Raman Spectroscopy Signal Research Articles

Surface-enhanced Raman scatting microfluidic chip based on the identification competition strategy were used for rapid and simultaneous detection of liver cancer related proteins

Biomarker testing plays a crucial role in the early detection of liver cancer. Herein, we developed a dual-signal amplification approach utilizing magnetic aggregation and a recognition competition strategy for the simultaneous detection of alpha-fetoprotein (AFP) and manganese superoxide dismutase (MnSOD) in serum. 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 were synthesized by functionalizing Raman signaling molecules and aptamer complementary chains onto the surface of gold nanoparticles (AuNPs). The detection complex Raman signal molecule@AuNPs@H-Fe3O4@cDNA was assembled by conjugating 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 with two nucleic acid aptamers (cDNA1 and cDNA2) modified with Fe3O4 through partial base complementary pairing. The target protein exhibited specific binding with the aptamer, leading to the competitive displacement of 4-MBA@AuNPs@H1 and DTNB@AuNPs@H2 from the Fe3O4 array surface, consequently resulting in a reduction of the Surface-Enhanced Raman Spectroscopy (SERS) signal. Through this approach, the limit of detection (LOD) for AFP and MnSOD in serum was achieved at remarkably low levels of 5.89 pg/mL and 6.23 pg/mL, respectively, with a reaction incubation period of only 5 min. Finally, the platform was utilized for the quantification of AFP and MnSOD in the serum of a nude mouse hepatocellular carcinoma model. The results obtained through SERS were consistent with those from enzyme-linked immunosorbent assay (ELISA), validating its accuracy. This methodology presents a novel approach for the swift and concurrent detection of proteins, holding significant clinical promise for the early diagnosis of hepatocellular carcinoma.

Unraveling RNA contribution to the molecular origins of bacterial surface-enhanced Raman spectroscopy (SERS) signals

Surface-enhanced Raman spectroscopy (SERS) is widely utilized in bacterial analyses, with the dominant SERS peaks attributed to purine metabolites released during sample preparation. Although adenosine triphosphate (ATP) and nucleic acids are potential molecular origins of these metabolites, research on their exact contributions remains limited. This study explored purine metabolite release from E. coli and RNA integrity following various sample preparation methods. Standard water washing generated dominant SERS signals within 10 s, a duration shorter than the anticipated RNA half-lives under starvation. Evaluating RNA integrity indicated that the most abundant ribosomal RNA species remained intact for hours post-washing, whereas messenger RNA and transfer RNA species degraded gradually. This suggests that bacterial SERS signatures observed after the typical washing step could originate from only a small fraction of endogenous purine-containing molecules. In contrast, acid depurination led to degradation of most RNA species, releasing about 40 times more purine derivatives than water washing. Mild heating also instigated the RNA degradation and released more purine derivatives than water washing. Notably, differences were also evident in the dominant SERS signals following these treatments. This work provides insights into SERS-based studies of purine metabolites released by bacteria and future development of methodologies.

Active Surface-Enhanced Raman Spectroscopy (SERS): A Novel Concept for Enhancing Signal Contrast in Complex Matrices Using External Perturbation.

