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.

A Zr_58.5Nb_2.8Cu_15.6Ni_12.8Al_10.3 (Vitreloy 106a) bulk metallic glass (BMG) was successfully welded by friction stir welding (FSW) with a fixed polycrystalline cubic boron nitride (PCBN) pin tool below its crystallization temperature. The microstructure was analyzed by X-ray diffraction (XRD) to evaluate the crystallization of the weld. The reduced radial distribution function (RDF), short-range order (SRO) domain size, and atomic pair distribution function (PDF) were analyzed to evaluate the effect of FSW at atomic scale. As a result, the SRO domain size was reduced for the weld surface, while increased for the weld nugget.

Designing minimally invasive, defect-free coatings based on conformal graphene layers to shield metals from both abiotic and biotic forms of corrosion is a persistent challenge. Single-layer graphene (SLG) grown on polycrystalline copper (PC-Cu) surfaces often have inherent defects, particularly at Cu grain boundaries, which weaken their barrier properties and worsen corrosion through grain-dependent mechanisms. Here, we report that an SLG grown via chemical vapor deposition (CVD) on Cu (111) single crystal serves as a high-performance coating to lower corrosion by nearly 4–6 times (lower than bare Cu (111)) in abiotic (sulfuric acid) and microbiologically influenced corrosion (MIC) environments. For example, the charge transfer resistance for SLG/Cu (111) (3.95 kΩ cm²) was 2.5-fold higher than for bare Cu (111) (1.71 kΩ cm²). Tafel analysis corroborated a reduced corrosion current (42 ± 3 µA cm⁻²) for SLG/Cu (111) compared to bare Cu (111) (115 ± 7 µA cm⁻²). These findings are consistent with the results based on biofilm measurements. The SLG/Cu (111) reduced biofilm formation by 3-fold compared to bare Cu (111), increasing corrosion resistance, and effectively mitigating pitting corrosion. The average depths of the pits (3.4 ± 0.6 µm) for SLG/Cu (111) were notably shallower than those of bare Cu (111) (6.5 ± 1.2 µm). Surface analysis of the corrosion products corroborated these findings, with copper sulfide identified as a major component across both surfaces. The absence of grain boundaries in Cu (111) resulted in high-quality SLG manifesting higher barrier properties compared to SLG on PC-Cu. Our findings show promise for using the presented strategy for developing durable graphene coatings against diverse forms of corrosion.