Mechanical nanolattices printed using nanocluster-based photoresists.
Science (New York, N.Y.)
2022; 378 (6621): 768-773
Natural materials exhibit emergent mechanical properties as a result of their nanoarchitected, nanocomposite structures with optimized hierarchy, anisotropy, and nanoporosity. Fabrication of such complex systems is currently challenging because high-quality three-dimensional (3D) nanoprinting is mostly limited to simple, homogeneous materials. We report a strategy for the rapid nanoprinting of complex structural nanocomposites using metal nanoclusters. These ultrasmall, quantum-confined nanoclusters function as highly sensitive two-photon activators and simultaneously serve as precursors for mechanical reinforcements and nanoscale porogens. Nanocomposites with complex 3D architectures are printed, as well as structures with tunable, hierarchical, and anisotropic nanoporosity. Nanocluster-polymer nanolattices exhibit high specific strength, energy absorption, deformability, and recoverability. This framework provides a generalizable, versatile approach for the use of photoactive nanomaterials in additive manufacturing of complex systems with emergent mechanical properties.
View details for DOI 10.1126/science.abo6997
View details for PubMedID 36395243
Nanoparticle-enhanced absorptivity of copper during laser powder bed fusion
View details for DOI 10.1016/j.addma.2021.102562
View details for Web of Science ID 000752324000003
Machine learning analysis of self-assembled colloidal cones.
Optical and confocal microscopy is used to image the self-assembly of microscale colloidal particles. The density and size of self-assembled structures is typically quantified by hand, but this is extremely tedious. Here, we investigate whether machine learning can be used to improve the speed and accuracy of identification. This method is applied to confocal images of dense arrays of two-photon lithographed colloidal cones. RetinaNet, a deep learning implementation that uses a convolutional neural network, is used to identify self-assembled stacks of cones. Synthetic data is generated using Blender to supplement experimental training data for the machine learning model. This synthetic data captures key characteristics of confocal images, including slicing in the z-direction and Gaussian noise. We find that the best performance is achieved with a model trained on a mixture of synthetic data and experimental data. This model achieves a mean Average Precision (mAP) of ∼85%, and accurately measures the degree of assembly and distribution of self-assembled stack sizes for different cone diameters. Minor discrepancies between machine learning and hand labeled data is discussed in terms of the quality of synthetic data, and differences in cones of different sizes.
View details for DOI 10.1039/d1sm01466h
View details for PubMedID 35103741
Diffusion of Anisotropic Colloidal Microparticles Fabricated Using Two-Photon Lithography
PARTICLE & PARTICLE SYSTEMS CHARACTERIZATION
View details for DOI 10.1002/ppsc.202100033
View details for Web of Science ID 000674188100001
Ductile Metallic Glass Nanoparticles via Colloidal Synthesis.
The design of ductile metallic glasses has been a longstanding challenge. Here, we use colloidal synthesis to fabricate nickel-boron metallic glass nanoparticles that exhibit homogeneous deformation at room temperature and moderate strain rates. In situ compression testing is used to characterize the mechanical behavior of 90-260 nm diameter nanoparticles. The force-displacement curves consist of two regimes separated by a slowly propagating shear band in small, 90 nm particles. The propensity for shear banding decreases with increasing particle size, such that large particles are more likely to deform homogeneously through gradual shape change. We relate this behavior to differences in composition and atomic bonding between particles of different size using mass spectroscopy and XPS. We propose that the ductility of the nanoparticles is related to their internal structure, which consists of atomic clusters made of a metalloid core and a metallic shell that are connected to neighboring clusters by metal-metal bonds.
View details for DOI 10.1021/acs.nanolett.0c02177
View details for PubMedID 32786936
Hardening in Au-Ag nanoboxes from stacking fault-dislocation interactions.
2020; 11 (1): 2923
Porous, nano-architected metals with dimensions down to ~10nm are predicted to have extraordinarily high strength and stiffness per weight, but have been challenging to fabricate and test experimentally. Here, we use colloidal synthesis to make ~140nm length and ~15nm wall thickness hollow Au-Ag nanoboxes with smooth and rough surfaces. In situ scanning electron microscope and transmission electron microscope testing of the smooth and rough nanoboxes show them to yield at 130±45MPa and 96±31MPa respectively, with significant strain hardening. A higher strain hardening rate is seen in rough nanoboxes than smooth nanoboxes. Finite element modeling is used to show that the structure of the nanoboxes is not responsible for the hardening behavior suggesting that material mechanisms are the source of observed hardening. Molecular dynamics simulations indicate that hardening is a result of interactions between dislocations and the associated increase in dislocation density.
View details for DOI 10.1038/s41467-020-16760-1
View details for PubMedID 32522992
Stress-Induced Structural Transformations in Au Nanocrystals.
Nanocrystals can exist in multiply twinned structures like icosahedron or single crystalline structures like cuboctahedron. Transformations between these structures can proceed through diffusion or displacive motion. Experimental studies on nanocrystal structural transformations have focused on high-temperature diffusion-mediated processes. Limited experimental evidence of displacive motion exists. We report structural transformation of 6 nm Au nanocrystals under nonhydrostatic pressure of 7.7 GPa in a diamond anvil cell that is driven by displacive motion. X-ray diffraction and transmission electron microscopy were used to detect the structural transformation from multiply twinned to single crystalline. Single crystalline nanocrystals were recovered after unloading, then quickly reverted to the multiply twinned state after dispersion in toluene. The dynamics of recovery was captured using TEM which showed surface recrystallization and rapid twin boundary motion. Molecular dynamics simulations showed that twin boundaries are unstable due to defects nucleated from the interior of the nanocrystal.
View details for DOI 10.1021/acs.nanolett.0c03371
View details for PubMedID 33016704
Nucleation of Dislocations in 3.9 nm Nanocrystals at High Pressure.
Physical review letters
2020; 124 (10): 106104
As circuitry approaches single nanometer length scales, it has become important to predict the stability of single nanometer-sized metals. The behavior of metals at larger scales can be predicted based on the behavior of dislocations, but it is unclear if dislocations can form and be sustained at single nanometer dimensions. Here, we report the formation of dislocations within individual 3.9 nm Au nanocrystals under nonhydrostatic pressure in a diamond anvil cell. We used a combination of x-ray diffraction, optical absorbance spectroscopy, and molecular dynamics simulation to characterize the defects that are formed, which were found to be surface-nucleated partial dislocations. These results indicate that dislocations are still active at single nanometer length scales and can lead to permanent plasticity.
View details for DOI 10.1103/PhysRevLett.124.106104
View details for PubMedID 32216385