Engineering topics

Mirkin, Chad A.

Publications and source records attributed to Mirkin, Chad A..

3 records

DOE OSTI2024

High Temperature, Isothermal Growth Promotes Close Packing and Thermal Stability in DNA-Engineered Colloidal Crystals

Here, we report a strategy to accelerate the synthesis and increase the crystallinity of colloidal crystals engineered with DNA. Specifically, by holding the DNA-modified Au particle building blocks above the T m of the individual nanoparticle building blocks, but slightly below the T m of the anticipated colloidal crystal during the assembly process, crystallinity is increased, and enthalpically-favored phases with high degrees of facet registration are observed. We studied the utility of this approach with systems for which the commonly adopted slow-cooling approach yielded primarily amorphous aggregates. In particular, we used it to synthesize high-volume fraction colloidal crystals from large (80 nm) anisotropic nanoparticles (cubes and rhombic dodecahedra) with short (<14 nm) DNA designed to restrict the degrees of freedom for the DNA bonds and maintain the anisotropy of the particle building block. Small-angle X-ray scattering and electron microscopy studies show that the crystalline phases synthesized via this method are more thermally stable than their corresponding aggregate phases, likely due to an increased number of DNA-DNA bonds between particles. Crystal size tunability (between 0.5 and 15 µm edge lengths) and epitaxial growth were demonstrated using this strategy by modulating the NaCl concentration in tandem with previously synthesized colloidal crystal nuclei. Taken together, this isothermal strategy provides a route to deliberately crystallize a wide variety of anisotropic colloidal materials and expands the phase space accessible to nanoparticles modified with DNA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Unveiling Spatial and Temporal Dynamics of Plasmon-Enhanced Localized Fields in Metallic Nanoframes through Ultrafast Electron Microscopy

Plasmonic nanomaterials, particularly noble metal nanoframes (NFs), are important for applications such as catalysis, biosensing, and energy harvesting due to their ability to enhance localized electric fields and atomic efficiency via localized surface plasmon resonance (LSPR). Yet the fundamental structure-function relationships and plasmonic dynamics of the NFS are difficult to study experimentally and thus far rely predominately on computational methodologies, limiting their utilization. This study leverages the capabilities of ultrafast electron microscopy (UEM), specifically photon-induced near-field electron microscopy (PINEM), to probe the light-matter interactions within plasmonic NF structures. Here, the effects of shape, size, and plasmonic coupling of Pt@Au core-shell NFs on spatial and temporal characteristics of plasmon-enhanced localized electric fields are explored. Importantly, time-resolved PINEM analysis reveals that the plasmonic fields around hexagonal NF prisms exhibit a spatially dependent excitation and decay rate, indicating a nuanced interplay between the spatial geometry of the NF and the temporal evolution of the localized electric field. These results and observations uncover nanophotonic energy transfer dynamics in NFs and highlight their potential for applications in biosensing and photocatalysis.

Plasmonics↗

DOE OSTI2024

Machine Learning-Enabled Image Classification for Automated Electron Microscopy

Abstract Traditionally, materials discovery has been driven more by evidence and intuition than by systematic design. However, the advent of “big data” and an exponential increase in computational power have reshaped the landscape. Today, we use simulations, artificial intelligence (AI), and machine learning (ML) to predict materials characteristics, which dramatically accelerates the discovery of novel materials. For instance, combinatorial megalibraries, where millions of distinct nanoparticles are created on a single chip, have spurred the need for automated characterization tools. This paper presents an ML model specifically developed to perform real-time binary classification of grayscale high-angle annular dark-field images of nanoparticles sourced from these megalibraries. Given the high costs associated with downstream processing errors, a primary requirement for our model was to minimize false positives while maintaining efficacy on unseen images. We elaborate on the computational challenges and our solutions, including managing memory constraints, optimizing training time, and utilizing Neural Architecture Search tools. The final model outperformed our expectations, achieving over 95% precision and a weighted F-score of more than 90% on our test data set. This paper discusses the development, challenges, and successful outcomes of this significant advancement in the application of AI and ML to materials discovery.

Materials Science↗