer a very advanced alternative. Still, up to now it was not clear if the optical anisotropy perpendicular to the layers might be a hindering factor for the development of vdW nanophotonics. Here, we studied WS2-based waveguides in terms of their optical properties and, particularly, in terms of possible crosstalk distance. Surprisingly, we discovered that the low refractive index in the direction perpendicular to the atomic layers improves the characteristics of such devices, mainly due to expanding the range of parameters at which single-mode propagation can be achieved. Thus, using anisotropic materials offers new opportunities and novel control knobs when designing the nanophotonic devices.

Mechanical exfoliation yields high‐quality 2D materials but is challenging to scale up due to the small lateral size and low yield of the exfoliated crystals. Gold‐mediated exfoliation of macroscale monolayer MoS 2 and related crystals addresses this problem. However, it remains unclear whether this method can be extended to other metals. Herein, mechanical exfoliation of MoS 2 on a range of metallic substrates is studied. It is found that Au outperforms all the other metals in their ability to exfoliate macroscale monolayer MoS 2 . This is rationalized by gold's ability to resist oxidation, which is compromised on other metals and leads to a weakened binding with MoS 2 . An anomalously high monolayer yield found for Ag suggests that the large interfacial strain in the metal–MoS 2 heterostructures measured by Raman spectroscopy also is a critical factor facilitating the exfoliation, while the relative differences in the metal–MoS 2 binding play only a minor role. These results provide a new incentive for investigations of 2D material‐substrate combinations applicable where high‐quality 2D crystals of macroscopic dimensions are of importance.

Strong light-matter interaction in two-dimensional molybdenum diselenide (MoSe2) is observed using a tunable optical microcavity. Polariton states with a Rabi splitting of 20 and 29 meV are observed for a monolayer MoSe2 and a 'double-well' MoSe2/hBN/MoSe2, respectively.

Graphene‐based textiles show promise for next‐generation wearable electronic applications due to their advantages over metal‐based technologies. However, current reduced graphene oxide (rGO)‐based electronic textiles (e‐textiles) suffer from poor electrical conductivity and higher power consumption. Here, highly conductive, ultraflexible, and machine washable graphene‐based wearable e‐textiles are reported. A simple and scalable pad−dry−cure method with subsequent roller compression and a fine encapsulation of graphene flakes is used. The graphene‐based wearable e‐textiles thus produced provide lowest sheet resistance (≈11.9 Ω sq −1 ) ever reported on graphene e‐textiles, and highly conductive even after 10 home laundry washing cycles. Moreover, it exhibits extremely high flexibility, bendability, and compressibility as it shows repeatable response in both forward and backward directions before and after home laundry washing cycles. The scalability and multifunctional applications of such highly conductive graphene‐based wearable e‐textiles are demonstrated as ultraflexible supercapacitor and skin‐mounted strain sensors.

In this Letter, we show that the standard Dynes-Fulton analysis, commonly used to reconstruct the critical current density from interference patterns, breaks down in Josephson junctions with nonlinear phase distributions, leading to non-physical artifacts. To address this, we developed a simple iterative reconstruction algorithm and validated it both numerically and experimentally using a planar Josephson junction model. Unlike conventional approaches based on the logarithmic Hilbert transform, the proposed method allows for incorporating prior knowledge about the system and addresses the fundamental issue of ambiguity in reconstructing the critical current density from interference patterns.

Membranes are crucial in permselective processes, such as water purification, gas sequestration, ion separation, membrane distillation and pervaporation, and energy conversion and storage. The two-decade development of two-dimensional (2D) membranes has shifted the membrane transport from solution-diffusion in the polymer matrix to size-dependent migration in Ångström-to-nanoscale slit-like channels, delivering distinctive fundamentals within nanoconfinements and potential implementations. In this review, we delve into the comprehensive information that facilitates the transformation of layered crystals into continuous thin-film architectures boasting well-defined nanopores. We trace the evolutionary history of these technologies, engage in debates on their underlying principles, and reveal obstacles that demand attention. Starting with the preparation of "single-layer carbon," we explore an array of protocols designed for the sizable production of 2D nanosheets. Our focus then shifts toward unveiling the solutions available for membrane fabrication and control, specifically focusing on the chemical and structural properties of 2D membranes that dictate their mass transportation behaviors in various scenarios. This is followed by a brief comparative discussion on the development of atomically thin and 2D-based mixed matrix membranes. Finally, we conclude by outlining the challenges and limitations associated with streamlining the interlocked exfoliation-reconstruction-control processes to consolidate their practicalities for use in environment and energy-related applications.

No abstract is provided for this article.

In quantizing magnetic fields, graphene superlattices exhibit a complex fractal spectrum often referred to as the Hofstadter butterfly. It can be viewed as a collection of Landau levels that arise from quantization of Brown-Zak minibands recurring at rational (p/q) fractions of the magnetic flux quantum per superlattice unit cell. Here we show that, in graphene-on-boron-nitride superlattices, Brown-Zak fermions can exhibit mobilities above 106 cm2 V-1 s-1 and the mean free path exceeding several micrometers. The exceptional quality of our devices allows us to show that Brown-Zak minibands are 4q times degenerate and all the degeneracies (spin, valley and mini-valley) can be lifted by exchange interactions below 1 K. We also found negative bend resistance at 1/q fractions for electrical probes placed as far as several micrometers apart. The latter observation highlights the fact that Brown-Zak fermions are Bloch quasiparticles propagating in high fields along straight trajectories, just like electrons in zero field.

Tunable lasers Lasers emit coherent light at wavelengths that are well defined. These wavelengths are usually fixed once the device has been fabricated. Now, Chakraborty et al. have combined an atomically thin graphene sheet with terahertz quantum cascade lasers to realize a terahertz laser that can be tuned via the carrier doping level of the graphene layer (see the Perspective by Polini). The demonstration opens up the possibility of reversible control over the laser emission through the integration of graphene waveguides. Science , this issue p. 246 ; see also p. 229

Wearable e-textiles have gained huge tractions due to their potential for non-invasive health monitoring. However, manufacturing of multifunctional wearable e-textiles remains challenging, due to poor performance, comfortability, scalability, and cost. Here, we report a fully printed, highly conductive, flexible, and machine-washable e-textiles platform that stores energy and monitor physiological conditions including bio-signals. The approach includes highly scalable printing of graphene-based inks on a rough and flexible textile substrate, followed by a fine encapsulation to produce highly conductive machine-washable e-textiles platform. The produced e-textiles are extremely flexible, conformal, and can detect activities of various body parts. The printed in-plane supercapacitor provides an aerial capacitance of ∼3.2 mFcm−2 (stability ∼10,000 cycles). We demonstrate such e-textiles to record brain activity (an electroencephalogram, EEG) and find comparable to conventional rigid electrodes. This could potentially lead to a multifunctional garment of graphene-based e-textiles that can act as flexible and wearable sensors powered by the energy stored in graphene-based textile supercapacitors.