Interactions between bacterial surface and nanoparticles govern the performance of 'chemical nose' biosensors

Rapid and portable diagnosis of pathogenic bacteria can save lives lost from infectious diseases. Biosensors based on a "chemical nose" approach are attracting interest because they are versatile but the governing interactions between bacteria and the biosensors are poorly understood. Here, we use a "chemical nose" biosensor based on gold nanoparticles to explore the role of extracellular polymeric substances in bacteria-nanoparticle interactions. We employ simulations using Maxwell-Garnett theory to show how the type and extent of aggregation of nanoparticles influence their colorimetric response to bacteria. Using eight different species of Gram-positive and Gram-negative bacteria, we demonstrate that this "chemical nose" can detect and identify bacteria over two orders of magnitude of concentration (89% accuracy). Additionally, the "chemical nose" differentiates between binary and tertiary mixtures of the three most common hospital-isolated pathogens: Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa (100% accuracy). We demonstrate that the complex interactions between nanoparticles and bacterial surface determine the colorimetric response of gold nanoparticles and thus, govern the performance of "chemical nose" biosensors.