A facile and novel method to synthesize core/satellite (CS) nanoparticles via a linker-free method is reported. Au spheres on the tips of nanostars can gradually grow bigger and eventually transform into gap-enhanced Raman tags (GERTs) to form stable and ultrabright Au nanostar/GERTs CS nanoparticles. Au nanostar/Ag sphere CS nanoparticles can also be prepared via the growth of Ag spheres on the tips, which provides a direct route to new porous Au nanostar/Ag-Au, Ag-Pt, or Ag-Pd sphere CS nanoparticles through galvanic replacement of Ag. In situ surface-enhanced Raman spectroscopy monitoring on CS nanoparticles with different noble metals demonstrates single-particle photocatalysis; among them, the hybrid Ag-Pt CS nanoparticles show the fastest photocatalytic rates for the complete conversion of 4-nitrothiophenol (4-NTP) to 4,4-dimercaptoazobenzene (DMAB) at the single-particle level. This method provides a direct synthetic route to these complex nanoparticles without interference from external linker molecules and opens up new possibilities in single-particle analysis.

Determining the effects of ionizing radiation from unintended exposure in a nuclear event requires the identification of relevant biomarkers and development of methods to retrospectively estimate the absorbed dose. Melanin, a biologically important natural pigment found in hair, shows promise as a biomarker to assess potential radiation exposure. We investigated Raman spectroscopy as a rapid and noninvasive technique to assess changes in melanin from the hair of C57BL/6 mice to gamma radiation between 0 and 4 Gy. Two excitation wavelengths (532 and 785 nm) were employed to probe the melanin response for changes with radiation exposure. Excitation wavelength-dependent variation in Raman features indicates resonance Raman effects, where a 785-nm excitation is more sensitive to the effects of gamma radiation. Melanin-specific Raman features were identified as potential biomarkers for gamma-radiation exposure and used to distinguish between irradiated and nonirradiated mice. Partial least square discriminant analysis models of exposure exhibited enhanced sensitivity to irradiation at 785 nm excitation and yielded a sensitivity of 88% and a specificity of 83%. Mice were classified with 100% sensitivity and specificity up to day 7 at a known time point. A decline in specificity and classification accuracy correlated with alterations in melanin's spectra after >7 days following irradiation. Regression models of the Raman spectrum determined the exposed dose with a precision of <1 Gy at a known exposure time point. This noninvasive approach offers promising applications in radiation biodosimetry and medical monitoring, providing retrospective detection of gamma-radiation exposure at clinically relevant doses.

Tandem electrocatalysis, also referred to as sequential or cascade electrocatalysis, is a process in which multiple catalytic steps take place consecutively at distinct active sites within the same system. Inspired by enzymatic processes, this approach relies on the intermediate formed at one catalytic site serving as a reactant for the subsequent stage. By carefully integrating electrocatalysts and co-catalysts, tandem systems enhance reaction efficiency through synergistic effects, lower energy barriers, and improve selectivity toward targeted products. This study presents four case studies developed in our laboratory, focusing on the electrochemical reduction of CO₂ and N₂. Both transformations are pivotal for the energy transition, as they provide alternative electrochemical routes for carbon dioxide capture and conversion into valuable compounds while also promoting more sustainable methods for ammonia synthesis, moving away from the conventional Haber-Bosch process. In the first example, copper nanocubes are synthesized on copper electrodes and subsequently doped with silver nanoparticles via a galvanic displacement reaction. Analysis using Raman spectro-electrochemistry (Raman-SEC), electrochemical impedance spectroscopy (EIS), and differential electrochemical mass spectrometry (DEMS) reveals that at approximately -0.8 V vs. RHE, the reaction mechanism favors maximum CO generation before shifting selectivity toward ethylene formation. The second system involves copper nanocube electrodes doped with Zn nanoparticles, electrodeposited from ethaline. Electrolysis, combined with Raman-SEC, EIS, and DEMS studies, indicates that the density of active Zn sites plays a key role in directing the reaction pathway toward ethylene production. Additionally, the integration of Cu₂O nanocubes with Bi microparticles—electrodeposited through a pulsed potential protocol using ethaline as a solvent—enables selective methanol production at -0.2 V vs. RHE, achieving a faradaic efficiency of 70%. For the electrochemical reduction of N₂, a composite material composed of MoS₂ doped with FeS₂ was employed as the electrocatalyst. When FeS₂ doping reached 10%, a maximum faradaic efficiency of 13% for ammonia formation was observed, demonstrating a pronounced synergistic effect. In all cases, experiments were designed to provide empirical evidence supporting the selective generation of key intermediates, confirming the cascade reaction mechanism. Acknowledgments This research was supported by FONDECYT Regular Project 1221179, the Millennium Institute of Green Ammonia as an Energy Vector (ICN2021_023, MIGA), and the ANID National PhD Scholarship 21221294.

