---
title: "FiberPro 1.0: AI-Guided Multiagent Platform for High-Throughput Protein Micro/Nanofiber Production"
id: "biorxiv-11-fiberpro-1-0-multiagent-ai-guided-design-for-high-throughput-production-and"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-11-fiberpro-1-0-multiagent-ai-guided-design-for-high-throughput-production-and"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.18.752775v1?rss=1"
published_at: "2026-09-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# FiberPro 1.0: AI-Guided Multiagent Platform for High-Throughput Protein Micro/Nanofiber Production
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-11-fiberpro-1-0-multiagent-ai-guided-design-for-high-throughput-production-and
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.18.752775v1?rss=1)
- **Published At:** 2026-09-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- FiberPro 1.0 is a multiagent framework driven by a large language model that integrates high-throughput spinning with real-time experimental feedback to enable autonomous design, execution, and optimization of protein micro/nanofiber production. - The platform targets limitations of protein fiber manufacture: low throughput and processing instability, especially when using focused rotary jet spinning (**FRJS**) which has narrow processing windows. - FiberPro 1.0 applies closed-loop experimental guidance to navigate FRJS parameter spaces, reducing failure-prone trial-and-error in protein systems. - Across three distinct protein systems, FiberPro converged on **spinnable formulations** within an average of two experimental iterations, indicating rapid optimization capability. - To demonstrate translational potential and **high-throughput conformal deposition**, the system designed a zein‑based active packaging coating applied directly onto diverse food matrices. - The zein-based conformal coating combined antibacterial activity with real-time freshness monitoring; the authors report marked suppression of **Escherichia coli** and an extension of shelf life in the tested application. - The work is presented as establishing a verifiable route for scalable manufacturing and conformal coating of functional protein micro/nanofibers by connecting autonomous AI reasoning with high-throughput processing. - The preprint reports no declared competing interests and is available under a CC-BY 4.0 International license.
## Clinical Analysis & Structured Key Points
FiberPro 1.0: Multiagent AI-Guided Design for High-Throughput Production and Conformal Deposition of Functional Protein Micro/Nanofibers | bioRxiv Skip to main content New Results FiberPro 1.0: Multiagent AI-Guided Design for High-Throughput Production and Conformal Deposition of Functional Protein Micro/Nanofibers Longwen Li , Carter Moses , Yechan Noh , Charlie Hsu , Fatima Calderon Gutierrez , Cody Ruiz , Juan Felipe Mogollon Molina , Kaiyu Fu , Huibin Chang doi: https://doi.org/10.64898/2026.09.18.752775 Longwen Li 1 University of Notre Dame; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Carter Moses 1 University of Notre Dame; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yechan Noh 1 University of Notre Dame; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Charlie Hsu 1 University of Notre Dame; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Fatima Calderon Gutierrez 2 Bethel University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cody Ruiz 2 Bethel University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Juan Felipe Mogollon Molina 1 University of Notre Dame; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kaiyu Fu 1 University of Notre Dame; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Huibin Chang 1 University of Notre Dame; Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: hchang8{at}nd.edu Abstract Info/History Metrics Supplementary material Preview PDF 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. Competing Interest Statement The authors have declared no competing interest. Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license . Back to top Previous Next Posted September 22, 2026. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share FiberPro 1.0: Multiagent AI-Guided Design for High-Throughput Production and Conformal Deposition of Functional Protein Micro/Nanofibers Longwen Li , Carter Moses , Yechan Noh , Charlie Hsu , Fatima Calderon Gutierrez , Cody Ruiz , Juan Felipe Mogollon Molina , Kaiyu Fu , Huibin Chang bioRxiv 2026.09.18.752775; doi: https://doi.org/10.64898/2026.09.18.752775 Share This Article: Copy Citation Tools FiberPro 1.0: Multiagent AI-Guided Design for High-Throughput Production and Conformal Deposition of Functional Protein Micro/Nanofibers Longwen Li , Carter Moses , Yechan Noh , Charlie Hsu , Fatima Calderon Gutierrez , Cody Ruiz , Juan Felipe Mogollon Molina , Kaiyu Fu , Huibin Chang bioRxiv 2026.09.18.752775; doi: https://doi.org/10.64898/2026.09.18.752775 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8019) Biochemistry (18749) Bioengineering (14905) Bioinformatics (44471) Biophysics (22615) Cancer Biology (19733) Cell Biology (26913) Clinical Trials (138) Developmental Biology (13974) Ecology (21013) Epidemiology (2067) Evolutionary Biology (25466) Genetics (16177) Genomics (23527) Immunology (18720) Microbiology (42540) Molecular Biology (18075) Neuroscience (93552) Paleontology (701) Pathology (2984) Pharmacology and Toxicology (5102) Physiology (8123) Plant Biology (16006) Scientific Communication and Education (2095) Synthetic Biology (4562) Systems Biology (10239) Zoology (2392)
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