Noninvasive detection of surface-enhanced Raman spectroscopy (SERS) signals from deep within tissue represents a common challenge in many biological and clinical applications including disease diagnosis and therapy monitoring. Such signals are typically weak and not readily discernible from often much larger Raman and fluorescence background signals (e.g., from surrounding tissue). Consequently, suboptimal sensitivity in the detection of SERS signals is often achieved in these situations. Similar issues can arise in SERS measurements in other diffusely scattering samples and complex matrices. Here, we propose a novel concept, active SERS, for the efficient retrieval of SERS signals from deep within complex matrices such as biological tissues that mitigates these issues. It relies on applying an external perturbation to the sample to alter the SERS signal from nanoparticles (NPs) deep inside the matrix. A measurement with and without, or before and after, such perturbation then can provide powerful contrasting data enabling an effective elimination of the matrix signals to reveal more clearly the desired SERS signal without the interfering background and associated artifacts. The concept is demonstrated using ultrasound (US) as an external source of perturbation and SERS NPs inserted deep within a heterogeneous tissue phantom mimicking a cluster of NPs accumulated within a small target lesion. The overall SERS signal intensity induced by the applied US perturbation decreased by ∼21% and the SERS signal contrast was considerably improved by eliminating subtraction artifacts present in a conventional measurement performed at a neighboring spatial location in a heterogeneous tissue sample. Although the technique was demonstrated with SERS gold NPs with a standard Raman label, it is envisaged that active SERS NPs (both the nanoscale metal geometry and Raman label) could be specifically designed to deliver an augmented response to the external stimulus to further enhance the achievable SERS signal contrast and yield even greater improvement in detection sensitivity. The method was demonstrated using transmission Raman spectroscopy; however, it is also applicable to other Raman implementations including spatially offset Raman spectroscopy and conventional Raman spectroscopy performed both at depth and at surfaces of complex matrices.

Inhalable explosive nanosensor for real-time visual monitoring of therapeutic effects of lung cancer

Lung cancer, as one of the major diseases that endanger human health, has a very high fatality rate. Consequently, effective evaluation of clinical treatment outcomes for lung cancer is crucial for the formulation of subsequent treatment plans for patients. In this work, we utilized amorphous calcium carbonate nanoparticles (CaCO3 NPs) as sources of calcium ions (Ca2+) to develop CPs (CaCO3@PDA) core–shell nanoprobes. Concurrently, we functionalized the surface of gold nanoparticles with Cy5.5-peptide, AS1411, and (4-aminosulfonylphenyl) boronic acid (4-APBA) to produce APAAs (Au NPs@pep-AS1411-(4-APBA)), which were further assembled with CPs using PEG to construct an innovative explosive nanosensor, CPAs (CaCO3@PDA-APAAs). The CPs component of the nanosensor could cause the release of a substantial amount of Ca2+ in response to the tumor microenvironment (TME), which in turn induced cellular Ca2+ overload and subsequent apoptosis. This event triggers caspase-3 activation, causing the cleavage of a specific peptide sequence (DEVD), resulting in the fluorescence signal being reinstated. Additionally, the 4-APBA moiety on the probe interacted with H2O2 resulting in alterations in surface-enhanced Raman spectroscopy (SERS) signals, which aided in the detection of reactive oxygen species (ROS) during the physiological processes. By utilizing atomization, the nanoprobes were strategically deposited in the affected lung regions to enhance the fluorescence imaging capabilities and effectively mirror the therapeutic outcomes. Overall, the CPAs explosive nanosensor has a potential in advancing the non-invasive visual monitoring of lung cancer prognosis and can be a valuable tool in ongoing efforts to improve the management of this challenging disease.

Diagnosis and classification of kidney transplant rejection using machine learning-assisted surface-enhanced Raman spectroscopy using a single drop of serum

The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the development of non-invasive, precise diagnostic technologies. These technologies aim to detect antibody-mediated rejection (ABMR) and T-cell-mediated rejection (TCMR), which are asymptomatic and pose a risk of potential kidney damage. The protocols for managing rejection caused by ABMR and TCMR differ, and diagnosis has traditionally relied on invasive biopsy procedures. Therefore, a convergence system using a nano-sensing chip, Raman spectroscopy, and AI technology was introduced to facilitate diagnosis using serum samples obtained from patients with no major abnormality, ABMR, and TCMR after kidney transplantation. Tissue biopsy and Banff score analysis were performed across the groups for validation, and 5 μL of serum obtained at the same time was added onto the Au–ZnO nanorod-based Surface-Enhanced Raman Scattering sensing chip to obtain Raman spectroscopy signals. The accuracy of machine learning algorithms for principal component-linear discriminant analysis and principal component-partial least squares discriminant analysis was 93.53% and 98.82%, respectively. The collagen (an indicative of kidney injury), creatinine, and amino acid-derived signals (markers of kidney function) contributed to this accuracy; however, the high accuracy was primarily due to the ability of the system to analyze a broad spectrum of various biomarkers.

