Strange IndiaStrange India

Mineral synthesis

Ferrihydrite (2-line) was synthesized by the method of Schwertmann and Cornell44. Briefly, KOH solution was titrated with Fe(NO3)3·9H2O solution until the pH reached 7.0 ± 0.3 while stirring vigorously. The resulting precipitate was left to settle for 1–2 h before the overlying supernatant was syphoned off. The precipitate slurry was then transferred to a beaker, immersed in 5 l of ultrapure water (18.2 MΩ cm−1) and left to settle. The overlying supernatant was then removed and the beaker refilled with ultrapure water (18.2 MΩ cm1). The wash cycle was repeated two or three times a day until the pH of the supernatant was between 5 and 7 (normally 3 to 4 days). The precipitate was then centrifuged at 2,000g for 20 min and the supernatant discarded. Disordered birnessite (δMnO2) was synthesized through the method of Villalobos45. Briefly, KMnO4 solution was slowly (maximum time 5 min) added to NaOH solution while stirring vigorously, then MnCl2.4H2O solution was added (maximum time 30 min) while stirring vigorously to form a black precipitate. The precipitate was left to settle for 4 h and the overlying supernatant syphoned off. The remaining precipitate was then centrifuged at 2,000g for 20 min and the supernatant discarded. The residue was then shaken with 1 mol l−1 NaCl solution for 1 h and centrifuged. The NaCl wash was repeated five times, with the final wash shaken overnight. The centrifuge–wash cycle was then repeated 10 more times with ultrapure water (18.2 MΩ cm1) in place of NaCl, until the supernatant had a pH of approximately 12.8. The precipitate was then dialysed in ultrapure water (18.2 MΩ cm−1) using 12,000–14,000 g mol−1 of cellulose membrane tubing until the external water conductivity was less than 0.1 µS cm−1. Minerals were stored as wet slurries at 4 °C and mineral identity and purity were confirmed by X-ray diffraction using a Bruker D8 Diffractometer with Cu-Kα radiation (λ ≈ 0.154 nm). Diffractograms were recorded from 2° to 90° 2θ with 0.02° 2θ step size and 930 ms of acquisition time. Silicon dioxide was used as an analytical standard. The densities (g ml−1) of the final mineral precipitate slurries were determined by pipetting 1 ml of each slurry 10 times into preweighed weighing boats that were then left at 45 °C for 24 h before reweighing.

Abiotic Fe and Mn catalyst batch experiments

Glucose and glycine were used as representative moieties for dissolved monomeric reducing sugars and dissolved free amino acids in continental margin sediment pore waters, respectively. Equimolar (0.05 M) solutions of d-glucose (Sigma Aldrich, more than 99%) and glycine (Sigma Aldrich, more than or equal to 98.5%) were made up in autoclaved Schott bottles using 10% stock solutions. For the experiments using dissolved Fe or Mn as catalysts, 2,000-ppm stock solutions of either MnCl2 or FeCl2 were added to the Schott bottles to produce concentrations of 150, 300 and 400 μM l−1 of Fe or Mn. For the experiments using mineral Fe or Mn as catalysts, the minerals were added to the Schott bottles to produce solid solution ratios of 0.5, 1.5 and 2.5 g l−1. After the experimental solutions were prepared, the pH of each experiment was measured and adjusted to pH 8.2 ± 0.1 using either NaOH or HCl buffer solutions. Experiments were then placed on a shaker table in an incubator at 10 °C and 10-ml aliquots were taken daily. Aliquot samples were centrifuged at 2,000g for 30 min. The centrifuged supernatants from the dissolved Fe and Mn catalyst experiments and the mineral Fe and Mn catalyst experiments were dialysed against ultrapure water (18.2 MΩ cm−1) using 1,000-g-mol−1 dialysis tubing. Dialysis was continued until the resistance of the dialyte was around 18 MΩ cm−1. This size dialysis tubing was chosen as an operationally defined cutoff for geopolymerized molecules produced by means of the Maillard reaction16. The glucose and glycine reactants are around 180 g mol−1 and around 75 g mol−1 respectively, and any unreacted glucose and glycine remaining in the experimental solutions were therefore effectively separated from the reaction products. The dialyte was kept in solution to measure the concentration of GPS and aliquots were also freeze-dried for elemental and spectroscopic analysis, as described below. The centrifuged residue from the mineral Fe and Mn catalyst experiments was also repeatedly washed in ultrapure water (18.2 MΩ cm−1), recentrifuged and freeze-dried for elemental and spectroscopic analysis, as described below. To ensure that the experiments proceeded abiotically, all glassware was acid-washed and autoclaved, and all stock solutions, buffer solutions and experimental solutions were prepared using autoclaved ultrapure water (18.2 MΩ cm−1).

