1.
Testing the effect of water on XRF data from water-rich diatom ooze
Water-rich diatom ooze sediments present a special problem for XRF instruments. In
several cases, water has been shown to strongly attenuate emitted X-rays, particularly for the
low-atomic-mass elements, such as Al, Si, and S. In conventional XRF analysis, standard
preparation techniques remove water either through oven drying or freeze drying. However, in
the case of XRF core scanners, the only sample preparation that is performed is the application
of a thin X-ray transparent film to prevent contamination of the XRF sensor apparatus. This film
has been shown to form a thin layer of water derived directly from the water-rich sediments that
the film overlies [Kido et al., 2006].
Given the high water contents of EW0408-32MC and -43MC, a series of X-ray
simulations were designed to study the effects of water content on emitted Si, Cl, and Br
intensities in an organic-rich matrix. These simulations were conducted using the CASINO
software program [v. 2.42; Hovington et al., 1997; Drouin et al., 2007], with the following
conditions similar to all simulations:
Run 1a – Effect of water under film on Si X-ray intensity
 Initial set-up:
1) Layered sample composition
a. Layer 1 = 1-micron (= 1000 nm) polypropylene [(C3H6)n] film with a density
of 0.905 g/cm3
b. Layer 2 = water, varied between 0 to 1 microns thick, at 250 nm steps
c. Layer 3 = quartz substrate
2) Microscope & simulation properties
a. 10 keV accelerating voltage (Si K1 = 1.740 KeV)
b. 200,000 electrons
c. Specimen tilted at 0° and detector at 45°
d. Beam size of 200 microns (= 200,000 nm)
3) Distributions, options, physics models = default settings
 Plotted Si K X-ray depth distribution function [z) curve] under different water
thickness conditions.
Run 1b – Effect of water under film on Cl X-ray intensity
 Initial set-up:
1) Layered sample composition
a. Layer 1 = 1-micron polypropylene [(C3H6)n] film with a density of 0.905
g/cm3
b. Layer 2 = water, varied between 0 to 1 microns thick, at 250 nm steps
c. Layer 3 = halite (NaCl) substrate, density = 2.17 g/cm3
2) Microscope & simulation properties
a. 10 keV accelerating voltage (Cl K1 = 2.622 KeV)
b. 200,000 electrons
c. Specimen tilted at 0° and detector at 45°
d. Beam size of 200 microns
3) Distributions, options, physics models = default settings

Plotted Cl K X-ray depth distribution function [z) curve] under different water
thickness conditions
Run 1c – Effect of water under film on Br X-ray intensity
 Initial set-up:
1) Layered sample composition
a. Layer 1 = 1-micron polypropylene [(C3H6)n] film with a density of 0.905
g/cm3
b. Layer 2 = water, varied between 0 to 1 microns thick, at 250 nm steps
c. Layer 3 = organic methyl bromide (CH3Br) substrate, density = 1.73 g/cm3
2) Microscope & simulation properties
a. 30 keV accelerating voltage (Br K1 = 11.924 KeV)
b. 200,000 electrons
c. Specimen tilted at 0° and detector at 45°
d. Beam size of 200 microns
3) Distributions, options, physics models = default settings
 Plotted Br K X-ray depth distribution function [z) curve] under different water
thickness conditions
Figure S1: Effect of water thickness under X-ray film on the emitted X-ray intensities of Si, Cl,
and Br. Experimental set-ups indicated at top of each frame. Note the strong attenuation effect
for both Si and Cl as the water films increase, while there is negligible attenuation for Br.
2.
Examining the effect of sediment porosity on surface multicore geochemical data
The high water contents of surface multicore samples also present a problem for
interpreting concentration data due to the dilution effect caused by high porosity. In particular,
high porewater concentrations can unexpectedly cause correlations between certain geochemical
parameters that normally are uncorrelated. To test if the high porosity was the cause of
collinearity between opal and lithogenic proxies (Al, Ti, Fe, etc.) measured by scanning XRF, we
used a combination of the [i] GRA wet bulk density, [ii] clr Ti data, and [iii] opal concentrations
from EW0408-32MC.
After fitting an exponential equation to the measured GRA wet bulk density of EW040832MC, the following equation was applied to solve for the porosity (fliquid):
wbd = [solid * fsolid] + [liquid * fliquid]
such that fsolid + fliquid = 1, and where
solid = [lithogenic * flithogenic] + [biogenic * fbiogenic]
and assuming that flithogenic + fbiogenic = 1, as well as that flithogenic = fbiogenic (based on biogenic
fraction calculations in Eqn. 3 from the text; this is a special case for EW0408-32MC, and likely
does not apply to all multicore-type samples).
To calculate the % Ti and the % opal, we applied the following relationships:
% Ti = k1 * flithogenic * fsolid
% Opal = k2 * fbiogenic * fsolid
where k1 and k2 are constant scaling terms (e.g., the concentrations of Ti and opal remain
constant throughout the core).
List of terms in equations:
wbd = linearized GRA wet bulk density (empirical) in g/cm3
solid = a mixture of the lithogenic and biogenic contributions in g/cm3
fsolid = 1- fliquid
liquid = 1.024 g/cm3
fliquid = bulk porosity
lithogenic = 2.65 g/cm3
biogenic = 1.5 g/cm3
flithogenic = fbiogenic = 0.5 (special case for EW0408-32MC)
Figure S2: Effects of porewater dilution on geochemical parameters measured in EW040832MC. (a) All experimental data exhibit a similar increase in values downcore, including the
wet bulk density. (b) After fitting an exponential equation to the measured density, modeled
porosity values were calculated assuming that flithogenic = fbiogenic. The concentrations of opal and
Ti were then determined using constant values proportional to the relative changes in the
biogenic and lithogenic phases caused by changes in porosity.
