About

Joanna Aizenberg is the Amy Smith Berylson Professor of Materials Science at Harvard’s School of Engineering and Applied Sciences. She is a Core Faculty Member of the Wyss Institute for Biologically Inspired Engineering and Co-Director of the Kavli Institute for Bionano Science and Technology. Her research group focuses on understanding the basic principles of biological architectures and how biology solves complex problems in the design of multifunctional, adaptive materials. The goal of her research is to use biological principles as guidance in developing new, bio-inspired synthetic routes and nanofabrication strategies that lead to advanced materials and devices. Her lab pursues a wide range of research interests including adaptive materials, biomineralization, surface science and self-cleaning materials, bio-inspired optics, self-assembly, nanofabrication, and bio-nano interfaces.

Research topics

• Computer Science
• Materials science
• Nanotechnology
• Composite material
• Mathematics
• Physics
• Optics
• Chemistry
• Organic chemistry
• Polymer chemistry
• Biology
• Biological system
• Thermodynamics
• Paleontology
• Oceanography
• Photochemistry
• Optoelectronics
• Statistics
• Geometry
• Crystallography
• Statistical physics
• Chemical physics
• Geology
• Chemical engineering

Selected publications

• New Bio-inspired Materials: When Biology Meets Chemistry, Physics, Engineering, and Design

  Proceedings of the American Philosophical Society held at Philadelphia for promoting useful knowledge · 2026-03-01

  article1st authorCorresponding

  Abstract: Living systems sense, respond to, and harvest energy from the changing environment by interweaving chemistry, mechanics, optics, and fluid dynamics across time and length scales. In this lecture, materials scientist Joanna Aizenberg gives a taste of how inspiration from nature teaches us to break barriers between these fields in the synthetic realm and leads to fascinating new concepts in materials design. She looks at a deep-sea sponge and envisions a green, illuminated skyscraper that harvests energy from the wind. The brittle star’s intricate skeleton inspires dynamic optical systems that can collect light. She presents cilia-inspired adaptive hairy surfaces that alter their wetting, optical, and adhesive behavior via reconfiguration of tiny nanostructures. Creating liquid-sensing “noses” from chemically patterned photonic crystals inspired by butterflies, or ultra-slippery, antifouling surfaces with self-tuning transparency inspired by pitcher plant and cacti are just the beginning of the multifunctional, dynamic materials possibilities waiting to be explored at the intersection of biology, engineering, chemistry, and physics.

  PublisherDOI

• Rotational 3D printing of active–passive filaments and lattices with programmable shape morphing

  Proceedings of the National Academy of Sciences · 2026-04-22

  articleOpen access

  Natural filaments, such as proteins, plant tendrils, octopus tentacles, and elephant trunks, can transform into arbitrary three-dimensional shapes that carry out vital functions. Their shape-morphing behavior arises from intricate patterning of active and passive regions, which are difficult to replicate in synthetic matter. Here, we introduce a filament-centric strategy for programmable shape morphing in which intrinsic curvature and twist are directly encoded within multimaterial elastomeric filaments during fabrication. By harnessing rotational multimaterial 3D printing, we directly prescribe the filament’s natural curvature–twist field κ(s) through controlled material distribution and helical liquid crystal mesogen alignment. When heated above their nematic-to-isotropic transition temperature ( T NI ), the helically aligned liquid crystal elastomer regions contract along their local director field, while passive regions remain essentially unchanged. This approach enables independent control of bending and torsion at every cross-section along the filament centerline: the principal natural curvatures of the filament along two orthogonal axes as well as the local twist. Next, we printed architected lattices composed of unit cells formed by sinusoidal filaments that either reversibly contract, expand, or exhibit out-of-plane deformations. Discrete elastic rod simulations of Janus filaments with different natural curvatures and twist, which are interconnected within the printed lattices, allow accurate prediction of their observed shape-morphing behavior. By integrating active–passive elastomers, additive manufacturing, and computational modeling, we have created shape-morphing matter with complex programmable responses for applications that rely on adaptive, robotic, or deployable architectures.

  PublisherOA PDFDOI

• Indoor thermoregulatory homeostasis using hydrodynamic instability

  Proceedings of the National Academy of Sciences · 2026-05-22

  articleOpen accessCorresponding

  Branching patterns can emerge when one fluid is injected into a more viscous one within a quasi-two-dimensional cavity. While these patterns have dazzled physicists for decades, modern engineering efforts have focused on suppressing, rather than leveraging, these flow instabilities. Here, by designing fluidic devices with calibrated geometries, liquid absorptivities, and rheology, we exploit the thermal sensitivity of the Saffman-Taylor instability to achieve thermoregulatory shading systems with self-adjustment capabilities. Our devices produce negative feedback branching patterns that reduce indoor solar heating when warm but increase it when cool. Moreover, compared to existing temperature-responsive shading approaches with fixed thermal behaviors, our system can switch its thermal sensitivity and indoor temperature setpoints on-demand by adjusting the rate that patterns are grown. Experiments and models reveal the energy savings and indoor climate control capabilities enabled by this thermoregulatory framework. Overall, our work provides a blueprint for designing materials with self-regulatory behaviors based on flow instabilities.

