Zheng Lab - Research

2 Ph.D Positions are Available Immediately, Details in "Position" Page

Research Overview

The B³ Lab—Biomaterials, Biointerfaces, and Biophysics Laboratory—conducts highly interdisciplinary research at the convergence of bio-functional materials, biomolecular interfaces, disease-related proteins, and sustainable materials. Our work integrates biomaterials design, biointerface engineering, and biophysical characterization with data-driven modeling and molecular simulations to elucidate sequence/composition–structure–function relationships across multiple length and time scales, from molecular interactions to macroscopic material performance. This mechanistic understanding guides the development of next-generation biomedical devices, therapeutic materials, adaptive soft matter systems, and environmentally benign, energy-sustainable polymer platforms.

Smart, Tough, and Adaptive Polymer Networks

Soft polymer networks offer unique combinations of compliance, responsiveness, transport, and interfacial functionality, but their practical use is often limited by mechanical weakness, fatigue, environmental sensitivity, and irreversible damage. Our research seeks to establish fundamental design principles for creating mechanically robust and adaptive soft materials whose structure, properties, and functions can evolve in response to chemical, thermal, mechanical, and environmental stimuli.

We integrate molecular simulations, data-driven modeling, polymer synthesis, and multiscale experiments to investigate double-network and multi-network architectures, physically and dynamically crosslinked systems, phase-separated and hierarchical structures, and hybrid polymer networks. A central focus is to understand how molecular interactions, network topology, phase behavior, interfacial organization, and nonequilibrium processes govern mechanical reinforcement, energy dissipation, fracture, self-recovery, and adaptive response. Our research spans several interconnected directions:

Mechanically robust and fatigue-resistant networks, including double-network and hybrid architectures that combine sacrificial interactions, stress redistribution, and hierarchical energy dissipation.
Adaptive and self-reinforcing materials, in which mechanical deformation, bond rearrangement, polymerization, or structural reorganization can trigger recovery, strengthening, or changes in material function.
Environmentally resilient soft materials capable of maintaining functionality under freezing, high-salinity, dehydration, cyclic loading, and other extreme or nonequilibrium conditions.
Multifunctional polymer systems integrating conductivity, adhesion, mechanoresponsiveness, antifouling behavior, optical response, actuation, and interfacial functionality.
Fundamental structure–property–process relationships, with emphasis on how network formation sequence, crosslinking chemistry, entanglement, phase separation, solvent organization, and interfacial interactions determine macroscopic behavior.

Molecular Biophysics of Disease-Associated Proteins

Protein misfolding, aberrant self-assembly, and pathological protein–protein interactions are central to many human diseases, including neurodegenerative disorders, metabolic diseases, cancer, and other protein-associated pathologies. Disease-related proteins and peptides can undergo complex structural transitions, form transient oligomers and higher-order aggregates, interact with cellular membranes, and cross-talk with other biomolecular systems. Despite extensive study, the molecular mechanisms connecting protein structure, aggregation, membrane interactions, and cellular dysfunction remain incompletely understood, particularly at the atomic and molecular levels.

Our research combines molecular simulations, biophysical characterization, biochemical experiments, and data-driven approaches to understand how disease-associated proteins assemble, interact, and evolve across multiple spatial and temporal scales. A major focus is to uncover the molecular principles governing protein misfolding and aggregation and to determine how these processes contribute to disease progression and cellular toxicity. Our research spans several interconnected directions:

Protein misfolding and pathological self-assembly. We investigate the conformational transitions and intermolecular interactions that drive oligomerization, fibrillation, and higher-order assembly of disease-associated proteins, including Aβ, hIAPP, α-synuclein, and other amyloidogenic systems.
Structure and dynamics of toxic protein oligomers. We seek atomic- and molecular-level understanding of transient oligomeric states that are difficult to characterize experimentally but are often strongly associated with cytotoxicity and disease progression.
Protein–membrane interactions and membrane disruption. We study how disease-associated proteins adsorb onto cellular membranes, reorganize at interfaces, alter membrane structure and dynamics, and trigger membrane permeabilization or other pathological responses.
Cross-seeding and molecular connections among diseases. We investigate heterologous interactions between distinct proteins and peptides, including amyloid proteins, antimicrobial peptides, and cancer-associated proteins, to understand how cross-seeding, structural compatibility, and interfacial recognition may connect seemingly different pathological processes.
Molecular inhibition and therapeutic intervention. We design and evaluate peptides, small molecules, nanoparticles, and repurposed drugs that interfere with pathological protein assembly, stabilize non-toxic states, or redirect aggregation pathways.
Molecular probes and disease detection. We develop fluorescent and aggregation-induced emission probes to recognize disease-associated protein structures and explore strategies for sensitive detection of early aggregation states.

