Face masks will be used to prevent pandemic recurrence and outbreaks of mutant SARS-CoV-2 strains until mass immunity is confirmed. The polypropylene (PP) filter is a representative disposable mask material that traps virus-containing bioaerosols, preventing secondary transmission. In this study, a copper thin film (20 nm) was deposited via vacuum coating on a spunbond PP filter surrounding a KF94 face mask to provide additional protection and lower the risk of secondary transmission. Film adhesion was improved using oxygen ion beam pretreatment, resulting in cuprous oxide formation on the PP fiber without structural deformation. The copper-coated mask exhibited filtration efficiencies of 95.1 ± 1.32% and 91.6 ± 0.83% for NaCl and paraffin oil particles, respectively. SARS-CoV-2 inactivation was evaluated by transferring virus-containing media onto the copper-coated PP filters and subsequently adding Vero cells. Infection was verified using real-time polymerase chain reaction and immunochemical staining. Vero cells added after contact with the copper-coated mask did not express the RNA-dependent RNA polymerase and envelope genes of SARS-CoV-2. The SARS-CoV-2 nucleocapsid immunofluorescence results indicated a reduction in the amount of virus of more than 75%. Therefore, copper-coated antiviral PP filters could be key materials in personal protective equipment, as well as in air-conditioning systems.

Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. Existing diagnostic techniques are often invasive and carry risks of complications. Herein, we introduce a plasmonic gold nanopolyhedron (AuNH)-coated needle-based surface-enhanced Raman scattering (SERS) sensor, integrated with endoscopy, for direct mucus sampling and label-free detection of CRC. The thin and flexible stainless-steel needle is coated with polymerized dopamine, which serves as an adhesive layer and simultaneously initiates the nucleation of gold nanoparticle (AuNP) seeds on the needle surface. The AuNP seeds are further grown through a surface-directed reduction using Au ions-hydroxylamine hydrochloride solution, resulting in the formation of dense AuNHs. The formation mechanism of AuNHs and the layered structure of the plasmonic needle-based SERS (PNS) sensor are thoroughly analyzed. Furthermore, a strong field enhancement of the PNS sensor is observed, amplified around the edges of the polyhedral shapes and at nanogap sites between AuNHs. The feasibility of the PNS sensor combined with endoscopy system is further investigated using mouse models for direct colonic mucus sampling and verifying noninvasive label-free classification of CRC from normal controls. A logistic regression-based machine learning method is employed and successfully differentiates CRC and normal mice, achieving 100% sensitivity, 93.33% specificity, and 96.67% accuracy. Moreover, Raman profiling of metabolites and their correlations with Raman signals of mucus samples are analyzed using the Pearson correlation coefficient, offering insights for identifying potential cancer biomarkers. The developed PNS-assisted endoscopy technology is expected to advance the early screening and diagnosis approach of CRC in the future.

Effective hotspot engineering with facile and cost-effective fabrication procedures is critical for the practical application of surface-enhanced Raman spectroscopy (SERS). We propose a SERS substrate composed of a metal film over polyimide nanopillars (MFPNs) with three-dimensional (3D) volumetric hotspots for this purpose. The 3D MFPNs were fabricated through a two-step process of maskless plasma etching and hydrogel encapsulation. The probe molecules dispersed in solution were highly concentrated in the 3D hydrogel networks, which provided a further enhancement of the SERS signals. SERS performance parameters such as the SERS enhancement factor, limit-of-detection, and signal reproducibility were investigated with Cyanine5 (Cy5) acid Raman dye solutions and were compared with those of hydrogel-free MFPNs with two-dimensional hotspots. The hydrogel-coated MFPNs enabled the reliable detection of Cy5 acid, even when the Cy5 concentration was as low as 100 pM. We believe that the 3D volumetric hotspots created by introducing a hydrogel layer onto plasmonic nanostructures demonstrate excellent potential for the sensitive and reproducible detection of toxic and hazardous molecules.

With the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), disease prevention has become incredibly important. Consequently, mask and air-purifier use has increased. The filter is the core component of these items. However, most filter materials lack antimicrobial properties. Copper is a sustainable antimicrobial material. When copper is deposited onto the filter’s surface, the microorganisms that come into contact with it can be effectively inactivated. In this study, we used an oxygen ion beam with a controlled process temperature to treat filter surfaces with copper. This enabled a strong adhesion of at least 4 N/cm between the copper and the filter fibers without damaging them. Upon exposing the filter to bacteria (Staphylococcus aureus ATCC 6538, Klebsiella pneumoniae ATCC 4352, Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853) for one hour, a >99.99% removal rate was attained; when the filter was exposed to SARS-CoV-2 virus for one hour, it inactivated more than 99%. These beneficial properties minimize the risk of secondary infections, which are significantly more likely to occur when a conventional filter is replaced or removed.

Respiratory viruses, such as influenza A/B, RSV, SARS-CoV-2 and its variants, continue to be a major global health threat, highlighting the need for rapid and accurate variant-level diagnostics. Herein, we have developed a diagnostic platform for several respiratory viruses by integrating surface-enhanced Raman scattering (SERS) signals from three-dimensional (3D) plasmonic nanopillar substrates with interpretability-driven deep learning. The 3D plasmonic nanopillar array enables robust and reproducible capture of viral components, enhancing the SERS signal for virus-specific molecular fingerprinting. A one-dimensional convolutional neural network (1D-CNN) has been trained on SERS spectra from 13 respiratory virus types, including SARS-CoV-2 variants and sublineages, achieving over 98 % classification accuracy. To further improve model transparency, gradient-weighted class activation mapping (Grad-CAM) has been applied, revealing consistent Raman shift regions critical for virus discrimination across various media conditions. The platform has demonstrated reliable performance even in complex clinical samples, confirming its applicability for real-world diagnostics. The present approach offers a scalable and label-free solution for rapid virus detection, with potential for point-of-care applications and epidemiological surveillance.

The trade-off relationship between cost and performance is a major challenge in the development of surface-enhanced Raman spectroscopy (SERS) sensors for practical applications. We propose a roll-to-roll system with incorporated vacuum sputtering to manufacture Ag-coated nanodimples (Ag/NDs) on A4-scale films in a single step. The Ag/ND SERS platforms were prepared via O2 ion beam sputtering and Ag sputtering deposition. The concave three-dimensional spaces in the Ag/NDs functioned as hotspots, and their optimal fabrication conditions were investigated with two variables: moving speed and Ag thickness. The entire process was automated, which resulted in highly consistent optical responses (i.e., relative standard deviation of ∼10%). The activation of plasmonic hotspots was demonstrated by electric-field profiles calculated via the finite-difference time-domain method. The wavelength dependency of the Ag/ND platforms was also examined by dark-field microscopy. The results indicate that the developed engineering technique for the large-scale production of Ag/ND plasmonic chips would likely be competitive in the commercial market.