The availability and accessibility of economical renewable energy remains a key driving factor towards encouraging the uptake of clean energy. By incorporating economical hard carbon (HC), recycled from waste into anodes for lithium-ion batteries (LIBs), and treating the resulting HC-anode with a focused laser beam, the functionalised HC-anode exhibits enhanced electrochemical performance with a specific capacity of 516 mAh g- 1 at 0.1 A g- 1. It achieves over 100% capacity retention for 700 cycles at 1 A g- 1, and demonstrates super-long durability for 4000 cycles at 2 A g- 1. The improvements are attributed to laser-tunable expanded interlayer spacing and fluorescing defects in the engineered HC-anode. DFT calculations further established that these fluorescent defects correspond to carbon vacancies (cyan fluorescence), and their complexes with H heteroatoms (green fluorescence). These defects lead to the improved electrochemical performance via enhancing Li+ adsorption energies. Given such correlation, fluorescence studies are proposed as an interesting mechanism for guiding the development of carbon materials for energy applications, which serves as a highly efficient tool for assessing the electrochemical performance, eliminating the need for costly battery fabrication and testing processes. The performance achieved and its correlation to the observable fluorescence will not only contribute towards the effort of making cheaper batteries with better performance, but also serves as a rapid and scalable probe for preliminary evaluation of battery performance.

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic potential models have been developed using equivariant graph neural networks. These models are trained on extensive datasets of first-principles force and energy calculations. One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. However, the generalization error from model inference on diverse atomic structures arising in such calculations can compromise the reliability of the results. In this work, we propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential. Our method incorporates a set of heuristic structure descriptors that effectively employ the rich knowledge of the underlying model while requiring minimal generalization capabilities. Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations. Notably, our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and it is at least 3000 times faster compared to first-principles molecular dynamics.

The large-scale implementation of 2D material-based membranes is hindered by mechanical stability and mass transport control challenges. This work describes the fabrication, characterisation, and testing of self-standing graphene oxide (GO) membranes cross-linked with oxides such as Fe2O3, Al2O3, CaSO4, Nb2O5, and a carbide, SiC. These cross-linking agents enhance the mechanical stability of the membranes and modulate their mass transport properties. The membranes were prepared by casting aqueous suspensions of GO and SiC or oxide powders onto substrates, followed by drying and detachment to yield self-standing films. This method enabled precise control over membrane thickness and the formation of laminated microstructures with interlayer spacings ranging from 0.8 to 1.2 nm. The resulting self-standing membranes, with areas between 0.002 m2 and 0.090 m2 and thicknesses from 0.6 μm to 20 μm, exhibit excellent flexibility and retain their chemical and physical integrity during prolonged testing in direct contact with ethanol/water and methanol/water mixtures in both liquid and vapour phases, with stability demonstrated over 24 h and up to three months. Gas permeation and chemical characterisation tests evidence their suitability for gas separation applications. The interactions promoted by the oxides and carbide with the functional groups of GO confer great stability and unique mass transport properties—the Nb2O5 cross-linked membranes present distinct performance characteristics—creating the potential for scalable advancements in cross-linked 2D material membranes for separation technologies.

We present a novel approach to address the challenges of variable occupation numbers in direct optimization of density functional theory (DFT). By parametrizing both the eigenfunctions and the occupation matrix, our method minimizes the free energy with respect to these parameters. As the stationary conditions require the occupation matrix and the Kohn-Sham Hamiltonian to be simultaneously diagonalizable, this leads to the concept of "self-diagonalization," where, by assuming a diagonal occupation matrix without loss of generality, the Hamiltonian matrix naturally becomes diagonal at stationary points. Our method incorporates physical constraints on both the eigenfunctions and the occupations into the parametrization, transforming the constrained optimization into an fully differentiable unconstrained problem, which is solvable via gradient descent. Implemented in JAX, our method was tested on aluminum and silicon, confirming that it achieves efficient self-diagonalization, produces the correct Fermi-Dirac distribution of the occupation numbers and yields band structures consistent with those obtained with SCF eigensolver methods in Quantum Espresso.

Kaolinite is a single 2D layer of kaolin or metakaolin (MK), common clays that can be characterized as layered 3D materials. We show that because of its chemical composition, kaolinite can be converted into an amorphous 3D material by chemical means. This dimensional transformation is possible due to the large surface to volume ratio and chemical reactivity of kaolinite. We investigate the formation and influence of quasi- or nanocrystalline phases in MK-based alkali-activated materials (AAM) that are related to the Si/Al ratio. We analyze the formation of an AAM from a MK precursor, which is a 3D bonded network that preserves the layered structure at the nanometer scale. We also exfoliate the remaining layered phase to examine the effects of the alkali-activation in the final sheet structures embedded within the amorphous network. The final material can be used as a cement with no carbon dioxide produced by the transformation reaction.

