SUPPLEMENTARY MATERIALS FOR:
Bioleaching of Rare Earth Elements from Monazite Sand
Vanessa L. Brissona, Wei-Qin Zhuanga, Lisa Alvarez-Cohena,b*
a
Department of Civil and Environmental Engineering, University of California, Berkeley, CA
94720-1710
b
Earth Sciences Division, Lawrence Berkeley National Laboratory, Cyclotron Rd., Berkeley, CA
94720
*
Mailing address: 726 Davis Hall, Department of Civil and Environmental Engineering,
University of California, Berkeley, CA 94720-1710. Phone: (510) 643-5969. Fax: (510) 6435264. E-mail: alvarez@ce.berkeley.edu.
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SUPPLEMENTARY METHODS
Enrichment and Isolation of REE Solubilizing Microorganisms
Inoculating source material was placed in 50 mL centrifuge tubes, covered with NBRIP
medium, and shaken to release cells. Soil and sand particles were allowed to settle and
supernatant was collected and used to inoculate enrichment bottles containing NBRIP medium
with 10 g/L glucose (added as carbon and energy source) and insoluble NdPO4 (phosphate
source) [Sigma-Aldrich, St. Louis, MO]. Enrichment cultures were shaken continuously and
approximately 50% of the growth medium was exchanged for fresh medium weekly to provide
fresh nutrients.
REE-phosphate solubilizing microorganisms were isolated from enrichment cultures on
selective plates containing NBRIP medium solidified with 1.5% agar and containing powdered
NdPO4 as the phosphate source and 10 g/L glucose as carbon and energy source. Plates were
inoculated with 100 µL of enrichment culture and incubated at 30 °C. Once growth was
observed, individual colonies were selected and transferred to new plates. 10 µL sterile water
was used to help fungal spores to adhere to the sterile loop for transfer. This process was
repeated for several transfers to achieve isolated strains. Once fungal strains were isolated, they
were maintained on potato dextrose agar plates.
Statistical Analysis
i. Biomass Growth During Bioleaching
Biomass (measured as VS) results (Figure 1) were analyzed by performing pairwise
comparisons to positive and negative controls using a two-tailed T-test for independent samples
with unequal variance. This analysis was performed on the log transformed data, a common
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transformation for biomass and cell count data due to the typical positive skew of such data
(Olsen 2003; Olsen 2014).
ii. Bioleaching Performance under Different Growth Conditions
Differences in performance under different growth conditions (Figure 2) were analyzed
using a two-tailed T-test for independent samples with unequal variance to compare total REE
concentrations at the end of six days of incubation. For each organism, data from different
growth conditions (e.g. different medium compositions) were compared by pairwise
comparisons.
iii. Proportional Release of REEs and Thorium During Bioleaching
Differences in the release of Th in proportion to REE release (Figure 3b) were analyzed
using a two-tailed Student’s T-test for independent samples with unequal variance. Pairwise
comparisons were performed to compare Th in leachate from each of the organisms to the
monazite composition and to compare leachate from each of the organisms to each other.
iv. Abiotic Leaching with Hydrochloric Acid, Organic Acids, and Spent Medium from
Bioleaching
For analysis of abiotic leaching of REEs (Figure 4), a weighted linear model was used in
order to analyze the effects of one continuous (pH) and one categorical (different acids or spent
supernatant from different organisms) independent variable on the dependent variable (REE
concentration). The sample variances were first estimated for each acid and for the supernatant
from each organism. These variances were then used to determine weights in the model. In
order to estimate the sample variances, a least squares linear fit between pH and REE
concentration was first performed for the HCl data. The slope of this line was then used to
estimate a linear fit for each of the other acids and for the supernatant data. The sum squared
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error in relation to that linear fit was used to estimate the variance for each and those variance
estimates were used to weight the model.
