Search for: All records

Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver predictive microstructure evolution dynamics with huge savings in computational costs. Taking 2D and 3D grain growth simulations as an example, we present a completely overhauled computational framework based on graph neural networks with not only excellent agreement to both the ground truth phase-field methods and theoretical predictions, but enhanced accuracy and efficiency compared to previous works based on convolutional neural networks. These improvements can be attributed to the graph representation, both improved predictive power and a more flexible data structure amenable to adaptive mesh refinement. As the simulated microstructures coarsen, our method can adaptively adopt remeshed grids and larger timesteps to achieve further speedup. The data-to-model pipeline with training procedures together with the source codes are provided.

 

Photon upconversion in systems incorporating inorganic quantum dots (QDs) is of great interest for applications in solar energy conversion, bioimaging, and photodynamic therapy. Achieving high up-conversion efficiency requires not only high-quality inorganic nanoparticles, but also precise control of their surface functional groups. Gas-phase surface functionalization provides a new pathway towards controlling the surface of small inorganic nanoparticles. In this contribution, we utilize a one-step low-temperature plasma technique for the synthesis and in-flight partial functionalization of silicon QDs with alkyl chains. The partially functionalized surface is then modified further with 9-vinylanthracene via thermal hydrosilylation resulting in the grafting of 9-ethylanthracene (9EA) groups. We have found that the minimum alkyl ligand density necessary for quantum dot solubility also gives the maximum upconversion quantum yield, reaching 17% for silicon QDs with Si-dodecyl chains and an average of 3 9EA molecules per particle.