References:
Drouin, D., A. R. Couture, D. Joly, X. Tastet, V. Aimez, and R. Gauvin (2007), CASINO V2.42
- A fast and easy-to-use modeling tool for scanning electron microscopy and microanalysis users,
Scanning, 29, 92-101.
Hovington, P., D. Drouin, and R. Gauvin (1997), CASINO: A new Monte Carlo code in C
language for electron beam interaction - Part I: description of the program, Scanning, 19, 1-14.
Kido, Y., T. Koshikawa, and R. Tada (2006), Rapid and quantitative major element analysis
method for wet fine-grained sediments using an XRF microscanner, Marine Geology, 229, 209225.
3.
Additional scanning XRF datasets for EW0408-32MC and -43MC
4.
Table S1: Carbonate-free organic elemental and isotopic data from EW0408-32MC and 43MC. TOC is total organic carbon and TN is total nitrogen. *: samples occur within
interval missing age constraints between ~AD 1956 to 1972.
5.
Table S2: Variation arrays of scanning XRF data for EW0408-32MC and EW0408-43MC, as determined with CoDaPack v2
[Thio-Henestrosa and Martin-Fernandez, 2005], where values are calculated as var [ln(Xi / Xj)]. Bold values are below the
5th percentile (e.g., high covariance), and italic values are those elements that exceed the 95th percentile (e.g., low covariance).
EW0408-32MC
Xj
Si
S
Cl
K
Ca
Ti
Mn
Fe
Cu
Zn
Br
Rb
Sr
Y
Zr
Pb
Ba
0.003
0.020
0.016
0.011
0.006
0.021
0.005
0.001
0.016
0.004
0.010
0.006
0.011
0.014
0.008
0.005
0.002
0.016
0.004
0.001
0.009
0.013
0.008
0.025
0.010
0.008
0.016
0.008
0.004
0.001
0.014
0.004
0.001
0.006
0.001
0.007
0.043
0.036
0.057
0.027
0.034
0.046
0.033
0.036
0.034
0.023
0.016
0.038
0.010
0.015
0.027
0.014
0.019
0.015
0.026
0.030
0.024
0.058
0.011
0.021
0.040
0.021
0.025
0.023
0.029
0.013
0.025
0.019
0.045
0.013
0.018
0.031
0.017
0.022
0.018
0.036
0.017
0.014
0.014
0.008
0.032
0.004
0.008
0.017
0.007
0.013
0.008
0.027
0.010
0.009
0.011
0.065
0.057
0.087
0.050
0.056
0.070
0.054
0.065
0.057
0.073
0.057
0.047
0.061
0.044
0.021
0.014
0.038
0.009
0.014
0.024
0.013
0.017
0.014
0.032
0.015
0.012
0.016
0.007
0.041
0.032
0.027
0.047
0.020
0.025
0.038
0.025
0.029
0.024
0.041
0.023
0.028
0.029
0.021
0.068
0.027
0.018
0.013
0.039
0.011
0.013
0.022
0.013
0.016
0.012
0.038
0.020
0.017
0.019
0.011
0.050
0.014
0.030
clr
variance
Xi
Al
Si
S
Cl
K
Ca
Ti
Mn
Fe
Cu
Zn
Br
Rb
Sr
Y
Zr
Pb
Ba
Total Variance =
0.010
0.007
0.016
0.006
0.007
0.011
0.007
0.009
0.007
0.018
0.010
0.012
0.011
0.007
0.028
0.009
0.015
0.010
0.199
EW0408-43MC
Xj
Si
S
Cl
Ca
Ti
Cr
Mn
Fe
Cu
Zn
Br
Sr
Mo
Ba
0.091
0.105
0.013
0.119
0.024
0.006
0.154
0.065
0.061
0.064
0.134
0.056
0.062
0.074
0.075
0.137
0.041
0.024
0.026
0.114
0.102
0.180
0.086
0.072
0.076
0.162
0.148
0.076
0.274
0.197
0.245
0.262
0.156
0.164
0.343
0.386
0.184
0.092
0.085
0.091
0.156
0.148
0.099
0.150
0.341
0.149
0.061
0.072
0.083
0.113
0.102
0.109
0.158
0.207
0.139
0.115
0.021
0.036
0.043
0.085
0.066
0.072
0.118
0.160
0.113
0.064
0.186
0.105
0.100
0.105
0.048
0.088
0.166
0.210
0.127
0.194
0.132
0.105
0.185
0.085
0.068
0.072
0.143
0.139
0.078
0.128
0.357
0.147
0.148
0.113
0.184
0.135
0.043
0.038
0.043
0.097
0.091
0.061
0.108
0.258
0.119
0.096
0.058
0.123
0.105
clr
variance
Xi
Al
Si
S
Cl
Ca
Ti
Cr
Mn
Fe
Cu
Zn
Br
Sr
Mo
Ba
Total Variance =
0.072
0.033
0.033
0.036
0.050
0.048
0.048
0.069
0.116
0.069
0.054
0.039
0.063
0.065
0.046
0.839
References:
Thio-Henestrosa, S., and J. A. Martin-Fernandez (2005), Dealing with compositional data: the freeware CoDaPack, Math. Geol.,
37(7), 773-793.