Search

The inherently weak signal present in Raman spectroscopy makes spectral resolution susceptible to noise. Hence, efficient denoising techniques for post‐processing of spectral data are required. We introduce two efficient approaches to remove noise from graphene Raman spectra, based on deep neural network architectures using supervised and unsupervised learning. We compared the performance of these approaches with three traditional noise removal methods. The experimental results demonstrate the effectiveness of deep‐learning models in the denoising task, which is crucial in interpreting characterization data of mass‐produced graphene. Overall, our supervised approach outperforms all considered baselines, as well as the unsupervised method, providing significant improvement in noise reduction. [ABSTRACT FROM AUTHOR]

The deposition of amyloid plaques is considered one of the main microscopic features of Alzheimer's disease (AD). Since plaque formation can precede extensive neurodegeneration and it is the main clinical manifestation of AD, it constitutes a relevant target for new treatment and diagnostic approaches. Micro-Raman spectroscopy, a label-free technique, is an accurate method for amyloid plaque identification and characterization. Here, we present a high spatial resolution micro-Raman hyperspectral study in transgenic APPswePS1ΔE9 mouse brains, showing details of AD tissue biochemical and histological changes without staining. First we used stimulated micro-Raman scattering to identify the lipid-rich halo surrounding the amyloid plaque, and then proceeded with spontaneous (conventional) micro-Raman spectral mapping, which shows a cholesterol and sphingomyelin lipid-rich halo structure around dense-core amyloid plaques. The detailed images of this lipid halo relate morphologically well with dystrophic neurites surrounding plaques. Principal Component Analysis (PCA) of the micro-Raman hyperspectral data indicates the feasibility of the optical biomarkers of AD progression with the potential for discriminating transgenic groups of young adult mice (6-month-old) from older ones (12-month-old). Frequency-specific PCA suggests that plaque-related neurodegeneration is the predominant change captured by Raman spectroscopy, and the main differences are highlighted by vibrational modes associated with cholesterol located majorly in the lipid halo. [ABSTRACT FROM AUTHOR]

A metrological framework for statistical analysis of number of layers and stacking order in mass-produced graphene using Raman spectroscopy is presented. The method is based on two complementary protocols, denominated by 2D and G. The 2D–protocol is based on the parameterized principal component analysis of the two-phonon 2D band, and it measures interlayer coupling. A neural-network algorithm for spectral denoising was also developed to improve the outcome. The G–protocol explores the intensity of the bond-stretching G band, and provides information about the number of layers. The method is suitable for automated statistical analysis of heterogeneous graphene-based systems with relatively low computational cost, as shown here for graphene flakes prepared by the liquid-phase exfoliation of graphite. Image 1 [ABSTRACT FROM AUTHOR]

The global prevalence of Alzheimer's disease (AD) points to endemic levels, especially considering the increase of average life expectancy worldwide. AD diagnosis based on early biomarkers and better knowledge of related pathophysiology are both crucial in the search for medical interventions that are able to modify AD progression. In this study we used unsupervised spectral unmixing statistical techniques to identify the vibrational spectral signature of amyloid β aggregation in neural tissues, as early biomarkers of AD in an animal model. We analyzed spectral images composed of a total of 55 051 Raman spectra obtained from the frontal cortex and hippocampus of five bitransgenic APPswePS1ΔE9 mice, and colocalized amyloid β plaques by other fluorescence techniques. The Raman signatures provided a multifrequency fingerprint consistent with the results of synthesized amyloid β fibrils. The fingerprint obtained from unmixed analysis in neural tissues is shown to provide a detailed image of amyloid plaques in the brain, with the potential to be used as biomarkers for non-invasive early diagnosis and pathophysiology studies in AD on the retina. [ABSTRACT FROM AUTHOR]

