Surface-enhanced Raman Scattering Research Articles

Light-dependent electron transfer mechanism on a Z-scheme MIL-100(Fe)/AgCl/Ag heterostructure for photocatalytic degradation

Research on the changes in material properties caused by different light sources and the various photocatalytic mechanisms generated is of great significance for exploring new catalysts and improving catalytic efficiency. In this study, a novel composite of MIL-100(Fe)/AgCl/Ag was synthesized for photocatalytic degradation of organic pollutants. Techniques such as ultraviolet (UV)-visible spectroscopy and surface-enhanced Raman spectroscopy (SERS) were employed to monitor the degradation process of small molecule organic pollutants in real-time under different light sources. The research found that the catalytic efficiency of the catalyst under visible light is markedly higher than that under UV light. This phenomenon can attributed to the dynamic changes in the material’s properties, particularly the adjustment of the interface electric field under different light sources. Specifically, under UV light irradiation, the catalyst follows a Z-scheme electron transfer pathway to achieve interband transitions. In contrast, under visible light irradiation, it operates through a Z-scheme electron transfer mechanism related to surface plasmon resonance (SPR), which effectively promotes separation of electrons and holes. As a results, the apparent reaction rate is approximately 2.5 times higher compared to that under UV light conditions. This study contributes to a deeper understanding of charge transfer mechanisms in photocatalytic reactions under different wavelength light sources, and could provide valuable insights for designing new light-responsive catalysts to improve their efficiency.

Simultaneous detection of mixed colorants adulterated in black tea based on various morphological SERS sensors

Colorant adulteration is a common problem in tea safety control; thus, a rapid identification method is required. In this study, we optimized the fabrication parameters of various sensors to enhance their performance. R6G was used as a probe molecule, demonstrating that the sensnor remained stable for 120 days. Based on surface-enhanced Raman spectroscopy, the optimized sensors were used to identify and quantify mixed colorants (sunset yellow, lemon yellow, carmine, and erythrosine). Partial least squares prediction models were developed for each colorant (0.5–300 μg/mL), with R2 > 0.900 and RPD > 2.27; these indicated the accuracy of the sensors. The results also revealed a model recovery range of 95.9 % to 116 %, with RSD < 3.94 %, indicating the universality of our proposed method. Overall, the proposed method enables the detection of mixed-colorant adulteration in black tea within 3 min, thereby representing a novel method for the assessment of tea quality.

Gap-free hybridized plasmonics with tunable decay channels for surface-enhanced Raman spectroscopy

Gap-free hybridized plasmonics with tunable decay channels for surface-enhanced Raman spectroscopy

Pyroelectrically Driven Charge Transfer and its Advantages on SERS and Self‐Cleaning Property

AbstractThe fabrication of reusable SERS substrates with sensitivity at the single‐molecule level remains challenging but promising. Herein, a composite surface‐enhanced Raman scattering (SERS) substrate is proposed that includes Ag nanoparticle (Ag NPs) /Graphene/BaTiO3 (Ag/G/BTO) from a chemical enhancement perspective. The pyroelectric field generated by BaTiO3 drives the charge transfer between the SERS substrate and molecules, achieving a significant improvement in the SERS performance and self‐cleaning properties. The SERS signals of rhodamine 6G (R6G) molecules are further amplified up to 70‐fold. Thus, the detection limit is reduced by three orders of magnitude in this study, reaching 10−14 m after applying a pyroelectric field. In addition, the substrate exhibits a higher degradation efficiency than previous self‐cleaning SERS substrates because of the outstanding catalytic properties of BaTiO3. Target molecules are degraded effectively after several temperature cycles. A detailed mechanism analysis of pyroelectric SERS is conducted based on theoretical simulations and experimental results. This study is considered to deepen the understanding of SERS mechanisms and boost the application of SERS technology.

An AI-assisted microfluidic paper-based multiplexed surface-enhanced raman scattering (SERS) biosensor with electrophoretic removal and electrical modulation for accurate acute myocardial infarction (AMI) diagnosis and prognosis

