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.

Highly porous gold supraparticles as surface-enhanced Raman spectroscopy (SERS) substrates for sensitive detection of environmental contaminants.

Surface-enhanced Raman spectroscopy (SERS) has great potential as an analytical technique for environmental analyses. In this study, we fabricated highly porous gold (Au) supraparticles (i.e., ∼100 µm diameter agglomerates of primary nano-sized particles) and evaluated their applicability as SERS substrates for the sensitive detection of environmental contaminants. Facile supraparticle fabrication was achieved by evaporating a droplet containing an Au and polystyrene (PS) nanoparticle mixture on a superamphiphobic nanofilament substrate. Porous Au supraparticles were obtained through the removal of the PS phase by calcination at 500 °C. The porosity of the Au supraparticles was readily adjusted by varying the volumetric ratios of Au and PS nanoparticles. Six environmental contaminants (malachite green isothiocyanate, rhodamine B, benzenethiol, atrazine, adenine, and gene segment) were successfully adsorbed to the porous Au supraparticles, and their distinct SERS spectra were obtained. The observed linear dependence of the characteristic Raman peak intensity for each environmental contaminant on its aqueous concentration reveals the quantitative SERS detection capability by porous Au supraparticles. The limit of detection (LOD) for the six environmental contaminants ranged from ∼10 nM to ∼10 µM, which depends on analyte affinity to the porous Au supraparticles and analyte intrinsic Raman cross-sections. The porous Au supraparticles enabled multiplex SERS detection and maintained comparable SERS detection sensitivity in wastewater influent. Overall, we envision that the Au supraparticles can potentially serve as practical and sensitive SERS devices for environmental analysis applications.

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

Multicellular systems, such as microbial biofilms and cancerous tumors, feature complex biological activities coordinated by cellular interactions mediated via different signaling and regulatory pathways, which are intrinsically heterogeneous, dynamic, and adaptive. However, due to their invasiveness or their inability to interface with native cellular networks, standard bioanalysis methods do not allow in situ spatiotemporal biochemical monitoring of multicellular systems to capture holistic spatiotemporal pictures of systems-level biology. Here, a high-throughput reverse nanoimprint lithography approach is reported to create biomimetic transparent nanoplasmonic microporous mesh (BTNMM) devices with ultrathin flexible microporous structures for spatiotemporal multimodal surface-enhanced Raman spectroscopy (SERS) measurements at the bio-interface. It is demonstrated that BTNMMs, supporting uniform and ultrasensitive SERS hotspots, can simultaneously enable spatiotemporal multimodal SERS measurements for targeted pH sensing and non-targeted molecular detection to resolve the diffusion dynamics for pH, adenine, and Rhodamine 6G molecules in agarose gel. Moreover, it is demonstrated that BTNMMs can act as multifunctional bio-interfaced SERS sensors to conduct in situ spatiotemporal pH mapping and molecular profiling of Escherichia coli biofilms. It is envisioned that the ultrasensitive multimodal SERS capability, transport permeability, and biomechanical compatibility of the BTNMMs can open exciting avenues for bio-interfaced multifunctional sensing applications both in vitro and in vivo.

Microporous Multiresonant Plasmonic Meshes by Hierarchical Micro-Nanoimprinting for Bio-Interfaced SERS Imaging and Nonlinear Nano-Optics.

Microporous mesh plasmonic devices have the potential to combine the biocompatibility of microporous polymeric meshes with the capabilities of plasmonic nanostructures to enhance nanoscale light-matter interactions for bio-interfaced optical sensing and actuation. However, scalable integration of dense and uniformly structured plasmonic hotspot arrays with microporous polymeric meshes remains challenging due to the processing incompatibility of conventional nanofabrication methods with flexible microporous substrates. Here, scalable nanofabrication of microporous multiresonant plasmonic meshes (MMPMs) is achieved via a hierarchical micro-/nanoimprint lithography approach using dissolvable polymeric templates. It is demonstrated that MMPMs can serve as broadband nonlinear nanoplasmonic devices to generate second-harmonic generation, third-harmonic generation, and upconversion photoluminescence signals with multiresonant plasmonic enhancement under fs pulse excitation. Moreover, MMPMs are employed and explored as bio-interfaced surface-enhanced Raman spectroscopy mesh sensors to enable in situ spatiotemporal molecular profiling of bacterial biofilm activity. Microporous mesh plasmonic devices open exciting avenues for bio-interfaced optical sensing and actuation applications, such as inflammation-free epidermal sensors in conformal contact with skin, combined tissue-engineering and biosensing scaffolds for in vitro 3D cell culture models, and minimally invasive implantable probes for long-term disease diagnostics and therapeutics.

A digital SERS sensing platform using 3D nanolaminate plasmonic crystals coupled with Au nanoparticles for accurate quantitative detection of dopamine.

