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MXenes have demonstrated potential for various applications owing to their tunable surface chemistry and metallic conductivity. However, high temperatures can accelerate MXene film oxidation in air. Understanding the mechanisms of MXene oxidation at elevated temperatures, which is still limited, is critical in improving their thermal stability for high-temperature applications. Here, we demonstrate that Ti ${}_3$C ${}_2$T ${}_x$MXene monoflakes have exceptional thermal stability at temperatures up to 600 ${}^{°}$C in air, while multiflakes readily oxidize in air at 300 ${}^{°}$C. Density functional theory calculations indicate that confined water between Ti ${}_3$C ${}_2$T ${}_x$flakes has higher removal energy than surface water and can thus persist to higher temperatures, leading to oxidation. We demonstrate that the amount of confined water correlates with the degree of oxidation in stacked flakes. Confined water can be fully removed by vacuum annealing Ti ${}_3$C ${}_2$T ${}_x$films at 600 ${}^{°}$C, resulting in substantial stability improvement in multiflake films (can withstand 600 ${}^{°}$C in air). These findings provide fundamental insights into the kinetics of confined water and its role in Ti ${}_3$C ${}_2$T ${}_x$oxidation. This work enables the use of stable monoflake MXenes in high-temperature applications and provides guidelines for proper vacuum annealing of multiflake films to enhance their stability. 

At low guest atom concentrations, Si clathrates can be viewed as semiconductors, with the guest atoms acting as dopants, potentially creating alternatives to diamond Si with exciting optoelectronic and spin properties. Studying Si clathrates with different guest atoms would not only provide insights into the electronic structure of the Si clathrates but also give insights into the unique properties that each guest can bring to the Si clathrate structure. However, the synthesis of Si clathrates with guests other than Na is challenging. In this study, we have developed an alternative approach, using thermal diffusion into type II Si clathrate with an extremely low Na concentration, to create Si clathrate with Li guests. Using time-of-flight secondary-ion mass spectroscopy, X-ray diffraction, and Raman scattering, thermal diffusion of Li into the nearly empty Si clathrate framework is detected and characterized as a function of the diffusion temperature and time. Interestingly, the Si clathrate exhibits reduced structural stability in the presence of Li, converting to polycrystalline or disordered phases for anneals at temperatures where the starting Na guest Si clathrate is quite stable. The Li atoms inserted into the Si clathrate lattice contribute free carriers, which can be detected in Raman scattering through their effect on the strength of Si−Si bonds in the framework. These carriers can also be observed in electron paramagnetic resonance (EPR). EPR shows, however, that Li guests are not simple analogues of Na guests. In particular, our results suggest that Li atoms, with their smaller size, tend to doubly occupy cages, forming “molecular-like” pairs with other Li or Na atoms. Results of this work provide a deeper insight into Li guest atoms in Si clathrate. These findings are also relevant to understanding how Li moves through and interacts with Si clathrate anodes in Li-ion batteries. Additionally, techniques presented in this work demonstrate a new method for filling the Si clathrate cages, enabling studies of a broad range of other guests in Si clathrates. 

We analyze the regression accuracy of convolutional neural networks assembled from encoders, decoders and skip connections and trained with multifidelity data. These networks benefit from a significant reduction in the number of trainable parameters with respect to an equivalent fully connected network. These architectures are also versatile with respect to the input and output dimensionality. For example, encoder-decoder, decoder-encoder or decoder-encoder-decoder architectures are well suited to learn mappings between input and outputs of any dimensionality. We demonstrate the accuracy produced by such architectures when trained on a few high-fidelity and many low-fidelity data generated from models ranging from one-dimensional functions to Poisson equation solvers in two-dimensions. We finally discuss a number of implementation choices that improve the reliability of the uncertainty estimates generated by a dropblock regularizer, and compare uncertainty estimates among low-, high- and multi-fidelity approaches. 

An outer-membrane complex exposes the cell-surface CglB adhesin at bacterial focal-adhesion sites to mediate gliding motility.