'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.

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.

Vibrational spectroscopic profiling of biomolecular interactions between oak powdery mildew and oak leaves.

Oak powdery mildew, caused by the biotrophic fungus Erysiphe alphitoides, is a prevalent disease affecting oak trees, such as English oak (Quercus robur). While mature oak populations are generally less susceptible to this disease, it can endanger young oak seedlings and new leaves on mature trees. Although disruptions of photosynthate and carbohydrate translocation have been observed, accurately detecting and understanding the specific biomolecular interactions between the fungus and the leaves of oak trees is currently lacking. Herein, via hybrid Raman spectroscopy combined with an advanced artificial neural network algorithm, the underpinning biomolecular interactions between biological soft matter, i.e., Quercus robur leaves and Erysiphe alphitoides, are investigated and profiled, generating a spectral library and shedding light on the changes induced by fungal infection and the tree's defence response. The adaxial surfaces of oak leaves are categorised based on either the presence or absence of Erysiphe alphitoides mildew and further distinguishing between covered or not covered infected leaf tissues, yielding three disease classes including healthy controls, non-mildew covered and mildew-covered. By analysing spectral changes between each disease category per tissue type, we identified important biomolecular interactions including disruption of chlorophyll in the non-vein and venule tissues, pathogen-induced degradation of cellulose and pectin and tree-initiated lignification of cell walls in response, amongst others, in lateral vein and mid-vein tissues. Via our developed computational algorithm, the underlying biomolecular differences between classes were identified and allowed accurate and rapid classification of disease with high accuracy of 69.6% for non-vein, 73.5% for venule, 82.1% for lateral vein and 85.6% for mid-vein tissues. Interfacial wetting differences between non-mildew covered and mildew-covered tissue were further analysed on the surfaces of non-vein and venule tissue. The overall results demonstrated the ability of Raman spectroscopy, combined with advanced AI, to act as a powerful and specific tool to probe foliar interactions between forest pathogens and host trees with the simultaneous potential to probe and catalogue molecular interactions between biological soft matter, paving the way for exploring similar relations in broader forest tree-pathogen systems.

Development and application of an optimised Bayesian shrinkage prior for spectroscopic biomedical diagnostics.

BACKGROUND AND OBJECTIVE: Classification of vibrational spectra is often challenging for biological substances containing similar molecular bonds, interfering with spectral outputs. To address this, various approaches are widely studied. However, whilst providing powerful estimations, these techniques are computationally extensive and frequently overfit the data. Shrinkage priors, which favour models with relatively few predictor variables, are often applied in Bayesian penalisation techniques to avoid overfitting. METHODS: Using the logit-normal continuous analogue of the spike-and-slab (LN-CASS) as the shrinkage prior and modelling, we have established classification for accurate analysis, with the established system found to be faster than conventional least absolute shrinkage and selection operator, horseshoe or spike-and-slab. These were examined versus coefficient data based on a linear regression model and vibrational spectra produced via density functional theory calculations. Then applied to Raman spectra from saliva to classify the sample sex. RESULTS: Subsequently applied to the acquired spectra from saliva, the evaluated models exhibited high accuracy (AUC>90 %) even when number of parameters was higher than the number of observations. Analyses of spectra for all Bayesian models yielded high-classification accuracy upon cross-validation. Further, for saliva sensing, LN-CASS was found to be the only classifier with 100 %-accuracy in predicting the output based on a leave-one-out cross validation. CONCLUSIONS: With potential applications in aiding diagnosis from small spectroscopic datasets and are compatible with a range of spectroscopic data formats. As seen with the classification of IR and Raman spectra. These results are highly promising for emerging developments of spectroscopic platforms for biomedical diagnostic sensing systems.

Hybrid use of Raman spectroscopy and artificial neural networks to discriminate Mycobacterium bovis BCG and other Mycobacteriales.

Even in the face of the COVID-19 pandemic, Tuberculosis (TB) continues to be a major public health problem and the 2nd biggest infectious cause of death worldwide. There is, therefore, an urgent need to develop effective TB diagnostic methods, which are cheap, portable, sensitive and specific. Raman spectroscopy is a potential spectroscopic technique for this purpose, however, so far, research efforts have focused primarily on the characterisation of Mycobacterium tuberculosis and other Mycobacteria, neglecting bacteria within the microbiome and thus, failing to consider the bigger picture. It is paramount to characterise relevant Mycobacteriales and develop suitable analytical tools to discriminate them from each other. Herein, through the combined use of Raman spectroscopy and the self-optimising Kohonen index network and further multivariate tools, we have successfully undertaken the spectral analysis of Mycobacterium bovis BCG, Corynebacterium glutamicum and Rhodoccocus erythropolis. This has led to development of a useful tool set, which can readily discern spectral differences between these three closely related bacteria as well as generate a unique spectral barcode for each species. Further optimisation and refinement of the developed method will enable its application to other bacteria inhabiting the microbiome and ultimately lead to advanced diagnostic technologies, which can save many lives.

