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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Surface acoustic waves and lattice vibrations in two-dimensional Ti 3 ⁢C 2 ⁢𝑇 𝑥 (𝑇 =O, F) MXene films

We investigated surface acoustic wave (SAW) propagation and lattice vibrations in two-dimensional (2D) titanium carbide (Ti 3 ⁢C 2 ⁢𝑇 x ) MXene films as a function of surface termination and layer stacking, using atomistic simulations. We found that SAW propagation velocity is highly sensitive to both single-layer properties and interlayer bonding. Surface terminations significantly modulate wave behavior, with oxygen and fluorine terminations producing distinct effects on wave propagation, with oxygen-terminated monolayers exhibiting 20% higher wave speeds than fluorine counterparts due to strengthened intralayer bonds. Key observations include the transition from one to two layers causing wave speed variations, and the development of interlayer modes that generate more dispersed lattice vibrations. As the film layer thickness increases, SAW propagation becomes predominantly confined to the upper surface, with coherence of vibrational modes diminishing in multilayer structures. These findings suggest MXene terminations and layer stacking are crucial parameters for controlling SAW behavior, offering promising avenues for novel acoustic wave device applications.

2-dimensional systems↗
DOE OSTI2025

A Sensitive Electrochemical Cholinesterase-Inhibiting Biosensor for Organophosphorus Pesticides Based on Ti 3 C 2 TX MXene Quantum Dots

Organophosphorus pesticides (OPs) pose significant environmental and health risks due to their widespread use and toxicity, primarily by inhibiting acetylcholinesterase. Traditional detection methods are often slow and costly, highlighting the urgent need for advanced, sensitive, and accessible technologies. This study developed a highly sensitive electrochemical cholinesterase-inhibiting biosensor for OP pesticides, utilizing Ti 3 C 2 T x MXene Quantum Dots (MQDs), which was synthesized via a hydrothermal method. The biosensor’s performance was characterized using electrochemical impedance spectroscopy, differential pulse voltammetry (DPV), and cyclic voltammetry. DPV proved to be the optimal technique, exhibiting an ultralow detection limit of 1 × 10 −17 M and a wide linear range (10 −14 –10 −8 M) for chlorpyrifos (a model OP) with an estimated inhibition constant of 62 nM. The biosensor demonstrated high selectivity for OPs (chlorpyrifos, acephate, glyphosate) over a non-target pyrethroid (permethrin), confirmed by distinct electrochemical signatures and compared to in vitro cholinergic activity assays in bean beetle homogenates. The enhanced performance is attributed to the high surface-to-volume ratio, quantum confinement effects, and superior conductivity of the MQDs, as well as the robust enzyme immobilization facilitated by glutaraldehyde cross-linking and a chitosan matrix. This work presents a promising platform for rapid, sensitive, and selective detection of OP pesticides, with potential applications in environmental monitoring and public health protection.

Chemistry↗
DOE OSTI2025

Investigation of Electrocatalytic CO 2 Reduction on MXene Materials via First-Principles Simulations

Computational studies of CO 2 reduction to yield various products were carried out on the basal plane and edges of different MXene materials. The impact of vacancies upon Mo 2 TiC 2 T x , W 2 TiC 2 T x , and Ti 3 C 2 T x (T x = O and OH) was also examined for both edge and basal sites. Initial calibrations were carried out to generate surfaces with optimal oxide/hydroxide ratios and proper termination sites upon which various vacancy sites were explored to ensure an accurate model of the surfaces’ resting states. From this work, Mo 2 TiC 2 O was determined to exhibit the lowest theoretical overpotential for methane as determined by volcano plot analyses. At a large enough vacancy concentration, the CO 2 reduction reaction (CO 2 RR) is predicted to outcompete the hydrogen evolution reaction (HER) as the predominant reaction on the surface. In conclusion, when examining the edge of Mo 2 TiC 2 O, stronger CO 2 binding was exhibited to such an extent that the reaction was predicted to terminate after the generation of formate on the edge.

Lund, Colton [Argonne National Laboratory (ANL), A↗
DOE OSTI2026

Revealing the Hidden Third Dimension of Point Defects in Two-Dimensional MXenes

Point defects govern many important functional properties of two-dimensional (2D) materials. However, resolving the three-dimensional (3D) arrangement of these defects in multi-layer 2D materials remains a fundamental challenge, hindering rational defect engineering. Here, we overcome this limitation using an artificial intelligence-guided electron microscopy workflow to map the 3D topology and clustering of atomic vacancies in Ti3C2TX MXene. Our approach reconstructs the 3D coordinates of vacancies across hundreds of thousands of lattice sites, generating robust statistical insight into their distribution that can be correlated with specific synthesis pathways. This large-scale data enables us to classify a hierarchy of defect structures-from isolated vacancies to nanopores-revealing their preferred formation and interaction mechanisms, as corroborated by molecular dynamics simulations. This work provides a generalizable framework for understanding and ultimately controlling point defects across large volumes, paving the way for the rational design of defect-engineered functional 2D materials.

2D materials↗
DOE OSTI2024

Optimizing protic ionic liquid electrolyte for pre-intercalated Ti 3 C 2 T x MXene supercapacitor electrodes

Electrical double-layer capacitors (EDLCs) are of increasing importance in energy storage from renewable sources. The properties of the electrode and electrolyte materials influence the energy and power densities of EDLCs. We examined the specific capacitance and ion dynamics of a protic ionic liquid confined in pre-intercalated Ti 3 C 2 T x MXene. Our electrochemical measurements demonstrated that the creation of a protic ionic liquid, 1-butyl-3-H-imidazolium bis(trifluoromethanesulfonyl)imide (BuIMH-NTf2), using a mixture of ionic liquid, 1-butyl imidazole (BuIM), and salt, bis(trifluoromethanesulfonyl)imide (HNTf2), in a ratio of 0.8:0.2 led to the optimal capacitance. Remarkably, quasi-elastic neutron scattering measurements revealed increased particle mobility at this composition, attributed to the more efficient accumulation of BuIMH + on the electrode surface. This deposit of additional ions results in fewer BuIM molecules away from the surface, enhancing their mobility due to reduced crowding. This composition-dependent electrochemical behavior will guide the formulation of more efficient protic ionic liquid systems, enabling faster ion transport in energy storage devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Confinement of Ti 3 C 2 T x MXene Nanosheets within a Polymer Matrix via In-situ Polymerization and Deposition on Carbon Cloth for Supercapacitors

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.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring nonlinear Rashba effect and spin Hall conductivity in Janus MXenes W 2 ⁢CO ⁢𝑋 (𝑋=S, Se, Te)

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 ⁢CO⁢𝑋 (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.

Electronic structure↗
DOE OSTI2025

Nacre-like MXene/Polyacrylic Acid Layer-by-Layer Multilayers as Hydrogen Gas Barriers

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.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Describing Point Defect Topology in 2D Energy Materials Through Computer Vision

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.

2D materials↗