Exfoliation Energy as a Descriptor of MXenes Synthesizability and Surface Chemical Activity

Atomic Scale Design of MXenes and Their Parent Materials─From Theoretical and Experimental Perspectives

More than a decade after the discovery of MXene, there has been a remarkable increase in research on synthesis, characterization, and applications of this growing family of two-dimensional (2D) carbides and nitrides. Today, these materials include one, two, or more transition metals arranged in chemically ordered or disordered structures of three, five, seven, or nine atomic layers, with a surface chemistry characterized by surface terminations. By combining M, X, and various surface terminations, it appears that a virtually endless number of MXenes is possible. However, for the design and discovery of structures and compositions beyond current MXenes, one needs suitable (stable) precursors, an assessment of viable pathways for 3D to 2D conversion, and utilization or development of corresponding synthesis techniques. Here, we present a critical and forward-looking review of the field of atomic scale design and synthesis of MXenes and their parent materials. We discuss theoretical methods for predicting MXene precursors and for assessing whether they are chemically exfoliable. We also summarize current experimental methods for realizing the predicted materials, listing all verified MXenes to date, and outline research directions that will improve the fundamental understanding of MXene processing, enabling atomic scale design of future 2D materials, for emerging technologies.

Surface Stability and Exfoliability of Non-van der Waals Magnetic Chromium Tellurides

Exfoliation of two-dimensional (2D) magnetic materials from non-van der Waals (non-vdW) materials has attracted increasing attention because it provides a great platform for the construction of 2D magnetic materials. For non-vdW magnetic chromium tellurides with high Curie temperatures, their few-layer samples show promising applications in the field of spintronics. However, there is still no consensus on whether the surface structures of few-layer chromium tellurides should be terminated by Cr or Te atoms. By calculating the surface and exfoliation energy, we find that which structure is more stable depends greatly on the value of the chemical potential of Te atoms, and the few-layer sample with a Cr-terminated surface is easier to exfoliate than that with both Te-terminated surfaces. Finally, we propose that different exfoliated structures can be identified by using the atomic number ratio of Cr to Te and the average magnetic moment of Cr atoms in few-layer samples.

Computationally Guided Synthesis of MXenes by Dry Selective Extraction

MXenes are a rapidly growing family of 2D transition metal carbides and nitrides that are promising for various applications, including energy storage and conversion, electronics, and healthcare. Hydrofluoric-acid-based etchants are typically used for large-scale and high-throughput synthesis of MXenes, which also leads to a mixture of surface terminations that impede MXene properties. Herein, a computational thermodynamic model with experimental validation is presented to explore the feasibility of fluorine-free synthesis of MXenes with uniform surface terminations by dry selective extraction (DSE) from precursor MAX phases using iodine vapors. A range of MXenes and respective precursor compositions are systematically screened using first-principles calculations to find candidates with high phase stability and low etching energy. A thermodynamic model based on the "CALculation of PHAse Diagrams" (CALPHAD) approach is further demonstrated, using Ti3 C2 I2 as an example, to assess the Gibbs free energy of the DSE reaction and the state of the byproducts as a function of temperature and pressure. Based on the assessment, the optimal synthesis temperature and vapor pressure are predicted and further verified by experiments. This work opens an avenue for scalable, fluorine-free dry synthesis of MXenes with compositions and surface chemistries that are not accessible using wet chemical etching.

Special Issue "Theoretical Calculation and Molecular Modeling of Nanomaterials"

The continuous advancement of computational chemistry and the chemical modeling of materials is closely aligned with the ever-evolving computational power and related techniques [...].

Machine Learning-Driven Discovery of Key Descriptors for CO2 Activation over Two-Dimensional Transition Metal Carbides and Nitrides

Fusing high-throughput quantum mechanical screening techniques with modern artificial intelligence strategies is among the most fundamental ─yet revolutionary─ science activities, capable of opening new horizons in catalyst discovery. Here, we apply this strategy to the process of finding appropriate key descriptors for CO₂ activation over two-dimensional transition metal (TM) carbides/nitrides (MXenes). Various machine learning (ML) models are developed to screen over 114 pure and defective MXenes, where the random forest regressor (RFR) ML scheme exhibits the best predictive performance for the CO₂ adsorption energy, with a mean absolute error ± standard deviation of 0.16 ± 0.01 and 0.42 ± 0.06 eV for training and test data sets, respectively. Feature importance analysis revealed d-band center (ε_d), surface metal electronegativity (χ_M), and valence electron number of metal atoms (M_V) as key descriptors for CO₂ activation. These findings furnish a fundamental basis for designing novel MXene-based catalysts through the prediction of potential indicators for CO₂ activation and their posterior usage.

Synthesis of Synthetic Musks: A Theoretical Study Based on the Relationships between Structure and Properties at Molecular Scale

Synthetic musks (SMs), as an indispensable odor additive, are widely used in various personal care products. However, due to their physico-chemical properties, SMs were detected in various environmental media, even in samples from arctic regions, leading to severe threats to human health (e.g., abortion risk). Environmentally friendly and functionally improved SMs have been theoretically designed in previous studies. However, the synthesizability of these derivatives has barely been proven. Thus, this study developed a method to verify the synthesizability of previously designed SM derivatives using machine learning, 2D-QSAR, 3D-QSAR, and high-throughput density functional theory in order to screen for synthesizable, high-performance (odor sensitivity), and environmentally friendly SM derivatives. In this study, three SM derivatives (i.e., D52, D37, and D25) were screened and recommended due to their good performances (i.e., high synthesizability and odor sensitivity; low abortion risk; and bioaccumulation ability in skin keratin). In addition, the synthesizability mechanism of SM derivatives was also analyzed. Results revealed that high intramolecular hydrogen bond strength, electrostatic interaction, qH⁺ value, energy gap, and low E_HOMO would lead to a higher synthesizability of SMs and their derivatives. This study broke the synthesizability bottleneck of theoretically designed environment-friendly SM derivatives and advanced the mechanism of screening functional derivatives.