Hair is an attractive sample for determining exposure to ionizing radiation due to its non-invasive nature. A biological tissue comprised mainly of keratin protein, hair is susceptible to oxidative or reductive stress by direct or indirect damage mechanisms. In this report, changes observed in the Raman spectra associated with hair protein from ionizing radiation were assessed for biodosimetry. Raman spectra were obtained from the hairs of a mixed sex cohort of irradiated C57BL/6 mice (N = 32 total) with doses of gamma rays ranging from 0-4 Gy. Radiation-dependent changes in the Raman spectra of the hairs provided molecular-specific signals that can inform about the damage mechanism. Partial least-squares discriminant analysis (PLS-DA) models incorporating automated variable selection for each sex showed classification of controls or exposed at 80% accuracy based on cross-validation. Models show only slight differences in performance based on the mouse's sex from which the sample originated. This slight difference is consistent with PLS-DA models that show marginal cross-validation sensitivity (∼60%) in predicting the sex of the mouse from the Raman hair spectrum. Utilizing PLS regression, a dose-response model including both sexes showed root-mean-squared error (RMSE) ±1 Gy. The ability to determine dose or exposure from plucked hair with Raman spectroscopy would provide a needed tool for rapid medical triage after unexpected exposure.

The ability to combine microscopy and spectroscopy is beneficial for directly monitoring physical and biological processes. Spectral imaging approaches, where a transmission diffraction grating is placed near an imaging sensor to collect both the spatial image and spectrum for each object in the field of view, provide a relatively simple method to simultaneously collect images and spectroscopic responses on the same sensor. Initially demonstrated with fluorescence spectroscopy, the use of spectral imaging in Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) can provide a vibrational spectrum containing molecularly specific information that can inform on chemical changes. However, a major complication to this approach is the spectral overlap that occurs when objects are spaced closely together horizontally. In this work, we add a dove prism to a spectral imaging instrument developed for SERS imaging, enabling rotation of the collected SERS image and dispersed spectrum onto the imaging complementary metal-oxide semiconductor (CMOS) sensor. We demonstrate that this effectively reduces spectral overlap for emitters with clear separation between them and emitters with slightly overlapping point spread functions thereby facilitating collection of unambiguous spectra from each emitter.

Surface enhanced Raman scattering (SERS) provides a label free method of analyzing molecules from diverse and complex signals, potentially with single molecule sensitivity. The chemical specificity inherent in the SERS spectrum can identify molecules; however signal variability arising from the diversity of plasmonic environments can limit quantification, particularly at low concentrations. Here we show that digitizing, or counting SERS events, can decrease the limit of detection in flowing solutions enabling quantification of single molecules. By using multivariate curve resolution and establishing a score threshold, each individual spectrum can be classified as containing an event or not. This binary "yes/no" can then be quantified, and a linear region can be established. This method was shown to lower the limit of detection to the lowest physical limit, and lowered the limit of detection by an order of magnitude from the traditional, intensity based LOD calculations.

Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm−1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.