We report a digital surface-enhanced Raman spectroscopy (SERS) sensing platform using the arrays of 3D nanolaminate plasmonic crystals (NLPC) coupled with Au nanoparticles and digital (on/off) SERS signal analysis for the accurate quantitative detection of dopamine (DA) at ultralow concentrations. 3D NLPC SERS substrates were fabricated to support the optically dense arrays of vertically-stacked multi-nanogap hotspots and combined with Raman tag-conjugated Au nanoparticles for NLPC-based dual-recognition structures. We demonstrate that the 3D NLPC-based dual-recognition structures including Au nanoparticle-induced additional hotspots can enable more effective SERS enhancement through the molecular recognition of DA. For the accurate quantification of DA at ultralow concentrations, we conducted digital SERS analysis to reduce stochastic signal variation due to various microscopic effects, including molecular orientation/position variation and the spatial distribution of nanoparticle-coupled hotspots. The digital SERS analysis allowed the SERS mapping results from the DA-specific dual-recognition structures to be converted into binary "On/Off" states; the number of "On" events was directly correlated with low-abundance DA molecules down to 1 pM. Therefore, the digital SERS platform using the 3D NLPC-based dual-recognition structures coupled with Au nanoparticles and digital SERS signal analysis can be used not only for the ultrasensitive, accurate, and quantitative determination of DA, but also for the practical and rapid analysis of various molecules on nanostructured surfaces.

Plasmonically Calibrated Label-Free Surface-Enhanced Raman Spectroscopy for Improved Multivariate Analysis of Living Cells in Cancer Subtyping and Drug Testing.

Plasmonic nanostructure-enabled label-free surface-enhanced Raman spectroscopy (SERS) emerges as a rapid nondestructive molecular fingerprint characterization technique for complex biological samples. However, label-free SERS bioanalysis faces challenges in reliability and reproducibility due to SERS signals' high susceptibility to local optical field variations at plasmonic hotspots, which can bias correlations between the measured spectroscopic features and the actual molecular concentration profiles of complex biochemical matrices. Herein, we report that plasmonically enhanced electronic Raman scattering (ERS) signals from metal nanostructures can serve as a SERS calibration internal standard to improve multivariate analysis of living biological systems. Through side-by-side comparisons with noncalibrated SERS datasets, we demonstrate that the ERS-based SERS calibration can enhance supervised learning classification of label-free living cell SERS spectra in (1) subtyping breast cancer cells with different degrees of malignancy and (2) assessing cancer cells' drug responses at different dosages. Notably, the ERS-based SERS calibration has the advantages of excellent photostability under laser excitation, no spectral interference with biomolecule Raman signatures, and no occupation competition with biomolecules at hotspots. Therefore, we envision that the ERS-based SERS calibration can significantly boost the multivariate analysis performance in label-free SERS measurements of living biological systems and other complex biochemical matrices.

Reusable Surface-Enhanced Raman Spectroscopy Membranes and Textiles via Template-Assisted Self-Assembly and Micro/Nanoimprinting.

Surface-enhanced Raman spectroscopy (SERS) has emerged as a powerful tool for ultrasensitive fingerprint recognition of molecules with considerable potential in wearable biochemical sensing. However, previous efforts to fabricate wearable SERS devices by directly treating fabrics with plasmonic nanoparticles have generated a nonuniform assembly of nanoparticles, weakly adsorbed on fabrics via van der Waals forces. Here, we report the creation of washing reusable SERS membranes and textiles via template-assisted self-assembly and micro/nanoimprinting approaches. Uniquely, we employ the capillary force driven self-assembly process to generate micropatch arrays of Au nanoparticle (NP) aggregates within hydrophobic microstructured templates, which are then robustly bonded onto semipermeable transparent membranes and stretchable textiles using the UV-resist based micro/nanoimprinting technique. A mild reactive ion etching (RIE) treatment of SERS membranes and textiles can physically expose the SERS hotspots of Au NP-aggregates embedded within the polymer UV resist for further improvement of their SERS performance. Also, we demonstrate that the semipermeable transparent SERS membranes can keep the moisture content of meat from evaporating to enable stable in situ SERS monitoring of biochemical environments at the fresh meat surface. By contrast, stretchable SERS textiles can allow the spreading, soaking, and evaporation of solution analyte samples on the fabric matrix for continuous enrichment of analyte molecules at the hotspots in biochemical SERS detection. Due to the mechanical robustness of the UV-resist immobilized Au NP aggregates, simple detergent-water washing with ultrasound sonication or mechanical stirring can noninvasively clean contaminated hot spots to reuse SERS textiles. Therefore, we envision that washing reusable SERS membranes and textiles by template-assisted self-assembly and micro/nanoimprinting fabrication are promising for wearable biochemical sensing applications, such as wound monitoring and body fluid monitoring.

Plasmonic Electronic Raman Scattering as Internal Standard for Spatial and Temporal Calibration in Quantitative Surface-Enhanced Raman Spectroscopy.