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.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury.

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

Window into the mind: Advanced handheld spectroscopic eye-safe technology for point-of-care neurodiagnostic.

Traumatic brain injury (TBI), a major cause of morbidity and mortality worldwide, is hard to diagnose at the point of care with patients often exhibiting no clinical symptoms. There is an urgent need for rapid point-of-care diagnostics to enable timely intervention. We have developed a technology for rapid acquisition of molecular fingerprints of TBI biochemistry to safely measure proxies for cerebral injury through the eye, providing a path toward noninvasive point-of-care neurodiagnostics using simultaneous Raman spectroscopy and fundus imaging of the neuroretina. Detection of endogenous neuromarkers in porcine eyes' posterior revealed enhancement of high-wave number bands, clearly distinguishing TBI and healthy cohorts, classified via artificial neural network algorithm for automated data interpretation. Clinically, translating into reduced specialist support, this markedly improves the speed of diagnosis. Designed as a hand-held cost-effective technology, it can allow clinicians to rapidly assess TBI at the point of care and identify long-term changes in brain biochemistry in acute or chronic neurodiseases.

Optimisation of GraPhage13 macro-dispersibility via understanding the pH-dependent ionisation during self-assembly: towards the manufacture of graphene-based nanodevices.

GraPhage13 aerogels (GPAs) are micro-porous structures generated through the self-assembly of graphene oxide (GO) and M13 bacteriophage. As GPA fabrication involves the aggregation of GO and M13 in aqueous solution, we aim to understand its dispersibility across a wide pH range. Herein, a novel technique has been developed to relate the ionisation of functional groups to the surface charge, offering insights into the conditions required for GPA fabrication and the mechanism behind its self-assembly. The aggregation of GO and M13 was observed between pH 2-6 and exhibited dependence on the surface charge of the resulting aggregate with the M13 bacteriophage identified as the primary factor contributing to this, whilst originating from the ionisation of its functional groups. In contrast, GO exhibited a lesser impact on the surface charge due to the deprotonation of its carboxylic, enolic and phenolic functional groups at pH 6 and above, which falls outside the required pH range for aggregation. These results enhance our understanding of the GPA self-assembly mechanism, the conditions required for their fabrication and the optimal processability, laying the foundation towards its broad range of applications and the subsequent manufacture of graphene-based nanodevices.

Raman Spectroscopy as a Neuromonitoring Tool in Traumatic Brain Injury: A Systematic Review and Clinical Perspectives.

Traumatic brain injury (TBI) is a significant global health problem, for which no disease-modifying therapeutics are currently available to improve survival and outcomes. Current neuromonitoring modalities are unable to reflect the complex and changing pathophysiological processes of the acute changes that occur after TBI. Raman spectroscopy (RS) is a powerful, label-free, optical tool which can provide detailed biochemical data in vivo. A systematic review of the literature is presented of available evidence for the use of RS in TBI. Seven research studies met the inclusion/exclusion criteria with all studies being performed in pre-clinical models. None of the studies reported the in vivo application of RS, with spectral acquisition performed ex vivo and one performed in vitro. Four further studies were included that related to the use of RS in analogous brain injury models, and a further five utilised RS in ex vivo biofluid studies for diagnosis or monitoring of TBI. RS is identified as a potential means to identify injury severity and metabolic dysfunction which may hold translational value. In relation to the available evidence, the translational potentials and barriers are discussed. This systematic review supports the further translational development of RS in TBI to fully ascertain its potential for enhancing patient care.

Photobiomodulation reduces hippocampal apoptotic cell death and produces a Raman spectroscopic "signature".

Apoptotic cell death within the brain represents a significant contributing factor to impaired post-traumatic tissue function and poor clinical outcome after traumatic brain injury. After irradiation with light in the wavelength range of 600-1200 nm (photobiomodulation), previous investigations have reported a reduction in apoptosis in various tissues. This study investigates the effect of 660 nm photobiomodulation on organotypic slice cultured hippocampal tissue of rats, examining the effect on apoptotic cell loss. Tissue optical Raman spectroscopic changes were evaluated. A significantly higher proportion of apoptotic cells 62.8±12.2% vs 48.6±13.7% (P<0.0001) per region were observed in the control group compared with the photobiomodulation group. After photobiomodulation, Raman spectroscopic observations demonstrated 1440/1660 cm-1 spectral shift. Photobiomodulation has the potential for therapeutic utility, reducing cell loss to apoptosis in injured neurological tissue, as demonstrated in this in vitro model. A clear Raman spectroscopic signal was observed after apparent optimal irradiation, potentially integrable into therapeutic light delivery apparatus for real-time dose metering.

