We investigate the formation of chains of few plasmonic nanoparticles-so-called plasmonic oligomers-by strain-induced fragmentation of linear particle assemblies. Detailed investigations of the fragmentation process are conducted by in situ atomic force microscopy and UV-vis-NIR spectroscopy. Based on these experimental results and mechanical simulations computed by the lattice spring model, we propose a formation mechanism that explains the observed decrease of chain polydispersity upon increasing strain and provides experimental guidelines for tailoring chain length distribution. By evaluation of the strain-dependent optical properties, we find a reversible, nonlinear shift of the dominant plasmonic resonance. We could quantitatively explain this feature based on simulations using generalized multiparticle Mie theory (GMMT). Both optical and morphological characterization show that the unstrained sample is dominated by chains with a length above the so-called infinite chain limit-above which optical properties show no dependency on chain length-while during deformation, the average chain length decrease below this limit and chain length distribution becomes more narrow. Since the formation mechanism results in a well-defined, parallel orientation of the oligomers on macroscopic areas, the effect of finite chain length can be studied even using conventional UV-vis-NIR spectroscopy. The scalable fabrication of oriented, linear plasmonic oligomers opens up additional opportunities for strain-dependent optical devices and mechanoplasmonic sensing.

No abstract

Suspensions of soft and highly deformable microgels can be concentrated far more than suspensions of hard colloids, leading to their unusual mechanical properties. Microgels can accommodate compression in suspensions in a variety of ways such as interpenetration, deformation, and shrinking. Previous experiments have offered insightful, but somewhat conflicting, accounts of the behavior of individual microgels in compressed suspensions. We develop a mesoscale computational model to probe the behavior of compressed suspensions consisting of microgels with different architectures at a variety of packing fractions and solvent conditions. We find that microgels predominantly change shape and mildly shrink above random close packing. Interpenetration is only appreciable above space filling, remaining small relative to the mean distance between cross-links. At even higher packing fractions, microgels solely shrink. Remarkably, irrespective of the single-microgel properties, and whether the suspension concentration is changed via changing the particle number density or the swelling state of the particles, which can even result in colloidal gelation, the mechanics of the suspension can be quantified in terms of the single-microgel bulk modulus, which thus emerges as the correct mechanical measure for these type of soft-colloidal suspensions. Our results rationalize the many and varied experimental results, providing insights into the relative importance of effects defining the mechanics of suspensions comprising soft particles.

A data-driven framework is presented for building magneto-elastic machine-learning interatomic potentials (ML-IAPs) for large-scale spin-lattice dynamics simulations. The magneto-elastic ML-IAPs are constructed by coupling a collective atomic spin model with an ML-IAP. Together they represent a potential energy surface from which the mechanical forces on the atoms and the precession dynamics of the atomic spins are computed. Both the atomic spin model and the ML-IAP are parametrized on data from first-principles calculations. We demonstrate the efficacy of our data-driven framework across magneto-structural phase transitions by generating a magneto-elastic ML-IAP for α-iron. The combined potential energy surface yields excellent agreement with first-principles magneto-elastic calculations and quantitative predictions of diverse materials properties including bulk modulus, magnetization, and specific heat across the ferromagnetic–paramagnetic phase transition.

We have demonstrated the facile formation of reversible and fast self-rolling biopolymer microstructures from sandwiched active-passive, silk-on-silk materials. Both experimental and modeling results confirmed that the shape of individual sheets effectively controls biaxial stresses within these sheets, which can self-roll into distinct 3D structures including microscopic rings, tubules, and helical tubules. This is a unique example of tailoring self-rolled 3D geometries through shape design without changing the inner morphology of active bimorph biomaterials. In contrast to traditional organic-soluble synthetic materials, we utilized a biocompatible and biodegradable biopolymer that underwent a facile aqueous layer-by-layer (LbL) assembly process for the fabrication of 2D films. The resulting films can undergo reversible pH-triggered rolling/unrolling, with a variety of 3D structures forming from biopolymer structures that have identical morphology and composition.