Concentration of GPS

Previous methods used to measure the production of Maillard reaction products (for example, Browning Index or E4/E6) are unable to provide absolute quantification of the geopolymers produced and also neglect GPS that are non-chromophoric46,47. Previous measurements of Maillard reaction rate are thus only inferred, and subsequent evaluations of the potential of the Maillard reaction to generate complex OC molecules may be underestimated5. To overcome these problems, we used nanoparticle tracking analysis to precisely quantify the concentration of products in the greater than or equal to 1,000-g-mol−1 molecular weight range. Nanoparticle tracking analysis is shown to successfully measure the concentration of Maillard reaction products48.

The concentration of GPS particles in the dialyte from the dissolved and mineral Fe and Mn catalyst experiments was calculated by tracking particles in a known volume of solution. Samples were diluted as required to 107–109 particles ml−1 before being immediately introduced into the sample chamber of a Malvern Nanosight NS300 (Malvern Instruments Limited) with a beam wavelength of 405 nm. Samples were then left to equilibrate for 30 s before analysis began. Each experiment was measured in triplicate with each video lasting for 215 s. To ensure that the analyses counted only organic reaction products, and not any nanoparticles of Fe or Mn mineral catalysts that may have remained in the experimental supernatants after centrifugation and subsequently passed through the 1,000-g-mol−1 dialysis membrane, particles that created flare and/or noise during analysis were automatically discounted by the analytical software during particle counting. Nanoparticles of Fe(III) (oxyhydr)oxides and Mn oxide have much higher refractive indices (RIs) (RI 2.32 and 3.35, respectively)49,50 than Maillard reaction products produced from glucose and glycine (melanoidins, RI 1.62)51,52, and thus create greater flare/noise during analysis. All dilutions were conducted using 0.2 μm of filtered ultrapure water (18.2 MΩ cm−1), which had previously been examined on the instrument to determine that it was free from contaminant nanoparticles.

Sediment sample preparation

Bulk surface sediment samples were collected from a variety of continental margins using either multicore or grab samplers. Sediments were freeze-dried, stored at −18 °C and subsequently fumigated to remove inorganic C before NEXAFS analysis. Fumigation was achieved by weighing 20 mg of sediment into Ag cups held in a glass tray, which was then placed in a glass desiccator above a glass beaker containing 25 ml of 37% concentrated HCl for 6 h. Fumigation is shown to reduce the risk of alteration of organic molecules in coastal sediments during inorganic C removal, compared with suspension in HCl53.

Carbon and nitrogen content of GPS

The C and N content of the dialyte from the dissolved and mineral Fe and Mn catalyst experiments, and the residues from the mineral Fe and Mn catalyst experiments, was determined on freeze-dried samples using a Vario PYRO cube CNS elemental analyser (Elementar).

Molecular weight of GPS

The hydrodynamic radius of GPS was measured using dynamic light scattering (Zetasizer Nano-ZS, Model ZEN3600, Malvern Instrument Ltd). Samples were dissolved in ultrapure water (18.2 MΩ cm−1) and then pipetted into disposable low-volume cuvettes (ID of 1.5 cm) and measured for 180 s while keeping the solution at a constant temperature of 25 °C. The range of particle radii was found to be 3.25–4.36 nm with a maximum peak intensity at 3.77 nm. The hydrodynamic radius was then used to calculate the diffusion coefficient of GPS (DGPS) on the basis of the Stokes–Einstein equation, which in turn was used to calculate the molecular weight (MWGPS) following Alperin et al.9 (Supplementary Table 5):

$${{\rm{M}}{\rm{W}}}_{{\rm{G}}{\rm{P}}{\rm{S}}}=\frac{{R}^{3}\times {T}^{3}\times {\rho }_{{\rm{G}}{\rm{P}}{\rm{S}}}}{162{\pi }^{2}\times {N}^{2}\times {\eta }^{3}\times {{D}_{{\rm{G}}{\rm{P}}{\rm{S}}}}^{3}}$$

where R = gas constant; T = absolute temperature (K); ρGPS = density of GPS, which is assumed to be the same as for typical biomolecules9,54 (1.5 g cm−3); N = Avogadro’s constant and η = the dynamic viscosity of the medium.

