Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on addressing these issues with recent Raman spectroscopy breakthroughs enabled by machine learning (ML), particularly deep learning methods. Given the regulatory requirements, consumer demand for safe food products, and growing awareness of risks with environmental pathogens, this study emphasizes addressing pathogen detection in clinical, food safety, and environmental settings. Here, we highlight the use of convolutional neural networks for analyzing complex clinical data and surface enhanced Raman spectroscopy for sensitizing early and rapid detection of pathogens and analyzing food safety and potential environmental risks. Deep learning methods can tackle issues with the lack of adequate Raman datasets and adaptability across diverse bacterial samples. We highlight pending issues and future research directions needed for accelerating real-world impacts of ML-enabled Raman diagnostics for rapid and accurate diagnosis and surveillance of pathogens across critical fields.

Human society annually produces nearly 100 billion gallons of wastewater, containing approximately 3600 GWh of energy. This study introduces a proof of concept utilizing graphene materials to extract and instantly store this energy. A hybrid device, mimicking a microbial fuel cell, acts as both a battery and supercapacitor. Wastewater serves as the electrolyte, with indigenous microorganisms on the graphene electrode acting as biocatalysts. The device features a capacitive electrode using a 3D nickel foam modified with a plasma-exfoliated graphene mixture. Compared to controls, the Gr/Ni configuration shows a 150-fold increase in power output (2.58 W/m²) and a 48-fold increase in current density (12 A/m²). The Gr/Ni/biofilm interface demonstrates outstanding charge storage capability (19,400 F/m²) as confirmed by electrochemical impedance spectroscopy. Microscopy, spectroscopy, and electrochemical tests were employed to elucidate the superior performance of Gr/Ni electrodes. Ultimately, the capacitive energy extracted from wastewater can power small electrical equipment in water infrastructure, addressing energy needs in remote regions without access to a typical power grid.