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Engineering of plasmonic properties of gold nanostructures expands the field of their applications from photocatalysis and photothermal effects to ultrasensitive surface-enhanced Raman spectroscopy (SERS). The known methods of preparation of gold nanobowls involve the deposition of gold layer on polymers or silicon nanotemplates and the removal of the top layer of gold together with the template. Such gold nanobowls are characterized by very broad plasmonic bands due to the plasmon hybridization. The sharp edges on the top of nanobowls are potential sources of the strong electromagnetic field beneficial for SERS. We present a novel template-free synthesis of gold nanobowls (AuNBs). The AuNB layers are deposited on graphene oxide (GO) layers. We compare AuNBs with gold nanospheres (AuNSs) and gold nanourchins (AuNUs) having similar size. The gold nanoparticles are combined with pristine GO or graphene oxide conditioned in ammonia (GONH 3 ) or graphene oxide conditioned in sodium hydroxide (GONaOH). The SERS properties of the hybrid supports were studied using rhodamine 6G (R6G) as the SERS probe. The 633 nm laser line was used, which falls out of the molecular resonance with R6G. The results indicate that AuNBs show largely higher enhancement factors when compared to AuNUs and AuNSs. Furthermore, the GO materials are able to modify the SERS enhancement by 1 order of magnitude. We explain the influence of the GO material by three factors: (1) enabling or disabling the charge transfer between gold and R6G, which is crucial for the chemical part of SERS enhancement; (2) causing the aggregation of gold nanoparticles and formation of hot spots; (3) dipole contribution to the electromagnetic enhancement through the abundance of polar groups on the surface., Competing Interests: The authors declare no competing financial interest., (© 2023 The Authors. Published by American Chemical Society.)

Gamification is known to enhance users' participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process.

Graphene oxide-silver nanoparticle nanohybrids were synthesized by simple reduction of the silver nitrate and graphene oxide (GO) mixture in water using the mild reducing agent ascorbic acid. The concentration of ascorbic acid was varied to verify the possible influence of the GO surface composition on the efficiency of the hybrid material as substrates for surface enhanced Raman spectroscopy (SERS). Furthermore, the composites were conditioned in ammonia solution or in potassium hydroxide diluted solution. For comparison, the graphene oxide-silver nanoparticle composite has been synthesized using the ammonia-treated GO. All materials were characterized using spectroscopic and microscopic methods including UV-Vis, infrared, and Raman spectroscopy and scanning electron microscopy. The SERS efficiency of the nanohybrids was tested using 4-aminothiophenol (PATP). The optimal synthesis conditions were found. Ammonia and potassium peroxide drop-casted on the composite changed the SERS properties. The sample treated with KOH showed the best SERS enhancement. The variation of the SERS enhancement was correlated with the shape of the UV-Vis characteristics and the surface structure of the composites., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kasztelan, Studzinska, Żukowska and Pałys.)

Surface Enhanced Raman Spectroscopy (SERS) has a long history as an ultrasensitive platform for the detection of biological species from small aromatic molecules to complex biological systems as circulating tumor cells. Thanks to unique properties of graphene, the range of SERS applications has largely expanded. Graphene is efficient fluorescence quencher improving quality of Raman spectra. It contributes also to the SERS enhancement factor through the chemical mechanism. In turn, the chemical flexibility of Reduced Graphene Oxide (RGO) enables tunable adsorption of molecules or cells on SERS active surfaces. Graphene oxide composites with SERS active nanoparticles have been also applied for Raman imaging of cells. This review presents a survey of SERS assays employing graphene or RGO emphasizing the improvement of SERS enhancement brought by graphene or RGO. The structure and physical properties of graphene and RGO will be discussed too., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)