SERS detects single molecules with exceptional sensitivity. To counter the issue of selectivity faced by point-of-care, herein, an externally applied electric field that allows electrical modulation and electromigrates unbound SERS tags without multiple washing steps is successfully developed and demonstrated to improve the biosensor's selectivity and sensitivity in multiplexed detection of cTnI, HDL, and LDL in human serum at a low LoD. Ultra-sensitive detectors can detect signals from non-specifically absorbed species, and these species can cover up overlapping analyte peaks, amplifying the effect of non-specific binding. Even though antifouling molecules can prevent non-specific adsorption at the sensor interface, this approach does not completely eliminate it. Our significant findings show that an electrically regulated device can electromigrate non-specifically bound species without cross-reacting with endogenous albumin proteins. Stability, repeatability, and reproducibility were good, with an RSD of 10%. Artificial intelligence was employed to interpret and analyze high-dimensional fingerprint SERS spectra using feature selection and dimensionality reduction for accurate acute myocardial infarction diagnosis and prognosis. These machine learning methods allow quantification of cTnI, HDL, and LDL biomarkers with low RMSE. Machine learning classifiers showed strong AUROC values of 0.950 ± 0.111 and 0.884 ± 0.139 for early and recurrent AMI detection, respectively. A high negative predictive value (NPV) of ≥99% indicates an effective early AMI rule-out. In short, this work demonstrated that a simple, low-cost, electrophoretic modulated biosensor with machine learning can diagnose, rule out, and predict recurring AMI.

Neurotransmitters and neural hormone-based probes for quadruplex DNA sequences associated with neurodegenerative diseases

The potential of neurotransmitters and neural hormones as possible G-quadruplex DNA binders was analyzed using fluorescence spectroscopy, surface-enhanced Raman spectroscopy (SERS), DNA melting analysis, and molecular docking. G-quadruplex sequences, (GGC)3 and G4C2, with roles in Fragile X syndrome and amyotrophic lateral sclerosis (ALS), respectively, were selected, and their interactions with melatonin, serotonin, and gamma-aminobutyric acid (GABA), were studied. Both melatonin and serotonin demonstrated strong interactions with the DNA sequences with hydrogen bonding being the primary mode of interaction, with some non-intercalative interactions involving the π systems. GABA demonstrated much weaker interactions and may not be a suitable candidate as a probe for low concentrations of G-quadruplex DNA.

Shell-Isolated Nanoparticle-Enhanced Raman Spectroscopy: Toward High Sensitivity and Broad Applicability.

As a nondestructive and ultrasensitive technique, surface-enhanced Raman spectroscopy (SERS) has captivated the attention of the global scientific community for over 50 years. Among the various spectroscopic techniques derived from SERS, shell-isolated nanoparticle-enhanced Raman spectroscopy (SHINERS) stands as a cutting-edge advancement. The innovative and versatile core-shell nanoparticle structures used in SHINERS have emerged as an ideal platform for interfacial research, offering high sensitivity and broad applicability across diverse materials and single-crystal surfaces. Consequently, SHINERS has seen widespread adoption in pivotal fields, such as interface chemistry, electrocatalysis, biomedicine, materials, and food safety. In this Perspective, we outline the evolutionary journey of SHINERS, delve deep into its applications in fundamental research for interface characterization and catalysis, and explore its practical utility in critical areas of food safety and biomedicine analysis. Additionally, we map out the prospective trajectory and future milestones that await SHINERS as it continues to revolutionize the landscape of scientific exploration.

Simple and straightforward synthesis of magnetoplasmonic nanostars Fe3O4@Au NSs for enhanced SERS detection

This work introduces a simple and efficient protocol for synthesizing dual-functional magnetic gold nanostars Fe3O4@Au NSs, overcoming the challenges of previous complex synthesis protocols. By analyzing key synthetic variables and their impact on the structure and plasmonic properties, we optimized the production of these nanostars. The synthesized nanostructures exhibit significant surface-enhanced resonance Raman spectroscopy (SERRS) effects, with an enhancement factor of 1 × 105 using malachite green as a model analyte. The inherent magnetic properties enable efficient pre-concentration, leading to a 13-fold increase in Raman signal intensity and a low detection limit of 33 nM. Through the synergistic combination of magnetic separation and SERS/SERRS analysis, these nanostructures offer a robust tool for sensitive and selective analyte determination in complex matrices, making them valuable for applications in environmental monitoring, sensing, imaging, and biomedical research.

