Abstract
Lateral Flow Assays (LFAs) have become the foundation of point-of-care (POC) diagnostics, valued for their user-friendly format, cost-effectiveness, and rapid turnaround time. However, despite their widespread success, most notably in pregnancy testing and infectious disease monitoring, conventional colorimetric LFAs often suffer from insufficient sensitivity, limiting their utility for the early-stage detection of low-abundance biomarkers. This review provides a critical analysis of recent strategies have been developed to improve the sensitivity of these assays. Enhancement strategies are systematically classified into six distinct domains: (i) flow modulation techniques that optimise reaction kinetics; (ii) sample preconcentration methods; (iii) advanced signal transduction reporters beyond traditional gold nanoparticles; (iv) chemical signal amplification including nanozyme and enzymatic catalysis; (v) structural strategies for maximising label accumulation; and (vi) the engineering of aptamers as programmable recognition elements. Special emphasis is placed on ”reagent-free” and ”equipment-free” innovations that boost performance without compromising the inherent simplicity of the device. Finally, the emerging transition from passive biological selection to active molecular engineering is discussed, outlining the future trajectory of ultra-sensitive next-generation LFAs.
Full text
6,603 characters
· extracted from
preprint-html
· click to expand
Strategies for Enhancing Sensitivity in Lateral Flow Assays | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 8 April 2026 V1 Latest version Share on Strategies for Enhancing Sensitivity in Lateral Flow Assays Authors : Aylar Eslami Saed , Jacopo Giaretta 0000-0003-0006-2132 , Syamak Farajikhah 0000-0002-2997-5931 [email protected] , and Fariba Dehghani Authors Info & Affiliations https://doi.org/10.22541/au.177564092.22756389/v1 184 views 109 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Lateral Flow Assays (LFAs) have become the foundation of point-of-care (POC) diagnostics, valued for their user-friendly format, cost-effectiveness, and rapid turnaround time. However, despite their widespread success, most notably in pregnancy testing and infectious disease monitoring, conventional colorimetric LFAs often suffer from insufficient sensitivity, limiting their utility for the early-stage detection of low-abundance biomarkers. This review provides a critical analysis of recent strategies have been developed to improve the sensitivity of these assays. Enhancement strategies are systematically classified into six distinct domains: (i) flow modulation techniques that optimise reaction kinetics; (ii) sample preconcentration methods; (iii) advanced signal transduction reporters beyond traditional gold nanoparticles; (iv) chemical signal amplification including nanozyme and enzymatic catalysis; (v) structural strategies for maximising label accumulation; and (vi) the engineering of aptamers as programmable recognition elements. Special emphasis is placed on ”reagent-free” and ”equipment-free” innovations that boost performance without compromising the inherent simplicity of the device. Finally, the emerging transition from passive biological selection to active molecular engineering is discussed, outlining the future trajectory of ultra-sensitive next-generation LFAs. Supplementary Material File (manuscript.pdf) Download 1.25 MB Information & Authors Information Version history V1 Version 1 08 April 2026 Copyright This work is licensed under a Keywords aptamer lateral flow assay point-of-care sensitivity enhancement signal amplification Authors Affiliations Aylar Eslami Saed The University of Sydney View all articles by this author Jacopo Giaretta 0000-0003-0006-2132 The University of Sydney View all articles by this author Syamak Farajikhah 0000-0002-2997-5931 [email protected] The University of Sydney View all articles by this author Fariba Dehghani The University of Sydney View all articles by this author Metrics & Citations Metrics Article Usage 184 views 109 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Aylar Eslami Saed, Jacopo Giaretta, Syamak Farajikhah, et al. Strategies for Enhancing Sensitivity in Lateral Flow Assays. Authorea . 08 April 2026. DOI: https://doi.org/10.22541/au.177564092.22756389/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.177564092.22756389/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fdf3feedb8d06cf',t:'MTc3OTE1MzMxOA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();
Text is read by the "Ask this paper" AI Q&A widget below.
Extraction quality varies by source — PMC NXML preserves structure
cleanly, OA-HTML may include some navigation residue, and OA-PDF can
have broken hyphenation. The publisher copy
(via DOI)
is the canonical version.