Lateral flow assay sensitivity and signal enhancement via laser µ-machined constrains in nitrocellulose membrane
Lateral flow assay (LFA) is a handful diagnostic technology that can identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and other common respiratory viruses in one strip, which can be tested at the point-of-care without the need for equipment or skilled personnel outside the laboratory. Although its simplicity and practicality make it an appealing solution, it remains a grand challenge to substantially enhance the colorimetric LFA sensitivity. In this work, we present a straightforward approach to enhance the sensitivity of LFA by imposing the flow constraints in nitrocellulose (NC) membranes via a number of vertical femtosecond laser micromachined microchannels which is important for prolonged specific binding interactions. Porous NC membrane surfaces were structured with different widths and densities µ-channels employing a second harmonic of the Yb:KGW femtosecond laser and sample XYZ translation over a microscope objective-focused laser beam. The influence of the microchannel parameters on the vertical wicking speed was evaluated from the video recordings. The obtained results indicated that µ-channel length, width, and density in NC membranes controllably increased the immunological reaction time between the analyte and the labeled antibody by 950%. Image analysis of the colorimetric indicators confirmed that the flow rate delaying strategy enhanced the signal sensitives by 40% compared with pristine NC LFA.
Authors
- Gazy Khatmi (ORCID: https://orcid.org/0000-0001-6989-9286)
- Laimis Silimavičius (ORCID: https://orcid.org/0009-0005-8368-8350)
- Martynas Simanavičius (ORCID: https://orcid.org/0000-0003-2996-229X)
- Asta Tamulevičienė (ORCID: https://orcid.org/0000-0003-4152-1382)
- Tomas Klinavičius (ORCID: https://orcid.org/0000-0002-7925-1691)
- Indrė Kučinskaitė-Kodzė (ORCID: https://orcid.org/0000-0002-1761-0089)
- Tomas Tamulevičius (ORCID: https://orcid.org/0000-0003-3879-2253)
- Gintautas Gylys
- Agnė Rimkutė (ORCID: https://orcid.org/0009-0003-7022-3701)
Institutions
- Kaunas University of Technology (LT)
- Vilnius University (LT)
- Vilnius Business College (LT)
Publication Details
- Journal
- Scientific Reports
- Published
- 2024-10-02
- DOI
- https://doi.org/10.1038/s41598-024-74407-3
- Citations
- 17
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 3.14