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Virus detection is highly important; the last several years, since the onset of the SARS-CoV-2 pandemic, have highlighted a weakness in the field: the need for highly specialized and complex methodology for sensitive virus detection, which also manifests as sacrifices in limits of detection made to achieve simple and rapid sensing. Surface-enhanced Raman spectroscopy (SERS) has the potential to fill this gap, and two novel approaches to the development of a detection scheme are presented in this study. First, the physical entrapment of vesicular stomatitis virus (VSV) and additional virus-like particles through substrate design to localize virus analytes into SERS hotspots is explored. Then, the use of nonspecific linear polymers as affinity agents to facilitate polymer-enabled capture of the VSV for SERS detection is studied. Quantitative detection of the VSV is achieved down to 10 1 genetic copies per milliliter with an R 2 of 0.987 using the optimized physical entrapment method. Physical entrapment of two more virus-like particles is demonstrated with electron microscopy, and distinctive SERS fingerprints are shown. This study shows great promise for the further exploration of label-free virus detection methods involving thoughtful substrate design and unconventional affinity agents.

Neurotransmitters act as chemical messengers, determining human physiological and psychological function, and abnormal levels of neurotransmitters are related to conditions such as Parkinson's and Alzheimer's disease. Biologically and clinically relevant concentrations of neurotransmitters are usually very low (nM), so electrochemical and electronic sensors for neurotransmitter detection play an important role in achieving sensitive and selective detection. Additionally, these sensors have the distinct advantage to potentially be wireless, miniaturized, and multichannel, providing remarkable opportunities for implantable, long-term sensing capabilities unachievable by spectroscopic or chromatographic detection methods. In this article, we will focus on advances in the development and characterization of electrochemical and electronic sensors for neurotransmitters during the last five years, identifying how the field is progressing as well as critical knowledge gaps for sensor researchers.

Investigating the interactions between small, charged molecules and aptamers using surface plasmon resonance (SPR) is limited by the inherent low response of small molecules and difficulties with nonspecific electrostatic interactions between the aptamer, analyte, and sensor surface. However, aptamers are increasingly being used in sensors for small molecule detection in critical areas like healthcare and environmental safety. The ability to probe these interactions through simple, direct SPR assays would be greatly beneficial and allow for the development of improved sensors without the need for complicated signal enhancement. However, these assays are nearly nonexistent in the current literature and are instead surpassed by sandwich or competitive binding techniques, which require additional sample preparation and reagents. In this work, we develop a method to characterize the interaction between the charged small molecule serotonin (176 Da) and an aptamer with SPR using streptavidin-biotin capture and a high-ionic-strength buffer. Additionally, other methods, such as serotonin immobilization and thiol-coupling of the aptamer, were investigated for comparison. These techniques give insight into working with small molecules and allow for quickly adapting a binding affinity assay into a direct SPR sensor.

Carbon dots (CDs) have attracted great attention in a range of applications due to their bright photoluminescence, high photostability, and good biocompatibility. However, it is challenging to design CDs with specific emission properties because the syntheses involve many parameters, and it is not clear how each parameter influences the CD properties. To help bridge this gap, machine learning, specifically an artificial neural network, is employed in this work to characterize the impact of synthesis parameters on and make predictions for the emission color and wavelength for CDs. The machine reveals that the choice of reaction method, purification method, and solvent relate more closely to CD emission characteristics than the reaction temperature or time, which are frequently tuned in experiments. After considering multiple models, the best performing machine learning classification model achieved an accuracy of 94% in predicting relative to actual color. In addition, hybrid (two-stage) models incorporating both color classification and an artificial neural network k -ensemble model for wavelength prediction through regression performed significantly better than either a standard artificial neural network or a single-stage artificial neural network k -ensemble regression model. The accuracy of the model predictions was evaluated against CD emission wavelengths measured from experiments, and the minimum mean average error is 25.8 nm. Overall, the models developed in this work can effectively predict the photoluminescence emission of CDs and help design CDs with targeted optical properties.