For analysis of abiotic leaching of Th, an initial least squares regression analysis of the
data from HCl solutions ranging in pH from 1.8 to 3.7 showed no correlation between pH and Th
solubilization (Figure S3). Therefore, data (Figure S4) were analyzed using a two-tailed
Student’s T-test for independent samples with unequal variance to do pairwise comparisons of
each organic acid and supernatant solution to the HCl solution. Different concentrations of each
organic acid were analyzed separately.
v. Šidák Adjustment for Multiple Comparisons
For all analyses involving multiple comparisons, p-values were adjusted using the Šidák
correction (Šidák 1967) to maintain a significance level of α = 0.05. The correction is given by:
padjusted = 1 − (1 − punadjusted )
n
where n is the number of comparisons performed.
REFERENCES FOR SUPPLEMENTARY MATERIALS
Olsen CH. 2003. Review of the use of statistics in Infection and Immunity. Infection and
Immunity 71(12):6689-6692.
Olsen CH. 2014. Statistics in Infection and Immunity Revisited. Infection and Immunity
82(3):916-920.
Šidák Z. 1967. Rectangular Confidence Regions for the Means of Multivariate Normal
Distributions. Journal of the American Statistical Association 62(318):626-633.
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SUPPLEMENTARY TABLES AND FIGURES
Supplementary Table SI. P-values for statistical analyses reported in the text. For analyses
involving multiple comparisons, p-values are Šidák adjusted. Unless otherwise noted, only pvalues indicating statistical significance (p < 0.05) are given.
Comparison
Condition
p-value
(Šidák adjusted for multiple
comparisons)
Growth difference between monazite
and negative control (Figure 1)
A. niger
0.0028
ML3-1
0.0013
WE3-F
0.017
A. niger
0.0029
ML3-1
0.065 (marginally significant)
WE3-F
0.0018
A. niger
0.0013
ML3-1
<0.0001
WE3-F
<0.0001
Linear correlation between REE
solubilization and pH (Figure 4a)
HCl solutions
<0.0001
REE solubilization differences between
organic acids / spent medium and HCl
control (Figure 4a and 4b)
citric acid
0.0001
ML3-1
0.0003
WE3-F
<0.0001
2 mM citric acid
0.0079
20 mM citric acid
0.0005
2 mM oxalic acid
0.0008
20 mM oxalic acid
0.0019
A. niger
0.015
REE solubilization differences between
AMS and NBRIP (Figure 2a)
Proportional release of Th during
bioleaching in comparison to Th
content of monazite (Figure 3b)
Th solubilization difference between
organic acids / spent medium and HCl
(Figure 5)
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Supplementary Figure S1. Total sugar concentrations during bioleaching of monazite. Error bars
indicate standard deviations around the means. (a) Different growth media: PVK medium, PVK medium
without Fe or Mn, AMS medium, and NBRIP medium, all containing 10 g/L glucose as carbon source.
(b) Different carbon sources: glucose, fructose, sucrose, xylose, and starch, all in AMS medium with
initial carbon source concentrations of 10 g/L. For the sucrose medium, data include the sum of glucose,
fructose and sucrose concentrations. For starch medium, data include only glucose concentrations rather
than starch, which could not be determined by the detection method used. (c) Different initial glucose
concentrations: 5 g/L, 10 g/L, and 100 g/L, all in AMS medium. For the 100g/L glucose condition,
glucose concentration remained above the scale of the graph throughout bioleaching.
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Supplementary Figure S2. pH during bioleaching of monazite. Error bars indicate standard
deviations around the means. (a) Different growth media: PVK medium, PVK medium without
Fe or Mn, AMS medium, and NBRIP medium, all containing 10 g/L glucose as carbon source.
(b) Different carbon sources: glucose, fructose, sucrose, xylose, and starch, all in AMS medium
with initial carbon source concentrations of 10 g/L. (c) Different initial glucose concentrations:
5 g/L, 10 g/L, and 100 g/L, all in AMS medium.
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Supplementary Figure S3. Relationship between pH and solubilization of Th for abiotic
leaching of monazite with solutions of HCl. (a) All data (b) Data excluding apparent outlier at
pH = 2.8, [Th] = 0.41 mg/L. Lines show least squares linear fits to the data. Neither linear fit is
statistically significant.
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