FiberPro 1.0: Multiagent AI-Guided Design for High-Throughput Production and Conformal Deposition of Functional Protein Micro/Nanofibers
- 1University of Notre Dame
Abstract
Functional protein micro/nanofibers integrate high specific surface areas with bioactive architectures but remain hampered low production throughput and severe processing instability. Focused rotary jet spinning (FRJS) shows promising to break these throughput constraints while enabling direct, conformal deposition onto complex, irregular substrates. However, navigating FRJS's narrow processing windows in proteins remains failure-prone without closed-loop experimental guidance. Here, we report FiberPro 1.0, a large language model driven multi agent framework that unites high-throughput spinning with real-time experimental feedback for autonomous design, execution, and optimization. Across three protein systems, FiberPro 1.0 converged on spinnable formulations within an average of two iterations. To demonstrate high-throughput conformal deposition in a translational setting, FiberPro designed a zein-based active packaging system applied directly onto diverse food matrices. The resulting conformal coating combined potent antibacterial activity with real-time freshness monitoring, markedly suppressing Escherichia coli and extending shelf life. This work connects autonomous AI reasoning with high-throughput processing, establishing a verifiable route for scalable manufacturing and conformal coating of functional protein micro/nanofibers.
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BibTeX
@misc{li2026fiberpro,
title = {{FiberPro 1.0: Multiagent AI-Guided Design for High-Throughput Production and Conformal Deposition of Functional Protein Micro/Nanofibers}},
author = {L. Li and C. Moses and Y. Noh and C. Hsu and F. Calderon Gutierrez and C. Ruiz and J. F. Mogollon Molina and K. Fu and H. Chang},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.18.752775},
url = {https://www.biorxiv.org/content/10.64898/2026.09.18.752775v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - FiberPro 1.0: Multiagent AI-Guided Design for High-Throughput Production and Conformal Deposition of Functional Protein Micro/Nanofibers
AU - L. Li
AU - C. Moses
AU - Y. Noh
AU - C. Hsu
AU - F. Calderon Gutierrez
AU - C. Ruiz
AU - J. F. Mogollon Molina
AU - K. Fu
AU - H. Chang
PY - 2026
DA - 2026/09/18
PB - bioRxiv
DO - 10.64898/2026.09.18.752775
UR - https://www.biorxiv.org/content/10.64898/2026.09.18.752775v1
AB - Functional protein micro/nanofibers integrate high specific surface areas with bioactive architectures but remain hampered low production throughput and severe processing instability. Focused rotary jet spinning (FRJS) shows promising to break these throughput constraints while enabling direct, conformal deposition onto complex, irregular substrates. However, navigating FRJS's narrow processing windows in proteins remains failure-prone without closed-loop experimental guidance. Here, we report FiberPro 1.0, a large language model driven multi agent framework that unites high-throughput spinning with real-time experimental feedback for autonomous design, execution, and optimization. Across three protein systems, FiberPro 1.0 converged on spinnable formulations within an average of two iterations. To demonstrate high-throughput conformal deposition in a translational setting, FiberPro designed a zein-based active packaging system applied directly onto diverse food matrices. The resulting conformal coating combined potent antibacterial activity with real-time freshness monitoring, markedly suppressing Escherichia coli and extending shelf life. This work connects autonomous AI reasoning with high-throughput processing, establishing a verifiable route for scalable manufacturing and conformal coating of functional protein micro/nanofibers.
ER -