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Supercapacitors are widely recognized as a favorable option for energy storage due to their higher power density compared to batteries, despite their lower energy density.
Here, with the promising preliminary electrochemical performance, further investigation into polymer-filler interfacial regulation and confined ion transport in the CC/polymer/MXene system could provide deeper insights, paving the way for its application in flexible supercapacitors and other energy storage devices requiring highly adaptable electrodes.
Rashba spin-orbit coupling (RSOC) facilitates spin manipulation without relying on an external magnetic field, opening up exciting possibilities for advanced spintronic devices. In this paper, we examine the effects of crystal momentum (𝑘) nonlinearity and anisotropy on the conventional Rashba effect, with a particular focus on their impact on the spin Hall conductivity (SHC) in a newly predicted family of 2D Janus materials, W 2 CO𝑋 (𝑋 =S, Se, Te). Using first-principles density functional theory calculations, we confirm the dynamical and mechanical stability of the studied 2D materials. Strikingly, this materials family exhibits pronounced nonlinear Rashba spin splitting at the Γ point of Brillouin zone near the Fermi level, which cannot be adequately described by the linear-𝑘 Rashba model. Therefore, third-order momentum contributions (𝑘 3 ) must be incorporated into the Rashba Hamiltonian. Our analysis reveals that among the studied systems, W 2 COS exhibits the highest 𝑘 3 contribution of −45.9 eV Å 3 , despite having the lowest linear Rashba constant. Here, a detailed analysis of electronic structure reveals topologically nontrivial behavior in these 2D materials, yielding sizable SHC that is primarily governed by the nonlinear Rashba effect. Notably, these materials also exhibit large spin Hall angle (0.018–2.5 at E 𝐹 ), which is comparable to that of in bulk topological insulators like Bi 2 Se 3 and Bi 2 Te 3 , and surpassing those in narrow bandgap bulk semiconductors GeTe and SnTe, as well as heavy metals such as Pt. Sizable SHC, large spin Hall angles, and the ability to tune SHC via electric fields without altering the topological properties, rooted in the crystal field splitting, underscore the potential of these materials for spintronic applications.
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Here, hydrogen (H 2 ) energy has emerged as a promising alternative in the automotive sector to replace fossil fuel; however, its storage with volumetric efficiency remains a challenge. The latest type IV storage vessel featuring a polymeric liner with composite overwrap incurs hydrogen saturation, which eventually causes failure. To prevent the H 2 dissolution in the liner, we have developed a barrier coating comprised of polyurethane/epoxy semi-interpenetrating network (S-IPN) combined with hyperbranched polyglycerol grafted MXene (h-MXene). The hyperbranched structure facilitated the dispersion of MXene in the coating solution by enhancing the exfoliation and improved the polymer filler interaction by forming covalent and H-bonding through end-terminal hydroxyl groups. Leveraging the dense network of S-IPN combined with the dispersed layer structured h-MXene significantly increased the tortuosity of the spray-coated barrier film applied to the nylon 6 liner. The coating exhibited a 90 % reduction in the H 2 gas permeability at only 2 wt% h-MXene concentration, which further improved to above 98 % at 10 wt% loading. Additionally, the h-MXene considerably improved the adhesion with the liner even in a highly stretched condition, signifying the durability of the coating under cyclic pressurization and depressurization of the storage vessel.
MXenes are a promising class of 2D nanomaterials and are of particular interest for gas barrier applications due to their high aspect ratio. However, MXene nanosheets naturally bear a negative charge, which prevents assembly with negatively charged polymers, such as polyacrylic acid (PAA), into gas barrier coatings. Here, we present MXene- and PAA-based layer-by-layer (MXene/ PAA LbL) multilayers formed by leveraging hydrogen bonding interactions. When assembled in acidic conditions, MXene/PAA LbL multilayers exhibit conformal, pinhole-free, nacre-like structures. The MXene/PAA LbL multilayers yield high blocking capability and low permeability (0.14 ± 0.01 cc·mm·m −2 ·day −1 · MPa −1 ) for hydrogen gas which is over 9000 times lower than uncoated niobium (Nb) substrate. These nacre-like structures are also electronically conductive (σ DC , up to 370 ± 30 S cm −1 ). Because these multilayers utilize hydrogen bonding, their properties are highly sensitive to the pH of the assembly and its external environment. Specifically, the reversible deconstruction of these multilayers under basic conditions is experimentally verified. This study shows that hydrogen bonding interactions can be leveraged to form MXene LbL multilayers as gas barriers, electronically conductive coatings, and deconstructable thin films via pH control.
Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.
The exploration of MXenes for electronic applications is a rapidly growing field in materials science. However, most research has focused on MXene films, with only a limited number of studies addressing the characterization of single-flake devices. In this work, we investigate the electronic and magnetotransport properties of Ti 3 C 2 T x single-flake devices, exploring the influence of structural defectivity on their transport mechanisms. We show that negative magnetoresistance present at low temperatures in single flake samples arises from weak localization, which we analyze to extract the phase coherence length of single-layer and multi-layer flakes. The study of magnetoresistance for this metallic MXene shows that the material exhibits quantum transport phenomena when intrinsic electronic behavior dominates. Moreover, by increasing the defect density via thermal annealing in ultrahigh vacuum, we uncover and characterize the metal-to-disordered metal transition in Ti 3 C 2 T x , shedding light on new properties and enriching fundamental knowledge about MXenes.
Textile-based supercapacitors (TSCs) are being used to meet the ever-increasing demand for mobile, safe, and convenient energy sources to power personal electronic devices. To that end, the smart textiles used in wearable technology need to be made from highly conductive yarns that are easily manufacturable. To date, synthetic- and cellulosic-based yarns have been exclusively used for the fabrication of TSCs, while other yarns have not been explored. Here, we used conductive protein-based yarns for TSCs and report on the use of wool coated with Ti 3 C 2 T x MXene as a potential electrode material. To knit TSCs, wool and cotton yarns were coated with MXene flakes and their surfaces were characterized using Scanning Electron Microscopy (SEM) and X-Ray Photoelectron Spectroscopy (XPS). The electrochemical characterization was conducted to examine the performance of wool- and cotton-based MXene electrodes as substrates and determine charge storage and resistive behavior. These tests showed that wool TSCs exhibited more pseudocapacitive behavior, while cotton TSCs exhibited a wider current range. At a scan rate of 5 mV/s, cotton TSCs presented an areal capacitance of 823.9 mF/cm 2 while this value for the wool TSCs was 284 mF/cm 2 . The performance of yarns was also tested under various mechanical deformation conditions and after washing in order to assess the stability of TSCs. This study indicates the potential of protein-based yarns as electrode substrates for integration of MXene to fabricate smart textile-based devices.
Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.
Herein, we report MXene-filled epoxy-based vitrimeric nanocomposites featuring a semi-interpenetrating network (S-IPN) to develop a hydrogen gas (H 2 ) barrier coating with self-healing characteristics for compressed H 2 storage applications. The reversible epoxy network was formed by synthesizing linear epoxy chains with pendent bis-hydroxyl groups using amino diol, which were then crosslinked with 1,4-benzenediboronic acid to generate dynamic boronic ester linkages. To achieve the S-IPN-type molecular arrangement, the epoxy chains were in situ crosslinked in the presence of poly(ethylene-co-vinyl alcohol) (EVOH), giving rise to a self-healing network (EEP) with a healing efficiency of 87%. Into the S-IPN vitrimer (EEP), a 2D platelet-type nanofiller MXene was incorporated to introduce a tortuous path for H 2 gas diffusion along with improved mechanical properties. The nanocomposite coating was applied to nylon 6 liner material, which is conventionally used in all-composite H 2 storage vessels. Further, the application of a 2 wt% MXene/EEP nanocomposite coating showed a permeability coefficient of 0.062 cm 3 mm m -2 d -1 atm -1 exhibiting ~96% reduction in gas permeability compared to uncoated nylon 6. The same nanocomposite exhibited a healing efficiency of 79%. Increasing the MXene loading to 10 wt% further reduced the permeability coefficient to 0.002 cm 3 mm m -2 d -1 atm -1 ; however, the healing efficiency decreased due to restricted chain mobility. In essence, the current work highlights the potential of vitrimeric S-IPN nanocomposite coatings for H 2 gas-barrier applications, enhancing safety and performance.