Abstract: A method including detecting contaminants in a water sample using a machine learning algorithm having a Laplacian operator configured to extract Raman peak data, a deep neural network, and a K nearest neighbors (KNN) cluster model. The method may include a test system having a silicon nanofiber film, a plurality of ZnO nanorods arranged in an array on the silicon nanofiber film, and a plurality of silver particles disposed on the plurality of ZnO nanorods. A water sample may be applied onto the test system, and the water sample may be measured using surface-enhanced Raman spectroscopy to generate the Raman peak data used in the machine learning algorithm.

Abstract: Embodiments of the present disclosure pertain to a composition that includes: silica nanofibers; and one or more detection particles associated with the silica nanofibers. Additional embodiments of the present disclosure pertain to methods of sensing one or more analytes and/or one or more environmental conditions from a sample by associating the sample with a composition of the present disclosure; detecting a change in a property of the detection particles; and correlating the change in the property of the detection particles to a presence or absence of one or more analytes, one or more environmental conditions, or combinations thereof. Further embodiments of the present disclosure pertain to methods of making the compositions of the present disclosure by growing silica nanofibers from at least one precursor material; and associating one or more detection particles with the silica nanofibers.