Multifunctional (Bio)materials

The design of highly bioinert, biocompatible, and multifunctional biomaterials is essential for a wide range of biomedical and industrial applications. A central challenge in this field is to create materials that can resist nonspecific biofouling while simultaneously providing desirable mechanical, chemical, and biological functions for use in complex physiological environments. Our research addresses this challenge through an integrated platform that combines computational modeling, data-driven design, polymer synthesis, and experimental characterization to establish fundamental design principles and engineer advanced biomaterials with predictable performance.

Our work has led to the development of a diverse family of biomaterials based on polyacrylamide/acrylate, polyethylene glycol (PEG), and zwitterionic polymers, as well as smart polymer systems incorporating stimuli-responsive functionalities. These materials are engineered into a variety of formats, including surfaces and coatings, brushes, fibers, nanogels, hydrogels, and nanochannels, enabling broad versatility across applications. A major emphasis of our research is understanding and controlling polymer–water, polymer–protein, and polymer–cell interactions, which are key to antifouling performance, biocompatibility, and biological function. Our research spans several interconnected directions:

Fundamental antifouling materials design, including the development of highly bioinert and biocompatible polymer systems and the elucidation of molecular mechanisms governing antifouling behavior.
Smart and stimuli-responsive biomaterials, especially zwitterionic and other functional polymers that respond to environmental or biological cues.
Multifunctional soft biomaterials, including tough, self-healing, adhesive, antimicrobial, and antifouling hydrogels and nanogels.
Biointerfaces and biomimetic systems, such as antifouling coatings, polymer brushes, biomimetic nanochannels, and engineered interfaces for controlling biological interactions.
Biomedical materials engineering, with applications in wound dressings, contact-lens substitutes, drug and gene delivery systems, and other translational biomaterial platforms.

Computational and Data-Driven Design of Materials and Biomolecular Systems

Computational modeling and data-driven approaches are increasingly central to understanding complex molecular systems and accelerating the discovery of new materials. Our research develops and applies multiscale molecular simulations, machine learning, statistical modeling, and computational design strategies to connect molecular structure and interactions with mesoscale organization, macroscopic properties, and biological function. Rather than treating computation solely as a supporting tool, we use it as an integrated component of the design–predict–validate–refine cycle, where simulations and machine-learning models generate mechanistic insight, guide experiments, identify promising candidates, and progressively improve predictive understanding. Our computational research spans several interconnected directions:

Molecular and multiscale simulations. We employ atomistic molecular dynamics, coarse-grained simulations, Monte Carlo methods, hybrid Monte Carlo–molecular dynamics approaches, and reactive or network-level models to investigate molecular interactions, self-assembly, polymer-network formation, interfaces, transport, aggregation, and mechanical behavior across multiple spatial and temporal scales.
Machine learning and data-driven discovery. We develop QSAR/QSPR models, graph-based learning, neural networks, active learning, and other data-mining approaches to uncover structure–property and structure–activity relationships and accelerate the design of polymers, soft materials, peptides, biomolecules, and functional materials.
Physics-informed and mechanism-guided modeling. Experimental and simulation-derived descriptors are integrated into predictive models to move beyond empirical correlations and identify the molecular mechanisms governing material properties, biological activity, and system-level behavior.
Inverse design and closed-loop materials discovery. Computational models are used to propose new molecular structures, polymer compositions, network architectures, and processing conditions with targeted properties. Predictions are experimentally tested and then fed back into the model, enabling iterative computational–experimental optimization and discovery.
Computational polymer and soft-material design. We model polymerization, crosslinking, network topology, phase separation, mechanochemical bond activation, fracture, transport, and environmental response to establish molecular-to-macroscale design principles for adaptive hydrogels, organogels, elastomers, membranes, and other polymeric materials.
Computational biophysics and biomolecular assembly. We investigate protein and peptide folding, aggregation, oligomerization, membrane interactions, cross-seeding, and molecular recognition using multiscale simulation and data-driven approaches. Computational peptide-packing and self-assembly models are also used to predict supramolecular structures and guide inhibitor or probe design.
Biointerfaces and interfacial modeling. We develop computational approaches to understand polymer–water, polymer–protein, membrane–protein, and material–biological interface interactions, providing molecular insight into antifouling, adhesion, transport, membrane disruption, and interfacial functionality.
Computational design across multifunctional materials. Machine learning and molecular simulation are increasingly integrated into our work on antifouling materials, biodegradable polymers, membranes, environmental materials, battery-related materials, and other multifunctional systems to identify transferable structure–property–function relationships.

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