Issue 7, 2023

A thread-based electrofluidic platform for direct transfer, separation, and pre-concentration of materials from sample swabs

Abstract

A new method and platform has been developed for direct transfer, electrophoretic separation, and pre-concentration of swabbed samples using the principles of thread-based electrofluidics. A direct electrokinetic injection has been observed for a variety of analytes ranging from small molecules to proteins. The effect of physicochemical interactions of the analyte with the swab and the thread on the transfer efficiency has been studied by exploring different swab and thread combinations. For fluorescein, using a polyurethane swab, 98% and 94% transfer efficiencies were observed on mercerised cotton and nylon thread, while only 80% transfer efficiency was observed on polyester thread, respectively. A 97% transfer of fluorescein was observed on the nylon thread when a flocked nylon swab was used, while only 47% transfer was observed when a cotton swab was used. A successful transfer has been observed for both liquid and dry samples from either pre-wetted or dry swabs in both the presence and absence of any surrounding electrolytes. The platform has been further adapted for multiplexed analysis, where a sample from a single swab was transferred onto two parallel thread systems with ca. 50% distribution between them. The method has been validated for transfer, separation, and pre-concentration of DNA from blood. It has also been successfully used to directly analyse dried blood samples using a commercial sampling device, Neoteryx Mitra.

Graphical abstract: A thread-based electrofluidic platform for direct transfer, separation, and pre-concentration of materials from sample swabs

Supplementary files

Article information

Article type
Paper
Submitted
12 Nov 2022
Accepted
13 Feb 2023
First published
14 Feb 2023

Analyst, 2023,148, 1543-1551

A thread-based electrofluidic platform for direct transfer, separation, and pre-concentration of materials from sample swabs

A. Manchanda, V. Gupta, L. Wu and B. Paull, Analyst, 2023, 148, 1543 DOI: 10.1039/D2AN01856J

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