Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

The biological impact of microplastic pollution in human food and water sources is largely unknown, and drinking water sources are not exempt from this microplastic contamination. Here, we demonstrate a streamlined approach for capturing, quantifying, and identifying microplastics in drinking water. We present an analytical workflow termed the Silicon Nanomembrane Analysis Pipeline (SNAP) that takes advantage of novel Silicon nitride nanomembranes that enable a significant "concentration factor," which consolidates suspended particles into a planarized observation area for individuated, quantifiable, and multimodal particle analysis on the same substrate. SNAP's primary advantages derive from its use of ultrathin, Silicon nitride-based membranes housed in conventionally sized filter disks, enabling the direct capture and analysis of polymeric MPs on a non-polymeric background. Drinking water samples sourced in the Rochester, NY region, were collected from residential tap sources and analyzed using SNAP. Particles in each sample were characterized by optical and scanning electron microscopy (SEM), Raman spectroscopy, and energy-dispersive X-ray spectroscopy (EDX), and various identified constituents were quantified in proportion to the total captured particles.

Authors

Institutions

Publication Details

Journal
Journal of Visualized Experiments
Published
2025-06-13
DOI
https://doi.org/10.3791/68200
Primary Topic
Microplastics and Plastic Pollution
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

Gregory Madejski, Samantha Romanick, Teagan Horan, James Roussie et al.
Journal of Visualized Experiments
Microplastics and Plastic Pollution
article

Multimodal Analysis of Microplastics in Drinking Water using a Silicon Nanomembrane Analysis Pipeline

Gregory Madejski, Samantha Romanick, Teagan Horan, James Roussie, James L. McGrath, Ahmet Gurcan, Kathryn Neville
article en

Abstract

The biological impact of microplastic pollution in human food and water sources is largely unknown, and drinking water sources are not exempt from this microplastic contamination. Here, we demonstrate a streamlined approach for capturing, quantifying, and identifying microplastics in drinking water. We present an analytical workflow termed the Silicon Nanomembrane Analysis Pipeline (SNAP) that takes advantage of novel Silicon nitride nanomembranes that enable a significant "concentration factor," which consolidates suspended particles into a planarized observation area for individuated, quantifiable, and multimodal particle analysis on the same substrate. SNAP's primary advantages derive from its use of ultrathin, Silicon nitride-based membranes housed in conventionally sized filter disks, enabling the direct capture and analysis of polymeric MPs on a non-polymeric background. Drinking water samples sourced in the Rochester, NY region, were collected from residential tap sources and analyzed using SNAP. Particles in each sample were characterized by optical and scanning electron microscopy (SEM), Raman spectroscopy, and energy-dispersive X-ray spectroscopy (EDX), and various identified constituents were quantified in proportion to the total captured particles.

Journal of Visualized Experiments(220)
SiMPore (United States) (US), University of Rochester (US)
Clean water and sanitation
Openalex Percentile: Top 90%
Microplastics and Plastic Pollution
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.