AN EVALUATION OF A GOLD SURFACE FUNCTIONALIZATION PROCEDURE FOR ANTIBODY BINDING AND PROTEIN DETECTION USING 11-MERCAPTOUNDECANOIC ACID (11-MUA)

Immunosensors play an essential role in biological element recognition systems. Among the numerous types of electrodes utilized for immunosensor, gold electrodes with surface modification have attracted considerable attention and have experienced various improvements and developments due to their advantages, compromising high conductivity and chemical stability. Self-assembled monolayer (SAM) has been used as an effective approach for biomarker immobilization onto electrodes. In this study, two types of gold electrodes, namely, sputtered gold on a glass slide and screen-printed gold electrodes, were utilized to assess the performance of the proposed procedure utilizing 11-Mercaptoundecanoic acid (11-MUA). Bovine serum albumin–fluorescein isothiocyanate conjugate (BSA-FITC) and anti-albumin antibody (anti-BSA) were employed as the antigen–antibody pair. Microscope-based fluorescent, Raman spectroscopy and electrical measurements, including cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS), were conducted to validate the success of each step in the surface functionalization process. The results showed that BSA proteins were immobilized on the modified electrode, as indicated by the fluorescent green color observed under the microscope and the changes in surface impedance recorded in Raman spectroscopy, CV and EIS signal graphs. Additionally, the influence of the 11-MUA incubation time and the protein concentration on the performance of the gold electrode surface functionalization process were investigated. The results indicate that an incubation time exceeding 24 h and a protein concentration of 5 [Formula: see text]M was found to be the most optimal conditions. The screen-printed gold electrode exhibited a slightly superior performance in comparison to the sputtered gold electrode. This work has verified the proposed gold surface functionalization process and can serve as a solid foundation for future immunosensor research studies.

Machine learning assisted dual-functional nanophotonic sensor for organic pollutant detection and degradation in water

This study presents a dual-functional thin film, known as Ag nanoparticles decorated, ZnO nanorods coated silica nanofibers (AgNP-ZnONR-SNF), which demonstrates remarkable capabilities in both water purification and organic pollutants sensing. The 3D fibrous structure of ZnONR-SNF provides a large surface-area-to-volume ratio for piezo- and photo-catalytic degradation of organic pollutants under UV irradiation, achieving over 98% efficiency. Ag nanoparticles decorated on ZnONR-SNF form “hot-spot” that significantly enhance the surface-enhanced Raman spectroscopy (SERS) signal, resulting in an enhancement factor of 1056 and an experimental detection limit of 1 pg mL−1. Furthermore, a machine learning algorithm is developed for the qualitative and quantitative detection of multiple contaminants, achieving high accuracy (92.3%) and specificity (89.3%) without the need for preliminary processing of Raman spectra. This work provides a promising nanoengineering solution for water purification and sensing with improved detection accuracy, purification efficiency, and cost-effectiveness.

Transfer Contrastive Learning for Raman Spectroscopy Skin Cancer Tissue Classification.

Using Raman spectroscopy (RS) signals for skin cancer tissue classification has recently drawn significant attention, because of its non-invasive optical technique nature using molecular structures and conformations within biological tissue for diagnosis. In reality, RS signals are noisy and unstable for training machine learning models. The scarcity of tissue samples also makes it challenging to learn reliable deep-learning networks for clinical usages. In this paper, we advocate a Transfer Contrasting Learning Paradigm (TCLP) to address the scarcity and noisy characteristics of the RS for skin cancer tissue classification. To overcome the challenge of limited samples, TCLPleverages transfer learning to pre-train deep learning models using RS data from similar domains (but collected from different RS equipments for other tasks). To tackle the noisy nature of the RS signals, TCLPuses contrastive learning to augment RS signals to learn reliable feature representation to represent RS signals for final classification. Experiments and comparisons, including statistical tests, demonstrate that TCLPoutperforms existing deep learning baselines for RS signal-based skin cancer tissue classification. Both code and data used in our study are available at: https://github.com/yeyimilk/tclp.

