Periodic Nucleation of Calcium Phosphate in a Stirred Biocatalytic Reaction

Article abstract of DOI:10.1002/anie.201911213

Highly ordered superstructures composed of inorganic nanoparticles appear in natural and synthetic systems, however the mechanisms of non-equilibrium self-organization that may be involved are still poorly understood. Herein, we performed a kinetic investigation of the precipitation of calcium phosphate using a process widely found in microorganisms: the hydrolysis of urea by enzyme urease. With high initial ratio of calcium ion to phosphate, periodic precipitation was obtained accompanied by pH oscillations in a well-stirred, closed reactor. We propose that an internal pH-regulated change in the concentration of phosphate ion is the driving force for periodicity. A simple model involving the biocatalytic reaction network coupled with burst nucleation of nanoparticles above a critical supersaturation reproduced key features of the experiments. These findings may provide insight to the self-organization of nanoparticles in biomineralization and improve design strategies of biomaterials for medical applications.

Full text of DOI:10.1002/anie.201911213

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Accepted Article
Title: Periodic nucleation of calcium phosphate in a stirred bio-catalytic
reaction
Authors: Biborka Bohner, Tamas Bansagi, Agota Toth, Dezso Horvath,
and Annette Taylor
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10.1002/anie.201911213
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RESEARCH ARTICLE
Periodic nucleation of calcium phosphate in a stirred bio-catalytic
reaction
Bíborka Bohner,[a] Tamás Bánsági Jr.[b], Ágota Tóth[a], Dezső Horváth[a] and Annette F. Taylor[c]*
systems, mass transport plays an important role in the
development of structure. Here we report the first observations of
periodic precipitation in a stirred biocatalytic reaction. The results
Abstract: Highly ordered superstructures composed of inorganic
illustrate how coupled chemical reactions can be used to generate
nanoparticles appear in natural and synthetic systems, however the
oscillations in the production of nanoparticles, driven by
mechanisms of non-equilibrium self-organization that may be involved
competition between a reaction producing inorganic ions and a
are still poorly understood. Herein, we performed
a kinetic
phase change process consuming them.
investigation of the precipitation of calcium phosphate using a process
widely found in microorganisms: the hydrolysis of urea by enzyme
urease. With high initial ratio of calcium ion to phosphate, periodic
precipitation was obtained accompanied by pH oscillations in a well-
stirred, closed reactor. We propose that an internal pH-regulated
change in the concentration of phosphate ion is the driving force for
periodicity. A simple model involving the biocatalytic reaction network
coupled with burst nucleation of nanoparticles above a critical
supersaturation reproduced key features of the experiments. These
findings may provide insight to the self-organization of nanoparticles
in biomineralization and improve design strategies of biomaterials for
medical applications.
Bacteria use networks of enzyme-catalyzed reactions to
drive mineral formation. One well known reaction is that of urease
which breaks down urea and forms ammonia, raising the pH.[8]
This leads to precipitation resulting in stone formation and
catheter blockages. Urease has also been used to produce
calcium carbonate and other minerals including calcium
phosphate, both as a study of the biomineralization process but
also for applications in engineering such as cement
strengthening.[9]
We investigated the kinetics of reaction-induced
precipitation of calcium phosphate, driven by urease. Calcium
phosphate is an important biomineral produced by bacteria, often
found intracellularly as amorphous calcium phosphate (ACP), a
precursor to the crystalline forms.[10] ACP is also implicated in the
early stages of bone development in vertebrates.[11] There are
numerous methods for the synthesis of calcium phosphate and its
various polymorphs in the laboratory, but fewer studies modelling
its formation, particularly under biologically relevant conditions.[12]
It is widely used in biomaterials applications such as bone and
dental cements and coatings for metallic prostheses.[10]
We found that under certain conditions periodic precipitation
of calcium phosphate occurred along with pH oscillations. Internal
pH changes caused by competing reactions – production of
ammonia and removal of phosphate – resulted in periodic
changes in supersaturation and nucleation of nanoparticles. The
results were reproduced in a simple model inspired by the LaMer
theory for the formation monodisperse colloids involving burst
nucleation above a critical supersaturation.[13] Our results may
help uncover the mechanisms that give rise to self-organization in
some natural and synthetic biomineral systems.