This work utilizes the collection of Raman spectra directly from thin layer chromatography (TLC) plates for quantitative determination of the pigment content of plant leaves.

Strongly confined electric fields resulting from nanogaps within nanoparticle aggregates give rise to significant enhancement of surface-enhanced Raman scattering (SERS). Nanometer differences in gap sizes lead to drastically different confined field strengths; so much attention has been focused on the development and understanding of nanostructures with controlled gap sizes. In this work, we report a novel petal gap-enhanced Raman tag (GERT) consisting of a bipyramid core and a nitrothiophenol (NTP) spacer to support the growth of hundreds of small petals and compare its SERS emission and localization to a traditional bipyramid aggregate. To do this, we use super resolution spectral SERS imaging that simultaneously captures the SERS images and spectra while varying the incident laser polarization. Intensity fluctuations inherent of SERS enabled super resolution algorithms to be applied, which revealed subdiffraction limited differences in the localization with respect to polarization direction for both particles. Interestingly, however, only the traditional bipyramid aggregates experienced a strong polarization dependence in their SERS intensity and in the plasmon-induced conversion of NTP to dimercaptoazobenzene (DMAB), which was localized with nanometer precision to regions of intense electromagnetic fields. The lack of polarization dependence (validated through electromagnetic simulations) and surface reactions from the bipyramid-GERTs suggests that the emissions arising from the bipyramid-GERTs are less influenced by confined fields.

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

Raman scattering provides a chemical-specific and label-free method for identifying and quantifying molecules in flowing solutions. This review provides a comprehensive examination of the application of Raman spectroscopy and surface-enhanced Raman scattering (SERS) to flowing liquid samples. We summarize developments in online and at-line detection using Raman and SERS analysis, including the design of microfluidic devices, the development of unique SERS substrates, novel sampling interfaces, and coupling these approaches to fluid-based chemical separations (e.g., chromatography and electrophoresis). The article highlights the challenges and limitations associated with these techniques and provides examples of their applications in a variety of fields, including chemistry, biology, and environmental science. Overall, this review demonstrates the utility of Raman and SERS for analysis of complex mixtures and highlights the potential for further development and optimization of these techniques.

SERS substrates with silver nanosheets (AgNS) on a copper surface were synthesized. A quantitative analysis of the pesticide imidacloprid was then performed by applying a PLSR chemometric model.

Immortalized cell lines are commonly used for in vitro studies such as drug efficacy, toxicology, and life cycle due to their cost effectiveness and accessibility; however, subpopulations within a cell line can arise from random mutations or asynchronous cell cycles which may lead to results that make interpretation difficult. A method that could classify these differences and separate unique subpopulations would increase our understanding of heterogeneous cellular responses. In the present work, we explore spectroscopic signals associated with subpopulations of cells magnetically sorted on the basis of alpha 5 beta 1 integrin binding to cyclic-RGDfC which mimics fibronectin in the extracellular matrix. SW620 colon cancer cells were incubated with cyclic-RGDfC functionalized gold-coated, iron core nanoparticles and magnetically sorted. The subpopulations from the sort were imaged (N = 10 positive and N = 10 negative, number of cells) via simultaneous surface-enhanced Raman scattering (SERS) and optical-photothermal infrared spectroscopy (O-PTIR). Pearson correlations of the standard peptide-protein interaction in the SERS channel allowed for visualization of the cyclic RGDfC-integrin alpha 5 beta 1 interaction. Partial least-squares discriminant analysis of the O-PTIR spectra collected from cell maps successfully classified the positively or negatively sorted cells. These results demonstrate that biochemical changes within a single cell line can be sorted via an integrin-activity-based assay using simultaneous SERS and O-PTIR.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool that provides valuable insight into the molecular contents of chemical and biological samples. However, interpreting Raman spectra from complex or dynamic datasets remains challenging, particularly for highly heterogeneous biological samples like extracellular vesicles (EVs). To overcome this, we developed a tunable and interpretable deep autoencoder for the analysis of several challenging Raman spectroscopy applications, including synthetic datasets, chemical mixtures, a chemical milling reaction, and mixtures of EVs. We compared the results with classical methods (PCA and UMAP) to demonstrate the superior performance of the proposed technique. Our method can handle small datasets, provide a high degree of generalization such that it can fill unknown gaps within spectral datasets, and even quantify relative ratios of cell line-derived EVs to fetal bovine serum-derived EVs within mixtures. This simple yet robust approach will greatly improve the analysis capabilities for many other Raman spectroscopy applications.

