Engineering topics

Yao, Lehan

Publications and source records attributed to Yao, Lehan.

10 records

DOE OSTI2026

Raw electron microscopy images for "The Importance of Nano-edges in Atomic Stencilling and Chiroptically Active Assembly of Patchy Gold Tetrahedra"

This dataset contains the raw transmission electron microscopy (TEM) and scanning electron microscopy (SEM) images used in the main figures of the paper “The Importance of Nano-edges in Atomic Stencilling and Chiroptically Active Assembly of Patchy Gold Tetrahedra (2026).” All the images were acquired at the Materials Research Laboratory, University of Illinois at Urbana-Champaign, by Qian Chen group. 1. We provide five subfolders, each named according to the corresponding figure numbers in the paper. 2. All files in the subfolders for Figures 1–3 and 5 are named as "Panel [letter]_*", where [letter] (e.g., a, b, c) represents the raw images used for the corresponding panels. 3. All files in the subfolder for Figure 4 correspond to panel f and show the configurations of patchy tetrahedra synthesized at varying concentrations of iodide and 2-naphthalenethiol. They are named "Experiment_[number]", where [number] represents the corresponding data points in the phase diagram. 4. In TEM images, the bright and dark regions indicate the polymer patches and nanoparticle cores, respectively. 5. In SEM images, the bright and dark regions indicate the nanoparticle cores and polymer patches, respectively. 6. Abbreviations in file names: HAADF-STEM (high-angle annular dark-field scanning transmission electron microscopy), PINEM (photon-induced near-field electron microscopy), and RCP/LCP (left-/right-handed circularly polarized).

chirality↗

DOE OSTI2025

Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy

Microscopy is one of the primary sources of information on materials structure and functionality at the nanometer and atomic scales. The data generated through microscopy is often contained in well-structured datasets, enriched with extensive metadata and sample histories, although not always with the same level of detail or storage format. The broad incorporation of data management plans by major funding agencies ensures the preservation and accessibility of this data. However, deriving insights from these rich datasets remains challenging due to the lack of established code ecosystems, standardized benchmarks, and integration strategies. Correspondingly, the efficiency of data usage is very low, and time expenditures at the analysis stage are enormous. In addition to post-acquisition data analysis, the emergence of application programming interfaces by major microscope manufacturers now creates opportunities for real-time ML-based data analytics to enable automated decision making, and particularly ML-agent controlled real-time microscope operation. Despite these opportunities, there is a significant gap in integrating the ML community with the broader microscopy community, limiting the value that these methods bring to physics and materials discovery and materials optimization. Hackathons address these challenges by fostering collaboration between ML experts and microscopy professionals, encouraging the development of innovative solutions that leverage ML for microscopy and preparing the workforce of the future both for microscopy-intensive domains areas, instrument manufacturers, and ML scientists interested in real world applications for fundamental research, materials optimization, and manufacturing. The hackathon generated benchmark datasets and digital twins of microscopes that further contribute to the development of the field and establish data analysis ecosystems. All the codes can be found at GitHub(https://github.com/KalininGroup/Mic-hackathon-2024-codes-publication/tree/1.0.0.1) and Zenodo (https://zenodo.org/records/15579940).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Patchy nanoparticles by atomic stencilling

Stencilling, in which patterns are created by painting over masks, has ubiquitous applications in art, architecture and manufacturing. Modern, top-down microfabrication methods have succeeded in reducing mask sizes to under 10 nm, enabling ever smaller microdevices as today’s fastest computer chips. Meanwhile, bottom-up masking using chemical bonds or physical interactions has remained largely unexplored, despite its advantages of low cost, solution-processability, scalability and high compatibility with complex, curved and three-dimensional (3D) surfaces. Here we report atomic stencilling to make patchy nanoparticles (NPs), using surface-adsorbed iodide submonolayers to create the mask and ligand-mediated grafted polymers onto unmasked regions as ‘paint’. We use this approach to synthesize more than 20 different types of NP coated with polymer patches in high yield. Polymer scaling theory and molecular dynamics (MD) simulation show that stencilling, along with the interplay of enthalpic and entropic effects of polymers, generates patchy particle morphologies not reported previously. These polymer-patched NPs self-assemble into extended crystals owing to highly uniform patches, including different non-closely packed superlattices. We propose that atomic stencilling opens new avenues in patterning NPs and other substrates at the nanometre length scale, leading to precise control of their chemistry, reactivity and interactions for a wide range of applications, such as targeted delivery, catalysis, microelectronics, integrated metamaterials and tissue engineering.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Nanoscopic Imaging of Self-Propelled Ultrasmall Catalytic Nanomotors