NEXAFS spectroscopy

The C and N 1s NEXAFS spectra of the freeze-dried dialyte from the dissolved and mineral Fe and Mn catalyst experiments, and of the freeze-dried residue from the mineral Fe catalyst experiment, were recorded on I08 beamline at Diamond Light Source Synchrotron, UK. For analysis, around 2 mg of freeze-dried sample residue was redissolved (dialyte) or suspended (residue) in 500 μl of ultrapure water (18.2 MΩ cm−1) water. Aliquots of 0.2 µl were then pipetted onto silicon windows (50 nm thick) and left to air-dry. Windows were glow discharged before loading with sample to improve particle distribution. Windows were then inserted into a high vacuum environment (less than 1 × 10−5 mBar) and samples were analysed in scanning transmission mode. Stacked datasets for C were collected between 275 eV and 320 eV, using varied energy resolution across 275–280 eV (1 eV), 280–284 eV (0.5 eV), 284–286.8 eV (0.2 eV), 286.8–290 eV (0.1 eV) and 290–320 eV (0.5 eV). Stacked datasets for N were collected between 385 eV and 430 eV, at a coarser energy resolution of 385–400 eV (1 eV), 400–415 eV (0.2 eV), 415–420 (0.5 eV) and 420–430 eV (1 eV) as the N 1s edge is more sensitive to beam damage. To maximize spectral resolution, the beamline uses Fresnel zone plates to focus the beam and a collimated plane grating monochromator of SX700-type with an undulator that provides a source size of 300 μm in the horizontal and 50 μm in the vertical plane, which are then refocused into a secondary source with a 50-μm slit, providing an energy resolution of better than 50 meV at the C k-edge. To minimize beam damage on the sample, dwell times were set to 10 ms per energy step following beam damage tests conducted by repeatedly measuring the same area of sacrificial samples. Beam damage manifests as a C NEXAFS peak at an absolute energy of 285.2 eV, attributable to the formation of aromatic C in the beam55. Sacrificial spectra with beam damage were discarded, but the position of the aromatic C peak was used for absolute energy calibration by shifting all spectra in the energy space by the required energy to align the beam damage peak to 285.2 eV. Reference spectra for the unreacted glucose and glycine were obtained from unmodified solids. The dark signal was measured routinely before the collection of sample spectra. X-ray absorption stacks were aligned using the Axis2000 software. Spectra were extracted and the dark signal was subtracted from the raw data using the Mantis software. Spectra were then exported for baseline correction, alignment, calibration and normalization using the Athena software. Baseline correction and normalization avoid spectral dependence on the total C and N content; as a result, spectral features and peak shifts are indicative of C and N molecular structure and chemistry, and not C or N concentration effects occurring during NEXAFS measurement. Peak identification for the normalized spectra was achieved with reference to literature assignations (Supplementary Tables 3 and 4).

Application of experimental reaction rates to continental margin sediments

To provide a first attempt to estimate the potential scale and importance of GPS production in oxygenated surface sediments on the continental margins, a total reactive pore-water volume within which GPS production might occur was calculated, and the spatial variation of the experimentally determined GPS production rates within this volume were modelled as a function of pore-water and sediment properties.

To calculate the total reactive pore-water volume, the following equation was used:

$${\rm{P}}{\rm{V}}=\varphi \times {Z}_{{\rm{O}}2}\times S$$

where PV is the pore-water volume (m3), \(\varphi \) is the porosity (dimensionless), ZO2 is the OPD (m), and S is the surface area of sediment (m2). Porosity (\(\varphi \)) was accounted for using a globally gridded map14. The impact of compaction (variability of porosity with sediment depth) in the calculation was not considered because the depth for calculation of the pore volume, that is, OPD (maximum 1.10 cm; see below), was smaller than the depth over which the porosity map has been estimated (5 cm)56. The model results were therefore insensitive to variations in compaction length scale, which is thought to be important over sediment depth of tens to hundreds of metres, rather than millimetres and centimetres57. OPD (ZO2) was determined from an empirical relationship related to water depth37, which was then converted to a globally gridded dataset58. This yielded OPDs within the range of 0.56–1.10 cm, with an average of 0.66 cm and standard deviation of 0.13 cm. Area of the sediment surface was determined from the areal extent of continental shelf (water depth of 0–200 m)34 and upper slope (water depth of 200–1,000 m)35 sediments. Using the equation and considerations above, pore-water volume was calculated in the oxic zone of each global grid point.

