The majority of analytical chemistry methods requires presence of target molecules directly at a sensing surface. Diffusion of analyte from the bulk towards the sensing layer is random and might be extremely lengthy, especially in case of low concentration of molecules to be detected. Thus, even the most sensitive transducer and the most selective sensing layer are limited by the efficiency of deposition of molecules on sensing surfaces. However, rapid development of new sensing technologies is rarely accompanied by new protocols for analyte deposition. To bridge this gap, we propose a method for fast and efficient deposition of variety of molecules (e.g. proteins, dyes, drugs, biomarkers, amino acids) based on application of the alternating electric field. We show the dependence between frequency of the applied electric field, the intensity of the surface enhanced Raman spectroscopy (SERS) signal and the mobility of the studied analyte. Such correlation allows for a priori selection of parameters for any desired compound without additional optimization. Thanks to the application of the electric field, we improve SERS technique by decrease of time of deposition from 20 h to 5 min, and, at the same time, reduction of the required sample volume from 2 ml to 50 μl. Our method might be paired with number of analytical methods, as it allows for deposition of molecules on any conductive surface, or a conductive surface covered with dielectric layer.

An assessment of the feasibility of using modified surface enhanced Raman scattering substrates (Ag nanoparticles on indium‑tin-oxide glass) for quantitative nanoparticle-enhanced laser induced breakdown spectroscopy (NELIBS) was carried out. Substrates were prepared with different surface coverage from various nanoparticle sizes, and their laser ablation behaviour was tested in detail. It was found that use of those combinations are most beneficial in terms of the signal enhancement factor, which provide the shortest interparticle distances. With the application of 266 nm laser wavelength, long (ms-range) gate width, and optimized laser pulse energy, the best NELIBS signal enhancement was found to be about a factor of three. By using liquid sample deposition by spraying, which was found to provide an even distribution of liquid samples on the substrate surface, successful calibration for Mn, Zn and Cr was performed. The NELIBS signal repeatability from five repeated measurements was found to be comparable to that of LIBS (5–10% RSD). These observations indicate that the NELIBS signal enhancement approach can be used in quantitative analytical applications for liquid samples, if i) the substrate fabrication procedure has good repeatability, ii) surface coverage and nanoparticle size is tightly controlled, iii) a homogenous liquid sample deposition is achieved.

The development of recyclable surface enhanced Raman scattering (SERS) based sensors has been in huge demand for trace level explosives detection. A simple, hybrid Silicon (Si) nanotextured target-based SERS platform is fabricated through patterning micro square arrays (MSA) on Si using femtosecond (fs) laser ablation technique at different fluences. Using the hybrid target Si MSA substrate loaded/decorated with Ag-Au alloy NPs (obtained using femtosecond ablation in liquids) we demonstrate the trace level detection of organic nitro-explosives [picric acid (PA), 2,4-dinitrotoluene (DNT), and 1, 3, 5-trinitroperhydro-1, 3, 5-triazine (RDX)] and their mixtures. The microstructures/nanostructures of MSA fabricated at an input fluence of 9.55 J/cm2, and decorated with Ag-Au alloy NPs, exhibited exceptional SERS enhancement factors (EFs) up to ∼1010 for MB, ∼106 for PA, and ∼104 for RDX with the detection limits obtained being ∼5 pM, ∼36 nM, and ∼400 nM for MB, PA and RDX respectively. Furthermore, we demonstrate these SERS substrates possess good reproducibility (RSD values < 15%) and a superior performance compared to a commercial Ag substrate (SERSitive, Poland). Three binary mixtures, i.e. MB-PA, MB-DNT, PA-DNT at different concentrations, were also investigated using the same SERS substrate to test the efficacy. Further, the SERS spectra of dyes, explosives, and complex mixtures were utilized for discrimination/classification using principal component analysis.

The paper reports on the features and advantages of horizontally oriented flexible silicon nanowires (SiNWs) substrates for surface-enhanced Raman spectroscopy (SERS) applications. The novel SERS substrates are described in detail considering three main aspects. First, the key synthesis parameters for the flexible nanostructure SERS substrates were optimized. It is shown that fabrication temperature and metal-plating duration significantly influence the flexibility of the SiNWs and, consequently, determine the SERS enhancement. Second, it is demonstrated how the immersion in a liquid followed by drying results in the formation of SiNWs bundles influencing the surface morphology. The morphology changes were described by fractal dimension and lacunar analyses and correlated with the duration of Ag plating and SERS measurements. SERS examination showed the optimal intensity values for SiNWs thickness values of 60–100 nm. That is, when the flexibility of the self-assembly SiNWs allowed hot spots occurrence. Finally, the test with 4-mercaptophenylboronic acid showed excellent SERS performance of the flexible, horizontally oriented SiNWs in comparison with several other commercially available substrates.

We show that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast, reliable, and easy method for detection and identification of food-borne bacteria, namely Salmonella spp., Listeria monocytogenes, and Cronobacter spp., in different types of food matrices (salmon, eggs, powdered infant formula milk, mixed herbs, respectively). The main aim of this work was to introduce the SERS technique into three ISO (6579:2002; 11290–1:1996/A1:2004; 22964:2006) standard procedures required for detection of these bacteria in food. Our study demonstrates that the SERS technique is effective in distinguishing very closely related bacteria within a genus grown on solid and liquid media. The advantages of the proposed ISO-SERS method for bacteria identification include simplicity and reduced time of analysis, from almost 144 h required by standard methods to 48 h for the SERS-based approach. Additionally, PCA allows one to perform statistical classification of studied bacteria and to identify the spectrum of an unknown sample. Calculated first and second principal components (PC-1, PC-2) account for 96, 98, and 90% of total variance in the spectra and enable one to identify the Salmonella spp., L. monocytogenes, and Cronobacter spp., respectively. Moreover, the presented study demonstrates the excellent possibility for simultaneous detection of analyzed food-borne bacteria in one sample test (98% of PC-1 and PC-2) with a goal of splitting the data set into three separated clusters corresponding to the three studied bacteria species. The studies described in this paper suggest that SERS represents an alternative to standard microorganism diagnostic procedures.

This paper demonstrates that surface-enhanced Raman spectroscopy (SERS) coupled with principal component analysis (PCA) can serve as a fast and reliable technique for the detection and identification of human fungal pathogens, such as Trichophyton rubrum, Candida krusei, Scopulariopsis brumptii, and Aspergillus flavus. Fungal infections have become one of the leading infectious causes of morbidity and mortality among hospitalized patients and/or immunocompromised hosts. Hence, there is a strong need for the development of new technologies allowing for fast and reliable diagnosis of fungal diseases. Our study shows that the SERS technique effectively distinguishes between selected common fungal pathogens and thus offers taxonomic affiliation of fungi within several minutes. Additionally, the PCA analysis allows performing statistical classification of fungal pathogens studied and identifying the fungal spectrum directly from a clinical sample. Calculated two principal components (PCs) (PC-1, PC-2) are the most diagnostically significant, explain 97% of the variability and enable, with very high probability, discrimination between the four mentioned fungal species. Moreover, the results of this study demonstrate the excellent possibility for the identification of fungi from human skin samples. The research presented in this paper offers an alternative for conventional fungal diagnostics and paves the way for the development of a new, fast, robust, and cost-effective diagnostic test for the detection and identification of fungal pathogens.