Plane-wave density functional theory is a computational quantum mechanical modeling method used to investigate the electronic structure of solids. It employs plane-waves as the basis set for representing electronic wave functions and leverages density functional theory to compute the electronic structure properties of many-body systems. Traditionally, the Self-Consistent Field (SCF) method is predominantly adopted for optimization in current DFT computations. However, this method encounters notable convergence and computational challenges, and its iterative nature obstructs the incorporation of emergent deep learning enhancements. To address these challenges, we introduce a fully differentiable optimization method tailored to resolve the intrinsic challenges associated with the optimization of plane-wave density functional methods. This methodology includes a direct total energy minimization approach for solving Kohn-Sham equations in periodic crystalline systems, which is coherent with deep learning infrastructures. The efficacy of our approach is illustrated through its two applications in solid-state physics: electron band structure prediction and geometry optimization. Our enhancements potentially pave the way for various gradient-based applications within deep learning paradigms in solid-state physics, extending the boundaries of material innovation and design. We illustrate the utility and diverse applications of our method on real crystal structures and compare its effectiveness with several established SCF-based packages, demonstrating its accuracy and robust convergence property.

We have investigated the lithium capacity of the 2H phase of niobium sulfide (NbS2) using density functional theory calculations and experiments. Theoretically, this material is found to allow the intercalation of a double layer of Li in between each NbS2 layer when in equilibrium with metal Li. The resulting specific capacity (340.8 mAh/g for the pristine material, 681.6 mAh/g for oxidized material) can reach more than double the specific capacity of graphite anodes. The presence of various defects leads to an even higher capacity with a partially reversible conversion of the material, indicating that the performance of the anodes is robust with respect to the presence of defects. Experiments in battery prototypes with NbS2-based anodes find a first specific capacity of about 1,130 mAh/g, exceeding the theoretical predictions.

This dataset provides a comprehensive account of the intricate processes involved in the rational design, synthesis, and characterization of anisotropic metallic carbon materials. The materials were derived through the hydrolytic oxidation of graphene sheets, followed by meticulous self-assembly and mild annealing. The resulting products are highly percolated carbon networks, preserving the essential basal area of the source graphene. Structured into various sections, this dataset aims to furnish detailed insights crucial for supporting extensive investigations into these carbon materials. Section S1 delves into simulations that elucidate the reactivity of hydroxyl radicals in the hydrolytic oxidation process, pinpointing optimal conditions for their selective use in edge-hydrolysis of graphene. Additionally, it explores the molecular dynamics of edge-hydrolyzed graphene sheets, unraveling their self-assembly behavior and the formation of highly ordered films. Section S2 meticulously describes the source materials and optimal protocols, aligning with insights gained from simulations. In Section S3, the dataset explores the impact of synthesis protocols on the processability of hydrolyzed graphene and anticipates potential applications. Sections S4 to S7 present detailed characterization protocols, meticulously divided into morphology, composition, mechanical properties, and thermal/electronic transport, ensuring the inclusion of all essential details for reproducibility in core characterizations. Finally, Section S8 presents a table summarizing the general properties of the final annealed metallic carbon film (G0). This dataset thus serves as a valuable resource, providing a robust foundation for in-depth studies and fostering a comprehensive understanding of the multifaceted aspects of anisotropic metallic carbon materials.

Harnessing the phenomena of quantum coherence and destructive interference, we have successfully engineered and synthesized a three-dimensional (3D) graphene-based film exhibiting remarkable properties, including metallic thermal conductivity (kappa approximate to 150 Wm-1K-1) and electrical conductivity (sigma approximate to 320 kSm-1) at room temperature. Notably, these films demonstrate colossal transport anisotropies, reaching approximately 103 for thermal and 105 for electrical conductivity. This places them among the conducting materials with the highest anisotropies known to date, surpassing even the performance of one-dimensional (1D) carbon nanotubes and two-dimensional (2D) materials like h-BN and MoS2. These films are synthesized by self-assembly and crosslinking of edge-hydrolyzed graphene flakes. The electron transport between flakes is phonon mediated and at low temperatures the films present quantum critical behavior of a metal to Anderson insulator transition. We measure the electron transport properties in a Hall bar geometry and extract the critical exponents as a function of the sample mobility.