To model spatial variation of the experimentally determined GPS production rates as a function of continental margin sediment temperature, the Arrhenius equation and globally gridded data for continental margin bottom water temperature59 was used to determine GPS production rates at each grid point60:

$$R={R}_{{\rm{L}}{\rm{a}}{\rm{b}}}\times \,\exp \left(-\frac{{E}_{{\rm{a}}}}{{R}_{{\rm{G}}}}(\frac{1}{T}-\frac{1}{{T}_{{\rm{L}}{\rm{a}}{\rm{b}}}})\right)$$

where R is the GPS production rate at any given grid point (mol m−3 yr−1) and RLab is the GPS production rate obtained from our experiments (mol m−3 yr−1), Ea is the activation energy (J mol−1), RG is the universal gas constant (J K−1 mol−1), T is the absolute temperature (K) at any given grid point and TLab is the absolute temperature (K) of our experiments (283.15 K). The temperature map was obtained from the interpolation of data for global bottom water temperature, which is commonly used as the temperature of surface sediments59. The GPS production rates were determined from the linear regressions of the experimental reaction rates using each of the mineral catalyst concentrations (that is, 0.5, 1.5 and 2.5 g l−1) for both mineral Fe and mineral Mn (Fig. 2). Only the mineral Fe- and Mn-catalysed GPS production rates, which might effectively compete with microbial remineralization22, were used in the determination. The activation energy was taken from a collated list of Maillard reaction studies61. Regarding other spatially variable parameters, sedimentation rates strongly correlate with water column depth37, whereas bio-irrigation/bioturbation depths/coefficients are mostly synchronized and correlate with OPD62,63. The relationship between water column depth and OPD is used explicitly in the calculation of the total reactive pore-water volume as described above, and thus variations in sedimentation rate and bio-irrigation/bioturbation are implicitly included in our approach.

To estimate how much carbon might be preserved in sediments on the continental shelf and upper slope (water depth of 0–1,000 m) as a result of GPS formation, the above equation was integrated into the following:

$${C}_{{\rm{p}}{\rm{r}}{\rm{e}}{\rm{s}}}={{\rm{M}}{\rm{W}}\times {C}_{{\rm{c}}{\rm{o}}{\rm{n}}{\rm{t}}}\times {\rm{P}}{\rm{V}}\times R}_{{\rm{L}}{\rm{a}}{\rm{b}}}\times \,\exp \left(-\frac{{E}_{{\rm{a}}}}{{R}_{{\rm{G}}}}(\frac{1}{T}-\frac{1}{{T}_{{\rm{L}}{\rm{a}}{\rm{b}}}})\right)$$

where Cpres is the rate of carbon preservation (g C yr−1) as a result of GPS production, MW is the molecular weight of GPS (g mol−1) and Ccont is the carbon content of GPS (wt%). The molecular weight of GPS was determined from the experimental particle hydrodynamic radii measured using dynamic light scattering, which was then used to calculate the diffusion coefficient of GPS on the basis of the Stokes–Einstein equation, which in turn was used to calculate the molecular weight9 (Methods and Supplementary Table 5). The C content of GPS was determined from the experimental elemental analysis (Methods and Supplementary Table 1).

The rate of C preservation was determined in a Monte Carlo procedure in which the input dataset described above was run 1,000 times for each grid point of the global map (which is more than one million nodes for 0.25° × 0.25° resolution). In this approach, the input parameters for GPS production rates, GPS activation energy, GPS molecular weight and GPS C content were varied over a range determined either during the experiments or in the literature. Specifically, the range for GPS production rates was varied between those determined for the lowest and highest mineral catalyst concentrations (0.5–2.5 g l−1); for GPS activation energy was varied between a collated list of Maillard reaction activation energies, determined for a range of amino acid and reducing sugar pairings61; for GPS molecular weight was varied over the range of hydrodynamic radii measured using dynamic light scattering; and for GPS C content was varied over the instrument uncertainty (Supplementary Table 6). In the Monte Carlo procedure, the maximum and minimum of each experimental or literature range was then further increased or decreased by one standard deviation, respectively, in an attempt to reasonably cover the broadest extent of input parameter possibilities that may be encountered in margin sediments. If the latter led to a value less than zero, a value close to zero (10−15) was selected instead (Supplementary Table 6). The final data were generated randomly on the basis of a uniform distribution within the selected ranges for each parameter. At each grid point, after 1,000 Monte Carlo runs, the mean rate of C preservation (g C yr−1) as a result of GPS production was returned by the model. The uncertainties were determined from the confidence intervals on the basis of a 95% confidence level according to the 2.5th and 97.5th percentiles of the Student’s t-distribution. The sum of C preserved as a result of GPS production at all of the grid points active in the analysis yielded the global annual rate of C preserved as a result of GPS production.

Source link


Leave a Reply

Your email address will not be published. Required fields are marked *