Characterization of Chiral Near Field Within Nanogaps Using Surface‐Enhanced Raman Scattering

AbstractChiral nanogap antennas, which exhibit specific chiroptical responses, hold exceptional potential in various chiral applications, including polarization converters, asymmetric catalysis, label‐free chiral recognition, and chiral sensing. However, characterizing the chiral optical fields within nanogaps presents considerable challenges owing to their complex light field responses and spatial dimensions. In this study, the strong modulation of helicity‐resolved surface‐enhanced Raman spectroscopy (SERS) signals in graphene by chiral plasmonic antennas, offering a promising approach to characterize chiral near fields within nanogaps is demonstrated. The SERS of achiral monolayer graphene is performed within plasmonic nanogap antennas, including single gold nanorods (GNRs) and GNR dimers on gold microflakes. The Raman scattering intensity of graphene within these nanogap antennas is enhanced approximately sixfold. Furthermore, the degree of cross‐circularly polarized Raman intensity reaches up to ≈45%, indicating a robust chiral optical field. Moreover, under opposite circularly polarized light excitation, the SERS of graphene reveals varying emission intensities attributed to the modulation arising from the chiral antennas in both the excitation and emission processes of Raman scattering. Furthermore, the degree of chirality depends on the antenna configuration and the excitation laser wavelength.

Normothermic Ex Situ Machine Perfusion of Vascularized Composite Allografts with Oxygen Microcarriers for 12 Hours Using Real-Time Mitochondrial Redox Quantification.

Normothermic ex situ perfusion of vascularized composite allografts (VCAs) necessitates high oxygen demand and, thus, increased metabolic activity, which, in turn, requires the use of blood-based perfusion solutions. However, blood-derived perfusates, in turn, constitute an antigenic load. To circumvent this immunogenic problem, we used a perfusate enriched with acellular dextrane oxygen microcarriers to perfuse rat hindlimbs. Rat hindlimbs (n = 11) were perfused with either (non-), oxygenated dextrane-enriched Phoxilium, or Phoxilium enriched with dextrane oxygen microcarriers (MO2) for 12 h at 37 °C or stored on ice. Oxygenation of the skeletal muscle was assessed with Raman spectroscopy, tissue pO2-probes, and analysis of the perfusate. Transmission electronic microscopy was utilized to assess the ultrastructure of mitochondria of the skeletal muscle. For all evaluated conditions, ischemia time until perfusion was comparable (22.91 ± 1.64 min; p = 0.1559). After 12 h, limb weight increased significantly by at least 81%, up to 124% in the perfusion groups, and by 27% in the static cold storage (SCS) group. Raman spectroscopy signals of skeletal muscle did not differ substantially among the groups during either perfusion or static cold storage across the duration of the experiment. While the total number of skeletal muscle mitochondria decreased significantly compared to baseline, mitochondrial diameter increased in the perfusion groups and the static cold storage group. The use of oxygen microcarriers in ex situ perfusion of VCA with acellular perfusates under normothermic conditions for 12 h facilitates the maintenance of mitochondrial structure, as well as a subsequent recovery of mitochondrial redox status over time, while markers of muscle injury were lower compared to conventional oxygenated acellular perfusates.

