Biomimetic Transparent Nanoplasmonic Meshes by Reverse-Nanoimprinting for Bio-Interfaced Spatiotemporal Multimodal SERS Bioanalysis

Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning

Surface-enhanced Raman spectroscopy (SERS) is a powerful optical technique for non-invasive and label-free bioanalysis of liquid biopsy, facilitating to diagnosis of potential diseases. Neuropsychiatric systemic lupus erythematosus (NPSLE) is one of the subgroups of systemic lupus erythematosus (SLE) with serious manifestations for a high mortality rate. Unfortunately, lack of well-established gold standards results in the clinical diagnosis of NPSLE being a challenge so far. Here we develop a novel Raman fingerprinting machine learning (ML-) assisted diagnostic method. The microsphere-coupled SERS (McSERS) substrates are employed to acquire Raman spectra for analysis via convolutional neural network (CNN). The McSERS substrates demonstrate better performance to distinguish the Raman spectra from serums between SLE and NPSLE, attributed to the boosted signal-to-noise ratio of Raman intensities due to the multiple optical regulation in microspheres and AuNPs. Eight statistically-significant (p-value <0.05) Raman shifts are identified, for the first time, as the characteristic spectral markers. The classification model established by CNN algorithm demonstrates 95.0% in accuracy, 95.9% in sensitivity, and 93.5% in specificity for NPSLE diagnosis. The present work paves a new way achieving clinical label-free serum diagnosis of rheumatic diseases by enhanced Raman fingerprints with machine learning.

SERS-based microdevices for use as in vitro diagnostic biosensors

Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.

Simple Isopropanol-Assisted Direct Soft Imprint Lithography for Residue-Free Microstructure Patterning

A direct soft imprint lithography was proposed to realize the direct fabrication of residue-free, well-shaped functional patterns through a single step. This imprint method requires only a simply prepared isopropanol-treated polydimethylsiloxane (PDMS) stamp without any additional resists. Residue-free Ag patterns were successfully fabricated on different substrates by directly imprinting the Ag ink with the isopropanol-treated PDMS stamp. Furthermore, the coffee-ring effect of the imprinting Ag patterns can be eliminated by optimizing the imprinting time, isopropanol-treating time, and imprinting temperatures. Studies show that the residual Ag ink in the contact region can be absorbed by the isopropanol-treated PDMS stamp due to the "like dissolves like" principle. Finally, this method was employed to fabricate the Ag electrodes for the thin-film transistors, attaining a mobility of ∼8 cm2 V-1 s-1, which is comparable to those with vacuum-processed electrodes. This process provides a simple, low-cost, residue-free, coffee-ring-free, and fast patterning method in the field of microelectronics.

Voltage Modulation of Nanoplasmonic Metal Luminescence from Nano-Optoelectrodes in Electrolytes

Metallic nanostructures supporting surface plasmon modes can concentrate optical fields and enhance luminescence processes from the metal surface at plasmonic hotspots. Such nanoplasmonic metal luminescence contributes to the spectral background in surface-enhanced Raman spectroscopy (SERS) measurements and is helpful in bioimaging, nanothermometry and chemical reaction monitoring applications. Although there is growing interest in nanoplasmonic metal luminescence, its dependence on voltage modulation has received limited attention in research investigations. Also, the hyphenated electrochemical surface-enhanced Raman spectroscopy (EC-SERS) technique typically ignores voltage-dependent spectral background information associated with nanoplasmonic metal luminescence due to limited mechanistic understanding and poor measurement reproducibility. Here, we report a combined experiment and theory study on dynamic voltage-modulated nanoplasmonic metal luminescence from hotspots at the electrode-electrolyte interface using multiresonant nanolaminate nano-optoelectrode arrays. Our EC-SERS measurements under 785 nm continuous wavelength laser excitation demonstrate that short-wavenumber nanoplasmonic metal luminescence associated with plasmon-enhanced electronic Raman scattering (PE-ERS) exhibits a negative voltage modulation slope (up to ≈30% V^-1) in physiological ionic solutions. Furthermore, we have developed a phenomenological model to intuitively capture the plasmonic, electronic, and ionic characteristics at the metal-electrolyte interface to understand the observed dependence of the PE-ERS voltage modulation slope on voltage polarization and ionic strength. The current work represents a critical step toward the general application of nanoplasmonic metal luminescence signals in optical voltage biosensing, hybrid optical-electrical signal transduction, and interfacial electrochemical monitoring.

In Situ Spatiotemporal SERS Measurements and Multivariate Analysis of Virally Infected Bacterial Biofilms Using Nanolaminated Plasmonic Crystals

In situ spatiotemporal biochemical characterization of the activity of living multicellular biofilms under external stimuli remains a significant challenge. Surface-enhanced Raman spectroscopy (SERS), combining the molecular fingerprint specificity of vibrational spectroscopy with the hotspot sensitivity of plasmonic nanostructures, has emerged as a promising noninvasive bioanalysis technique for living systems. However, most SERS devices do not allow reliable long-term spatiotemporal SERS measurements of multicellular systems because of challenges in producing spatially uniform and mechanically stable SERS hotspot arrays to interface with large cellular networks. Furthermore, very few studies have been conducted for multivariable analysis of spatiotemporal SERS datasets to extract spatially and temporally correlated biological information from multicellular systems. Here, we demonstrate in situ label-free spatiotemporal SERS measurements and multivariate analysis of Pseudomonas syringae biofilms during development and upon infection by bacteriophage virus Phi6 by employing nanolaminate plasmonic crystal SERS devices to interface mechanically stable, uniform, and spatially dense hotspot arrays with the P. syringae biofilms. We exploited unsupervised multivariate machine learning methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), to resolve the spatiotemporal evolution and Phi6 dose-dependent changes of major Raman peaks originating from biochemical components in P. syringae biofilms, including cellular components, extracellular polymeric substances (EPS), metabolite molecules, and cell lysate-enriched extracellular media. We then employed supervised multivariate analysis using linear discriminant analysis (LDA) for the multiclass classification of Phi6 dose-dependent biofilm responses, demonstrating the potential for viral infection diagnosis. We envision extending the in situ spatiotemporal SERS method to monitor dynamic, heterogeneous interactions between viruses and bacterial networks for applications such as phage-based anti-biofilm therapy development and continuous pathogenic virus detection.