In this study, Raman micro spectroscopy is applied to investigate two manganese oxides: lithiophorite [(Al,Li)Mn4+O2(OH)2] and asbolane [(Ni,Co)xMn4+(O,OH)4.nH2O], along with their intermediates (“Asbolane-Lithiophorite Intermediates”: ALI). These oxides typically incorporate variable concentrations of Co, Ni, Cu and Li. They represent a group of economically interesting phases that are difficult to identify and characterize with classical X-ray diffraction techniques. We show that Raman micro spectroscopy is useful to the investigation of those phases, but they require to be tested in very low laser power conditions to avoid sample degradation (e.g. 0.2mW 532nm). We propose reference Raman spectroscopic signatures for lithiophorite, asbolane and ALI phases. These spectra are mainly composed of two spectral domains, the first one is located between 370-630 cm-1 and the second one between 900-1300 cm-1. We then assess the impact of their highly variable chemistry on their Raman peak positions, intensities and FWHM using a semi-systematic curve-fitting method profiled for these phases.
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RBINS Staff Publications
Subtidal natural hard substrates (SNHS) promote occupancy by rich benthic communities that provide irreplaceable and fundamental ecosystem functions, representing a global priority target for nature conservation and recognised in most European environmental legislation. However, scientifically validated methodologies for their quantitative spatial demarcation, including information on species occupancy and fine-scale environmental drivers (e.g., the effect of stone size on colonisation) are rare. This is, however, crucial information for sound ecological management. In this investigation, high-resolution (1 m) multibeam echosounder (MBES) depth and backscatter data and derivates, underwater imagery (UI) by video drop-frame, and grab sediment samples, all acquired within 32 km2 of seafloor in offshore Belgian waters, were integrated to produce a random forest (RF) spatial model, predicting the continuous distribution of the seafloor areal cover/m2 of the stones’ grain sizes promoting colonisation by sessile epilithic organisms. A semi-automated UI acquisition, processing, and analytical workflow was set up to quantitatively study the colonisation proportion of different grain sizes, identifying the colonisation potential to begin at stones with grain sizes Ø ≥ 2 cm. This parameter (i.e., % areal cover of stones Ø ≥ 2 cm/m2) was selected as the response variable for spatial predictive modelling. The model output is presented along with a protocol of error and uncertainty estimation. RF is confirmed as an accurate, versatile, and transferable mapping methodology, applicable to area-wide mapping of SNHS. UI is confirmed as an essential aid to acoustic seafloor classification, providing spatially representative numerical observations needed to carry out quantitative seafloor modelling of ecologically relevant parameters. This contribution sheds innovative insights into the ecologically relevant delineation of subtidal natural reef habitat, exploiting state-of-the-art underwater remote sensing and acoustic seafloor classification approaches.
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RBINS Staff Publications 2021
