About

Xuanhe Zhao is a professor of mechanical engineering and civil and environmental engineering (by courtesy) at MIT. His research focuses on soft materials and systems, including tough adhesive hydrogels, hard-magnetic soft materials, wearable ultrasound, bioelectronics, and soft medical robotics. He also studies solid mechanics topics such as large deformation, instability, fracture, fatigue, adhesion, and multiple-field coupling, with an emphasis on societal impacts related to health, sustainability, water, food, energy, and plastics. Zhao's lab at MIT aims to advance science and technology at the interface of humans and machines to address grand societal challenges. He holds a Ph.D. and M.Sc. from Harvard University, a M.Sc. from the University of British Columbia, and a B.E. from Tianjin University. Zhao has received numerous honors and awards, including the NSF CAREER Award, ONR Young Investigator Award, SES Young Investigator Medal, and the Clarivate Highly Cited Researcher designation. His work has led to innovations such as bioadhesive ultrasound devices and adhesive coatings that prevent scarring around medical implants, with some of his lab's inventions recognized among TIME's Best Inventions of 2022 and awarded the Nature Spinoff Prize in 2023. Zhao holds multiple patents licensed by companies and contributing to FDA-approved medical devices. He has also held faculty positions at Duke University and MIT, and serves on various editorial boards and professional committees.

Research topics

• Computer Science
• Nanotechnology
• Materials science
• Engineering
• Composite material
• Artificial Intelligence
• Systems engineering
• Business
• Acoustics
• Mechanical engineering
• Data science
• Risk analysis (engineering)
• Optoelectronics
• Engineering management
• Physics

Selected publications

• Biohybrid Tendons Enhance the Power‐to‐Weight Ratio and Modularity of Muscle‐Powered Robots (Adv. Sci. 15/2026)

  Advanced Science · 2026-03-01

  articleOpen access
  PublisherDOI

• Targeting key toxic nanoscale particulate matter for precision control of coal power emissions

  Communications Earth & Environment · 2026-04-27

  articleOpen access

  Coal-fired power plants represent a major anthropogenic source of nanoscale particulate matter, yet conventional mass-based regulations overlook the distinct and potent health risks posed by specific components. Here we combine single-particle elemental profiles (169 plants across China) with cellular toxicity (human lung cells). Using interpretable machine learning, we reveal iron-rich nanoparticles as key toxic driver, explaining 27.4% of the observed oxidative stress and 16.9% of cytotoxicity. We then develop a high-resolution national inventory of iron-rich nanoparticles, estimating total emissions of 236 tons in 2020, with Eastern China as a hotspot contributing 38.2%. Tailored regional strategies could achieve a 77.5% reduction in national emission, with electrostatic precipitator upgrades identified as the most cost-effective measure. Our findings provide an actionable framework to advance air pollution policy beyond total emissions control toward component-specific reduction of the most toxic nanoparticles, ultimately mitigating their associated public health impacts. Iron rich nanoscale particles from coal plants are identified as the main toxic component, revealed through single particle chemical profiling and cell-based toxicity analysis combined with interpretable machine learning.

  PublisherOA PDFDOI

• Iron-Rich Particles Drive Pulmonary Toxicity of Coal Combustion-Derived Fine Particles via Transferrin Receptor-Mediated Ferroptosis

  Environmental Science & Technology · 2026-03-02 · 1 citations

  article

  Coal-derived fine particles (FPs, <1 μm) are highly reactive and compositionally heterogeneous, yet their toxicity mechanisms remain poorly understood. Using single-particle ICP-TOF-MS, we profiled metal(loid)s in FPs from ten representative coal-fired power plants across China. Quantification showed that 57 ± 9% of FPs were multimetal(loid) (mmFPs), 84 ± 9% of which were Al/Si/Fe-rich and carried most toxic metals. Toxicology assays identified that Fe-rich FPs and associated toxic metals (Cr, Mn, and Pb) could be important contributors to cellular injury, accompanied by oxidative stress and in vitro transcriptomic enrichment of ferroptosis, inflammation, and small-cell lung cancer-related signaling pathways. As an easily separable Fe-rich FP fraction, magnetic FPs comprised only 15.8% of the mass yet contributed 74.2% of oxidative stress and 88.5% of the cytotoxicity. In vitro and in vivo experiments revealed their transferrin receptor (TFRC)-mediated uptake induced ferroptosis and pulmonary injury, which could be attenuated by a TFRC inhibitor. These results suggest Fe-rich FPs (together with associated toxic metals) as the significant contributor of coal-combustion FP toxicity and provide the mechanistic evidence pinpointing Fe-rich particles as key determinants.

