The Grainger College of Engineering
Qian Chen Group

Research

Overview: The Nanoscience of Soft Matter

We are fascinated by the fundamental rules governing the spatiotemporal complexities of synthetic soft materials and biological systems. We aim to answer questions of how to image, quantify, understand, and engineer such complexity from the atomic scale to composite structures, to design new properties. Our interest spans widely, from nanoparticles, polymers, to proteins and cells, from self-assembly, mechanical metamaterials, molecular separation to energy applications, from in-situ characterization to AI-guided automation. Particularly, by pushing the boundaries of innovative electron videography methods such as liquid-phase TEM, electron tomography, and 4D-STEM, we capture videos along the dimensions of time, composition, chemical reactivity, symmetry, defect, strain, and 3D shape, with unprecedented spatial resolutions, making the invisible visible. This imaging approach is coupled with statistical mechanics, machine learning, and computer simulations, enabling us to explore the frontiers of chemistry, materials science, and biology.

Our customized methodologies developed for the acquisition and analysis of high-dimensional electron microscopy data can be found at this GitHub website: https://github.com/chenlabUIUC

Read the following Review or Viewpoint papers of ours.

Beyond Snowflakes: Heterogeneity in Nanomaterials
Qian Chen*
Nano Letter, 22 (1), pp 3-5 (2022) [LINK] [PDF]

Electron Microscopy Studies of Soft Nanomaterials
Zhiheng Lyu, Lehan Yao, Wenxiang Chen, Falon C. Kalutantirige, and Qian Chen*
Chemical Reviews, 123 (7), 4051–4145  (2023) [LINK][PDF]

Hope to see the most up-to-date progress of our research? Come and join our weekly group meetings. See the group meeting schedule here. Reach out to Nick Menacher (menache1_AT_illinois.edu) for the time and location.

Nanoparticle self-assembly

and liquid-phase TEM

 

We pioneer the experimental paradigm to image, track, understand, and manipulate the self-assembly of nanoparticles in solution using liquid-phase TEM, which is of fundamental importance to introducing complexities in structure and property. The self-assembly pathways are governed by interactions at the nanoscale, which are difficult to model due to non-additivity and multiscale-coupling effects. We have developed the workflow of using liquid-phase TEM and machine-learning based single particle tracking to:

  1. Capture experimentally the phase transition pathways and nanoscale interactions governing assembly in equilibrium (Nat. Mater. 2020; Nat. Nanotechnol. 2023) and driven by external field (ACS Nano 2024).
  2. Engineer assembly pathways to favor functionally useful metastable structures, such as chiroptically active superlattices (Nature 2022).  
  3. Measure phonon dispersion relations of nanoparticle superlattices from particle vibrations, to design self-assembled topologically mechanical metamaterials (Nat. Mater. 2025). 

 


 

 

 

 

Precision synthesis of polymer patchy nanoparticles by atomic stenciling

We develop the atomic stencil method to selectively mask nanoparticle facets with ions and paint polymers at the unmasked areas with nanometer precision. This simple and scalable method allows the synthesis of patchy nanoparticles with deterministic control of patch placement, size, and shape. These patchy nanoparticles are synthetic analogues of proteins with multifunctionality and directional interactions, for targeted delivery, catalysis, microelectronics, integrated metamaterials and tissue engineering. We have integrated our wet chemistry synthesis with polymer physics-based scaling theory, density functional theory and simulation to:

  1. Synthesize a library of patchy nanoparticles for low index polyhedral gold nanoparticles and their assembly into large-scale crystals (Nature 2025).
  2. Capture the patch-clasping among patchy nanoparticles (ACS Nano 2024).
  3. Understand the critical importance of controlling the Flory-Huggins interaction parameter in symmetry-breaking patching behaviors (Nat. Commun. 2022).

 

Polymer separation membranes and electron tomography

 

As we apply electron tomography to polymeric samples, we find that much like how living cells undergo morphogenesis in organism formation, polymers can develop distinct 3D nanomorphology through folding or phase separation when they undergo processes such as chemical reaction, drying, and shearing. We use low-dose electron tomography of sub-nm voxel resolution and machine learning-based morphometry analysis to:

  1. Relate synthesis conditions of polymer membranes and separation performance with the otherwise missing middleman of 3D crumple morphology (Sci. Adv. 2022).
  2. Map practically complex polyamide membranes, both the interconnected networks and voids to enable prediction of separation performance (Nat. Commun. 2024).

 

 

 

 

Structural Biophysics

 

We use “dose-shielding” graphene liquid cell TEM, electron tomography, volume electron microscopy, and X-ray tomography to image the nanoscale shape transformation of biological systems. We work to resolve and understand the working mechanisms of proteins in various biological states via real-space and real-time experimental imaging. We extend such imaging to whole cell imaging and whole plant imaging to understand cell-cell, bacteria-inorganic minerals, and nanoparticle fertilizer and plant cell interactions. We have developed imaging analysis and biophysical analysis tools to:

  1. Capture the liquid-phase TEM movies of dynamic transformation of nanodiscs containing various membrane proteins, including rare events and biomechanical properties (Sci. Adv. 2024). 
  2. Whole cell/plant imaging of the interface of cellular interaction with kidney stones and nanoparticle fertilizers (PNAS 2026 ; Chem. Eng. J. 2026).

Strained metal and metal oxide materials and 4D-STEM

 

Nanoscale strain fields as well as associated structural defects are central to active-site chemistry, ion insertion, durability, as well as phase stability critical to separation and extraction of rare-earth elements. We have developed the sample preparation, imaging, and analysis protocols of 4D-STEM for these phase-complex materials to: 

  1. Map the oriented phase domains developed during the cycling of multivalent ion batteries, where the spatial patterns of strained regions can impact ion diffusivity by more than 10 fold (Nano Lett. 2019; Nat. Mater. 2022). 
  2. Integrate 4D-STEM with liquid-phase TEM to relate chemical reactivities with heterogeneity in nanostructures (Nano Lett. 2024).

 

 

 

 

New analysis tools: graph theory, machine learning, and automation

 

Soft materials are by nature complex, with extended heterogeneity in space and time, where traditional order parameters are insufficient and statistics are needed to fully capture the heterogeneity to relate to macroscopic properties. We have introduced graph theory metrics as a universal framework to quantify complex structures and developed a series of data science toolboxes for advanced autonomous imaging and analysis, tailored for soft materials to:

  1. Map multistage assembly pathways using graph theory metrics (Science, accepted).
  2. Achieve fast analysis of liquid-phase TEM movies to map out interaction landscape and self-assembly kinetic laws (ACS Cent. Sci. 2020).
  3. Map and analyze 3D nanomorphology in an automated way (Nanoscale 2022; npj Comput. Mater. 2024.)

 

 

Join Us!

We have openings for undergrads, PhD students, and postdocs with background in TEM, nanoscience or biology. Interested candidates can send your CV to Prof. Chen at qchen20@illinois.edu