Introduction
Self-organization refers to the structures found in chemical and
biological systems arising from internal feedback mechanisms far
from equilibrium.[1] Some interesting examples of self-
organization in materials have been obtained, inspired by natural
processes such as the oscillatory regulation of microtubule growth
in cells or the formation of coccoliths in marine algae.[2]
Understanding how self-organization arises in nature will aid in
the design of bioinspired materials for widespread applications.[3]
Many natural and synthetic processes involving mineral
precipitation develop remarkable superstructures composed of
nanoparticles.[4] Complex shapes, known as biomorphs, form
when barium carbonate nanocrystals precipitate in silica
solution.[5] Periodicity has been observed in the arrangement of
calcite nanocrystals in calcium carbonate composites[6] and in the
chemical garden structures or Leisegang patterns driven by the
coupling of chemical and physical processes.[7] In these synthetic
Results and Discussion
[a]
Dr B. Bohner1, Dr Á. Toth1, Dr D. Horváth2
1 Department of Physical Chemistry and 2 Department of Applied
and Environmental Chemistry
University of Szeged
Aradi Vértanúk tere 1., Szeged, H-6720, Hungary
Dr T. Bánsági Jr.
School of Chemistry
University of Birmingham
Edgbaston, Birmingham, B15 2TT, UK
Dr A.F. Taylor
Department of Chemical and Biological Engineering
University of Sheffield
Precipitation of calcium phosphate was achieved by addition of a
solution of the enzyme urease (Sigma, Type III) containing
sodium phosphate to a stirred solution of urea, calcium chloride
and hydrochloric acid (see experimental section and
supplementary information). For certain initial concentrations,
there was a delay before the increase in pH associated with the
enzyme catalyzed hydrolysis of urea:
[b]
[c]
urease 
CO(NH2)2 + H2O
2NH3 + CO2
(1)
Mappin Street, Sheffield, S1 3JD, UK
E-mail: A.F.Taylor@sheffield.ac.uk
Rapid precipitation was observed when the pH reached 7, and
there was a corresponding change in the intensity of images of
Supporting information for this article is given via a link at the end of
the document.
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10.1002/anie.201911213
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RESEARCH ARTICLE
the cuvette (Figure 1(a)). Turbidity measurements revealed an
increase in the amount of calcium phosphate formed with higher
initial concentrations of calcium ion (Figure S1). The washed and
dried particles were < 100 nm in diameter with a spherical
morphology (SEM in Figure 1(b) and Figure S2). The bands
obtained in the Raman spectrum of the particles are associated
3
with vibrational modes of PO4 ; the broad peak at 952 cm-1 (Figure
S3) is indicative of amorphous calcium phosphate (ACP) or poorly
crystalline hydroxyapatite (Ca10(PO4)6(OH)2, HAP).[14] Biological
macromolecules and carbonates can inhibit crystal growth so the
enzyme urease or the carbonate produced from hydrolysis of urea
may aid in the stabilisation of ACP.[15]
When the concentrations of urea and calcium chloride were
increased, a stepwise production of precipitate was observed
(Figure 1(c)), accompanied by oscillations in pH between 7.8 and
8.1. The Raman spectra of the dried precipitate taken during the
oscillatory reaction contained a phosphate band at 952 cm-1 and
characterstic calcite peak at 1086 cm-1, suggesting a mixture of
amorphous calcium phosphate and calcite was formed (Figure
1(d) and Figure S4). The SEM of the dried precipitates showed
submicron particles and micron sized rhombic structures (Figure
1(d) and Figure S5).