  PublisherDOI

• Source Apportionment of Lead-Containing Fine Particles from Typical Industrial Emissions: A Machine Learning Approach Based on Source-specific Fingerprints

  2026-03-13

  articleOpen access1st authorCorresponding

  Lead-containing fine particles (Pb-FPs) from industrial emissions pose significant health risks, but their source-specific characteristics and traceability remain significant knowledge gaps. This study constructed a nationwide Pb-FP multi-metal fingerprint dataset and developed a machine learning–based source apportionment approach for efficient and accurate source attribution of atmospheric Pb-containing particles. Specifically, we presented a comprehensive investigation of Pb-FPs derived from four major industrial sectors in China, i.e. coal-fired power (CFP), iron and steel smelting (ISS), waste incineration power (WIP), and biomass power generation (BP), through systematic analysis of 134 PM samples collected nationwide using single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOF-MS). Our results showed that WIP (5 ×107 particles/mg) and ISS (3.9 ×107 particles/mg) activities emitted significantly higher number concentrations of Pb-FPs compared to CFP and BP sources. Across all sources, Pb–multi-metal FPs accounted for 66.7–81.2 % of total Pb-FPs number concentrations, with the mass fraction of Pb was predominantly ≤ 10 %.Hierarchical clustering resolved 36 elemental fingerprint clusters with distinct source signatures (e.g., Fe/Mn/Zn-enriched ISS particles versus Si/Al-dominated CFP particles). Building on these fingerprints, we evaluated five machine learning algorithms for source apportionment, with XGBoost emerging as the optimal classifier (F1 score = 0.76, accuracy = 0.77) after intra-fold parameter optimization and cross-validation strategies. Application of the model to PM2.5 samples from Beijing and Shanghai revealed persistent and substantial contributions from ISS-derived Pb-FPs (6.7–38.1 % in Beijing, 10.5–33.7 % in Shanghai), with additional average inputs from CFP (7.4 %), WIP (5.8 %), and BP (12.1 %). These results highlight the dominant role of ISS in atmospheric Pb pollution across industrialized regions of China and provide a basis for explainable source-attribution analysis and future transfer-learning applications.

  PublisherDOI

• Extraordinarily high fractocohesive lengths in polymer-like networks

  Mathematics and Mechanics of Solids · 2026-03-03

  articleOpen accessSenior authorCorresponding

  The failure resistance of polymer networks dictates their utility as material candidates across industries. However, relating the key length scales driving crack growth to molecular mechanisms remains a key bottleneck in predicting and designing against fracture. The fractocohesive length—defined in terms of the ratio of fracture energy to the specific work to rupture—of a material correlates with the length scale of energy dissipation and controls fracture resistance. Although the Lake–Thomas model predicts the fractocohesive length of a perfect polymer network to match the undeformed mesh size, real soft materials exhibit values that far exceed this prediction. Here, we report extraordinarily high fractocohesive lengths in polymer-like networks with and without defects. We find that even perfect networks can have fractocohesive lengths orders of magnitude higher than the undeformed mesh size due to highly nonlinear chain behavior giving rise to nonlocal effects during fracture. Introducing defects further increases the fractocohesive length. We identify quantitative relations between nonlinear chain mechanics, defect length, defect density, and fractocohesive length. Overall, strain-stiffening chain behavior, defect density, and defect size independently correlate with larger fractocohesive lengths in polymer-like networks, and their individual effects can be collapsed into a single power law scaling. These outcomes point the way towards improved physics-informed design of soft yet tough polymers and metamaterials.

  PublisherDOI

• Author Correction: Adhesive anti-fibrotic interfaces on diverse organs

  Nature · 2025-07-11 · 2 citations

  erratumOpen accessSenior author

  of the article, as seen in Fig. 1, below.The change does not alter the results or conclusions of the paper.

  PublisherOA PDFDOI

• Critical insights on the chemistry and toxicity of fine particles from power and steel plant emissions in China

  Environment International · 2025-12-04 · 1 citations

  articleOpen access

  particles (ca. 5217 tons), annually. Among these emissions, ISPs contributed over 97 %, with sintering and ironmaking being the major contributors. Therefore, developing advanced filtration technologies and enhancing the monitoring of ISP emissions is strongly encouraged.

