Bioinspired superhydrophobic SERS substrates for machine learning assisted miRNA detection in complex biomatrix below femtomolar limit.

Surface-enhanced Raman spectroscopy (SERS) is an analytical method with high potential in the field of medicine. The design of SERS substrates, based on specific morphology and/or chemical modification, allow the recognition of the presence of specific analytes with precision close to a single-molecule detection limit. However, the SERS analysis of real samples is significantly complicated by the presence of a large number of "minor" molecules that can shield the signal from the target analyte and make it impossible to determine it in practice. In this work, an advanced SERS approach was used for the detection of cancer-related miRNA-21 in blood plasma, used as a molecular model background. The approach was based on the combination of the biomimetic plasmon-active SERS substrate, its tuned surface chemistry and advanced SERS data analysis, making use of artificial machine learning. In the first step, biomimetic SERS substrates were created using a butterfly wing as a starting template. The substrates were covered by thin Au layer and covalently grafted with hydrophobic chemical moieties to introduce superhydrophobic and water-adhesive properties. The self-concentration of the analyte on the substrates was achieved by minimizing the contact area between the analyte drop and the substrate, which is facilitated by surface superhydrophobicity and additionally enhanced by drop evaporation on the flipped over substrate. Due to the presence of cancer miRNA and blood plasma background, the measured SERS spectra represent a complex of interfering peaks. Thus, their interpretation was carried out using a specially trained machine learning model. As a result, reliable and repeatable quantitative detection of miRNAs below the femtomolar level (up to 10-16 M) on the background of human blood plasma becomes possible.

Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage.

Taking advantage of surface-enhanced Raman scattering (SERS) methodology with its unique ability to collect abundant intrinsic fingerprint information and noninvasive data acquisition we set up a SERS-based approach for recognition of physically induced DNA damage with further incorporation of artificial neural network (ANN). As a proof-of-concept application, we used the DNA molecules, where the one oligonucleotide (OND) was grafted to the plasmonic surface while complimentary OND was exposed to UV illumination with various exposure doses and further hybridized with the grafted counterpart. All SERS spectra of entrapped DNA were collected by several operators using the portable spectrometer, without any optimization of measurements procedure (e.g., optimization of acquisition time, laser intensity, finding of optimal place on substrate, manual baseline correction, etc.) which usually takes a significant amount of operator's time. The SERS spectra were employed as input data for ANN training, and the performance of the system was verified by predicting the class labels for SERS validation data, using a spectra dataset, which has not been involved in the training process. During that phase, accuracy higher than 98% was achieved with a level of confidence exceeding 95%. It should be noted that utilization of the proposed functional-SERS/ANN approach allows identifying even the minor DNA damage, almost invisible by control measurements, performed with common analytical procedures. Moreover, we introduce the advanced ANN design, which allows not only classifying the samples but also providing the ANN analysis feedback, which associates the spectral changes and chemical transformations of DNA structure.

Laser patterning of transparent polymers assisted by plasmon excitation.

Plasmon-assisted lithography of thin transparent polymer films, based on polymer mass-redistribution under plasmon excitation, is presented. The plasmon-supported structures were prepared by thermal annealing of thin Ag films sputtered on glass or glass/graphene substrates. Thin films of polymethylmethacrylate, polystyrene and polylactic acid were then spin-coated on the created plasmon-supported structures. Subsequent laser beam writing, at the wavelength corresponding to the position of plasmon absorption, leads to mass redistribution and patterning of the thin polymer films. The prepared structures were characterized using UV-Vis spectroscopy and confocal and AFM microscopy. The shape of the prepared structures was found to be strongly dependent on the substrate type. The mechanism leading to polymer patterning was examined and attributed to the plasmon-heating. The proposed method makes it possible to create different patterns in polymer films without the need for wet technological stages, powerful light sources or a change in the polymer optical properties.