Figure 1. Precipitation of calcium phosphate in a cuvette with high stirring rate
(1000 rpm). (a) Changes in pH and intensity of grayscale images with initial
concentrations of [urea] = 0.08 M, [CaCl2] = 4 mM, [HCl] = 3 mM, [urease] = 20
u/ml, [H2PO4¯] = 0.034 M. (b) Raman spectrum with PO4 peaks at ~420, 586
and 952 cm-1 and SEM of precipitates formed with concentrations in (a). (c)
Periodic, stepwise precipitation and pH oscillations with [urea] = 0.5 M, [CaCl2]
= 0.25 M, [HCl] = 5 mM, [urease] = 20 u/ml, [H2PO4¯] = 0.034 M. (d) Raman
spectrum from precipitate taken during the reaction with concentrations in (d)
except [urease] = 30 u/ml and [H2PO4¯] = 0.05 M with broad PO4 peaks at ~420,
586 and 952 cm-1 and CO3 peaks at 712, 1086, 1437 and 1747 cm-1. Inset: SEM
of precipitates.
The range of concentrations for which oscillations were
observed is shown in Table 1 and typical pH/intensity plots are
included in Figure S6 – S8. In all cases, precipitate was obtained
when the pH increased above 7, but stepwise production
commenced with a sharp drop in the pH when the pH reached ~8
to 8.4. The average amplitude of the pH oscillations was 0.3 ±
0.08 and did not vary greatly between experiments, but
dampened as the reaction progressed. The period of oscillations
was between 5 to 20 minutes and in general was higher with
decreases in [Ca2+] or [urea]. The period and number of
oscillations were found to be particularly sensitive to the nature of
the stirring, including the type of magnetic stirrer bar used and the
stirring rate.
If the stirring rate was reduced such that aggregation could
occur, clusters of particles deposited on the electrode and walls
of the cuvette (Figure 2). The amplitude of pH oscillations was the
same as with higher stirring rate and the period of oscillations was
greater. We found that there was a total increase in the amount
of precipitate in time, indicated by the stepwise change in baseline
intensity (red box around wall-coating aggregates in image and
red intensity curve 1 and Figure 2(d)). However, in the bulk
solution oscillations in intensity were observed with a return to the
same baseline intensity and an absence of precipitates (blue box
around bulk solution in image and blue intensity curve 2). In
repeat experiments, there was a change in baseline intensity after
a peak in areas of the cuvette with aggregate, whereas in areas
without aggregate it was close to zero (Figure 2(e) and Figure S9).
This suggests that the periodicity arose from rapid nucleation of
calcium phosphate in the bulk, stirred solution.
In order to gain a better understanding of the processes
involved, we devised a simple model of the reaction. The model
included the hydrolysis of urea by urease with well-known kinetics
and the ammonia, carbonate and phosphate equilibria (see
supplementary information, section 3). In addition, we included
precipitation of calcium phosphate and calcium carbonate. We
note that it is likely there are multiple calcium phosphate species
formed and there are various types of ACP including amorphous
tricalcium phosphate (ATCP):[16]
Table 1. Range of concentrations where oscillations were obtained under well-
stirred conditions (1000 rpm). HCl = 5 mM in all cases.
[urease]/
u/ml
[H2PO4-]/
M
[urea]/
M
[CaCl2]/
M
<Period>
/min
10 – 40a)
30b)
0.017 – 0.07
0.052
0.5
0.25
7 - 18
7 - 20
5 - 13
0.5
0.125 - 0.75
0.25
20c)
0.034
0.5 – 1
a) Figure S6; b) Figure S7 c) Figure S8 for typical plots.
3-
3Ca2+ + 2PO4 + nH2O → Ca3(PO4)2.nH2O (s)
(2)
3-
2-
where some PO4 ions can be replaced by HPO4 and OH¯ or
other ions giving different ratios of [Ca]/[P]T and [P]T is the total
concentration of phosphorous.
