skip to main content

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 4

Search for: All records

Creators/Authors contains: "Chen, Chi"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ; ( , The Journal of Physical Chemistry A)
    DNA nanotechnology has broad applications in biomedical drug delivery and pro- grammable materials. Characterization of the self-assembly of DNA origami and quan- tum dots (QDs) is necessary for the development of new DNA-based nanostructures. We use computation and experiment to show that the self-assembly of 3D hierarchi- cal nanostructures can be controlled by programming the binding site number and their positions on DNA origami. Using biotinylated pentagonal pyramid wireframe DNA origamis and streptavidin capped QDs, we demonstrate that DNA origami with 1 binding site at the outer vertex can assemble multi-meric origamis with up to 6 DNA origamis on 1 QD, and DNA origami with 1 binding site at the inner center can only assemble monomeric and dimeric origamis. Meanwhile, the yield percentages of differ- ent multi-meric origamis are controlled by the QD:DNA-origami stoichiometric mixing ratio. DNA origamis with 2 binding sites at the αγ positions (of the pentagon) make larger nanostructures than those with binding sites at the αβ positions. In general, increasing the number of binding sites leads to increases in the nanostructure size. At high DNA origami concentration, the QD number in each cluster becomes the limiting factor for the growth of nanostructures. We find that reducing the QD size can also affect the self-assembly because of the reduced access to the binding sites from more densely packed origamis. 
    more » « less
    Free, publicly-accessible full text available June 27, 2025
  2. ; ; ; ; ( , Physical Review X)
    Free, publicly-accessible full text available February 1, 2025
  3. ; ; ( , Developmental Science)
    Abstract Research Highlights

    Conceptual development in numeracy is associated with a shift in attention from objects to sets.

    Children acquire meanings of the first few number words through associations with parallel attentional individuation of objects.

    Understanding of cardinality is associated with attentional processing of sets rather than individuals.

    Brain signatures suggest children attribute exact rather than approximate numerical meanings to the first few number words.

    Number‐quantity relationship processing for the first few number words is evident in frontal but not parietal scalp electrophysiology of young children.

     
    more » « less
  4. ; ; ; ; ; ; ; ; ( , npj Computational Materials)
    Abstract Refractory multi-principal element alloys (RMPEAs) are promising materials for high-temperature structural applications. Here, we investigate the role of short-range ordering (SRO) on dislocation glide in the MoNbTi and TaNbTi RMPEAs using a multi-scale modeling approach. Monte carlo/molecular dynamics simulations with a moment tensor potential show that MoNbTi exhibits a much greater degree of SRO than TaNbTi and the local composition has a direct effect on the unstable stacking fault energies (USFEs). From mesoscale phase-field dislocation dynamics simulations, we find that increasing SRO leads to higher mean USFEs and stress required for dislocation glide. The gliding dislocations experience significant hardening due to pinning and depinning caused by random compositional fluctuations, with higher SRO decreasing the degree of USFE dispersion and hence, amount of hardening. Finally, we show how the morphology of an expanding dislocation loop is affected by the applied stress. 
    more » « less
    Full Text Available 
  5. ; ; ; ( , Communications in Computational Physics)
    Full Text Available 
  6. ; ; ; ; ; ( , Science Advances)

    Scalable fabrication of two-dimensional (2D) arrays of quantum dots (QDs) and quantum rods (QRs) with nanoscale precision is required for numerous device applications. However, self-assembly–based fabrication of such arrays using DNA origami typically suffers from low yield due to inefficient QD and QR DNA functionalization. In addition, it is challenging to organize solution-assembled DNA origami arrays on 2D device substrates while maintaining their structural fidelity. Here, we reduced manufacturing time from a few days to a few minutes by preparing high-density DNA-conjugated QDs/QRs from organic solution using a dehydration and rehydration process. We used a surface-assisted large-scale assembly (SALSA) method to construct 2D origami lattices directly on solid substrates to template QD and QR 2D arrays with orientational control, with overall loading yields exceeding 90%. Our fabrication approach enables the scalable, high fidelity manufacturing of 2D addressable QDs and QRs with nanoscale orientational and spacing control for functional 2D photonic devices.

     
    more » « less
    Full Text Available 
  7. ; ; ; ; ; ; ; ; ; ( , Nature Communications)
    Abstract Control over the copy number and nanoscale positioning of quantum dots (QDs) is critical to their application to functional nanomaterials design. However, the multiple non-specific binding sites intrinsic to the surface of QDs have prevented their fabrication into multi-QD assemblies with programmed spatial positions. To overcome this challenge, we developed a general synthetic framework to selectively attach spatially addressable QDs on 3D wireframe DNA origami scaffolds using interfacial control of the QD surface. Using optical spectroscopy and molecular dynamics simulation, we investigated the fabrication of monovalent QDs of different sizes using chimeric single-stranded DNA to control QD surface chemistry. By understanding the relationship between chimeric single-stranded DNA length and QD size, we integrated single QDs into wireframe DNA origami objects and visualized the resulting QD-DNA assemblies using electron microscopy. Using these advances, we demonstrated the ability to program arbitrary 3D spatial relationships between QDs and dyes on DNA origami objects by fabricating energy-transfer circuits and colloidal molecules. Our design and fabrication approach enables the geometric control and spatial addressing of QDs together with the integration of other materials including dyes to fabricate hybrid materials for functional nanoscale photonic devices. 
    more » « less
    Full Text Available 
  8. ; ; ; ; ; ; ; ; ; ; et al ( , npj Computational Materials)
    Abstract Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science. 
    more » « less
    Full Text Available 
  9. ; ; ; ; ; ; ( , Matter)
    Full Text Available 
  10. ; ; ( , Scientific Reports)
    null (Ed.)
    Abstract What we learn about the world is affected by the input we receive. Many extant category learning studies use uniform distributions as input in which each exemplar in a category is presented the same number of times. Another common assumption on input used in previous studies is that exemplars from the same category form a roughly normal distribution. However, recent corpus studies suggest that real-world category input tends to be organized around skewed distributions. We conducted three experiments to examine the distributional properties of the input on category learning and generalization. Across all studies, skewed input distributions resulted in broader generalization than normal input distributions. Uniform distributions also resulted in broader generalization than normal input distributions. Our results not only suggest that current category learning theories may underestimate category generalization but also challenge current theories to explain category learning in the real world with skewed, instead of the normal or uniform distributions often used in experimental studies. 
    more » « less
    Full Text Available