Ultrasmall nanomotors (<100 nm) are highly desirable nanomachines for their size-specific advantages over their larger counterparts in applications spanning nanomedicine, directed assembly, active sensing, and environmental remediation. While there are extensive studies on motors larger than 100 nm, the design and understanding of ultrasmall nanomotors have been scant due to the lack of high-resolution imaging of their propelled motions with orientation and shape details resolved. Here, we report the imaging of the propelled motions of catalytically powered ultrasmall nanomotors─hundreds of them─at the nanometer resolution using liquid-phase transmission electron microscopy. These nanomotors are Pt nanoparticles of asymmetric shapes (“tadpoles” and “boomerangs”), which are colloidally synthesized and observed to be fueled by the catalyzed decomposition of NaBH4 in solution. Statistical analysis of the orientation and position trajectories of fueled and unfueled motors, coupled with finite element simulation, reveals that the shape asymmetry alone is sufficient to induce local chemical concentration gradient and self-diffusiophoresis to act against random Brownian motion. Our work elucidates the colloidal design and fundamental forces involved in the motions of ultrasmall nanomotors, which hold promise as active nanomachines to perform tasks in confined environments such as drug delivery and chemical sensing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Electron Microscopy Studies of Soft Nanomaterials

This review highlights recent efforts on applying electron microscopy (EM) to soft (including biological) nanomaterials. We will show how developments of both the hardware and software of EM have enabled new insights into the formation, assembly, and functioning (e.g., energy conversion and storage, phonon/photon modulation) of these materials by providing shape, size, phase, structural, and chemical information at the nanometer or higher spatial resolution. Specifically, we first discuss standard real-space two-dimensional imaging and analytical techniques which are offered conveniently by microscopes without special holders or advanced beam technology. The discussion is then extended to recent advancements, including visualizing three-dimensional morphology of soft nanomaterials using electron tomography and its variations, identifying local structure and strain by electron diffraction, and recording motions and transformation by in situ EM. On these advancements, we cover state-of-the-art technologies designed for overcoming the technical barriers for EM to characterize soft materials as well as representative application examples. Here, the even more recent integration of machine learning and its impacts on EM are also discussed in detail. With our perspectives of future opportunities offered at the end, we expect this review to inspire and stimulate more efforts in developing and utilizing EM-based characterization methods for soft nanomaterials at the atomic to nanometer length scales in academic research and industrial applications.

Imaging↗

DOE OSTI2022

Electron Tomography and Machine Learning for Understanding the Highly Ordered Structure of Leafhopper Brochosomes

Insects known as leafhoppers (Hemiptera: Cicadellidae) produce hierarchically structured nanoparticles known as brochosomes that are exuded and applied to the insect cuticle, thereby providing camouflage and anti-wetting properties to aid insect survival. Although the physical properties of brochosomes are thought to depend on the leafhopper species, the structure–function relationships governing brochosome behavior are not fully understood. Brochosomes have complex hierarchical structures and morphological heterogeneity across species, due to which a multimodal characterization approach is required to effectively elucidate their nanoscale structure and properties. In this work, we study the structural and mechanical properties of brochosomes using a combination of atomic force microscopy (AFM), electron microscopy (EM), electron tomography, and machine learning (ML)-based quantification of large and complex scanning electron microscopy (SEM) image data sets. This suite of techniques allows for the characterization of internal and external brochosome structures, and ML-based image analysis methods of large data sets reveal correlations in the structure across several leafhopper species. Our results show that brochosomes are relatively rigid hollow spheres with characteristic dimensions and morphologies that depend on leafhopper species. Nanomechanical mapping AFM is used to determine a characteristic compression modulus for brochosomes on the order of 1–3 GPa, which is consistent with crystalline proteins. Altogether, this work provides an improved understanding of the structural and mechanical properties of leafhopper brochosomes using a new set of ML-based image classification tools that can be broadly applied to nanostructured biological materials.

