Layer-by-layer design and optimization of xerogel-based amperometric first generation biosensors for uric acid

•Highly performing uric acid (UA) amperometric biosensor - PIH diagnosis application•Successful adaptation of glucose model scheme/material to clinical target molecule•LbL optimization and characterization of xerogel and polymeric materials•High sensitivity, selectivity, linear response for physiological relevant UA levels•Demonstrated rapid response, 10 day stability, and 30 + day sensor shelf-life

A layer-by-layer (LbL) strategy of modifying an electrode with a specific combination of polymeric and xerogel materials is used to create an effective first generation biosensor for uric acid (UA) - a clinically relevant molecule implemented in pregnancy induced hypertension (PIH) diagnosis, a condition that can lead to a serious disorder called preeclampsia. In addition to offering a new, promising sensor for UA, this study represents significant progress for amperometric biosensor development since the strategy and materials employed were successfully and readily adapted from a glucose biosensor model scheme and used for the effective detection of UA, a fundamentally different molecule compared to glucose. Specifically, each of four, functional modifying layers (outer polyurethane (PU) selective membrane, the inner selective electropolymer, and the xerogel bi-layer) are systematically investigated and tailored for UA permeability and interferent discrimination. The role of PU hydrophobicity and its UA permeability are established while the enzyme-doped and outer diffusional xerogel layers are evaluated for uricase (UOx) species/loading and silane precursor dependence, respectively. LbL systematic evaluation reveals the specific combination of hydroxyl-methyl triethoxy silane (HMTES) xerogels, a polyluminol-aniline electropolymer, and 100% hydrophilic polyurethane yields impressive uric acid sensing performance: effective sensitivity (0.8 nA/μM), linear response across physiologically relevant UA concentrations (100–700 μM), fast response times (~ 10 s), low limits of detection (< 10 μM), and selectivity against most common interferents. Toward the specific application of PIH risk assessment, the optimized sensor exhibited 10 day stability as well as effective shelf-life exceeding 35 days. The presented system rivals or exceeds UA biosensor performance found in the literature and offers the possibility of miniaturization for in situ or in vivo remote diagnostic sensing. The successful adaptation suggests that the strategy and materials may be applicable to detecting/monitoring other medically significant molecules via sensor development.

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