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where KJ = AVc/Vm, and Vc is the volume of a particle of radius rc
and Vm is the molar volume. The exponential term and parameters
A and B arise from classical nucleation theory. The probability
term, p(S), was introduced in order to take into account the onset
of nucleation at a critical value of S:
S ≥ Scrit1
p = 1
(5)
3-
where the value of Scrit1 was given by Scrit1 = 1+R1[Ca2+]/[PO4
]
and R1 is a constant. The nucleation was terminated when S ≤
Scrit2 (given by 1 + R2[Ca2+]/[PO43-], where R2 << R1). The
dependence of Scrit on the ratio [Ca2+]/[PO43-] was intended to take
into account the fact that probability of precipitation was more
likely as the concentration of [PO43-] was increased for [Ca2+] >
[PO43-] i.e. Scrit decreased.[19] Further details of the model and all
parameters are given in the supplementary information.
The oscillatory process observed in experiments can be split
into two zones, A and B, as shown in Figure 3(a). In zone A, there
was no nucleation, and the pH was increasing as a result of the
urease reaction (reaction 1) and the production of ammonia. In
zone B, there was fast precipitation of calcium phosphate and the
pH dropped. The precipitation then terminated, there was no more
nucleation and the pH again increased. The distribution of
phosphate species is shown in Figure 3(b) and the key processes
Figure 2. Periodic nucleation and aggregation of calcium phosphate in a cuvette
with low stirring rate (200 rpm) where [urea] = 0.5 M, [HCl] = 5 mM, [CaCl2] =
0.25 M, [urease] = 30 u/ml, [H2PO4-] = 0.05 M. (a) image of the cuvette where
the white agglomerates of precipitate are deposited on the walls and pH
electrode. (b) Time series of pH, 1) average intensity of images of wall-coating
aggregates in the red box and 2) average intensity in images of the bulk solution
in the blue box. The blue line indicates the time point at which the image on the
left was taken. (c): images of part of the cuvette at time points indicated A, B
and C. (d) Baseline intensity after a peak from an area in the cuvette with
aggregate (red) and without aggregate (blue) for experiment in (a). Inset shows
peak and change in baseline intensity. (e) Average change in baseline intensity
from areas with aggegate (red) and areas without aggregate (blue) for three
repeats of the experiment. Error bars indicate standard deviation calculated
from multiple sample areas.
3-
in the model are in Figure 3(c). The concentration of PO4
increased when the pH reached high values. Then removal of
3-
PO4 by reaction with Ca2+ resulted in a pH drop, i.e. the net
process in B could be written as:
2-
3Ca2+ + 2HPO4 + nH2O → Ca3(PO4)2.nH2O (s) + 2H+ (6)
with the rate-determining nucleation reaction involving PO43-. The
model was able to reproduce oscillations in pH and a stepwise
production of precipitate (Figure 3(d)i). The simulations
suggested that only a small decrease in urea was observed during
the oscillatory reaction (Figure 3(d)ii). The supersaturation, S,
The solubility product Ksp for amorphous compounds cannot
be precisely determined but a range of estimates have been
reported, from 3 x 10-17 to 2 x10-33 [17]
.
For simplicity, we included
3-
increased as a result of the greater concentration of PO4 at
only ATCP in the model and took the value of Ksp = 3 x 10-17
.
higher pH. Nucleation was suppressed until the critical value Scrit1
.
Calcium carbonate also has various polymorphs; here there was
evidence of calcite from the Raman and SEM, hence we used the
Ksp corresponding to that of calcite (Ksp = 3.3 x 10-9), although this
may also be preceded by less stable amorphous species.[18]
At this point, the probability of nucleation was 1 and a rapid drop
3-
in S and pH was observed, along with consumption of PO4
(Figure 3d(iii)). When S fell below Scrit2, nucleation terminated.
The enzyme reaction then dominated again, with a renewed rise
in pH. Hence the instability occurs as a result of two competing
processes that cause PO43- formation and depletion.
