‘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

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.</p

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&gt;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

Tunable nanopatterning of conductive polymers via electrohydrodynamic lithography

[Image: see text] An increasing number of technologies require the fabrication of conductive structures on a broad range of scales and over large areas. Here, we introduce advanced yet simple electrohydrodynamic lithography (EHL) for patterning conductive polymers directly on a substrate with high fidelity. We illustrate the generality of this robust, low-cost method by structuring thin polypyrrole films via electric-field-induced instabilities, yielding well-defined conductive structures with feature sizes ranging from tens of micrometers to hundreds of nanometers. Exploitation of a conductive polymer induces free charge suppression of the field in the polymer film, paving the way for accessing scale sizes in the low submicron range. We show the feasibility of the polypyrrole-based structures for field-effect transistor devices. Controlled EHL pattering of conductive polymer structures at the micro and nano scale demonstrated in this study combined with the possibility of effectively tuning the dimensions of the tailor-made architectures might herald a route toward various submicron device applications in supercapacitors, photovoltaics, sensors, and electronic displays

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.</p

Collagen‐Electrohydrodynamic Hierarchical Lithography for Biomimetic Photonic Micro‐Nanomaterials

Biologically engineered nanomaterials give rise to unique and intriguing properties, which are not available in nature. The full‐realization of such has been hindered by the lack of robust and straightforward techniques to produce the required architectures. Here a new bottomup bionano‐engineering route is developed to construct nanomaterials using a guided assembly of collagen building blocks, establishing a lithographic process for three‐dimensional collagen‐based hierarchical micronano‐architectures. By introducing optimized hybrid electro‐hydrodynamic micronano‐lithography exploiting collagen molecules as biological building blocks to self‐assemble into a complex variety of structures, quasi‐ordered mimics of metamaterials‐like are constructed. The tailor‐designed engineered apparatus generates the underlying substrates with vertical orientation of collagen at controlled speeds. Templating these hierarchical structures into inorganic materials allows the replication of their network into periodic metal micronano‐assemblies. These generate substrates with interesting optical properties, suggesting that size‐and‐orientation dependent nanofilaments with varying degree of lateral order yield distinctly coloured structures with characteristic optical spectra correlated with observed colours, which varying diameters and interspacing, are attributable to coherent scattering by different periodicity of each fibrous micronano‐structure. The artificial mimics display similar optical characteristics to the natural butterfly wing's structure, known to exhibit extraordinary electromagnetic properties, driving future applications in cloaking, super‐lenses, photovoltaics and photodetectors