'When is a hotspot a good nanospot' - review of analytical and hotspot-dominated surface enhanced Raman spectroscopy nanoplatforms.

Substrate development in surface-enhanced Raman spectroscopy (SERS) continues to attract research interest. In order to determine performance metrics, researchers in foundational SERS studies use a variety of experimental means to characterize the nature of substrates. However, often this process would appear to be performed indiscriminately without consideration for the physical scale of the enhancement phenomena. Herein, we differentiate between SERS substrates whose primary enhancing structures are on the hundreds of nanometer scale (analytical SERS nanosubstrates) and those whose main mechanism derives from nanometric-sized gaps (hot-spot dominated SERS substrates), assessing the utility of various characterization methods for each substrate class. In this context, characterization approaches in white-light spectroscopy, electron beam methods, and scanning probe spectroscopies are reviewed. Tip-enhanced Raman spectroscopy, wavelength-scanned SERS studies, and the impact of surface hydrophobicity are also discussed. Conclusions are thus drawn on the applicability of each characterization technique regarding amenability for SERS experiments that have features at different length scales. For instance, while white light spectroscopy can provide an indication of the plasmon resonances associated with 10 s-100 s nm-scale structures, it may not reveal information about finer surface texturing on the true nm-scale, critical for SERS' sensitivity, and in need of investigation via scanning probe techniques.

Advances in lithographic techniques for precision nanostructure fabrication in biomedical applications.

Nano-fabrication techniques have demonstrated their vital importance in technological innovation. However, low-throughput, high-cost and intrinsic resolution limits pose significant restrictions, it is, therefore, paramount to continue improving existing methods as well as developing new techniques to overcome these challenges. This is particularly applicable within the area of biomedical research, which focuses on sensing, increasingly at the point-of-care, as a way to improve patient outcomes. Within this context, this review focuses on the latest advances in the main emerging patterning methods including the two-photon, stereo, electrohydrodynamic, near-field electrospinning-assisted, magneto, magnetorheological drawing, nanoimprint, capillary force, nanosphere, edge, nano transfer printing and block copolymer lithographic technologies for micro- and nanofabrication. Emerging methods enabling structural and chemical nano fabrication are categorised along with prospective chemical and physical patterning techniques. Established lithographic techniques are briefly outlined and the novel lithographic technologies are compared to these, summarising the specific advantages and shortfalls alongside the current lateral resolution limits and the amenability to mass production, evaluated in terms of process scalability and cost. Particular attention is drawn to the potential breakthrough application areas, predominantly within biomedical studies, laying the platform for the tangible paths towards the adoption of alternative developing lithographic technologies or their combination with the established patterning techniques, which depends on the needs of the end-user including, for instance, tolerance of inherent limits, fidelity and reproducibility.

Rapid Assessment of Fish Freshness for Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy and Fusion-Based Artificial Intelligence.

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and quadratic discriminant analyses, k-nearest neighbors, random forest, support vector machine, and linear regression, as well as ensemble and majority voting methods, were used to explore spectroscopy data measured on fillets and to train classification models to predict freshness. Our results show that multi-mode spectroscopy achieves 95% accuracy, improving the accuracies of the FL, VIS-NIR and SWIR single-mode spectroscopies by 26, 10 and 9%, respectively. We conclude that multi-mode spectroscopy and data fusion analysis has the potential to accurately assess freshness and predict shelf life for fish fillets and recommend this study be expanded to a larger number of species in the future.

Optimization of Nanosubstrates toward Molecularly Surface-Functionalized Raman Spectroscopy.

Diagnostic advancements require continuous developments of reliable analytical sensors, which can simultaneously fulfill many criteria, including high sensitivity and specificity for a broad range of target analytes. Incorporating the highly sensitive attributes of surface-enhanced Raman spectroscopy (SERS) combined with highly specific analyte recognition capabilities via molecular surface functionalization could address major challenges in molecular diagnostics and analytical spectroscopy fields. Herein, we have established a controllable molecular surface functionalization process for a series of textured gold surfaces. To create the molecularly surface-functionalized SERS platforms, self-assembled benzyl-terminated and benzoboroxole-terminated monolayers were used to compare which thicknesses and root-mean-square (RMS) roughness of planar gold produced the most sensitive and specific surfaces. Optimal functionalization was identified at 80 ± 8 nm thickness and 7.2 ± 1.0 nm RMS. These exhibited a considerably higher SERS signal (70-fold) and improved sensitivity for polysaccharides when analyzed using principal component analysis (PCA) and self-organizing maps (SOM). These findings lay the procedure for establishing the optimal substrate specifications as an essential prerequisite for future studies aiming at developing the feasibility of molecular imprinting for SERS diagnostic applications and the subsequent delivery of advanced, highly selective, and sensitive sensing devices and analytical platforms.

Detection of low-concentration contaminants in solution by exploiting chemical derivatization in surface-enhanced Raman spectroscopy.

A simple derivatization methodology is shown to extend the application of surface-enhanced Raman spectroscopy (SERS) to the detection of trace concentration of contaminants in liquid form. Normally in SERS the target analyte species is already present in the molecular form in which it is to be detected and is extracted from solution to occupy sites of enhanced electromagnetic field on the substrate by means of chemisorption or drop-casting and subsequent evaporation of the solvent. However, these methods are very ineffective for the detection of low concentrations of contaminant in liquid form because the target (ionic) species (a) exhibits extremely low occupancy of enhancing surface sites in the bulk liquid environment and (b) coevaporates with the solvent. In this study, the target analyte species (acid) is detected via its solid derivative (salt) offering very significant enhancement of the SERS signal because of preferential deposition of the salt at the enhancing surface but without loss of chemical discrimination. The detection of nitric acid and sulfuric acid is demonstrated down to 100 ppb via reaction with ammonium hydroxide to produce the corresponding ammonium salt. This yields an improvement of ~4 orders of magnitude in the low-concentration detection limit compared with liquid phase detection.