The pure and representative types of disordered platinum nanoparticles from machine learning.

The development of interpretable structure/property relationships is a cornerstone of nanoscience, but can be challenging when the structural diversity and complexity exceeds our ability to characterise it. This is often the case for imperfect, disordered and amorphous nanoparticles, where even the nomenclature can be unspecific. Disordered platinum nanoparticles have exhibited superior performance for some reactions, which makes a systematic way of describing them highly desirable. In this study we have used a diverse set of disorder platinum nanoparticles and machine learning to identify the pure and representative structures based on their similarity in 121 dimensions. We identify two prototypes that are representative of separable classes, and seven archetypes that are the pure structures on the convex hull with which all other possibilities can be described. Together these nine nanoparticles can explain all of the variance in the set, and can be described as either single crystal, twinned, spherical or branched; with or without roughened surfaces. This forms a robust sub-set of platinum nanoparticle upon which to base further work, and provides a theoretical basis for discussing structure/property relationships of platinum nanoparticles that are not geometrically ideal.

Metal-Organic Framework Nanoparticle-Assisted Cryopreservation of Red Blood Cells.

The development of hybrid nanomaterials mimicking antifreeze proteins that can modulate/inhibit the growth of ice crystals for cell/tissue cryopreservation has attracted increasing interests. Herein, we describe the first utilization of zirconium (Zr)-based metal-organic framework (MOF) nanoparticles (NPs) with well-defined surface chemistries for the cryopreservation of red blood cells (RBCs) without the need of any (toxic) organic solvents. Distinguishing features of this cryoprotective approach include the exceptional water stability, low hemolytic activity, and the long periodic arrangement of organic linkers on the surface of MOF NPs, which provide a precise spacing of hydrogen donors to recognize and match the ice crystal planes. Five kinds of Zr-based MOF NPs, with different pore size, surface chemistry, and framework topologies, were used for the cryoprotection of RBCs. A "splat" assay confirmed that MOF NPs not only exhibited ice recrystallization inhibition activities but also acted as a "catalyst" to accelerate the melting of ice crystals. The human RBC cryopreservation tests displayed RBC recoveries of up to ∼40%, which is higher than that obtained via commonly used hydroxyethyl starch polymers. This cryopreservation approach will inspire the design and utilization of MOF-derived nanoarchitectures for the effective cryopreservation of various cell types as well as tissue samples.

Sustaining GHz oscillation of carbon nanotube based oscillators via a MHz frequency excitation.

There have been intensive studies to investigate the properties of gigahertz nano-oscillators based on multi-walled carbon nanotubes (MWCNTs). Many of these studies, however, revealed that the unique telescopic translational oscillations in such devices would damp quickly due to various energy dissipation mechanisms. This challenge remains the primary obstacle against its practical applications. Herein, we propose a design concept in which a GHz oscillation could be re-excited by a MHz mechanical motion. This design involves a triple-walled CNT, in which sliding of the longer inner tube at a MHz frequency can re-excite and sustain a GHz oscillation of the shorter middle tube. Our molecular dynamics (MD) simulations prove this design concept at ∼10 nm scale. A mathematical model is developed to explore the feasibility at a larger size scale. As an example, in an oscillatory system with the CNT's length above 100 nm, the high oscillatory frequency range of 1.8-3.3 GHz could be excited by moving the inner tube at a much lower frequency of 53.4 MHz. This design concept together with the mechanical model could energize the development of GHz nano-oscillators in miniaturized electro-mechanical devices.

New Engineering Science Insights into the Electrode Materials Pairing of Electrochemical Energy Storage Devices.

Pairing the positive and negative electrodes with their individual dynamic characteristics at a realistic cell level is essential to the practical optimal design of electrochemical energy storage devices. However, the complex relationship between the performance data measured for individual electrodes and the two-electrode cells used in practice often makes an optimal pairing experimentally challenging. Taking advantage of the developed tunable graphene-based electrodes with controllable structure, experiments with machine learning are successfully united to generate a large pool of capacitance data for graphene-based electrode materials with varied slit pore sizes, thicknesses, and charging rates and numerically pair them into different combinations for two-electrode cells. The results show that the optimal pairing parameters of positive and negative electrodes vary considerably with the operation rate of the cells and are even influenced by the thickness of inactive components. The best-performing individual electrode does not necessarily result in optimal cell-level performance. The machine learning-assisted pairing approach presents much higher efficiency compared with the traditional trial-and-error approach for the optimal design of supercapacitors. The new engineering science insights observed in this work enable the adoption of artificial intelligence techniques to efficiently translate well-developed high-performance individual electrode materials into real energy storage devices.