A methodology for the structural analysis of graphene‐related materials using parameterized principal component analysis (PCA), ideal for large‐scale data treatment, is introduced. First, we review different aspects of Raman spectroscopy for structural and functional characterization of sp2‐bonded carbon materials, which are important for understanding the problem. The parameterized PCA is then introduced and applied to 2 different scenarios: to identify different sp2 carbon structures and to identify graphene samples with different numbers of layers. Automating these Raman spectroscopy analysis techniques is desired for large‐scale industrial applications. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This article offers a historical account of the evolution of defect metrology in graphene through Raman spectroscopy over the past five decades. The application of Raman scattering to the investigation of disorder levels in graphite materials dates back to the 1970s, and substantial advancements have occurred in this field, especially following the isolation of graphene in 2006. The article starts presenting the physics related to structural defects disrupting the translational symmetry in crystalline solids, introducing a relaxation of selection rules in Raman spectroscopy that manifests as peaks induced by disorder, then it delves into significant milestones and provides a practical summary of the principal existing protocols. Furthermore, we explore the contribution of tip-enhanced Raman spectroscopy to gaining deeper insights into fundamental aspects of defects in graphene materials, owing to its capacity for spectroscopic measurements with high spatial resolution. In conclusion, we outline prospects for the further utilization of this innovative technique in enhancing both the science and metrology of defects in graphene and its applications in other two-dimensional systems. [Display omitted] [ABSTRACT FROM AUTHOR]

In this work, chemical and structural properties of various biochars were analyzed and compared with those from a highly stable anthropic soil, Terra Preta de Índio (TPI). TPI is believed to be responsible for the fertility of Amazonian soils and their stability; therefore, the production of a synthetic TPI would be of great interest for agricultural applications. Biochar produced from different raw biomasses were comprehensively characterized and, based on the obtained results, a preliminary study was performed testing three different routes of chemical activation using nitric acid, phosphoric acid, and potassium hydroxide as activating agents. After chemical activations, metal contents in the biochars decreased, as expected, and high degrees of carbonization were observed. In the case of the activation performed with HNO 3 , intense signals related to carboxylic groups in TG-MS analysis and in potentiometric titrations point out to a highly oxygenated biochar. Structural analysis showed that activations generated point defects in sp2-carbon structures of biochar, with the material obtained after KOH activation showing a high surface area (569 m2 g−1), an important feature for the use as soil amendment. Image 1 • Synthetic production of a material similar to a fertile anthropic soil (TPI). • Characterization and comparison of biochars from different raw biomasses with TPI. • Chemical activations of biochars with HNO 3 , H 3 PO 4 , and KOH as a path to mimetize TPI. [ABSTRACT FROM AUTHOR]

• Physical-Chemical analyzes showing comparison among carbon microstructures. • Carbon grains in amazon rainforest soils displayed sp2 nanocrystallite structure. • Terra Preta-carbons have larger number of point defects than Rio Negro sediment. • Terra Preta do Índio-carbons are richer in nutrients than Rio Negro deposits-carbon. Driving practices of Amazon native inhabitants are an example of positive feedback in carbon storage acting as a key element in soil fertility and stability. The anthropogenic Amazonian Terra Preta do Índio (Indian Dark Earth) soils are rich in pyrogenic black carbon and the Rio Negro (Black River) also exhibits plentiful apparently similar black-carbon particles. In this context, we characterized the structural, morphological and elemental properties of the long-lived stable carbon-based structures present in sediments of the Amazonian Rio Negro and in two Terra Preta do Índio soils from two different geographic regions. Optical microscopy, Raman spectroscopy, scanning electron microscopy and energy dispersive X-ray spectroscopy techniques are utilized. The structural analysis displayed that these carbon structures exhibit similar nanocrystallite structures, despite their different geographic location and environment. The Terra Preta do Índio -carbons are however, more defective and the quantitative elemental analyzes indicate they have greater variety of nutrients, such as P and Ca, than Rio Negro sediment-carbons. [ABSTRACT FROM AUTHOR]