Abstract: The inhibitive action of 9H-pyrido[3,4-b]indole (norharmane) and 1-Methyl-9H-pyrido[3,4-b]indole (harmane) on corrosion of C38 steel in 0.5 M H2SO4 solution was investigated through electrochemical techniques. The experimental results obtained revealed that these compounds inhibited the steel corrosion in acid solution for all concentrations studied. Polarization measurements indicate that the examined compounds act as a mixed inhibitor and its inhibition efficiency increases with inhibitor concentration. Data obtained from ac impedance technique show a frequency distribution and therefore a modelling element with frequency dispersion behaviour, a constant phase element (CPE), has been used. The adsorption of used compounds on the steel surface obeys Langmuir's isotherm. The ΔG°ads values were calculated and discussed for both inhibitors. Significant correlations are obtained between inhibition efficiency and quantum chemical parameters using quantitative structure-activity relationship (QSAR) method. © 2011 by ESG.

Abstract: The present study examines the effect of alkaloids extract from Siparuna guianensis leaves and stems on corrosion of C38 steel in 0.1 M HCl using electrochemical impedance spectroscopy (EIS), potentiodynamic polarization technique and Raman spectroscopy. The protection efficiency is better with stems alkaloids extract. The inhibition was assumed to occur via adsorption of inhibitor molecules on the metal surface. The influence of stems alkaloids extract concentration on corrosion of low carbon steel in 0.1 M HCl was studied. The inhibition efficiency obtained from impedance and polarization measurements was in a good agreement and was found to increase with increasing concentration of alkaloids extract up to 50 mg/L for stems extract. The adsorption of the extract on the low carbon steel surface obeys the Langmuir adsorption.

Abstract: In this study, the corrosion of S355 carbon steel was monitored for 50 days in equatorial environment. The experiments were conducted under three complementary approaches in a natural environment: (i) bacterial diversity was assessed with Miseq sequencing, (ii) observations of the surface and identifications of deposit compounds were realised with scanning electron microscopy (SEM), Raman spectrometry and EDX analyses, (iii) electrochemical measurements were used to calculate corrosion rate and to identify the phenomena which control corrosion. The results showed that the bacterial population had changed over immersion time from a dominance of β-proteobacteria to α-proteobacteria. This evolution decreased charge transfer resistance at the metal/deposit from 41.6 to 20.8 Ω/cm2 in 50 days, caused an increase in the corrosion rate by a factor of 2, from 0.13 to 0.27 mm/year. Surface analyses showed a progressive appearance of MIC markers in the deposit such as iron sulphide and manganese oxides that were detected after 30 days of immersion in the natural environment. In conclusion, this study describes in detail, the first stage of MIC activity on carbon steel surface in an equatorial brackish water, under aerobic conditions. © 2016 Elsevier Ltd.

Abstract: Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (>150 Mg/ha, and >300 Mg/ha for P-band), which are commonly found in tropical forests. Studies have mapped the rough variations in AGB by combining optical and environmental data at regional and global scales. Nevertheless, these maps cannot represent local variations in AGB in tropical forests. In this paper, we hypothesize that the problem of misrepresenting local variations in AGB and AGB estimation with good precision occurs because of both methodological limits (signal saturation or dilution bias) and a lack of adequate calibration data in this range of AGB values. We test this hypothesis by developing a calibrated regression model to predict variations in high AGB values (mean >300 Mg/ha) in French Guiana by a methodological approach for spatial extrapolation with data from the optical geoscience laser altimeter system (GLAS), forest inventories, radar, optics, and environmental variables for spatial inter- and extrapolation. Given their higher point count, GLAS data allow a wider coverage of AGB values. We find that the metrics from GLAS footprints are correlated with field AGB estimations (R2 = 0.54, RMSE = 48.3 Mg/ha) with no bias for high values. First, predictive models, including remote-sensing, environmental variables and spatial correlation functions, allow us to obtain “wall-to-wall” AGB maps over French Guiana with an RMSE for the in situ AGB estimates of ∼50 Mg/ha and R2 = 0.66 at a 1-km grid size. We conclude that a calibrated regression model based on GLAS with dependent environmental data can produce good AGB predictions even for high AGB values if the calibration data fit the AGB range. We also demonstrate that small temporal and spatial mismatches between field data and GLAS footprints are not a problem for regional and global calibrated regression models because field data aim to predict large and deep tendencies in AGB variations from environmental gradients and do not aim to represent high but stochastic and temporally limited variations from forest dynamics. Thus, we advocate including a greater variety of data, even if less precise and shifted, to better represent high AGB values in global models and to improve the fitting of these models for high values. © 2016 Elsevier B.V.

Abstract: The Amazon basin harbors a diverse ecological community that has a critical role in the maintenance of the biosphere. Although plant and animal communities have received much attention, basic information is lacking for fungal or prokaryotic communities. This is despite the fact that recent ecological studies have suggested a prominent role for interactions with soil fungi in structuring the diversity and abundance of tropical rainforest trees. In this study, we characterize soil fungal communities across three major tropical forest types in the western Amazon basin (terra firme, seasonally flooded and white sand) using 454 pyrosequencing. Using these data, we examine the relationship between fungal diversity and tree species richness, and between fungal community composition and tree species composition, soil environment and spatial proximity. We find that the fungal community in these ecosystems is diverse, with high degrees of spatial variability related to forest type. We also find strong correlations between α- and β-diversity of soil fungi and trees. Both fungal and plant community β-diversity were also correlated with differences in environmental conditions. The correlation between plant and fungal richness was stronger in fungal lineages known for biotrophic strategies (for example, pathogens, mycorrhizas) compared with a lineage known primarily for saprotrophy (yeasts), suggesting that this coupling is, at least in part, due to direct plant-fungal interactions. These data provide a much-needed look at an understudied dimension of the biota in an important ecosystem and supports the hypothesis that fungal communities are involved in the regulation of tropical tree diversity. © 2013 International Society for Microbial Ecology.