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.

Development and Characterization of a Probe Device toward Intracranial Spectroscopy of Traumatic Brain Injury.

Traumatic brain injury is a leading cause of mortality worldwide, often affecting individuals at their most economically active yet no primary disease-modifying interventions exist for their treatment. Real-time direct spectroscopic examination of the brain tissue within the context of traumatic brain injury has the potential to improve the understanding of injury heterogeneity and subtypes, better target management strategies and organ penetrance of pharmacological agents, identify novel targets for intervention, and allow a clearer understanding of fundamental biochemistry evolution. Here, a novel device is designed and engineered, delivering Raman spectroscopy-based measurements from the brain through clinically established cranial access techniques. Device prototyping is undertaken within the constraints imposed by the acquisition and site dimensions (standard intracranial access holes, probe's dimensions), and an artificial skull anatomical model with cortical impact is developed. The device shows a good agreement with the data acquired via a standard commercial Raman, and the spectra measured are comparable in terms of quality and detectable bands to the established traumatic brain injury model. The developed proof-of-concept device demonstrates the feasibility for real-time optical brain spectroscopic interface while removing the noise of extracranial tissue and with further optimization and in vivo validation, such technology will be directly translatable for integration into currently available standards of neurological care.

Detection limits are central to improve reporting standards when using Nile red for microplastic quantification.

Beyond simple identification of either the presence or absence of microplastic particles in the environment, quantitative accuracy has been criticised as being neither comparable nor reproducible. This is, in part, due to difficulties in the identification of synthetic particles amidst naturally occurring organic and inorganic components. The fluorescent stain Nile red has been proposed as a tool to overcome this issue, but to date, has been used without consideration of polymer specific fluorescent variability. The aim of this study was to evaluate the efficacy of Nile red for microplastic detection by systematically investigating what drives variations in particle pixel brightness (PPB). The results showed that PPB varied between polymer type, shape, size, colour and by staining procedure. Sand, an inorganic component of the sample matrix does not fluoresce when stained with Nile red. In contrast the organic components, wood and chitin, fluoresce between 1.40 and 12 arbitrary units (a.u.) and 32 and 74 a.u. after Nile red staining, respectively. These data informed the use of a PPB threshold limit of 100 a.u., which improved the detection of EPS, HDPE, PP and PA-6 from the 6 polymers tested and reduced analysis time by 30-58% compared to unstained samples. Conversely, as with traditional illumination, PET and PVC were not accurately estimated using this approach. This study shows that picking a threshold limit is not arbitrary but rather must be informed by polymer specific fluorescent variability and matrix considerations. This is an essential step needed to facilitate comparability and reproducibility between individual studies.

Spectroscopic detection of traumatic brain injury severity and biochemistry from the retina.

Traumatic brain injury (TBI) is a major burden on healthcare services worldwide, where scientific and clinical innovation is needed to provide better understanding of biochemical damage to improve both pre-hospital assessment and intensive care monitoring. Here, we present an unconventional concept of using Raman spectroscopy to measure the biochemical response to the retina in an ex-vivo murine model of TBI. Through comparison to spectra from the brain and retina following injury, we elicit subtle spectral changes through the use of multivariate analysis, linked to a decrease in cardiolipin and indicating metabolic disruption. The ability to classify injury severity via spectra of the retina is demonstrated for severe TBI (82.0 %), moderate TBI (75.1 %) and sham groups (69.4 %). By showing that optical spectroscopy can be used to explore the eye as the window to the brain, we lay the groundwork for further exploitation of Raman spectroscopy for indirect, non-invasive assessment of brain chemistry.

Tuberculosis diagnostics: overcoming ancient challenges with modern solutions.

Rapid, sensitive, accurate and portable diagnostics are a mainstay of modern medicine. Tuberculosis is a disease that has been with us since time immemorial and, despite the fact that it can be treated and cured, it still remains the world's biggest infectious killer, taking the lives of millions annually. There have been important developments in the diagnostic devices for tuberculosis however, these are often prone to error, expensive, lack the necessary sensitivity or accuracy and, crucially, not sufficiently portable and thus not applicable in the remote, rural areas, where they are most needed. Modern solutions have been emerging in the past decade, seeking to overcome many of the inhibiting issues in this field by utilising recent advances in molecular biology, genetics and sequencing or even completely 'reinventing the wheel', by developing novel and unprecedented diagnostic techniques. In this mini review, the issues and challenges arising from the historical methods of diagnosing tuberculosis are discussed, followed by outlaying their particular lack of appropriateness for regions of the world where tuberculosis still remains endemic. Subsequently, more recent developments of new methods and technological advancements as 'modern weapons' in the battle to defeat this disease and associated challenges are reviewed, and finally an outlook is presented, highlighting the future of the modern solutions under development, which are envisioned to lay the platform for improvements in delivering timely intervention, reduce immense expense and burden on healthcare systems worldwide, while saving millions of lives and eventually, may enable the eradication of this ancient disease.

