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Two-dimensional, 2D, niobium carbide MXene, Nb2CTx, has attracted attention due to its extraordinarily high photothermal conversion efficiency that has applications ranging from medicine, for tumor ablation, to solar energy conversion. Here, we characterize its electronic properties and investigate the ultrafast dynamics of its photoexcitations with a goal of shedding light onto the origins of its unique properties. Through density functional theory, DFT, calculations, we find that Nb2CTx is metallic, with a small but finite DOS at the Fermi level for all experimentally relevant terminations that can be achieved using HF or molten salt etching of the parent MAX phase, including –OH, –O, –F, –Cl, –Br, –I. In agreement with this prediction, THz spectroscopy reveals an intrinsic long-range conductivity of ∼60 Ω−1 cm−1, with significant charge carrier localization and a charge carrier density (∼1020 cm−3) comparable to Mo-based MXenes. Excitation with 800 nm pulses results in a rapid enhancement in photoconductivity, which decays to less than 25% of its peak value within several picoseconds, underlying efficient photothermal conversion. At the same time, a small fraction of photoinjected excess carriers persists for hundreds of picoseconds and can potentially be utilized in photocatalysis or other energy conversion applications. 

Garnering attention for high conductivity, nonlinear optical properties, and more, MXenes are water-processable 2D materials that are considered candidates for applications in electromagnetic interference shielding, optoelectronic and photonic devices among others. Herein we investigate the intrinsic and photoexcited conductivity in Nb 2 CT x, a MXene with reported high photothermal conversion efficiency. DFT calculations show that hydroxyl and/or fluorine-terminated or is metallic, in agreement with THz spectroscopy, which reveals the presence of free charge carriers that are highly localized over mesoscopic length scales. Photoexcitation of Nb 2 CT x, known to result in rapid heating of the crystal lattice, is found to produce additional free carriers and a transient enhancement of photoconductivity. Most photoexcited carriers decay over the sub-picosecond time scales while a small fraction remain for much longer, sub-nanoseconds, times. 

Regression ensembles consisting of a collection of base regression models are often used to improve the estimation/prediction performance of a single regression model. It has been shown that the individual accuracy of the base models and the ensemble diversity are the two key factors affecting the performance of an ensemble. In this paper, we derive a theory for regression ensembles that illustrates the subtle trade-off between individual accuracy and ensemble diversity from the perspective of statistical correlations. Then, inspired by our derived theory, we further propose a novel loss function and a training algorithm for deep learning regression ensembles. We then demonstrate the advantage of our training approach over standard regression ensemble methods including random forest and gradient boosting regressors with both benchmark regression problems and chemical sensor problems involving analysis of Raman spectroscopy. Our key contribution is that our loss function and training algorithm is able to manage diversity explicitly in an ensemble, rather than merely allowing diversity to occur by happenstance. 

Stability is an important aspect of alloys, and proposed alloys may be unstable due to unfavorable atomic interactions. Segregation of an alloy may occur preferentially at specific exposed surfaces, which could affect the alloy's structure since certain surfaces may become enriched in certain elements. Using density functional theory (DFT), we modeled surface segregation in bimetallic alloys involving all transition metals doped in Pt, Pd, Ir, and Rh. We not only modeled common (111) surfaces of such alloys, but we also modeled (100), (110), and (210) facets of such alloys. Segregation is more preferred for early and late transition metals, with middle transition metals being most stable within the parent metal. We find these general trends in segregation energies for the parent metals: Pt > Rh > Pd > Ir. A comparison of different surfaces suggests no consistent trends across the different parent hosts, but segregation energies can vary up to 2 eV depending on the exposed surface. We also developed a statistical model to predict surface-dependent segregation energies. Our model is able to distinguish segregation at different surfaces (as opposed to generic segregation common in previous models), and agrees well with the DFT data. The present study provides valuable information about surface-dependent segregation and helps explain why certain alloy structures occur ( e.g. core–shell).