Ultrasensitive surface-enhanced Raman spectroscopy (SERS) still faces difficulties in quantitative analysis because of its susceptibility to local optical field variations at plasmonic hotspots in metallo-dielectric nanostructures. Current SERS calibration approaches using Raman tags have inherent limitations due to spatial occupation competition with analyte molecules, spectral interference with analyte Raman peaks, and photodegradation. Herein, we report that plasmon-enhanced electronic Raman scattering (ERS) signals from metal can serve as an internal standard for spatial and temporal calibration of molecular Raman scattering (MRS) signals from analyte molecules at the same hotspots, enabling rigorous quantitative SERS analysis. We observe a linear dependence between ERS and MRS signal intensities upon spatial and temporal variations of excitation optical fields, manifesting the |E|4 enhancements for both ERS and MRS processes at the same hotspots in agreement with our theoretical prediction. Furthermore, we find that the ERS calibration's performance limit can result from orientation variations of analyte molecules at hotspots.

Partial Leidenfrost Evaporation-Assisted Ultrasensitive Surface-Enhanced Raman Spectroscopy in a Janus Water Droplet on Hierarchical Plasmonic Micro-/Nanostructures.

The conventional methods of creating superhydrophobic surface-enhanced Raman spectroscopy (SERS) devices are by conformally coating a nanolayer of hydrophobic materials on micro-/nanostructured plasmonic substrates. However, the hydrophobic coating may partially block hot spots and therefore compromise Raman signals of analytes. In this paper, we report a partial Leidenfrost evaporation-assisted approach for ultrasensitive SERS detection of low-concentration analytes in water droplets on hierarchical plasmonic micro-/nanostructures, which are fabricated by integrating nanolaminated metal nanoantennas on carbon nanotube (CNT)-decorated Si micropillar arrays. In comparison with natural evaporation, partial Leidenfrost-assisted evaporation on the hierarchical surfaces can provide a levitating force to maintain the water-based analyte droplet in the Cassie-Wenzel hybrid state, i.e., a Janus droplet. By overcoming the diffusion limit in SERS measurements, the continuous shrinking circumferential rim of the droplet, which is in the Cassie state, toward the pinned central region of the droplet, which is in the Wenzel state, results in a fast concentration of dilute analyte molecules on a significantly reduced footprint within several minutes. Here, we demonstrate that a partial Leidenfrost droplet on the hierarchical plasmonic surfaces can reduce the final deposition footprint of analytes by 3-4 orders of magnitude and enable SERS detection of nanomolar analytes (10-9 M) in an aqueous solution. In particular, this type of hierarchical plasmonic surface has densely packed plasmonic hot spots with SERS enhancement factors (EFs) exceeding 107. Partial Leidenfrost evaporation-assisted SERS sensing on hierarchical plasmonic micro-/nanostructures provides a fast and ultrasensitive biochemical detection strategy without the need for additional surface modifications and chemical treatments.

Scalable nanolaminated SERS multiwell cell culture assay.

This paper presents a new cell culture platform enabling label-free surface-enhanced Raman spectroscopy (SERS) analysis of biological samples. The platform integrates a multilayered metal-insulator-metal nanolaminated SERS substrate and polydimethylsiloxane (PDMS) multiwells for the simultaneous analysis of cultured cells. Multiple cell lines, including breast normal and cancer cells and prostate cancer cells, were used to validate the applicability of this unique platform. The cell lines were cultured in different wells. The Raman spectra of over 100 cells from each cell line were collected and analyzed after 12 h of introducing the cells to the assay. The unique Raman spectra of each cell line yielded biomarkers for identifying cancerous and normal cells. A kernel-based machine learning algorithm was used to extract the high-dimensional variables from the Raman spectra. Specifically, the nonnegative garrote on a kernel machine classifier is a hybrid approach with a mixed nonparametric model that considers the nonlinear relationships between the higher-dimension variables. The breast cancer cell lines and normal breast epithelial cells were distinguished with an accuracy close to 90%. The prediction rate between breast cancer cells and prostate cancer cells reached 94%. Four blind test groups were used to evaluate the prediction power of the SERS spectra. The peak intensities at the selected Raman shifts of the testing groups were selected and compared with the training groups used in the machine learning algorithm. The blind testing groups were correctly predicted 100% of the time, demonstrating the applicability of the multiwell SERS array for analyzing cell populations for cancer research.

Refractive-Index-Insensitive Nanolaminated SERS Substrates for Label-Free Raman Profiling and Classification of Living Cancer Cells.

Surface-enhanced Raman spectroscopy (SERS) has emerged as an ultrasensitive molecular-fingerprint-based technique for label-free biochemical analysis of biological systems. However, for conventional SERS substrates, SERS enhancement factors (EFs) strongly depend on background refractive index (RI), which prevents reliable spatiotemporal SERS analysis of living cells consisting of different extra-/intracellular organelles with a heterogeneous distribution of local RI values between 1.30 and 1.60. Here, we demonstrate that nanolaminated SERS substrates can support uniform arrays of vertically oriented nanogap hot spots with large SERS EFs (>107) insensitive to background RI variations. Experimental and numerical studies reveal that the observed RI-insensitive SERS response is due to the broadband multiresonant optical properties of nanolaminated plasmonic nanostructures. As a proof-of-concept demonstration, we use RI-insensitive nanolaminated SERS substrates to achieve label-free Raman profiling and classification of living cancer cells with a high prediction accuracy of 96%. We envision that RI-insensitive high-performance nanolaminated SERS substrates can potentially enable label-free spatiotemporal biochemical analysis of living biological systems.