The addition of plasmonic nanoparticles into electrospun polymer fibers can have a significant impact on their properties relevant to applications in sensing, catalyst, and energy conversion. A Raman spectrometer incorporated into a photothermal heterodyne imaging system was used to study the hot electron transfer mechanism generated through the excitation of a localized surface plasmon resonance (LSPR) of gold and silver nanoparticles in polyacrylonitrile films and nanofibers. The ratio of anion nitrile radicals to neutral nitriles of polyacrylonitrile provides a measure of the ionization capabilities of the nanoparticles and was found to follow a Boltzmann distribution, indicating that the LSPR-mediated hot electron transfer mechanism is dependent on temperature. Silicon nanoparticles were used as a control for temperature and showed that heating itself, using 405 and 532 nm pump lasers, was not sufficient to ionize polyacrylonitriles, even at relatively high temperatures. The results provide insight into the roles of heating and electron transfer arising from nanoparticle additives in electrospun polymer fibers and other materials.

Hot spot engineering in plasmonic nanostructures plays a significant role in surface-enhanced Raman scattering (SERS) for bioanalysis and cell imaging. However, creating stable, reproducible, and strong SERS signals remains challenging due to the potential interference from surrounding chemicals and locating SERS-active analytes into hot-spot regions. Herein, we developed a straightforward approach to synthesize intra-gap nanoparticles encapsulating 4-nitrobenzenethiol (4-NBT) as a reporter molecule within these gaps to avoid outside interference. We made three kinds of intra-gap nanoparticles using nanorods, bipyramids, and nanospheres as cores, in which the nanorods based intra-gap nanoparticles exhibit the highest SERS activity. The advantage of our method is the ease of preparation of high-yield and stable intra-gap nanoparticles characterized by a short incubation time (10 min) with 4-NBT and quick synthesis without requiring an additional step to centrifuge for the purification of core nanoparticles. The intense localized field in the synthesized hot spots of these plasmonic gap nanostructures holds great promise as a SERS substrate for a broad range of quantitative optical applications.

Chemical transformations near plasmonic metals have attracted increasing attention in the past few years. Specifically, reactions occurring within plasmonic nanojunctions that can be detected via surface and tip-enhanced Raman (SER and TER) scattering were the focus of numerous reports. In this context, even though the transition between localized and nonlocal (quantum) plasmons at nanojunctions is documented, its implications on plasmonic chemistry remain poorly understood. We explore the latter through AFM-TER-current measurements. We use two molecules: i) 4-mercaptobenzonitrile (MBN) that reports on the (non)local fields and ii) 4-nitrothiophenol (NTP) that features defined signatures of its neutral/anionic forms and dimer product, 4,4'-dimercaptoazobenzene (DMAB). The transition from classical to quantum plasmons is established through our optical measurements: It is marked by molecular charging and optical rectification. Simultaneously recorded force and current measurements support our assignments. In the case of NTP, we observe the parent and DMAB product beneath the probe in the classical regime. Further reducing the gap leads to the collapse of DMAB to form NTP anions. The process is reversible: Anions subsequently recombine into DMAB. Our results have significant implications for AFM-based TER measurements and their analysis, beyond the scope of this work. In effect, when precise control over the junction is not possible (e.g., in SER and ambient TER), both classical and quantum plasmons need to be considered in the analysis of plasmonic reactions.