Amplification of the Surface-Enhanced Raman Scattering Signal from a Monolayer of Organic Molecules in Sandwich Structures Containing Plasmonic Silver Nanoplates

Amplification of the Surface-Enhanced Raman Scattering Signal from a Monolayer of Organic Molecules in Sandwich Structures Containing Plasmonic Silver Nanoplates

Liquid metasurface for size-independent detection of microplastics

Microplastics (MPs) are widely distributed in water, soil, and air, drawing a global concern as a cause of chronic diseases and immune system disruption. Though as one of the most promising techniques in MP detection, the surface-enhanced Raman scattering (SERS) is heavily dependent on the distribution of the “hot spots” and the size of MPs, known as “coffee ring effect” and “size effect” respectively, imposing major challenges in the quantitative detection of various sized MPs on conventional SERS substrates. Here we present a self-healing metasurface based on plasmonic nanoparticle (NP) array at the liquid-liquid interface (LLI) and air-liquid interface (ALI). The fluidic nature of the metasurface and the repulsive forces between NPs offer atomic-level flatness and uniform distribution for “hot spots”. Additionally, MPs are dissolved in the oil phase, uniformly enriched in the form of polymer molecular chains on the liquid metasurface, irrespective of the size of the MPs. This molecular dispersity of the dissolved MPs enhances the overlap between the “hot spots” and scattering volume of MPs, significantly improving the intensity and reproducibility of SERS. The sensing platform is successfully applied in trace detections of various MPs (PS, PET, PMMA, and PC), and validated in real samples.

Surface Plasmons in Two-Dimensional MXenes.

MXenes, a class of layered two-dimensional transition metal carbides and nitrides, exhibit excellent optoelectronic properties and show promise for fields ranging from photonics and communications to energy storage and catalysis. Some members of the MXene family are metallic and exhibit large in-plane conductivity, making them possibly suited for 2D plasmonics. The highly variable chemical structure of MXenes offers a broad chemical space to tune material properties for plasmonic applications, including plasmon-enhanced catalysis, surface-enhanced Raman spectroscopy (SERS), and electromagnetic shielding. However, this synthetic complexity has also presented several roadblocks in the process of moving MXene plasmonics into applications. For example, in the prototypical MXene Ti3C2Tx, there remains disagreement over the bulk plasmon energy and the assignment of a prominent resonance around 1.7 eV. We discuss fundamental models and theories of plasmon physics and apply these models to MXenes in order to clarify some of these problems. We outline the potential for hyperbolic plasmons in MXenes and propose new avenues for MXene photonics research.

Rapid Dynamic Surface-Enhanced Raman Spectroscopy Detection of Amoxicillin-Mediated Morphological Changes in a Pathogen for Diagnosis of Clinical Urine Samples.

The swift and stable detection of pathogens in urine samples holds significant implications for the immediate clinical diagnosis and treatment of urinary tract infections (UTIs). In this study, we propose a detection strategy utilizing a hybrid substrate composed of graphene oxide (GO) and silver nanoparticles (Ag NPs) for the detection of pathogens subjected to amoxicillin-mediated (amo-mediated) treatment. This strategy employs dynamic surface-enhanced Raman spectroscopy (D-SERS) for stable and rapid detection, capturing signal variations induced by amo-mediated changes in pathogen morphology. During the 5 min D-SERS detection time window, stable SERS signals were detected for three types of pathogens and four types of pathogens were successfully distinguished using principal component analysis (PCA). In comparison to conventional nanosubstrates, the GO/Ag NP hybrid substrate exhibits outstanding stability and enhancement effects. This approach enables the dual detection of the pathogen cell structure and metabolites, facilitating specific identification of pathogens in the urinary tract, with a detection limit for Escherichia coli reaching 1 × 104 colony-forming units (CFU)/mL, meeting the clinical microbiology laboratory diagnostic standards for UTIs (105 CFU/mL). Testing of 188 clinically collected urine samples using this strategy yielded a sensitivity (SENS) of 86.4% and a specificity (SPC) of 89.7%. This introduces a novel method for diagnosing UTIs, offering broad applications in the field of clinical pathogen detection.

A rapid dual-mode SERS/FL cytosensor assisted via DNA Walker-based plasmonic nanostructures

Various surface-enhanced Raman scattering (SERS) biosensors offer powerful tools for the ultrasensitive detection of circulating tumor cells (CTCs) and tumor diagnosis. Despite their efficacy, the swift and precise preparation of SERS plasmonic nanostructures poses an ongoing challenge. In this study, we introduce DNA-assisted plasmonic nanostructures capable of producing dual signals and facilitating DNA Walker signal amplification, resulting in the development of a SERS/Fluorescent (FL) dual-mode cytosensor for CTCs detection. Firstly, Au@Ag nanoparticle multimers (Au@AgNMs) featuring interparticle nano-gaps were synthesized through DNA self-assembly and in-situ deposition, which provided plasmonic nanostructures. Hence, the nano-gap distance among Au@AgNMs was meticulously regulated after optimization to achieve both SERS enhancement and fluorescence quenching. Subsequently, the aptamer (Apt) of MUC1 recognized CTCs specifically for strand displacement reaction (SDR) and further triggered the DNA Walker reaction for signal amplification. The limit of detection (LOD) of proposed cytosensor can be obtained as low as 5 cells/mL in SERS mode and 21 cells/mL in FL mode. Hence, SERS mode confers highly precise information, while FL mode allow for rapid quantitative analysis. This dual-mode cytosensor based on plasmonic nanostructures facilitates the early detection and precise treatment of cancer or infectious diseases.