The rate of formation of solid depends on the
supersaturation, S, defined here as the ratio of the ion product to
the solubility product:
3-
2
S = IP/Ksp
IP = {Ca2+}3{PO4
}
(3)
where {} denotes activities of the ions in solution. Precipitation is
thermodynamically favored for S > 1 and typically involves
primary and secondary nucleation, growth, aggregation/breakage
and Ostwald ripening. We consider only primary nucleation here
as the experiments with slow stirring rate suggest that this is the
driving force for periodicity. To imitate burst nucleation, the rate of
production of spheres of precipitate of a critical radius, rc, was
approximated by:
퐵
[
]
(−
)
2
)
푑 ATCP
(
ln푆
( )
= 푝 푆 퐾 푆푒
(4)
퐽
푑푡
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Figure 3. Periodic precipitation split into time zones A and B and (a) intensity
and pH from the experiments in Figure 1(c). (b) Distribution of phosphate
species as a function of pH calculated from the various equilibria, (c) basic
mechanism of pH increase in A and pH decrease in B, (d) simulations showing
pH and [ATCP] in time, urea and supersaturation S, and calcium and phosphate
ions for initial concentrations: [urea] = 0.5 M, [HCl] = 5 mM, [CaCl2] = 0.25 M,
[urease] = 30 u/ml, [H2PO4¯] = 0.05 M.
Figure 4. Comparison of reaction profiles in experiments and simulations for
different reaction conditions (a) pH profile in experiments with low calcium
chloride concentration where [urease] = 20 u/ml, [H2PO4¯] = 0.034 M, [urea] =
0.08 M, [HCl] = 3 mM, and [CaCl2] = 1 – 5 mM, (b) simulations with same
conditions as in Figure 3(d) but reduced calcium chloride [CaCl2] = 0.03 M. (c)
oscillations with [urea] = 0.5 M, [CaCl2] = 0.25 M, [HCl] = 5 mM, [urease] = 30
u/ml, [H2PO4¯] = 0.05 M. Inset shows optical microscope image of a sample of
the reaction after oscillations with small scale precipitates (calcium phosphate)
and larger structures (calcite) (d) Simulations showing production of calcium
phosphate (dashed) and calcite (orange) with [urea] = 0.5 M, [CaCl2] = 0.25 M,
[HCl] = 5 mM, [urease] = 30 u/ml, [H2PO4¯] = 0.05 M.
In order for oscillations to arise, the rate removal of
phosphate must overcome the rate of production of phosphate
from the biocatalytic process. When the ratio of [Ca]/[P]T was
reduced, nucleation occurred at lower values of Scrit1 and the rate
was reduced such that S fell to a steady state value above Scrit2
.
Transformations between phosphate polymorphs are
known to display autocatalytic features: a sigmoidal change in
Continuous rather than periodic nucleation was observed, in
agreement with experiments (Figure 4(a, b)). If the enzyme rate
(turnover number) was increased in simulations, or the
precipitation rate (KJ) decreased, then S rose above Scrit1 and
continuous production of calcium phosphate was also observed.
Hence oscillations only occurred for a finite range of values of Scrit1
that depended on the composition of the reaction mixture. We
found that in simulations the amplitude of pH oscillations was
governed primarily by Scrit1, and hence the ratio of [Ca]/[P]T and
the absolute pH by Ksp and the enzymatic rate. The period was
governed by a combination of the enzyme rate and the nucleation
rate, both of which are sensitive to the stirring rate.
Calcium carbonate was formed after calcium phosphate as
revealed by SEM (Figure S10), and analysis of a sample taken
after the oscillations with an optical microscope revealed that
growth of crystals of CaCO3 occurred at later stages of the
reaction, when nucleation and growth of ATCP appeared to have
terminated. When calcium carbonate nucleation was included in
the model, this species was produced more gradually than ATCP
(Figure 4(d)) and did not form stepwise – hence it is unlikely that
it contributed to the observed periodicity. The pre-exponential A
factor is expected to be smaller for a crystalline form than for an
amorphous phase, and the rate of nucleation of calcite was much
smaller than that of ATCP. This meant that the supersaturation
was always above the threshold for calcite, and continuous
nucleation was observed.
