Contact

  • Academic
    kulkwsk2@stanford.edu
    University - Student Department: Mechanical Engineering Position: Graduate

Additional Info

  • Mail Code: 3030

All Publications

  • Mechanical nanolattices printed using nanocluster-based photoresists. Science (New York, N.Y.) Li, Q., Kulikowski, J., Doan, D., Tertuliano, O. A., Zeman, C. J., Wang, M. M., Schatz, G. C., Gu, X. W. 2022; 378 (6621): 768-773

    Abstract

    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

  • Machine learning analysis of self-assembled colloidal cones. Soft matter Doan, D., Echeveste, D. J., Kulikowski, J., Gu, X. W. 2022

    Abstract

    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 Doan, D., Kulikowski, J., Gu, X. 2021

    View details for DOI 10.1002/ppsc.202100033

    View details for Web of Science ID 000674188100001