# A Machine Learning-Assisted Liquid Crystal Droplet Array Platform for the Sensitive and Selective Detection of Per- and Polyfluoroalkyl Substances (PFAS) in Water

## TL;DR

A machine learning-assisted liquid crystal platform can detect harmful PFAS chemicals in water at extremely low levels, even in complex environments.

## Contribution

A novel ML-assisted LC droplet array platform for sensitive and selective PFAS detection in water.

## Key findings

- The platform detects PFOA and PFOS at parts-per-trillion levels in various water types.
- Transfer learning enables differentiation between PFOA, PFOS, and their mixtures.
- The method works in water with interfering molecules and meets EPA contaminant level standards.

## Abstract

We report a machine learning (ML)-assisted liquid crystal
(LC)
droplet array platform for the detection of per- and polyfluoroalkyl
substances (PFAS) in water. Our approach uses an autoencoder network
to process thousands of images obtained from arrays of microscale
droplets of thermotropic LCs. The latent space obtained using the
autoencoder contains significant information that enables sensitive
and selective detection of two amphiphilic PFAS [perfluorooctanoic
acid (PFOA) and perfluorooctanesulfonic acid (PFOS)] at concentrations
as low as parts-per-trillion (ppt) in ultrapure water, municipal tap
water, and simulated river water containing dissolved organic matter.
Despite the absence of visually discernible changes in the optical
outputs of LC arrays at low PFAS concentrations, this approach accurately
predicts their presence, even in water containing interfering molecules.
We also demonstrate the use of transfer learning to differentiate
between PFOA, PFOS, and PFOA/PFOS mixtures, showcasing the potential
for practical environmental monitoring. This platform permits identification
of PFOA and PFOS below the maximum contaminant levels (4 ppt) established
by the U.S. Environmental Protection Agency. Our approach is compatible
with automated printing, treatment, and high-throughput optical and
ML analysis and could provide a basis for the development of low-cost
sensors to monitor PFAS and other amphiphilic contaminants in real-world
water samples.