[Ca2+], and enhanced rate in the presence of the crystalline
20]
product.[14a,
These processes likely occur by internal
rearrangement and expulsion of acid. Oscillations have not
previously been reported, probably because such systems either
do not involve continuous reactive supply of ions or employ the
relatively high metal:counterion ratio used here.[9b] In addition, the
burst nucleation observed here occurs on a faster timescale.
In classical nucleation theory, the steady state nucleation
rate is calculated from the number of clusters above a critical
radius. Both the critical nucleus size and the induction period
before the observable appearance of precipitate decrease with
increasing S. However, we found that the inclusion of a critical
supersaturation, Scrit, for nucleation was essential in the model in
order to reproduce the oscillations. LaMer proposed that for
systems in which the solute is supplied by e.g. chemical reaction,
the increase in S takes the system into a metastable region where
precipitation is negligible until a critical supersaturation, Scrit, is
reached and then rapid nucleation occurs.[13a] This removes ions
from solution until S falls below Scrit where nucleation is
terminated and growth of crystals can then occur. The LaMer
model separates the processes of nucleation and growth and is
often used to describe nanoparticle synthesis, where burst
nucleation results in uniform size particles from
a near
homogeneous solution.[13b] We have used a similar approach for
modelling nucleation of nanoparticles of ATCP. The probability
function used here allowed for a dynamic Scrit that depended on
the solution composition.
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The time before Scrit is reached may involve the
establishment of stable pre-nucleation clusters as the formation
of ACP is believed to occur via Posner clusters - Ca9(PO4)6 - of
size 1 nm.[11] Aggregates of various Ca:P stoichiometries have
been reported in systems with different initial compositions and
methods of preparation; and the presence of pre-nucleation
clusters was confirmed in these cases.[17a] Privman et al.
proposed a kinetic model for aggregation via pre-nucleation
clusters in colloidal systems which resulted in a similar expression
to the one used here.[21] This expression did not give oscillations
in our model without the inclusion of Scrit. A much steeper
dependence of the nucleation rate on S (e.g. S20) has also been
used to simulate the more step-like dependence of nucleation rate
on supersaturation in spatially distributed systems, as well as a
Heaviside function (for S > Scrit).[22] This scenario appears relevant
for solutions in which one ion is in large excess, and
supersaturation is rising as a result of the increase in the counter-
ion, either by reaction or by diffusion.
The mechanisms underlying oscillations in nanoparticle
production described here may find more general applications.
Coupled reaction networks, where the output of one process
regulates the input of another, are frequently found in nature and
self-organization may be used to arrange supramolecular
chemical structures such as nanoparticles into hierarchical
structures. Transport processes such as diffusion will then play
an important role. There are many natural materials that that
involve periodic deposition of nanoparticles. It may be that the
approach presented here can help describe some of these, or
allow us to develop a strategy for the control of superstructures
formed in synthetic, bioinspired mineral systems.
Experimental Section
Two solutions were prepared in ultrapure water (Milli-Q). In solution A,
urea (Sigma-Aldrich), calcium chloride dihydrate (Sigma-Aldrich) and
Other precipitation models include autocatalysis in growth
of colloids (Watzky-Finke model) and autocatalysis through
secondary nucleation which is responsible for rapid precipitation
in some inorganic salt systems.[23][24] However, the latter cannot
explain the periodicity described here as the product particles
were always present after the first nucleation event: there would
be no time delay necessary for oscillations. Oscillations in
precipitation and in nanoparticle aggregation have been obtained
in stirred systems involving pH oscillators – in this case the driving
force for the behavior was pH autocatalysis driven by inorganic
chemistry in open flow reactors.[25] Oscillations in pH have also
been achieved with enzyme catalyzed reactions involving
feedback through the rate-pH curve.[26] Here we presented the
first example of a system where the internally pH-regulated
formation of the counter-ion, phosphate, along with burst
nucleation are the driving force for the periodicity.