Strongly correlated electrons enable the realization of a plethora of quantum states of matter, such as Wigner crystallization, fractional quantum Hall effect, and high-temperature superconductivity. When correlated electrons and holes are allowed to coexist, they become intertwined and fuel the pursuit of quantum excitonic states harbouring counterflow superfluidity1,2 and topological orders with long-range quantum entanglement3,4. While such collective quantum states have been reported in sophisticated multi-layered heterostructures1,2,4–8, realizing and controlling such quantum states in a single natural strongly correlated material has remained challenging due to the fast particle recombination. Here, we report the creation of imbalanced electron-hole crystals in a doped multi-orbital honeycomb Mott insulator, α-RuCl3, through gate-tunable non-invasive van der Waals (vdW) doping from graphene. The absence of layer separation allows the immediate visualization of electron-hole crystals via scanning tunneling microscopy (STM). Real-space imaging reveals two completely different charge orderings at the lower Hubbard band (LHB) and the upper Hubbard band (UHB) energies, whose origin can be attributed to the correlation-driven honeycomb hole crystal composed of hole-rich Ru sites and rotational symmetry breaking paired electron crystal composed of electron-rich Ru-Ru bonds, respectively. Moreover, a gate-induced transition of electron-hole crystals can be directly visualized, further corroborating their nature as correlation-driven charge crystals9. The realization and atom-resolved visualization of imbalanced electron-hole crystals in a doped multi-orbital honeycomb Mott insulator, combined with a gate-tunable electron reservoir, opens new doors in the search for exotic correlated bosonic states within strongly correlated materials5,8,10–12.

2D materials are solid microscopic flakes with a-few-Angstrom thickness possessing some of the largest surface-to-volume ratios known. Altering their conformation state from a flat flake to a scroll or fiber offers a synergistic association of properties arising from 2D and 1D nanomaterials. However, a combination of the long-range electrostatic and short-range solvation forces produces an interlayer repulsion that has to be overcome, making scrolling 2D materials without disrupting the pristine structure a challenging task. Herein, a facile method is presented to alter the 2D materials' inter-layer interactions by confining organic salts onto their basal area, forming 2D-confined electrolytes. The confined electrolytes produce local charge inhomogeneities, which can conjugate across the interlayer gap, binding the two surfaces. This allows the 2D-confined electrolytes to behave as polyelectrolytes within a higher dimensional order (2D -> 1D) and form robust nanofibers with distinct electronic properties. The method is not material-specific and the resulting fibers are tightly bound even though the crystal structure of the basal plane remains unaltered.

Alkali-metal beta/beta '' aluminas are among the fastest ionic conductors, yet little is understood about the role of defects in the ion transport mechanism. Here, we use density functional theory (DFT) to investigate the crystal structures of the beta and beta '' phases and their vacancy and interstitial defects. We find that charge transport is likely to be dominated by alkali-metal interstitials in beta aluminas and by vacancies in beta '' aluminas. Lower bounds for the activation energy for diffusion are found by determining the minimum-energy paths for defect migration. The resulting migration barriers are lower than the experimental activation energies for conduction in Na beta and beta '' aluminas, suggesting a latent potential for optimization. The lowest activation energy of about 20 meV is predicted for correlated vacancy migration in K beta '' alumina.

Hydrogen gas (H2) is a promising energy carrier capable of replacing fossil fuels and achieving net zero emissions. However, purifying H2 for applications like fuel cells and industrial processes is challenging due to impurities affecting performance. Two-dimensional (2D) materials, particularly graphene-based membranes, are promising for H2 purification due to their unique properties. The hydrogen (H2) permeation capability of graphene-based membranes is particularly significant. This study examines the use of commercial and costeffective graphene oxide (GO) to fabricate multilayer graphene membranes, focusing on the impact of membrane thickness on H2 and CO2 separation. By using a scalable vacuum filtration method to coat porous ceramic substrates, membranes with controlled thicknesses were produced and characterised using AFM, FESEM, XRD, and gas permeation measurements. The study identified an optimal membrane thickness range (4 nm-250 nm) and the GO quantity (0.44 mu g/cm2 to 1.76 mu g/cm2) needed for effective H2/CO2 separation. This research aims to guide the development of cost-effective, mass-produced 2D-based membranes for industrial H2 purification.

Objective To evaluate the impact of sterilization methods on the structural integrity and antimicrobial properties of graphene nanocoating on titanium (GN). Methods GN was transferred to titanium using wet (WT) or dry transfer (DT) techniques and sterilized using an autoclave (AC), glutaraldehyde (GA), or ethylene oxide (EtO). The GN structure was characterized using Raman spectroscopy before and after sterilization. Additional specimens were characterized by Raman after AC and water jetting. Biofilm formation was assessed before and after AC using colony-forming units (CFU), biofilm biomass, and SEM (uncoated titanium was the control). Three independent samples were used for structural characterization and biofilm quantification. Statistical analyses were conducted using one-way analysis of variance (ANOVA) and Tukey's test (α = 0.05). Results WT and DT demonstrated high structural stability after sterilization and water jetting, with negligible coating quality or coverage loss. GN exhibited lower biofilm formation even after AC sterilization, as shown by the reduction in CFU counts, biofilm biomass, and SEM images compared to the control. Significance GN demonstrated high resistance to the stresses imposed by all sterilization methods tested, maintaining its structural integrity, resistance to water-jet cleaning, and antibiofilm potential. The findings suggest that standard industrial practices can effectively sterilize highly resilient GN on titanium implants and possibly other biomaterials.