Improvements in the production of purified M13 bacteriophage bio-nanoparticle.

M13 bacteriophage is a well-established versatile nano-building block, which can be employed to produce novel self-assembled functional materials and devices. Sufficient production and scalability of the M13, often require a large quantity of the virus and thus, improved propagation methods characterised by high capacity and degree of purity are essential. Currently, the 'gold-standard' is represented by infecting Escherichia coli cultures, followed by precipitation with polyethylene glycol (PEG). However, this is considerably flawed by the accumulation of contaminant PEG inside the freshly produced stocks, potentially hampering the reactivity of the individual M13 filaments. Our study demonstrates the effectiveness of implementing an isoelectric precipitation procedure to reduce the residual PEG along with FT-IR spectroscopy as a rapid, convenient and effective analytic validation method to detect the presence of this contaminant in freshly prepared M13 stocks.

Nanomechanics of graphene oxide-bacteriophage based self-assembled porous composites.

Graphene oxide, integrated with the filamentous bacteriophage M13, forms a 3D large-scale multifunctional porous structure by self-assembly, with considerable potential for applications. We performed Raman spectroscopy under pressure on this porous composite to understand its fundamental mechanics. The results show that at low applied pressure, the [Formula: see text] bonds of graphene oxide stiffen very little with increasing pressure, suggesting a complicated behaviour of water intercalated between the graphene layers. The key message of this paper is that water in a confined space can have a significant impact on the nanostructure that hosts it. We introduced carbon nanotubes during the self-assembly of graphene oxide and M13, and a similar porous macro-structure was observed. However, in the presence of carbon nanotubes, pressure is transmitted to the [Formula: see text] bonds of graphene oxide straightforwardly as in graphite. The electrical conductivity of the composite containing carbon nanotubes is improved by about 30 times at a bias voltage of 10 V. This observation suggests that the porous structure has potential in applications where good electrical conductivity is desired, such as sensors and batteries.

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

The unique attributes of surface enhanced Raman spectroscopy (SERS) make it well suited to address the challenges associated with portable diagnostics. However, despite the remarkable progress in this field, where the instrumentation has made great strides forward providing a route to the miniaturization of sensing devices, to date producing three-dimensional low-cost SERS substrates which simultaneously fulfill the multitude of criteria of high sensitivity, reproducibility, tunability, multiplexity, and integratability for rapid sensing has not yet been accomplished. Successful implementation of SERS requires readily fine-tuned nanostructures, which create a high enhancement. Here, an advanced electrofluidynamic patterning (EFDP) technique enables rapid fabrication of SERS active topographic morphologies with high throughput and at a nanoresolution via the spatial and lateral modulation of the dielectric discontinuity due to the high electric field generated across the polymer nanofilm and air gap. The subsequent formation of displacement charges within the nanofilm by coupling to the electric field yield a destabilizing electrostatic pressure and amplification of EFDP instabilities enabling the controllable pattern formation. The top of each gold coated EFDP fabricated pillar generates controllable high SERS enhancement by coupling of surface plasmon modes on top of the pillar, with each nanostructure acting as an individual sensing unit. The absolute enhancement factor depends on the topology as well as the tunable dimensions of the nanostructured units, and these are optimized in the design and engineering of the dedicated EFDP apparatus for reproducible, low-cost fabrication of the three-dimensional nanoarchitectures on macrosurfaces, rendering them for easy integration in further lab-on-a-chip devices. This unique combination of nanomaterials and nanospectroscopic systems lay the platform for a variety of applications in chemical and biological sensing.

Development of the Self Optimising Kohonen Index Network (SKiNET) for Raman Spectroscopy Based Detection of Anatomical Eye Tissue.

Raman spectroscopy shows promise as a tool for timely diagnostics via in-vivo spectroscopy of the eye, for a number of ophthalmic diseases. By measuring the inelastic scattering of light, Raman spectroscopy is able to reveal detailed chemical characteristics, but is an inherently weak effect resulting in noisy complex signal, which is often difficult to analyse. Here, we embraced that noise to develop the self-optimising Kohonen index network (SKiNET), and provide a generic framework for multivariate analysis that simultaneously provides dimensionality reduction, feature extraction and multi-class classification as part of a seamless interface. The method was tested by classification of anatomical ex-vivo eye tissue segments from porcine eyes, yielding an accuracy >93% across 5 tissue types. Unlike traditional packages, the method performs data analysis directly in the web browser through modern web and cloud technologies as an open source extendable web app. The unprecedented accuracy and clarity of the SKiNET methodology has the potential to revolutionise the use of Raman spectroscopy for in-vivo applications.