Effect of benzalkonium chloride adaptation on the tolerance of Cronobacter sakazakii exposed to subsequent lethal stresses

Benzalkonium Chloride (BC) is applied in food processing, clinical settings, and households to control the spread of pathogens. However, BC adaptation may promote bacterial resistance against biocides and physical stresses. In the study, the sensitivity of non-adapted and BC-adapted C. sakazakii to subsequent lethal stresses was assessed. Results indicated that BC adaptation provided C. sakazakii direct protection against high BC concentrations, but no cross-protection against heat, acid, osmotic, desiccative, and ethanol-related stress. BC adaptation reduced the proportion of saturated fatty acids (SFA) and the ratio of SFA to unsaturated fatty acids (SFA/USFA), whereas increasing the proportion of unsaturated fatty acids (USFA) in C. sakazakii. Six genes related to motility, metabolism, transport, and efflux were upregulated under BC adaptation. Some Surface-enhanced Raman spectroscopy (SERS) signal characteristics of the BC-adapted cell were significantly different from that of the non-adapted cell, which reflected the changes of intracellular proteins, nucleic acids, carbohydrates, and other cellular components. Consequently, the ability of BC-adapted C. sakazakii to withstand subsequent stresses should be taken into account when developing microbial control measures. A greater understanding of BC adaptation mechanisms in C. sakazakii will provide important insights for disease prevention.

Seeing Is Not Necessarily Believing: Is the Surface-Enhanced Raman Spectroscopy Signal Really from the Target?

Reagent purity is crucial to experimental research, considering that the ignorance of ultratrace impurities may induce wrong conclusions in either revealing the reaction nature or qualifying the target. Specifically, in the field of surface science, the strong interaction between the impurity and the surface will bring a non-negligible negative effect. Surface-enhanced Raman spectroscopy (SERS) is a highly surface-sensitive technique, providing fingerprint identification and near-single molecule sensitivity. In the SERS analysis of trace chloromethyl diethyl phosphate (DECMP), we figured out that the SERS performance of DECMP is significantly distorted by the trace impurities from DECMP. With the aid of gas chromatography-based techniques, one strongly interfering impurity (2,2-dichloro-N,N-dimethylacetamide), the byproduct during the synthesis of DECMP, was confirmed. Furthermore, the nonignorable interference of impurities on the SERS measurement of NaBr, NaI, or sulfadiazine was also observed. The generality ignited us to refresh and consolidate the guideline for the reliable SERS qualitative analysis, by which the potential misleading brought by ultratrace impurities, especially those strongly adsorbed on Au or Ag surfaces, could be well excluded.

Identification and Detection of Intracellular Reactive Sulfur Species Using a Reaction-Mediated Dual-Recognition Strategy.

Reactive sulfur species (RSS) are emerging as a potential key gasotransmitter in diverse physiological processes linking two signaling molecules H2S and SO2. However, the exact roles of H2S and SO2 remain unclear. A major hurdle is the shortage of accurate and robust approaches for sensing of H2S and SO2 in biological systems. Herein, we report a reaction-mediated dual-recognition strategy-based nanosensor, silver nanoparticles (AgNPs)-loaded MIL-101 (Fe) (ALM) hybrids, for the simultaneous detection of H2S and SO2 in a living cell. Upon exposure to H2S, AgNPs can be oxidized to form Ag2S, causing a decrease of surface enhanced Raman spectroscopy (SERS) signals of p,p'-dimercaptoazobenzene. Moreover, SO2 reacts with the amino moiety of MIL-101 to form charge-transfer complexes, resulting in an increment of fluorescent (FL) intensity. The ALM with dual-modal signals can simultaneously analyze H2S and SO2 at a concentration as low as 2.8 × 10-6 and 0.003 μM, respectively. Most importantly, the ALM sensing platform enables targeting mitochondria and detection multiple RSS simultaneously in living cells under external stimulation, as well as displays indiscernible crosstalk between SERS and FL signals, which is very beneficial for the comprehension of physiological issues related with RSS.

Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy

Raman spectroscopy is a kind of vibrational method that can rapidly and non-invasively gives chemical structural information with the Raman spectrometer. Despite its technical advantages, in practical application scenarios, Raman spectroscopy often suffers from interference, such as noises and baseline drifts, resulting in the inability to acquire high-quality Raman spectroscopy signals, which brings challenges to subsequent spectral analysis. The commonly applied spectral preprocessing methods, such as Savitzky–Golay smooth and wavelet transform, can only perform corresponding single-item processing and require manual intervention to carry out a series of tedious trial parameters. Especially, each scheme can only be used for a specific data set. In recent years, the development of deep neural networks has provided new solutions for intelligent preprocessing of spectral data. In this paper, we first creatively started from the basic mechanism of spectral signal generation and constructed a mathematical model of the Raman spectral signal. By counting the noise parameters of the real system, we generated a simulation dataset close to the output of the real system, which alleviated the dependence on data during deep learning training. Due to the powerful nonlinear fitting ability of the neural network, fully connected network model is constructed to complete the baseline estimation task simply and quickly. Then building the Unet model can effectively achieve spectral denoising, and combining it with baseline estimation can realize intelligent joint processing. Through the simulation dataset experiment, it is proved that compared with the classic method, the method proposed in this paper has obvious advantages, which can effectively improve the signal quality and further ensure the accuracy of the peak intensity. At the same time, when the proposed method is applied to the actual system, it also achieves excellent performance compared with the common method, which indirectly indicates the effectiveness of the Raman signal simulation model. The research presented in this paper offers a variety of efficient pipelines for the intelligent processing of Raman spectroscopy, which can adapt to the requirements of different tasks while providing a new idea for enhancing the quality of Raman spectroscopy signals.

Calibration of Raman spectroscopy signals for measurements of methane-air tubular flames

Calibration of Raman spectroscopy signals for measurements of methane-air tubular flames

Eco-friendly mass production of MoS2 flakes in pure water for performance enhancement of triboelectric nanogenerator

Among 2D materials, MoS2 has a conspicuous negative charging tendency owing to its large work function. This study investigates the modified work function of MoS2 caused by direct exfoliation and dispersion in pure water, which is an efficient mass production method from an environmental and economic perspective. We prepared a vacuum-filtered film using water dispersion of MoS2 flakes. An X-ray photoelectron spectroscopy analysis demonstrated that either or both hydroxyl and water molecules were adsorbed onto the MoS2, and a partial transformation from MoS2 to MoO3 occurred during the exfoliation process in pure water. The Raman spectroscopy signals also indicate the formation of MoO3. Kelvin probe force microscopy revealed that the work function of MoS2, exfoliated in pure water, was enhanced (5.14 eV) compared to that of the as-received MoS2 powder (4.56 eV). The filtered film was annealed at 650 °C under H2/H2S gas to remove functional groups and transform the Mo oxide to MoS2 by sulfurization. Accordingly, the work function of the annealed film returned to approximately that of the as-received powder (4.64 eV). To explore the potential application of MoS2 dispersion in water as triboelectric nanogenerators, we measured the output voltages generated from the filtered and annealed films when in contact with polyethylene terephthalate. The results showed that a higher voltage was generated from the filtered film (6.58 V) than from the annealed film (3.48 V). Furthermore, when using the MoS2 with enhanced work function in an electron-acceptor layer in polyimide, the triboelectric nanogenerator (TENG) with the PI/MoS2:PI/PI structure exhibited approximately 3.7 times greater power density than that with the MoS2 having non-enhanced work function. We envisage that the MoS2, which is produced in an eco-friendly in pure water at low cost, can be used in TENG applications without considerable technical difficulty because of the successful modification of its electron affinity.

Application of serum SERS technology based on thermally annealed silver nanoparticle composite substrate in breast cancer.