In unstirred systems, periodic precipitation is frequently
obtained. Leisegang patterns have been modelling using the
Cahn Hilliard equation which is used to effectively describe a
spatial phase separation process.[27] Biomorphs also show
periodic nanoparticle deposition and local pH oscillations have
been observed in experiments.[28] The main difference between
our system and these precipitation patterns is that spatial
gradients and diffusion are responsible for the changes in
supersaturation whereas here it was driven by chemical reaction.
The role of the biocatalytic process, then, was to maintain the
system far from equilibrium with the slow consumption of the
chemical fuel, urea, that regulated the pH and hence the
precipitation process under closed conditions.
hydrochloric acid (Sigma-Aldrich) were dissolved. Solution
B
contained urease/phosphate (from Canavalia ensiformis (Jack bean)
type III, Sigma-Aldrich, 31430 u/g, containing phosphate 0.17 mg/u).
For experiments in the cuvette, one ml of B was added to one ml of A
under constant stirring using a magnetic stirrer (HI-190M) with a
Spinfin® stirrer bar. Experiments were performed at room
temperature (20 ± 2 ˚C). The pH was monitored using a pH
microelectrode (Mettler Toledo and Pico Data logger software) and
images were taken using a PixeLINK® camera and analyzed using
ImageJ or MATLAB. Turbidity measurements were obtained using a
VWR UV-3100PC spectrophotometer. The precipitate was filtered,
rinsed with doubly deionized water and air dried. Raman spectroscopy
was performed on a Thermo ScientificTM DXRTM Raman microscope
using a green laser (λ = 532 nm), at 5 mW. Scanning electron
microscopic (SEM) images were recorded by a Hitachi S-4700 field
emission scanning electron microscope, at 10 kV. Optical microscope
images were produced from an aqueous sample of solution collected
after oscillations ceased using a Leica TCS SP8 Confocal Microscope.
For more details, see the supplementary information.
Acknowledgements
BB thanks COST action CM1304 Emergence and Evolution in
Complex Chemical Systems for funding a Short Term Scientific
Mission to the University of Sheffield and AFT and TB thank EPSRC
EP/K030574/2 for financial support. AT and DH thank National
Research, Development and Innovation Office (K119795)
and GINOP-2.3.2-15-2016-00013 project.
Keywords: biomineralization • systems chemistry • biocatalysis•
self-organization • reaction networks
Conclusion
Here, we have obtained periodic nucleation of calcium phosphate
and oscillations in pH by using a biocatalytic reaction to maintain
the system far from equilibrium. We presented a simple model
that reproduced the results by coupling the biocatalytic reaction to
[1]
[2]
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Nicolis,
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Prigogine,
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370, 2848-2865; bJ. Leira-Iglesias, A. Tassoni, T. Adachi,
M. Stich, T. M. Hermans, Nature Nanotechnology 2018; cJ.
burst nucleation of nanoparticles above
a
threshold
supersaturation, inspired by models for the formation of
monodisperse colloids proposed by LaMer.
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10.1002/anie.201911213
Angewandte Chemie International Edition
RESEARCH ARTICLE
Entry for the Table of Contents
Layout 1:
RESEARCH ARTICLE
Periodic precipitation of calcium
phosphate was obtained by using a
biocatalytic reaction to maintain the
system far from equilibrium. A simple
model involving burst nucleation of
nanoparticles above a critical
supersaturation reproduced the
results, suggesting a potential
mechanism for self-organization in
biomineralization.
Bíborka Bohner,[a] Tamás Bánsági Jr.[b],
Ágota Tóth[a], Dezső Horváth[a] and Annette
F. Taylor[c]*
Page No. – Page No.
Periodic nucleation of calcium
phosphate in a stirred bio-catalytic
reaction
This article is protected by copyright. All rights reserved.