# Degradable bioplastics for microfluidic diagnostic assays towards improved environmental and human health

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $638,942

## Abstract

PROJECT SUMMARY
 As POC diagnostics transitions towards mainstream use, it would be valuable to establish a credible
and validated set of biodegradable materials to sustain future innovations in monolithic microfluidic devices, in
order to reduce waste and curb the environmental impact of biomedical products. Bioplastics are an attractive
biodegradable material for microfluidic diagnostics. In particular, bio-based materials made of plant byproducts
are of interest due to their renewable material source to reduce reliance on fossil fuels. Existing
demonstrations of bioplastic microfluidics are limited; hence, despite the great potential of bioplastics for POC
diagnostics, there is a need for accessible fabrication, demonstration for diagnostic methods, and appropriate
degradability. Building on preliminary results with an accessible fabrication strategy, this proposal develops a
novel material fabrication method and device assembly strategy which direct each component of the life cycle
of a plastic microfluidic diagnostic device toward a more sustainable alternative to reduce the environmental
impacts of plastics and safeguard human health.

## Key facts

- **NIH application ID:** 10827115
- **Project number:** 1R01AI181318-01
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Samuel K Sia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $638,942
- **Award type:** 1
- **Project period:** 2024-06-28 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10827115

## Citation

> US National Institutes of Health, RePORTER application 10827115, Degradable bioplastics for microfluidic diagnostic assays towards improved environmental and human health (1R01AI181318-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10827115. Licensed CC0.

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