Chemical structure↗

DOE OSTI2022

Chiral assemblies of pinwheel superlattices on substrates

The unique topology and physics of chiral superlattices make their self-assembly from nanoparticles highly sought after yet challenging in regard to (meta)materials. In this report we show that tetrahedral gold nanoparticles can transform from a perovskite-like, low-density phase with corner-to-corner connections into pinwheel assemblies with corner-to-edge connections and denser packing. Whereas corner-sharing assemblies are achiral, pinwheel superlattices become strongly mirror asymmetric on solid substrates as demonstrated by chirality measures. Liquid-phase transmission electron microscopy and computational models show that van der Waals and electrostatic interactions between nanoparticles control thermodynamic equilibrium. Variable corner-to-edge connections among tetrahedra enable fine-tuning of chirality. The domains of the bilayer superlattices show strong chiroptical activity as identified by photon-induced near-field electron microscopy and finite-difference time-domain simulations. The simplicity and versatility of substrate-supported chiral superlattices facilitate the manufacture of metastructured coatings with unusual optical, mechanical and electronic characteristics.

42 ENGINEERING↗

DOE OSTI2022

Self-Assembly of Repetitive Segment and Random Segment Polymer Architectures

Recent advances in chemical synthesis have created new methodologies for synthesizing sequence-controlled synthetic polymers, but rational design of monomer sequence for desired properties remains challenging. In this work, we synthesize periodic polymers with repetitive segments using a sequence-controlled ring-opening metathesis polymerization (ROMP) method, which draws inspiration from proteins containing repetitive sequence motifs. The repetitive segment architecture is shown to dramatically affect the self-assembly behavior of these materials. In this work, our results show that polymers with identical repetitive sequences assemble into uniform spherical nanoparticles after thermal annealing, whereas copolymers with random placement of segments with different sequences exhibit disordered assemblies without a well-defined morphology. Overall, these results bring a new understanding to the role of periodic repetitive sequences in polymer assembly.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Symmetry-breaking in patch formation on triangular gold nanoparticles by asymmetric polymer grafting

Synthesizing patchy particles with predictive control over patch size, shape, placement and number has been highly sought-after for nanoparticle assembly research, but is fraught with challenges. Here we show that polymers can be designed to selectively adsorb onto nanoparticle surfaces already partially coated by other chains to drive the formation of patchy nanoparticles with broken symmetry. In our model system of triangular gold nanoparticles and polystyrene-b-polyacrylic acid patch, single- and double-patch nanoparticles are produced at high yield. These asymmetric single-patch nanoparticles are shown to assemble into self-limited patch-patch connected bowties exhibiting intriguing plasmonic properties. To unveil the mechanism of symmetry-breaking patch formation, we develop a theory that accurately predicts our experimental observations at all scales—from patch patterning on nanoparticles, to the size/shape of the patches, to the particle assemblies driven by patch-patch interactions. Both the experimental strategy and theoretical prediction extend to nanoparticles of other shapes such as octahedra and bipyramids. Our work provides an approach to leverage polymer interactions with nanoscale curved surfaces for asymmetric grafting in nanomaterials engineering.

42 ENGINEERING↗

DOE OSTI2022

Seeking regularity from irregularity: unveiling the synthesis–nanomorphology relationships of heterogeneous nanomaterials using unsupervised machine learning

Nanoscale morphology of functional materials determines their chemical and physical properties. However, despite increasing use of transmission electron microscopy (TEM) to directly image nanomorphology, it remains challenging to quantify the information embedded in TEM data sets, and to use nanomorphology to link synthesis and processing conditions to properties. We develop an automated, descriptor-free analysis workflow for TEM data that utilizes convolutional neural networks and unsupervised learning to quantify and classify nanomorphology, and thereby reveal synthesis–nanomorphology relationships in three different systems. While TEM records nanomorphology readily in two-dimensional (2D) images or three-dimensional (3D) tomograms, we advance the analysis of these images by identifying and applying a universal shape fingerprint function to characterize nanomorphology. After dimensionality reduction through principal component analysis, this function then serves as the input for morphology grouping through unsupervised learning. We demonstrate the wide applicability of our workflow to both 2D and 3D TEM data sets, and to both inorganic and organic nanomaterials, including tetrahedral gold nanoparticles mixed with irregularly shaped impurities, hybrid polymer-patched gold nanoprisms, and polyamide membranes with irregular and heterogeneous 3D crumple structures. In each of these systems, unsupervised nanomorphology grouping identifies both the diversity and the similarity of the nanomaterial across different synthesis conditions, revealing how synthetic parameters guide nanomorphology development. Our work opens possibilities for enhancing synthesis of nanomaterials through artificial intelligence and for understanding and controlling complex nanomorphology, both for 2D systems and in the far less explored case of 3D structures, such as those with embedded voids or hidden interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