Optically directed assembly of continuous mesoscale filaments

Defect Dynamics in 2-D MoS2 Probed by Using Machine Learning, Atomistic Simulations, and High-Resolution Microscopy

Structural defects govern various physical, chemical, and optoelectronic properties of two-dimensional transition-metal dichalcogenides (TMDs). A fundamental understanding of the spatial distribution and dynamics of defects in these low-dimensional systems is critical for advances in nanotechnology. However, such understanding has remained elusive primarily due to the inaccessibility of (a) necessary time scales via standard atomistic simulations and (b) required spatiotemporal resolution in experiments. Here, we take advantage of supervised machine learning, in situ high-resolution transmission electron microscopy (HRTEM) and molecular dynamics (MD) simulations to overcome these limitations. We combine genetic algorithms (GA) with MD to investigate the extended structure of point defects, their dynamical evolution, and their role in inducing the phase transition between the semiconducting (2H) and metallic (1T) phase in monolayer MoS₂. GA-based structural optimization is used to identify the long-range structure of randomly distributed point defects (sulfur vacancies) for various defect densities. Regardless of the density, we find that organization of sulfur vacancies into extended lines is the most energetically favorable. HRTEM validates these findings and suggests a phase transformation from the 2H-to-1T phase that is localized near these extended defects when exposed to high electron beam doses. MD simulations elucidate the molecular mechanism driving the onset of the 2H to 1T transformation and indicate that finite amounts of 1T phase can be retained by increasing the defect concentration and temperature. This work significantly advances the current understanding of defect structure/evolution and structural transitions in 2D TMDs, which is crucial for designing nanoscale devices with desired functionality.

Machine learnt bond order potential to model metal-organic (Co-C) heterostructures

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff-Brenner type bond order potential (BOP) for a Co-C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co₂C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure-property relationships, and temperature/pressure response of Co-C systems, as well as design organic-inorganic hybrid structures with a desired set of properties.

Nanostructured grating patterns over a large area fabricated by optically directed assembly

Optical trapping and manipulation of nanoparticles (NPs) have been widely used in nanotechnology and biology. Here, we demonstrate an optically directed assembly (ODA) route for bottom-up fabrication of stable nanostructured grating patterns in solution using laser standing evanescent wave (LSEW) fields. The control mechanism is the intriguing cooperative action of the periodically line-centered attractive optical gradient force and the near field dipolar coupling force induced by LSEW, which leads to assembly of the colloidal silver NPs into robust grating patterns within minutes. The anisotropic polarization nature of the grating patterns was studied further by examining the morphology correlation of the surface-enhanced Raman scattering (SERS)-based signal amplification. We show the LSEW ODA method can optimize and stabilize the strongest dipolar coupling style among the NPs during pattern assembly. These results advance the further understanding of ODA of colloid NPs and might have many potential applications in SERS, catalysis, nanophotonics and nano-fabrication.

Optical trapping and manipulation of plasmonic nanoparticles: fundamentals, applications, and perspectives

This feature article discusses the optical trapping and manipulation of plasmonic nanoparticles, an area of current interest with potential applications in nanofabrication, sensing, analytics, biology and medicine. We give an overview over the basic theoretical concepts relating to optical forces, plasmon resonances and plasmonic heating. We discuss fundamental studies of plasmonic particles in optical traps and the temperature profiles around them. We place a particular emphasis on our own work employing optically trapped plasmonic nanoparticles towards nanofabrication, manipulation of biomimetic objects and sensing.

Liu J, Dr. Yu M, Prof., Dr. Zheng J. One-step interfacial synthesis and assembly of ultrathin luminescent AuNPs/silica membranes

A facile one-step strategy is explored to achieve uniform luminescent Au nanoparticles (AuNPs) in a two-dimensional ultrathin silica matrix based on simultaneous reaction and assembly at the liquid-liquid interface. The as-prepared AuNPs/silica nanocomposites can be further employed to fabricate micrometer-thick films with bifunctional luminescent and superhydrophobic properties. Such a versatile concept is ideal for the development of multifunctional devices.