2025/08/17 by Sean D. Huver, Huver, Sean D.
Chemical Engineering · Chemistry · Engineering · #Analytical Chemistry and Sensors #Electrochemical Analysis and Applications #Electrochemical sensors and biosensors #FOS: Physical sciences #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2508.12499
openalex publication_date 2025/08/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The characterization of ligand--receptor interactions is a cornerstone of modern pharmacology; however, current methods are hampered by limitations such as ensemble averaging and invasive labeling. We propose a theoretical quantum sensing solution, the Quantum Ligand-Binding Interrogator (QLI), designed to overcome these challenges. The QLI is a differential sensor, or gradiometer, that uses a pair of co-trapped atomic ions to perform label-free detection of the electric field gradient produced by a single ligand binding to its receptor in vitrified samples. This gradiometric approach provides robust common-mode rejection of background electric field noise. To bridge the gap between the cryogenic, ultra-high-vacuum environment required for the sensor and the biological sample, we propose an architecture based on a vitrified sample mounted on a scanning probe. This enables the detection of the electrostatic signature of a single molecule in a specific conformational state (e.g., bound vs. unbound). This paper details the conceptual framework of the QLI, the experimental architecture, the measurement protocol using entangled two-ion spin states, and an analysis of key engineering risks. Anchoring to state-of-the-art single-ion low-frequency sensitivities (sub-mV m-1/ √(Hz)), we project SNR = 1 in tens of seconds at a 10 \textmu m ion--sample separation for Δp ∼ 20 D, with feasibility dominated by the (as yet unmeasured) electrostatic stability of vitrified samples. If realized, QLI would provide direct single-molecule measurements of binding-induced electric field changes, offering a new path for experimental validation of computational models of drug--receptor interactions.