MXenes à la Carte: Tailoring the Epitaxial Growth Alternating Nitrogen and Transition Metal Layers

A high-throughput analysis based on density functional simulations underscores the viable epitaxial growth of MXenes by alternating nitrogen and metal adlayers. This is supported by an exhaustive analysis of a number of thermodynamic and kinetic thresholds belonging to different critical key steps in the course of the epitaxial growth. The results on 18 pristine N- and C-based MXenes with M₂X stoichiometry reveal an easy initial N₂ fixation and dissociation, where N₂ adsorption is controlled by the MXene surface charge and metal d-band center and its dissociation controlled by the reaction energy change. Furthermore, formation energies indicate the plausible formation of N-terminated M₂XN₂ MXenes. Moreover, the further covering with metal adlayers is found to be thermodynamically driven and stable, especially when using early transition metal atoms. The most restrictive analyzed criterion is the N₂ adsorption and dissociation at nearly full N-covered adlayers, which is yet achievable for almost half of the explored M₂X seeds. The present results unfold the possibility of expanding, controlling, and tuning the composition, width, and structure of the MXene family.

Identifying the Atomic Layer Stacking of Mo2C MXene by Probe Molecule Adsorption

A density functional theory study is presented here aimed at investigating whether the atomic stacking on the new family of two-dimensional MXene materials has an influence on their adsorption properties and whether these properties can provide information about this structural feature. To this end, the Mo₂C MXene, exhibiting two nearly degenerate crystal structures with either ABC or ABA atomic stacking, is chosen as a case study. The study of the adsorption of CO, CO₂, and H₂O on both polymorphs of Mo₂C reveals substantial differences that could be used in experiments to provide information about the atomic stacking of a given sample. Particularly, we show that the asymmetric and symmetric stretching modes of the adsorbed CO₂ and the CO stretching mode are clear features that allow one to identify the stacking of atomic layers of the Mo₂C MXene. The present finding is likely to apply to other MXenes as well.

Adsorption and Activation of CO2 on Nitride MXenes: Composition, Temperature, and Pressure effects

The interaction of CO₂ with nitride MXenes of different thickness is investigated using periodic density functional theory‐based calculations and kinetic simulations carried out in the framework of transition state theory, the ultimate goal being predicting their possible use in Carbon Capture and Storage (CCS). We consider the basal (0001) surface plane of nitride MXenes with M_n+1N_n (n=1–3; M=Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, and W) stoichiometry and also compare to equivalent results for extended (001) and (111) surfaces of the bulk rock‐salt transition metal nitride compounds. The present results show that the composition of MXenes has a marked influence on the CO₂‐philicity of these substrates, whereas the thickness effect is, in general, small, but not negligible. The largest exothermic activation is predicted for Ti‐, Hf‐, and Zr‐derived MXenes, making them feasible substrates for CO₂ trapping. From an applied point of view, Cr‐, Mo‐, and W‐derived MXenes are especially well suited for CCS as the interaction with CO₂ is strong enough but molecular dissociation is not favored. Newly developed kinetic phase diagrams are introduced supporting that Cr‐, Mo‐, and W‐derived MXenes are appropriate CCS substrates as they are predicted to exhibit easy capture at mild conditions and easy release by heating below 500 K.

Machine Learning Prediction of the Exfoliation Energies of Two-Dimension Materials via Data-Driven Approach

Exfoliation energy is one of the fundamental parameters in the science and engineering of two-dimensional (2D) materials. Traditionally, it was obtained via indirect experimental measurement or first-principles calculations, which are very time- and resource-consuming. Herein, we provide an efficient machine learning (ML) method to accurately predict the exfoliation energies for 2D materials. Toward this end, a series of simple descriptors with explicit physical meanings are defined. Regression trees (RT), support vector machines (SVM), multiple linear regression (MLR), and ensemble trees (ET) are compared to develop the most suitable model for the prediction of exfoliation energies. It is shown that the ET model can efficiently predict the exfoliation energies through extensive validations and stability analysis. The influence of the defined features on the exfoliation energies is analyzed by sensitivity analysis to provide novel physical insight into the affecting factors of the exfoliation energies.

Nitrogen-Containing Gas Sensing Properties of 2-D Ti2N and Its Derivative Nanosheets: Electronic Structures Insight

In this work, the potentials of two-dimensional Ti2N and its derivative nanosheets Ti2NT2(T=O, F, OH) for some harmful nitrogen-containing gas (NCG) adsorption and sensing applications have been unveiled based on the quantum-mechanical Density Functional Theory calculations. It is found that the interactions between pure Ti2N and NCGs (including NO, NO2, and NH3 in this study) are very strong, in which NO and NO2 can even be dissociated, and this would poison the substrate of Ti2N monolayer and affect the stability of the sensing material. For the monolayer of Ti2NT2(T=O, F, OH) that is terminated by functional groups on surface, the adsorption energies of NCGs are greatly reduced, and a large amount of charges are transferred to the functional group, which is beneficial to the reversibility of the sensing material. The significant changes in work function imply the good sensitivity of the above mentioned materials. In addition, the fast response time further consolidates the prospect of two-dimensional Ti2NT2 as efficient NCGs' sensing materials. This theoretical study would supply physical insight into the NCGs' sensing mechanism of Ti2N based nanosheets and help experimentalists to design better 2-D materials for gas adsorption or sensing applications.