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Pinned Particles Impede Crystallization

Experiments challenge the assumption that crystals form more easily when some of the constituent particles are fixed in place.




A popular way to grow thin crystalline films is through physical vapor deposition, a process in which gaseous particles settle onto a surface and gradually arrange themselves into an ordered structure. Naively, one might expect this crystallization to be assisted by anchoring some of the same particles to the surface to serve as starting locations for crystal growth. But that is not the case according to new experimental work by Chandan Mishra at the Indian Institute of Technology Gandhinagar and his colleagues. The team’s counterintuitive findings could inspire improved strategies for material design.

Mishra and his colleagues pinned a few micrometer-sized beads of silica to a glass surface in a random, sparsely distributed pattern. They then suspended thousands of other silica beads in a liquid that they placed on the surface. These mobile beads descended onto the surface under gravity and then, via their mutual short-range attraction, clustered together with or without a pinned bead. The team studied the formation, evolution, and disintegration of these clusters at the single-bead level using video microscopy, supported by theoretical models and molecular-dynamics simulations.

Any given bead could serve as the starting point for a cluster, but the researchers found that, relative to the mobile beads, the pinned ones were less likely to do so. These pinned beads hindered crystallization because their inability to move made it harder for surrounding beads to form the specific arrangements required for crystal growth. The team says that future work could explore particle systems with long-range interactions and other features.

Website: International Research Awards on High Energy Physics and Computational Science.


#HighEnergyPhysics#ParticlePhysics#QuantumPhysics#AstroparticlePhysics#ColliderPhysics#HiggsBoson#LHC#QuantumFieldTheory#NeutrinoPhysics#PhysicsResearch#ComputationalScience#DataScience#ScientificComputing#NumericalMethods#HighPerformanceComputing#MachineLearningInScience#BigData#AlgorithmDevelopment#SimulationScience#ParallelComputing

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