ArticleLab on a chip2026
Nanomembrane-based microfluidic platform with embedded electrical pressure transducer for on-chip nanoparticle quantification.
Article in Lab on a chip, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
8 authors.
Funding
Abstract
Accurate quantification of nanoparticle concentration is important in a host of fields, particularly in nanomedicine, electronics, and catalysis. Microfluidic systems present an opportunity to develop low-cost tests for nanoparticle quantification but often suffer technical challenges related to small sample volumes and optical interference from materials used to construct the device. Here we introduce a microfluidic device that integrates an ultrathin silicon nitride nanoporous membrane (nanomembrane) with an on-chip pressure transducer, designed to precisely quantify nanoparticle concentrations within a microfluidic device using an electrical readout for quantification. As nanoparticles are captured by the membrane under pressure-driven flow, the pressure differential across it changes and is measured by an on-chip transducer. The pressure transducer utilizes a thin PDMS membrane that deflects under pressure to change the cross-section and ionic flow resistance of an adjacent channel. This enables the determination of nanoparticle concentration by analysis of the kinetics of trans-membrane pressure changes relative to particle blockage of the nanomembrane. We also propose a statistical model of partial blockage and particle caking in nanoporous membranes, which accounts for distributions in pore and particle sizes. This model provides a more detailed understanding of nanoparticle filtration behavior and the kinetics of nanopore blocking, enabling accurate concentration determination. Experimental validation of the model on the data acquired by the microfluidic device demonstrates a lower limit of detection on the order of 10
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