Iron mineral structure, reactivity, and isotopic composition in a South Pacific Gyre ferromanganese nodule over 4 Ma

Deep-sea ferromanganese nodules accumulate trace elements from seawater and underlying sediment porewaters during the growth of concentric mineral layers over millions of years. These trace elements have the potential to record past ocean geochemical conditions. The goal of this study was to determine whether Fe mineral alteration occurs and how the speciation of trace elements responds to alteration over ∼3.7 Ma of marine ferromanganese nodule (MFN) formation, a timeline constrained by estimates from 9Be/10Be concentrations in the nodule material. We determined Fe-bearing phases and Fe isotope composition in a South Pacific Gyre (SPG) nodule. Specifically, the distribution patterns and speciation of trace element uptake by these Fe phases were investigated. The time interval covered by the growth of our sample of the nodule was derived from 9Be/10Be accelerator mass spectrometry (AMS). The composition and distribution of major and trace elements were mapped at various spatial scales, using micro-X-ray fluorescence (μXRF), electron microprobe analysis (EMPA), and inductively coupled plasma mass spectrometry (ICP-MS). Fe phases were characterized by micro-extended X-ray absorption fine structure (μEXAFS) spectroscopy and micro-X-ray diffraction (μXRD). Speciation of Ti and V, associated with Fe, was measured using micro-X-ray absorption near edge structure (μXANES) spectroscopy. Iron isotope composition (δ56/54Fe) in subsamples of 1-3 mm increments along the radius of the nodule was determined with multiple-collector ICP-MS (MC-ICP-MS). The SPG nodule formed through primarily hydrogeneous inputs at a rate of 4.0 ± 0.4 mm/Ma. The nodule exhibited a high diversity of Fe mineral phases: feroxyhite (δ-FeOOH), goethite (α-FeOOH), lepidocrocite (γ-FeOOH), and poorly ordered ferrihydrite-like phases. These findings provide evidence that Fe oxyhydroxides within the nodule undergo alteration to more stable phases over millions of years. Trace Ti and V were spatially correlated with Fe and found to be adsorbed to Fe-bearing minerals. Ti/Fe and V/Fe ratios, and Ti and V speciation, did not vary along the nodule radius. The δ56/54Fe values, when averaged over sample increments representing 0.25 to 0.75 Ma, were homogeneous within uncertainty along the nodule radius, at -0.12 ± 0.07 ‰ (2sd, n=10). Our results indicate that the Fe isotope composition of the nodule remained constant during nodule growth and that mineral alteration did not affect the primary Fe isotope composition of the nodule. Furthermore, the average δ56/54Fe value of -0.12 ‰ we find is consistent with Fe sourced from continental eolian particles (dust). Despite mineral alteration, the trace element partitioning of Ti and V, and Fe isotope composition, do not appear to change within the sensitivity of our measurements. These findings suggest that Fe oxyhydroxides within hydrogenetic ferromanganese nodules are out of geochemical contact with seawater once they are covered by subsequent concentric mineral layers. Even though Fe-bearing minerals are altered, trace element ratios, speciation and Fe isotope composition are preserved within the nodule.

Comparison of adhesion layers of gold on silicate glasses for SERS detection

Gold is one of the most widely used metals for building up plasmonic devices. Although slightly less efficient than silver for producing sharp resonance, its chemical properties make it one of the best choices for designing sensors. Sticking gold on a silicate glass substrate requires an adhesion layer, whose effect has to be taken into account. Traditionally, metals (Cr or Ti) or dielectric materials (TiO2 or Cr2O3 ) are deposited between the glass and the nanoparticle. Recently, indium tin oxide and (3-mercaptopropyl)trimethoxysilane (MPTMS) were used as a new adhesion layer. The aim of this work is to compare these six adhesion layers for surface- enhanced Raman scattering sensors by numerical modeling. The near-field and the far-field optical responses of gold nanocylinders on the different adhesion layers are then calculated. It is shown that MPTMS leads to the highest field enhancement, slightly larger than other dielectric materials. We attributed this effect to the lower refractive index of MPTMS compared with the others.

Processing Bio-Argo nitrate concentration at the DAC Level

The only method used to date to measure dissolved nitrate concentration (NITRATE) with sensors mounted on profiling floats is based on the absorption of light at ultraviolet wavelengths by nitrate ion (Johnson and Coletti, 2002; Johnson et al., 2010; 2013; D’Ortenzio et al., 2012). Nitrate has a modest UV absorption band with a peak near 210 nm, which overlaps with the stronger absorption band of bromide, which has a peak near 200 nm. In addition, there is a much weaker absorption due to dissolved organic matter and light scattering by particles (Ogura and Hanya, 1966). The UV spectrum thus consists of three components, bromide, nitrate and a background due to organics and particles. The background also includes thermal effects on the instrument and slow drift. All of these latter effects (organics, particles, thermal effects and drift) tend to be smooth spectra that combine to form an absorption spectrum that is linear in wavelength over relatively short wavelength spans. If the light absorption spectrum is measured in the wavelength range around 217 to 240 nm (the exact range is a bit of a decision by the operator), then the nitrate concentration can be determined. Two different instruments based on the same optical principles are in use for this purpose. The In Situ Ultraviolet Spectrophotometer (ISUS) built at MBARI or at Satlantic has been mounted inside the pressure hull of a Teledyne/Webb Research APEX and NKE Provor profiling floats and the optics penetrate through the upper end cap into the water. The Satlantic Submersible Ultraviolet Nitrate Analyzer (SUNA) is placed on the outside of APEX, Provor, and Navis profiling floats in its own pressure housing and is connected to the float through an underwater cable that provides power and communications. Power, communications between the float controller and the sensor, and data processing requirements are essentially the same for both ISUS and SUNA. There are several possible algorithms that can be used for the deconvolution of nitrate concentration from the observed UV absorption spectrum (Johnson and Coletti, 2002; Arai et al., 2008; Sakamoto et al., 2009; Zielinski et al., 2011). In addition, the default algorithm that is available in Satlantic sensors is a proprietary approach, but this is not generally used on profiling floats. There are some tradeoffs in every approach. To date almost all nitrate sensors on profiling floats have used the Temperature Compensated Salinity Subtracted (TCSS) algorithm developed by Sakamoto et al. (2009), and this document focuses on that method. It is likely that there will be further algorithm development and it is necessary that the data systems clearly identify the algorithm that is used. It is also desirable that the data system allow for recalculation of prior data sets using new algorithms. To accomplish this, the float must report not just the computed nitrate, but the observed light intensity. Then, the rule to obtain only one NITRATE parameter is, if the spectrum is present then, the NITRATE should be recalculated from the spectrum while the computation of nitrate concentration can also generate useful diagnostics of data quality.