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3D-printed lung model helps researchers study aerosol deposition in the lungs

Feb 24, 2025

4 min

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Treating respiratory diseases is challenging. Inhalable medicines depend on delivering particles to the right lung areas, which is complicated by factors like the drug, delivery method and patient variability, or even exposure to smoke or asbestos particles. University of Delaware researchers have developed an adaptable 3D lung model to address this issue by replicating realistic breathing maneuvers and offering personalized evaluation of aerosol therapeutics.


“If it's something environmental and toxic that we're worried about, knowing how far and how deep in the lung it goes is important,” said Catherine Fromen, University of Delaware Centennial Associate Professor for Excellence in Research and Education in the Department of Chemical and Biomolecular Engineering. “If it's designing a better pharmaceutical drug for asthma or a respiratory disease, knowing exactly where the inhaled aerosol lands and how deep the medicine can penetrate will predict how well that works.”that can replicate realistic breathing maneuvers and offer personalized evaluation of aerosol therapeutics under various breathing conditions.


Fromen and two UD alumni have submitted a patent application on the 3D lung model invention through UD’s Office of Economic Innovation and Partnerships (OEIP), the unit responsible for managing intellectual property at UD.


In a paper published in the journal Device, Fromen and her team demonstrate how their new 3D lung model can advance understanding of how inhalable medications behave in the upper airways and deeper areas of the lung. This can provide a broader picture on how to predict the effectiveness of inhalable medications in models and computer simulations for different people or age groups. The researchers detail in the paper how they built the 3D structure and what they’ve learned so far.


Valuable research tool

The purpose of the lung is gas exchange. In practice, the lung is often approximated as the size of a tennis court that is exchanging oxygen and carbon dioxide with the bloodstream in our bodies. This is a huge surface area, and that function is critical — if your lungs go down, you're in trouble.


Fromen described this branching lung architecture like a tree that starts with a trunk and branches out into smaller and smaller limbs, ranging in size from a few centimeters in the trachea to about 100 microns (roughly the combined width of two hairs on your head) in the lung’s farthest regions. These branches create a complex network that filters aerosols as they travel through the lung. Just as tree branches end in leaves, the lung’s branches culminate in delicate, leaf-like structures called alveoli, where gases are exchanged.


“Those alveoli in the deeper airways make the surface area that provides this necessary gas exchange, so you don't want environmental things getting in there where they can damage these sensitive, finer structures,” said Fromen, who has a joint appointment in biomedical engineering.


Mimicking the complex structure and function of the lung in a lab setting is inherently challenging. The UD-developed 3D lung model is unique in several ways. First, the model breathes in the same cyclic motion as an actual lung. That’s key, Fromen said. The model also contains lattice structures to represent the entire volume and surface area of a lung. These lattices, made possible through 3D printing, are a critical innovation, enabling precise design to mimic the lung's filtering processes without needing to recreate its full biological complexity.


“There's nothing currently out there that has both of these features,” she explained. “This means that we can look at the entire dosage of an inhaled medicine. We can look at exposure over time, and we can capture what happens when you inhale the medication and where the medicine deposits, as well as what gets exhaled as you breathe.”


The testing process

Testing how far an aerosol or environmental particle travels inside the 3D lung model is a multi-step process. The exposure of the model to the aerosol only takes about five minutes, but the analysis is time-consuming. The researchers add fluorescent molecules to the solution being tested to track where the particles deposit inside the model’s 150 different parts.


“We wash each part and rinse away everything that deposits. The fluorescence is just a molecule in the solution. When it deposits, we know the concentration of that, so, when we rinse it out, we can measure how much fluorescence was recovered,” Fromen said.


This data allows them to create a heat map of where the aerosols deposit throughout the lung model’s airways, which then can be validated against benchmarked clinical data for where such aerosols would be expected to go in a human under similar conditions.


The team’s current model matches a healthy person under sitting/breathing conditions for a single aerosol size, but Fromen’s team is working to ensure the model is versatile across a much broader range of conditions.


