A Rational Framework for Optimizing Electrochemical Biosensor Performance via Systematically Tuning Linker Length and Probe Density on a Nanobody−MXene Platform
Abstract Electrochemical biosensors hold immense promise for sensitive, point-of-care detection of clinically relevant biomarkers from liquid biopsies; however, their performance is fundamentally constrained by charge screening and steric hindrance at the biointerface, particularly under physiological conditions where the Debye length is <1 nm. Herein, we establish a rational framework for engineering high-performance electrochemical biointerfaces by independently tuning two key parameters: linker-based bioreceptor spacing and probe loading density. This strategy is enabled by a modular nanobody−MXene platform, combining 2D MXene nanosheets with covalently conjugated nanobodies. Using anti-GFP nanobodies as model bioreceptors, we identified an intermediate linker regime that maximized signal transduction and a low-density probe regime that mitigated steric hindrance while enhancing dynamic range and sensitivity. Applied to a VHH72 nanobody−MXene biosensor, this framework enabled the detection of SARS-CoV-2 S1 with a 12 pM limit of detection, high specificity, and robust performance in saliva. Collectively, this work establishes a dual-parameter framework for advancing next-generation electrochemical biosensors.
Authors
- Erol Hasan (ORCID: https://orcid.org/0009-0003-7610-1887)
- Dana Alsulaiman (ORCID: https://orcid.org/0000-0002-3185-6288)
- Muhsin Ali (ORCID: https://orcid.org/0000-0002-1542-5138)
- Stefan T. Arold (ORCID: https://orcid.org/0000-0001-5278-0668)
- Ghanimah Abuhaimed
- Mohamed Nejib Hedhili (ORCID: https://orcid.org/0000-0002-3624-036X)
- Raik Grünberg
- Somayah Qutub
Institutions
- King Abdullah University of Science and Technology (SA)
Publication Details
- Journal
- ACS Materials Letters
- Published
- 2026-08-14
- DOI
- https://doi.org/10.1021/acsmaterialslett.6c00611
- Primary Topic
- MXene and MAX Phase Materials
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- King Abdullah University of Science and Technology