Revealing mitochondrial microenvironmental changes triggered by ferroptosis regulation.

Ferroptosis is a unique form of iron-dependent cell death characterized by dramatic ultrastructural changes in mitochondria. Since mitochondria are intracellular energy factories and binding sites for producing reactive oxygen species, there is increasing evidence that mitochondria are closely related to ferroptosis and play a crucial role in the regulation and execution of ferroptosis. The pH of the mitochondrial microenvironment is an important parameter for cellular physiological activities. Its abnormal fluctuations are commonly thought to be associated with cancers and other diseases. Herein, a surface-enhanced Raman scattering (SERS)-based pH nanosensor with high sensitivity and targeting function was utilized to quantify and monitor mitochondrial pH value. This nanosensor was constructed by gold nanorods (AuNRs) functionalized with pH-responsive molecules (4-mercaptopyridine, MPy) and mitochondrion-targeting peptides (AMMT) that can precisely deliver AuNRs to mitochondria. Super-resolution fluorescence imaging was employed to evidence the mitochondrial targeting feature of this nanosensor. Ferroptosis regulation induces intracellular accumulation of lipid peroxide (LPO) and reactive oxygen species (ROS), which cause changes in the mitochondrial pH. This method reveals that ferroptosis leads to the gradual acidification of the mitochondrial internal environment. The conclusion deduced by this study will be helpful for the evaluation and diagnosis of diseases according to intracellular abnormal microenvironments.

Machine Learning-Assisted Surface-Enhanced Raman Spectroscopy Detection for Environmental Applications: A Review.

Surface-enhanced Raman spectroscopy (SERS) has gained significant attention for its ability to detect environmental contaminants with high sensitivity and specificity. The cost-effectiveness and potential portability of the technique further enhance its appeal for widespread application. However, challenges such as the management of voluminous quantities of high-dimensional data, its capacity to detect low-concentration targets in the presence of environmental interferents, and the navigation of the complex relationships arising from overlapping spectral peaks have emerged. In response, there is a growing trend toward the use of machine learning (ML) approaches that encompass multivariate tools for effective SERS data analysis. This comprehensive review delves into the detailed steps needed to be considered when applying ML techniques for SERS analysis. Additionally, we explored a range of environmental applications where different ML tools were integrated with SERS for the detection of pathogens and (in)organic pollutants in environmental samples. We sought to comprehend the intricate considerations and benefits associated with ML in these contexts. Additionally, the review explores the future potential of synergizing SERS with ML for real-world applications.

Dual trace detection of hazardous substances by aptamer-functionalized gold nanofilms based on surface-enhanced Raman spectroscopy

This work proposes the application of multi-residue Surface-enhanced Raman spectroscopy (SERS) detection based on an internal standard and aptamer-functionalized gold nanofilms. First, two types of complementary DNA (cDNA) are fixed on the self-assembled monolayer of a halide ion-regulated nano-membrane through gold-sulfur bonds. Then, two signal probe molecules are prepared and modified. The aptamer specifically binds to cDNA, generating SERS signals. Detection of the target analytes is achieved by measuring the Raman intensity and using the internal standard method. The results show that the concentrations of thiamethoxam and dichlorodiphenyltrichloroethane (DDT) at 10-12 M can be effectively measured under optimal experimental conditions. The Raman intensity exhibits a linear relationship at the shifts of 1085 cm-1 for 4-mercaptopyridine (4-MPY) and 1583 cm-1 and 2223 cm-1 for 4-mercaptobenzoic acid (4-MBN). When applied to the detection of thiacloprid in actual lake water samples, the results show no significant difference compared to the standard.

Ultrasensitive Detection of Blood-Based Alzheimer's Disease Biomarkers: A Comprehensive SERS-Immunoassay Platform Enhanced by Machine Learning.