  PublisherDOI

• Biohybrid tendons enhance the power-to-weight ratio and modularity of muscle-powered robots

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-14

  preprintOpen access

  Abstract Biohybrid robots powered by tissue engineered skeletal muscle have historically relied on architectures in which muscle actuators are placed directly on skeletons, thus limiting the accessible design space for such machines. By contrast, native musculoskeletal architecture relies on tendons to bridge the interface between muscles and skeletons, enabling precise, space-efficient, and energy-efficient force transmission. In this study, we use a mathematical model of the muscle-tendon-skeleton interface to design a biohybrid muscle-tendon unit composed of tissue engineered muscle coupled to adhesive tough hydrogel tendons. We show how tuning tendon stiffness and pre-tension modulates actuator performance, measure fatigue characteristics of our actuators over &gt;7000 cycles, and tune skeleton stiffness to increase force transmission muscles to skeletons by ∼29X. Furthermore, we demonstrate an ∼11X improvement in power-to-weight ratio of muscle-tendon units as compared to previous demonstrations of robots powered by muscles alone. This work validates a robust approach for designing, manufacturing, and deploying muscle-tendon actuators that promises to enhance the modularity and efficiency of biohybrid robots.

  PublisherOA PDFDOI

• Scaling Law for Intrinsic Fracture Energy of Diverse Stretchable Networks

  Physical Review X · 2025-01-08 · 12 citations

  articleOpen accessSenior author

  Networks of interconnected materials permeate throughout nature, biology, and technology due to exceptional mechanical performance. Despite the importance of failure resistance in network design and utility, no existing physical model effectively links strand mechanics and connectivity to predict bulk fracture. Here, we reveal a scaling law that bridges these levels to predict the intrinsic fracture energy of diverse stretchable networks. Simulations and experiments demonstrate its remarkable applicability to a breadth of strand constitutive behaviors, topologies, dimensionalities, and length scales. We show that local strand rupture and nonlocal energy release contribute synergistically to the measured intrinsic fracture energy in networks. These effects coordinate such that the intrinsic fracture energy scales independent of the energy to rupture a strand; it instead depends on the strand rupture force, breaking length, and connectivity. Our scaling law establishes a physical basis for fracture of homogeneous networks with uniform strand mechanics and lattice connectivity throughout. The scaling also extends generally for fabricating tough materials from homogeneous networks across multiple length scales.

  PublisherDOI

• The Loop-Opening Model for the Intrinsic Fracture Energy of Elastomers

  Macromolecules · 2025-06-21 · 6 citations

  articleSenior authorCorresponding

  The intrinsic fracture energy of elastomers is a key factor in determining the mechanical durability of products such as rubber and tires. Historically, the intrinsic fracture energy has been described by the Lake–Thomas model, despite its known limitations. A loop-opening model has been proposed to describe the intrinsic fracture energy of gels prepared at semidilute conditions. In this work, we performed fatigue experiments on highly elastic end-linked polydimethylsiloxane (PDMS) elastomers and further combined these results with existing data from various elastomers to validate the applicability of the loop-opening model to elastomers. Our findings show that the intrinsic fracture energy per chain scales with the product of the fracture force and the contour length of the bridging chains between constraints, suggesting that unentangled and slightly entangled elastomers also follow the loop-opening model. However, in elastomers, overlapping chains constrain the opened loops and prevent them from fully extending. This result is supported by both experimental data and molecular dynamics simulations. This study extends the applicability of the loop-opening framework from gels to elastomers, providing a more quantitative and predictive description of intrinsic fracture energy across diverse polymer networks.

  PublisherDOI

Recent grants

• Transformative Skin: Controlled Electromechanical Instability on Polymer Surfaces

  NSF · $376k · 2012–2014

• EFRI C3 SoRo: Functional-Domain Soft Robots Precisely Controlled by Quantitative Dynamic Models and Data

  NSF · $2.0M · 2019–2024

• CAREER: Electroactive Graphene-Polymer System with Extreme Actuation and Tunable Properties

  NSF · $400k · 2013–2015

• Designing Extremely Robust Soft Wet Adhesives by Exploiting Molecular-Scale Reversible Crosslinks and Macro-Scale Instabilities

  NSF · $370k · 2017–2020

• CAREER: Electroactive Graphene-Polymer System with Extreme Actuation and Tunable Properties

  NSF · $355k · 2014–2018

Frequent coauthors

• Enrique Herrera‐Viedma

  Universidad de Granada

  571 shared

• Fei‐Yue Wang

  University of Chinese Academy of Sciences

  405 shared

• Yu Kang

  388 shared

• K Seow

  Royal Adelaide Hospital

  388 shared

• Giuseppe Nicosia

  388 shared

• Winston H. Hsu

  388 shared

• Maria Pia Fanti

  Polytechnic University of Bari

  388 shared

• Ieee Officers

  Institute of Electrical and Electronics Engineers

  388 shared

Labs

• Zhao LabPI

Awards & honors

• Uncas and Helen Whitaker Professorship, MIT (07/2024)
• Highly Cited Researcher 2023, Clarivate (11/2023)
• Nature Spinoff Prize (for SanaHeal), Nature (06/2023)
• Highly Cited Researcher 2022, Clarivate (11/2022)
• TIME Best Invention of 2022 (11/2022)