“An asthma attack, exercise, cystic fibrosis, chronic obstructive pulmonary disorder (COPD) — all those things are going to really affect where aerosols deposit. We want to make sure our model can capture those differences,” Fromen said.


The ability to examine disease features like airway narrowing or mucus buildup could lead to more personalized care, such as tailored medication doses or redesigned inhalers. Currently, inhaled medicines follow a one-size-fits-all approach, but the UD-developed model offers a tool to address these issues and understand why many inhaled medicines fail clinical trials.

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Is AI ethical? One philosophy professor has an answer. featured image

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Researchers detail why kissing bug far from home across Atlantic is an international concern featured image

3 min

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Cultivating justice and joy through critical AI use in the K-12 Classroom featured image

3 min

Cultivating justice and joy through critical AI use in the K-12 Classroom

Inspired by her middle school social studies teacher, University of Delaware Associate Professor Stephanie Smith Budhai has always believed that a classroom should be a space where students feel valued and nurtured. As a seasoned educator, she has helped teachers create these spaces using technology to support students in meeting learning outcomes, developing their skills and growing their confidence. Now, Budhai is mentoring overwhelmed teachers to reclaim the classroom as a nurturing environment for all students. Her book, "Critical AI in K-12 Classrooms: A Practical Guide for Cultivating Justice and Joy" with co-author Marie Heath of Loyola University Maryland, supports teachers and students as they navigate the complicated interactions of AI, education and justice. Budhai answered pressing questions about the already popular book. How would you describe the central message or takeaway of this book? "I believe that classrooms should be joyous spaces where all students feel heard, valued, seen and loved. Dealing with powerful technologies that exacerbates the biases and harms of society such as AI can impact teachers’ ability to nurture such spaces. Thus, this book was written to provide a practical guide for teachers and students as they navigate the complicated interactions of AI, education and justice. It is our duty as educators not to dismiss the technology altogether, but to help teachers take a critical lens, as they use AI for teaching and learning." What inspired you to pursue this line of research? "Before writing this book, I had already published 8 books and over 80 practitioner articles around technology, equity, teacher education and leadership. There had been other books that were quickly published about AI, but none focused on the harms of AI, how teachers could come to understand how AI really works and use it in ways that do not harm students." What are some of the key ways that K-12 teachers can cultivate justice and joy through AI? "In the book, we have many examples of how to cultivate justice and joy. For example, in chapter 3, we provide strategies for teachers to build an equity-mindset while supporting students in developing a critical consciousness around AI technologies, which are inherently biased. In chapters 4 and 5, we provide framing for using culturally-sustaining, abolitionist and fugitive pedagogies in designing curricula and learning activities that include the just use of AI technologies. And, in chapter 6, we provide teachers with myriad personal, professional, pedagogical and participatory actions they can take when using AI for teaching and learning." How could this work impact schools, teacher education programs and the broader public? "This work was created to support K-12 teachers, and many teachers have already shared how it has impacted them. K-12 teachers can immediately put the strategies and learning activities that we suggest in the book to use. We also situate AI in longer histories of technology injustice and provide accessible content on understanding how AI works through a critical algorithmic literacy perspective. Teacher education programs can use this work as they prepare teachers to navigate the complicated intersections of teaching, AI and justice. For the broader public, we also include content on justice-centered community activities and projects that are focused on critical AI." About Stephanie Smith Budhai Stephanie Smith Budhai is an associate professor in UD’s College of Education and Human Development, specializing in educational technology. Her interdisciplinary research and practice lies at the intersections of technology, equity and civic engagement, across myriad K-16 settings. She has published 10 books on teaching and digital technologies. Among other professional appointments, she serves as the Chair of the Information Technology Council for the Society for Information Technology and Teacher Education and is on the Board of Directors for the Civics of Technology. To speak with Smith further about AI in K-12 education, reach out to MediaRelations@udel.edu.

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