Accurate and early disease detection is crucial for improving patient care, but traditional diagnostic methods often fail to identify diseases in their early stages, leading to delayed treatment outcomes. Early diagnosis using blood derivatives as a source for biomarkers is particularly important for managing Alzheimer's disease (AD). This study introduces a novel approach for the precise and ultrasensitive detection of multiple core AD biomarkers (Aβ40, Aβ42, p-tau, and t-tau) using surface-enhanced Raman spectroscopy (SERS) combined with machine-learning algorithms. Our method employs an antibody-immobilized aluminum SERS substrate, which offers high precision, sensitivity, and accuracy. The platform achieves an impressive detection limit in the attomolar (aM) range and spans a wide dynamic range from aM to micromolar (μM) concentrations. This ultrasensitive and specific SERS immunoassay platform shows promise for identifying mild cognitive impairment (MCI), a potential precursor to AD, from blood plasma. Machine-learning algorithms applied to the spectral data enhance the differentiation of MCI from AD and healthy controls, yielding excellent sensitivity and specificity. Our integrated SERS-machine-learning approach, with its interpretability, advances AD research and underscores the effectiveness of a cost-efficient, easy-to-prepare Al-SERS substrate for clinical AD detection.

Highly Tunable, Nanomaterial-Functionalized Structural Templating of Intracellular Protein Structures Within Biological Species.

Inside living organisms, proteins are self-assembled into diverse 3D structures optimized for specific functions. This structure-function relationship can be exploited to synthesize functional materials through biotemplating and depositing functional materials onto protein structures. However, conventional biotemplating faces limitations due to the predominantly intracellular existence of proteins and associated challenges in achieving tunability while preserving functionality. In this study, Conversion to Advanced Materials via labeled Biostructures (CamBio), an integrated biotemplating platform that involves labeling target protein structures with antibodies followed by the growth of functional materials, ensuring outstanding nanostructure tunability is proposed. Protein-derived plasmonic nanostructures created by CamBio can serve as precise quantitative tools for assessing target species is demonstrated. The assessment is achieved through highly tunable and efficient surface-enhanced Raman spectroscopy (SERS). CamBio enables the formation of dense nanogap hot spots among metal nanoparticles, templated by diverse fibrous proteins comprising densely repeated monomers. Furthermore, iterative antibody labeling strategies to adjust the antibody density surrounding targets, amplifying the number of nanogaps and consequently improving SERS performance are employed. Finally, cell-patterned substrates and whole meat sections as SERS substrates, confirming their easily accessible, cost-effective, scalable preparation capabilities and dimensional tunability are incorporated.

SERS-Encoded Nanoprobes Based on Silver-Coated Gold Nanorods for Cell Sorting.

Optically-encoded probes have great potential for applications in the fields of biosensing and imaging. By employing specific encoding methods, these probes enable the detection of multiple target molecules and high-resolution imaging within the same sample. Among the various encoding methods, surface-enhanced Raman scattering (SERS) spectral encoding stands out due to its extremely narrow linewidth. Compared to fluorescence spectral encoding, SERS encoding significantly reduces crosstalk between adjacent peaks, thereby achieving a larger encoding capacity and enabling multi-channel parallel analysis. This article presents the design and construction of two novel sets of SERS-encoded probes based on noble metal core-shell nanostructures. Two different encoding strategies are successfully applied to encode the SERS spectra of the probes: 1D encoding based on the wavenumber of characteristic peaks in the SERS spectrum, and 2D encoding combining both wavenumber and intensity of characteristic peaks in the SERS spectrum. In addition, this study also demonstrates the potential application of 1D encoded probes in cell sorting. These studies verify the feasibility of applying these two encoding methods to SERS core-shell probes and provide new insights into the construction of optically encoded probes.

Surface-Enhanced Raman Scattering-Based Multimodal Techniques: Advances and Perspectives.

Surface-enhanced Raman scattering (SERS) spectroscopy is a versatile molecular fingerprinting technique with rapid signal readout, high aqueous compatibility, and portability. To translate SERS for real-world applications, it is pertinent to overcome inherent challenges, including high sample variability and heterogeneity, matrix effects, and nonlinear SERS signal responses of different analytes in complex (bio)chemical matrices with numerous interfering species. In this perspective, we highlight emerging SERS-based multimodal techniques to address the key roadblocks to improving the sensitivity, specificity, and reliability of (bio)chemical detection, bioimaging, theragnosis, and theragnostic. SERS-based multimodal techniques can be broadly categorized into two categories: (1) complementary methods or systems that work together to achieve a common goal where each method compensates for the weaknesses of the other to culminate in a single enhanced outcome or (2) orthogonal techniques that are independent and provide separate but corroborating results simultaneously without interfering with each other. These multimodal techniques maximize information gained from a single experiment to achieve enhanced qualitative or quantitative analysis and broaden the range of detectable analytes from small molecules to tissues. Finally, we discuss emerging directions in multimodal platform design, instrument integration, and data analytics that aim to push the analytical limits of holistic detection.