Electrofluidynamic Patterning of Tailorable Nanostructured Substrates for Surface-Enhanced Raman Scattering

Versatile methodology for the synthesis of stable magnetic SERS-encoded clusters for sensing applications

Surface-enhanced Raman scattering (SERS) substrates are garnering increasing interest for ultrasensitive high-throughput sensing. Notably, SERS-encoded nanostructures stand out due to their potential for nearly unlimited codification with excellent optical properties. In this paper we report a simple, versatile and cost-effective method for preparing SERS-encoded clusters. These clusters consist of encoded silver nanoparticles assembled onto magnetic microparticles, which are externally coated with oxide-based structures. We propose and compare diverse shell materials, including SiO2, ZnO and TiO2. This design results in a stable and robust system with excellent magnetic and optical properties, suitable for being used in multiple media and conditions. To enhance usability, the external coating was functionalized with dopamine, facilitating further modifications. Additionally, we developed a data analysis method based on machine learning and artificial neural networks, utilizing self-organizing maps to automate particle identification. This study provides valuable information for selecting the most appropriate magnetic SERS-encoded cluster for multiplex sensing applications.

Advanced Tuneable Micronanoplatforms for Sensitive and Selective Multiplexed Spectroscopic Sensing via Electro-Hydrodynamic Surface Molecular Lithography

Micro- and nanopatterning of materials, one of the cornerstones of emerging technologies, has transformed research capabilities in lab-on-a-chip diagnostics. Herein, a micro- and nanolithographic method is developed, enabling structuring materials at the submicron scale, which can, in turn, accelerate the development of miniaturized platform technologies and biomedical sensors. Underpinning it is the advanced electro-hydrodynamic surface molecular lithography, via inducing interfacial instabilities produces micro- and nanostructured substrates, uniquely integrated with synthetic surface recognition. This approach enables the manufacture of design patterns with tuneable feature sizes, which are functionalized via synthetic nanochemistry for highly sensitive, selective, rapid molecular sensing. The development of a high-precision piezoelectric lithographic rig enables reproducible substrate fabrication with optimum signal enhancement optimized for functionalization with capture molecules on each micro- and nanostructured array. This facilitates spatial separation, which during the spectroscopic sensing, enables multiplexed measurement of target molecules, establishing the detection at minute concentrations. Subsequently, this nano-plasmonic lab-on-a-chip combined with the unconventional computational classification algorithm and surface enhanced Raman spectroscopy, aimed to address the challenges associated with timely point-of-care detection of disease-indicative biomarkers, is utilized in validation assay for multiplex detection of traumatic brain injury indicative glycan biomarkers, demonstrating straightforward and cost-effective micro- and nanoplatforms for accurate detection.

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.

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.

Spectroscopic molecular-fingerprint profiling of saliva

Saliva analysis has been gaining interest as a potential non-invasive source of disease indicative biomarkers due to being a complex biofluid correlating with blood-based constituents on a molecular level. For saliva to cement its usage for analytical applications, it is paramount to gain underpinning molecular knowledge and establish a 'baseline' of the salivary composition in healthy individuals as well as characterize how these factors are impacting its performance as potential analytical biofluid. Here, we have systematically studied the molecular spectral fingerprint of saliva, including the changes associated with gender, age, and time. Via hybrid artificial neural network algorithms and Raman spectroscopy, we have developed a non-destructive molecular profiling approach enabling the assessment of salivary spectral changes yielding the determination of gender and age of the biofluid source. Our classification algorithm successfully identified the gender and age from saliva with high classification accuracy. Discernible spectral molecular 'barcodes' were subsequently constructed for each class and found to primarily stem from amino acid, protein, and lipid changes in saliva. This unique combination of Raman spectroscopy and advanced machine learning techniques lays the platform for a variety of applications in forensics and biosensing.

Spectroscopic profiling variations in extracellular vesicle biochemistry in a model of myogenesis

Extracellular vesicles (EVs) hold value as accessible biomarkers for understanding cellular differentiation and related pathologies. Herein, EV biomarkers in models of skeletal muscle dormancy and differentiation have been comparatively profiled using Raman spectroscopy (RS). Significant variations in the biochemical fingerprint of EVs were detected, with an elevation in peaks associated with lipid and protein signatures during early myogenic differentiation (day 2). Principal component analysis revealed a clear separation between the spectra of EVs derived from myogenic and senescent cell types, with non-overlapping interquartile ranges and population median. Observations aligned with nanoparticle tracking data, highlighting a significant early reduction in EV concentration in senescent myoblast cultures as well as notable variations in EV morphology and diameter. As differentiation progressed physical and biochemical differences in the properties of EVs became less pronounced. This study demonstrates the applicability of RS as a high-resolution analytical method for profiling biochemical changes in EVs during early myogenesis.