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Register a new userNanosized Monoatomic Palladium Metallic Glass
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Physically vitrifying single-element metallic glass requires ultrahigh cooling rates, which are still unachievable for most of the closest-packed metals. Here, we report a facile synthetic strategy for creating mono-atomic palladium metallic glass nanoparticles with a purity of 99.35 +/- 0.23 at% from palladium-silicon liquid droplets using a cooling rate below 1000 K/s. In-situ environmental transmission electron microscopy directly detected the leaching of silicon. Further hydrogen absorption experiment showed that this palladium metallic glass expanded little upon hydrogen uptake, exhibiting a great potential application for hydrogen separation. Our results provide insight into the formation of mono-atomic metallic glass at nanoscale.
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Crystallization from an amorphous atomic structure is usually seen as a spontaneous process in pursuit of a lower energy state, but for alloy systems it is often hard to elucidate because of the intrinsic structural and compositional complexity. Here, by means of electron beam irradiation, we found surface-limited, and thus size-dependent crystallization in a system of monoatomic Pd metallic glass, which is ascribed to the structural differences between the surface and the interior. The equilibrium thickness of the surface crystallization is controllable, presenting a promising approach to fabricate novel nanostructures. The investigation is believed to provide a general understanding of solid amorphous-to-crystalline phase transition from the nanoscale to the bulk size.
We demonstrate a remarkable equivalence in structure measured by total X-ray scattering methods between very small metallic nanoparticles and bulk metallic glasses (BMGs), thus connecting two disparate fields, shedding new light on both. Our results show that for nanoparticle diameters <5 nm the structure of Ni nanoparticles changes from fcc to the characteristic BMG-like structure, despite them being formed from a single element, an effect we call nano-metallic glass (NMG) formation. However, high-resolution TEM images of the NMG clusters exhibit lattice fringes indicating a locally well-ordered, rather than glassy, structure. These seemingly contradictory results may be reconciled by finding a locally ordered structure that is highly isotropic and we show that local icosahedral packing within 5 atomic shells explains this. Since this structure is stabilized only in the vicinity of a surface which highlights the importance of the presence of free volume in BMGs for stabilizing similar local clusters.
Micro- and nano-resonators have important applications including sensing, navigation, and biochemical detection. Their performance is quantified using the quality factor $Q$, which gives the ratio of the energy stored to the energy dissipated per cycle. Metallic glasses are a promising materials class for micro- and nano-scale resonators since they are amorphous and can be fabricated precisely into complex shapes on these lengthscales. To understand the intrinsic dissipation mechanisms that ultimately limit large $Q$-values in metallic glasses, we perform molecular dynamics simulations to model metallic glass resonators subjected to bending vibrations. We calculate the vibrational density of states, redistribution of energy from the fundamental mode of vibration, and $Q$ versus the kinetic energy per atom $K$ of the excitation. In the linear and nonlinear response regimes where there are no atomic rearrangements, we find that $Q rightarrow infty$ (since we do not consider coupling to the environment). We identify a characteristic $K_r$ above which atomic rearrangements occur, and there is significant energy leakage from the fundamental mode to higher frequencies, causing finite $Q$. Thus, $K_r$ is a critical parameter determining resonator performance. We show that $K_r$ decreases as a power-law, $K_rsim N^{-k},$ with increasing system size $N$, where $k approx 1.3$. We estimate the critical strain $langle gamma_r rangle sim 10^{-8}$ for micron-sized resonators below which atomic rearrangements do not occur, and thus large $Q$-values can be obtained when they are operated below $gamma_r$. We find that $K_r$ for amorphous resonators is comparable to that for resonators with crystalline order.
Various combinations of characteristic temperatures, such as the glass transition temperature, liquidus temperature, and crystallization temperature, have been proposed as predictions of the glass forming ability of metal alloys. We have used statistical approaches from machine learning to systematically explore a wide range of possible characteristic temperature functions for predicting glass forming ability in the form of critical casting diameter, $D_{max}$. Both linear and non-linear models were used to learn on the largest database of $D_{max}$ values to date consisting of 747 compositions. We find that no combination of temperatures for features offers a better prediction of $D_{max}$ in a machine learning model than the temperatures themselves, and that regression models suffer from poor performance on standard machine learning metrics like root mean square error (minimum value of $3.3 pm 0.1$ $mm$ for data with a standard deviation of 4.8 $mm$). Examination of the errors vs. database size suggest that a larger database may improve results, although a database significantly larger than that used here would likely be required. Shifting a focus from regression to categorization models learning from characteristic temperatures can be used to weakly distinguish glasses likely to be above vs. below our databases median $D_{max}$ value of 4.0 $mm$, with a mean F1 score of $0.77 pm 0.02$ for this categorization. The overall weak results on predicting $D_{max}$ suggests that critical cooling rate might be a better target for machine learning model prediction.
The design of multi-functional BMGs is limited by the lack of a quantitative understanding of the variables that control the glass-forming ability (GFA) of alloys. Both geometric frustration (e.g. differences in atomic radii) and energetic frustration (e.g. differences in the cohesive energies of the atomic species) contribute to the GFA. We perform molecular dynamics simulations of binary Lennard-Jones mixtures with only energetic frustration. We show that there is little correlation between the heat of mixing and critical cooling rate $R_c$, below which the system crystallizes, except that $Delta H_{rm mix} < 0$. By removing the effects of geometric frustration, we show strong correlations between $R_c$ and the variables $epsilon_- = (epsilon_{BB}-epsilon_{AA})/(epsilon_{AA}+epsilon_{BB})$ and ${overline epsilon}_{AB} = 2epsilon_{AB}/(epsilon_{AA}+epsilon_{BB})$, where $epsilon_{AA}$ and $epsilon_{BB}$ are the cohesive energies of atoms $A$ and $B$ and $epsilon_{AB}$ is the pair interaction between $A$ and $B$ atoms. We identify a particular $f_B$-dependent combination of $epsilon_-$ and ${overline epsilon}_{AB}$ that collapses the data for $R_c$ over nearly $4$ orders of magnitude in cooling rate.
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