There is a need to classify and standardize graphene-related materials giving the growing use of this materials industrially. One of the most used and more difficult to classify is graphene oxide (GO). Inconsistent definitions of GO, closely relating it to graphene, are found in the literature and industrial brochures. Hence, although they have very different physicochemical properties and industrial applications, commonly used classifications of graphene and GO definitions are not substantial. Consequently, the lack of regulation and standardization create trust issues among sellers and buyers that impede industrial development and progress. With that in mind, this study offers a critical assessment of 34 commercially available GOs, characterized using a systematic and reliable protocol for accessing their quality. We establish correlations between GO physicochemical properties and its applications leading to rationale for its classification.

2D materials can be functionalised with various ionisable functional groups of different formal charges, forming the so-called 2D electrolytes. In this study, 2D electrolytes based on functionalised graphene oxide (GO) with cationic groups (-NH3+) and molybdenum disulfide (MoS2) with anionic groups (-COO-) were used to form heterostructures through a self-assembly process. Due to the presence of opposite charges, heterostructures were formed by the predominantly attractive forces between the 2D electrolytes in a fluidic aqueous environment. With the application of sonication, both 2D materials were able to overcome the energy barrier offered by their bending stiffness, continuously assembling and scrolling into heterostructured nanofibers. The nanofibers were the product of the conjugated 2D electrolytes, which led to their phase separation and precipitation into highly ordered and high aspect ratio 1D structures. As the reaction proceeds, long nanofiber bundles with branches were formed, resembling the structures formed by naturally occurring polyelectrolytes such as amino acids forming proteins. This method offers a facile approach for the continuous processing of heterostructured nanofibers with a low production cost under flow that can be widely applied in textiles, encapsulation technologies, and nanosensors.

Two-dimensional materials offer a promising platform for the next generation of (opto-) electronic devices and other high technology applications. One of the most exciting characteristics of 2D crystals is the ability to tune their properties via controllable introduction of defects. However, the search space for such structures is enormous, and ab-initio computations prohibitively expensive. We propose a machine learning approach for rapid estimation of the properties of 2D material given the lattice structure and defect configuration. The method suggests a way to represent configuration of 2D materials with defects that allows a neural network to train quickly and accurately. We compare our methodology with the state-of-the-art approaches and demonstrate at least 3.7 times energy prediction error drop. Also, our approach is an order of magnitude more resource-efficient than its contenders both for the training and inference part.

Patterning antidots, which are regions of potential hills that repel electrons, into well-defined antidot lattices creates fascinating artificial periodic structures, leading to anomalous transport properties and exotic quantum phenomena in two-dimensional systems. Although nanolithography has brought conventional antidots from the semiclassical regime to the quantum regime, achieving precise control over the size of each antidot and its spatial period at the atomic scale has remained challenging. However, attaining such control opens the door to a new paradigm, enabling the creation of quantum antidots with discrete quantum hole states, which, in turn, offer a fertile platform to explore novel quantum phenomena and hot electron dynamics in previously inaccessible regimes. Here we report an atomically precise bottom-up fabrication of a series of atomic-scale quantum antidots through a thermal-induced assembly of a chalcogenide single vacancy in PtTe2. Such quantum antidots consist of highly ordered single-vacancy lattices, spaced by a single Te atom, reaching the ultimate downscaling limit of antidot lattices. Increasing the number of single vacancies in quantum antidots strengthens the cumulative repulsive potential and consequently enhances the collective interference of multiple-pocket scattered quasiparticles inside quantum antidots, creating multilevel quantum hole states with a tunable gap from the telecom to far-infrared regime. Moreover, precisely engineered quantum hole states of quantum antidots are geometry protected and thus survive on oxygen substitutional doping. Therefore, single-vacancy-assembled quantum antidots exhibit unprecedented robustness and property tunability, positioning them as highly promising candidates for advancing quantum information and photocatalysis technologies. We developed a technique to fabricate atomically precise quantum antidots with unprecedented robustness and tunable quantum hole states through self-assembled single vacancies in a two-dimensional transition metal dichalcogenide.