Pseudomonas aeruginosa (P. aeruginosa) is an opportunistic pathogen causing infections in blood and implanted devices. Traditional identification methods take more than 24 h to produce results. Molecular biology methods expedite detection, but require an advanced skill set. To address these challenges, this work demonstrates functionalization of laser-induced graphene (LIG) for developing flexible electrochemical sensors for P. aeruginosa based on phenazines. Electrodeposition as a facile approach is used to functionalize LIG with molybdenum polysulfide (MoS x ). The sensor's limit of detection (LOD), sensitivity, and specificity are determined in broth, agar, and wound simulating medium (WSM). Control experiments with Escherichia coli, which does not produce phenazines, demonstrate specificity of sensors for P. aeruginosa. The LOD for pyocyanin (PYO) and phenazine-1-carboxylic acid (PCA) is 0.19 × 10 −6 and 1.2 × 10 −6 m, respectively. Furthermore, the highly stable sensors enable real-time monitoring of P. aeruginosa biofilms over several days. Comparing square wave voltammetry data over time shows time-dependent generation of phenazines. In particular, two configurations-"Normal" and "Flipped"-are studied, showing that the phenazines time dynamics vary depending on how cells interact with sensors. The reported results demonstrate the potential of the developed sensors for integration with wound dressings for early diagnosis of P. aeruginosa infection.
Multiplexed detection of biomolecules is of great value in various fields, from disease diagnosis to food safety and environmental monitoring. However, accurate and multiplexed analyte detection is challenging to achieve in mixtures using a single device/material. In this paper, we demonstrate a machine learning (ML)-powered multimodal analytical device based on a single sensing material made of electrodeposited molybdenum polysulfide (eMoSx) on laser induced graphene (LIG) for multiplexed detection of tyrosine (TYR) and uric acid (UA) in sweat and saliva. Electrodeposition of MoSx shows an increased electrochemically active surface area (ECSA) and heterogeneous electron transfer rate constant, k^0. Features are extracted from the electrochemical data in order to train ML models to predict the analyte concentration in the sample (both singly spiked and mixed samples). Different ML architectures are explored to optimize the sensing performance. The optimized ML-based multimodal analytical system offers a limit of detection (LOD) that is two orders of magnitude better than conventional approaches which rely on single peak analysis. A flexible and wearable sensor patch is also fabricated and validated on-body, achieving detection of UA and TYR in sweat over a wide concentration range. While the performance of the developed approach is demonstrated for detecting TYR and UA using eMoSx-LIG sensors, it is a general analytical methodology and can be extended to a variety of electrochemical sensors to enable accurate, reliable, and multiplexed sensing.
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