  PublisherDOI

• Sniffing Out Risk: Development of an Electronic Nose to Detect Triggers of Airway Inflammation

  Journal of Allergy and Clinical Immunology · 2026-02-01

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Supplemental Data File for Self-regulated Dual-mode Solar Energy Harvesting

  Open MIND · 2026-01-01

  datasetSenior author
  DOI

• Supplemental Data File for Self-regulated Dual-mode Solar Energy Harvesting

  Zenodo (CERN European Organization for Nuclear Research) · 2026-01-01

  datasetOpen accessSenior author
  PublisherDOI

• Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery

  Zenodo (CERN European Organization for Nuclear Research) · 2026-05-26

  datasetOpen accessSenior author

  Version 2: Added missing files to sniffing_data.zip See GitHub repository for data processing functions and examples: https://github.com/soerenbrandt/sniffing-sensor Abstract:Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

  PublisherDOI

• Highly Selective Enteric Methane Monitoring Through Modular Sensor‐Filter Assembly (Adv. Mater. Technol. 10/2026)

  Advanced Materials Technologies · 2026-05-01

  articleSenior author

  Real-Time Methane Monitoring An ear-tag–mounted electronic nose monitors enteric methane from cattle in real time. The illustration highlights a materials-engineered, dual-layer filter that selectively permits methane transport while suppressing interfering vapors, enabling robust sensing in complex farm air. This work demonstrates a scalable, non-invasive approach to precision livestock methane monitoring. More details can be found in Research Article e02508 by Haritosh Patel, Joanna Aizenberg and co-workers.

  PublisherDOI

• Rotational 3D printing of active-passive filaments and lattices with programmable shape morphing

  Open MIND · 2026-03-05

  preprint

  Natural filaments, such as proteins, plant tendrils, octopus tentacles, and elephant trunks, can transform into arbitrary three-dimensional shapes that carry out vital functions. Their shape-morphing behavior arises from intricate patterning of active and passive regions, which are difficult to replicate in synthetic matter. Here, we introduce a filament-centric strategy for programmable shape morphing in which intrinsic curvature and twist are directly encoded within multimaterial elastomeric filaments during fabrication. By harnessing rotational multimaterial 3D printing (RM-3DP), we directly prescribe the filament's natural curvature--twist field $\mathbf{k}(s)$ through controlled material distribution and helical liquid crystal mesogen alignment. When heated above their nematic-to-isotropic transition temperature ($T_\mathrm{NI}$), the helically aligned LCE regions contract along their local director field, while passive regions remain essentially unchanged. This approach enables independent control of bending and torsion at every cross-section along the filament centerline: the principal natural curvatures of the filament along two orthogonal axes as well as the local twist. Next, we printed architected lattices composed of unit cells formed by sinusoidal filaments that either reversibly contract, expand, or exhibit out-of-plane deformations. Discrete elastic rod simulations of Janus filaments with different natural curvatures and twist, which are interconnected within the printed lattices, allow accurate prediction of their observed shape-morphing behavior. By integrating active-passive elastomers, additive manufacturing, and computational modeling, we have created shape-morphing matter with complex programmable responses for applications that rely on adaptive, robotic, or deployable architectures.

  DOI

• Highly Selective Enteric Methane Monitoring Through Modular Sensor‐Filter Assembly

  Advanced Materials Technologies · 2026-01-31

  articleSenior authorCorresponding

  ABSTRACT Accurate quantification of enteric methane emissions is essential for mitigating greenhouse gas outputs from livestock. However, existing methods such as respiration chambers and tracer gas systems remain costly, invasive, and impractical for large‐scale deployment. Here, we present a modular electronic nose (e‐nose) platform that introduces a materials‐informed sensor–filter framework for selective methane detection in complex gas environments relevant to livestock monitoring. The system integrates three modules: an environmental unit for temperature, humidity, and carbon dioxide tracking; a volatile organic compound module; and a methane‐selective unit comprising a metal oxide sensor with a dual‐layer adsorbent filter made of activated carbon spheres and a polyethylene terephthalate (PET) membrane. This hybrid, dual‐layer adsorptive filter enables a sensor–filter codesign strategy that suppresses both polar and non‐polar interferents while preserving methane transport, shifting the dominant selectivity mechanism from surface oxygen competitive depletion to diffusion‐mediated filtering. The methane‐selective sensor exhibited a strong signal‐to‐noise ratio (33.9 dB), low detection limit (7.8 ppm), and excellent linearity (R 2 = 0.999). In methane–pentane mixtures (20–1000 ppm), the filtered sensor maintained low error (∼13%), over 100‐fold lower than unfiltered counterparts. Controlled interferent studies elucidated how filters influence selectivity, offering a framework for designing sensor–filter pairs tailored to complex gas environments. Integrated with environmental compensation, this materials‐informed, ear‐tag‐shaped e‐nose establishes a deployment‐oriented framework for scalable, non‐invasive methane monitoring in livestock‐relevant environments.

  PublisherDOI

Recent grants

• Collaborative Research: CDI-Type I: Developing Computational Models to Guide the Design of Chemomechanically Responsive, Reconfigurable Surfaces

  NSF · $455k · 2011–2015

Frequent coauthors

• Neville Reid Moody

  65 shared

• Tak‐Sing Wong

  Pennsylvania State University

  64 shared

• Jack Alvarenga

  60 shared

• Philseok Kim

  Harvard University

  57 shared

• Michael Aizenberg

  Harvard University

  55 shared

• C. M. Friend

  Harvard University

  53 shared

• Feng Lin

  49 shared

• David S. Ginley

  National Renewable Energy Laboratory

  49 shared

Labs

• Aizenberg GroupPI

Education

• Ph.D., Materials Science and Engineering

  Massachusetts Institute of Technology

  1993

• M.S., Materials Science and Engineering

  Massachusetts Institute of Technology

  1989

• B.S., Chemical Engineering

  Technion-Israel Institute of Technology

  1986