Liquid biopsy is currently a non-destructive and convenient method of cancer screening, due to human blood containing a variety of cancer-related biomolecules. Therefore, the development of an accurate and rapid breast cancer screening technique combined with breast cancer serum is crucial for the treatment and prognosis of breast cancer patients. In this study, the surface enhanced Raman spectroscopy (SERS) technique is used to enhance the Raman spectroscopy (RS) signal of serum based on a high sensitivity thermally annealed silver nanoparticle/porous silicon bragg mirror (AgNPs/PSB) composite substrate. Compared with RS, SERS reflects more and stronger spectral peak information, which is beneficial to discover new biomarkers of breast cancer. At the same time, to further explore the diagnostic ability of SERS technology for breast cancer. In this study, the raw spectral data are processed by baseline correction, polynomial smoothing, and normalization. Then, the relevant feature information of SERS and RS is extracted by principal component analysis (PCA), and five classification models are established to compare the diagnostic performance of SERS and RS models respectively. The experimental results show that the breast cancer diagnosis model based on the improved SERS substrate combined with the machine learning algorithm can be used to distinguish breast cancer patients from controls. The accuracy, sensitivity, specificity and AUC values of the SVM model are 100%, 100%, 100% and 100%, respectively, as well as the training time of 4ms. The above experimental results show that the SERS technology based on AgNPs/PSB composite substrate, combined with machine learning methods, has great potential in the rapid and accurate identification of breast cancer patients.

Ultra-trace detection of diverse analyte molecules using femtosecond laser structured Ag–Au alloy substrates and SERRS

Choosing a wavelength close to the electronic excitation of the analyte molecules is known to enhance the Surface Enhanced Raman Spectroscopy signal further. This technique is often referred to as Surface Enhanced Resonance Raman Spectroscopy (SERRS). Here we use SERRS for sensing different classes of molecules and demonstrate how it can be used for ultra-trace detection. Diverse SERS substrates were fabricated by femtosecond laser ablation of Ag–Au alloy (1:1) in the air by changing the angle of incidence. Further, we have examined the SERRS performance of each substrate. Often in laser ablation in air, the surface debris is considered undesirable. Here we have shown that this debris of nanoparticles can be used as an advantage for ultra-trace detection of different molecules. Further, with the advantage of having randomly stacked nanoparticles in debris and periodic substrates without debris from a single experiment, we have examined the much-debated relationship between enhancement and reproducibility in SERS and found that they are inversely correlated. The sensitivity of the substrate for rhodamine 6G, crystal violet, picric acid, and cysteine was found to be 10 fM, 100 fM, 100 nM, and 100 nM, respectively, with good reproducibility.

Evaporation-induced self-assembly of gold nanorods on a hydrophobic substrate for surface enhanced Raman spectroscopy applications

The controllable assembly of plasmonic nanoparticles has developed as one of the most significant approaches for surface enhanced Raman spectroscopy (SERS) applications. This study developed a simple approach to improve a large-scale ordered assembly of gold nanorods (GNRs) by controlling the droplet evaporation mode on hydrophobic substrates. The hydrophobic substrate was efficiently produced by spin coating the silicone oil onto the glass slides and annealing them. The analyte molecule rhodamine (R6G) was employed as a surface-enhanced Raman scattering probe to demonstrate the potential effects of the synthesized arrays. This hydrophobic platform enables the concentration and delivery of analyte molecules into the surface enhanced Raman spectroscopy sensitive site while suppressing the coffee ring effect generated by the smooth contraction motion of the base contact radius of the droplet without any pinning. Thus, the limit of detection (LOD) of the R6G analyte was lowered to 10−10 M and the homogenous dispersion of surface enhanced Raman spectroscopy hotspots within the self-assembly reproducible surface enhanced Raman spectroscopy signal. This new method enables a broad range of packing patterns and mechanisms by changing the host nanoparticles in the dispersion.

Designed Growth of AgNP Arrays for Anti-counterfeiting Based on Surface-Enhanced Raman Spectroscopy Signals.

Based on etched PS sphere arrays, the different growths of Ag nanoparticles with tunable LSPR are designed when SiO2-25 nm/Ag-30 nm/SiO2-100 nm sandwich nanocavity structures are annealed at 500 °C, including the hexagonal silver nanoparticle rings, circular silver nanoparticle rings, and aggregated silver nanoparticles. The uniformity of particle size and regularity of position generate enhanced electromagnetic field and good surface-enhanced Raman spectroscopy signals as confirmed by UV-vis observation and finite difference time domain method simulation. The developed nanostructures are effectively used as stable, nonreproducible, and markable anti-counterfeiting signs.