Tingzheng Hou1,2, Wentao Xu3, Xiaokun Pei3, Lu Jiang1,4, Omar M Yaghi3, Kristin A Persson1,5. 1. Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States. 2. Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States. 3. Department of Chemistry, University of California, Berkeley, California 94720, United States. 4. Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States. 5. The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States.
Abstract
We report the theoretical and experimental investigation of two polyoxometalate-based metal-organic frameworks (MOFs), [(MnMo6)2(TFPM)]imine and [(AlMo6)2(TFPM)]imine, as quasi-solid-state electrolytes. Classical molecular dynamics coupled with quantum chemistry and grand canonical Monte Carlo are utilized to model the corresponding diffusion and ionic conduction in the two materials. Using different approximate levels of ion diffusion behavior, the primary ionic conduction mechanism was identified as solvent-assisted hopping (>77%). Detailed static and dynamic solvation structures were obtained to interpret Li+ motion with high spatial and temporal resolution. A rationally designed noninterpenetrating MOF-688(one-fold) material is proposed to achieve 6-8 times better performance (1.6-1.7 mS cm-1) than the current state-of-the-art (0.19-0.35 mS cm-1).
We report the theoretical and experimental investigation of two polyoxometalate-based metal-organic frameworks (MOFs), [(MnMo6)2(TFPM)]imine and [(AlMo6)2(TFPM)]imine, as quasi-solid-state electrolytes. Classical molecular dynamics coupled with quantum chemistry and grand canonical Monte Carlo are utilized to model the corresponding diffusion and ionic conduction in the two materials. Using different approximate levels of ion diffusion behavior, the primary ionic conduction mechanism was identified as solvent-assisted hopping (>77%). Detailed static and dynamic solvation structures were obtained to interpret Li+ motion with high spatial and temporal resolution. A rationally designed noninterpenetrating MOF-688(one-fold) material is proposed to achieve 6-8 times better performance (1.6-1.7 mS cm-1) than the current state-of-the-art (0.19-0.35 mS cm-1).
Solid-state electrolytes with
high mechanical strength and ionic conductivity are anticipated to
revolutionize the energy storage industry.[1−5] This is due to their significant contributions to
improved safety, low-temperature performance, and volumetric energy
density as compared to conventional liquid electrolytes. Recently,
anionic metal–organic frameworks (MOFs) with superior ionic
conductivity and Li+ transference numbers have opened a
new avenue in the development of quasi-solid-state electrolytes (QSSEs).[6−11] To immobilize anions on the backbone of these frameworks, one approach
is to directly link negatively charged building blocks, whereby the
lithium counterions are introduced as the only mobile species inside
the material.[9] For example, a three-fold
interpenetrating anionic MOF (MOF-688) was synthesized from Anderson
type polyoxometalate (POM) [N(C4H9)4]3[MnMo6O18{(OCH2)3CNH2}2] (MnMo6) and
tetrakis(4-formylphenyl)methane (TFPM) building units through imine
condensation.[12] With propylene carbonate
(PC) filling the pores, Li+-exchanged MOF-688 exhibited
a high ionic conductivity of 4.0 × 10–4 S cm–1 and high Li+ transference number of 0.87
at 298 K.In this new class of promising prototype QSSEs, it
is important
to understand the transport and conduction mechanisms, especially
given the characterization challenges associated with transport measurements.[13−15] Solid-state nuclear magnetic resonance characterizations have shown
that Li+ transport in the framework channels involves complex
interactions between cations, anions, and framework segments[16] and may exhibit a change in conduction mechanism
with varying temperature.[17] Moreover, given
the vast materials space that results from linking inorganic nodes
and organic ligands,[18] it is important
to develop theoretical methods that can predict and optimize the transport
properties of MOFs to support the experimental efforts.[9] Yuan et al.[19] computed
the energetics of Li+ hopping between binding sites in
Cu-MOF-74, which provides support for a hypnotized hopping mechanism
of Li+ conduction.[8] However,
the detailed ionic transport mechanisms and how exactly the anionic
species and solvent molecules cooperatively facilitate the Li+ diffusion are still unclear.[9]Classical molecular dynamics (MD) simulations have shown excellent
results in modeling the solvation and transport properties of liquid
electrolytes[20,21] as well as the diffusion and
adsorption properties of MOFs,[22,23] and have been considered
as a promising tool to provide in-depth understanding of MOF-related
QSSEs.[9] In this contribution, by combining
MD simulations with quantum chemistry and grand canonical Monte Carlo
(GCMC), we identified solvent-assisted hopping as the dominant pathway
for Li+ conduction in MOF-688 materials, revealing the
critical role of the solvent in MOF-based QSSEs. This work constitutes
the first theoretical model that accurately describes the ionic conduction
mechanism of MOF-based QSSEs at an atomistic level, which is challenging
to obtain from experimental results,[13,14] and provides
guidance for possible improvements.A molecular simulation model,
denoted as MOF-688(Mn), was created
from X-ray single crystal structure of MOF-688, [(MnMo6)2(TFPM)].[12] In parallel, an isoreticular structure [(AlMo6)2(TFPM)], termed
MOF-688(Al), was synthesized and modeled by substituting Mn3+ with Al3+ to investigate the influence of the POM center
metal ion on ionic conduction. Li+ counterions were introduced
in both MOFs with a POM/Li+ ratio of 1:3 (Supporting Information (SI) Section 1). Hybrid MD and GCMC
simulations[24] were performed to equilibrate
the content of PC solvent in the pores of MOFs. The obtained PC-infused
structures with a POM/PC ratio of 16:170 and a Li+ concentration
of 2.0 mol L–1 were then utilized to perform MD
simulations (SI Section 3). By analyzing
MD trajectories over a few tens of nanoseconds, three different types
of Li+ motion were observed (Scheme ): (1) Li+ hopping on the same
POM cluster between the outmost oxygens (i.e., binding sites); (2)
Li+ hopping between POM clusters; and (3) solvated Li+ diffusion, where Li+ is coordinated and separated
only by PC that can freely diffuse in bulk solvent.
Scheme 1
Three Proposed Conduction
Mechanisms for MOF-688
To identify the ionic conduction mechanism of the two model materials,
we calculated the ionic conductivity using different levels of approximation
(SI Section 5): first using rigorous Green–Kubo
(GK) relations which account for intermolecular transport correlations,
second using the dilute approximation Nernst–Einstein equation,
and third, using a single-mechanism hopping model. Comparing results
of the three models enabled us to conclusively determine the dominant
conduction mechanisms of both materials.The calculated GK conductivity
of MOF-688(Mn) at 298 K (Figure c) and as a function
of temperature (Figure S12) agree well
with the experimental trend, validating that the molecular model is
suitable for the quantitative study of transport phenomena of MOF-based
QSSEs. Moreover, MOF-688(Al) exhibits a slightly lower ionic conductivity
than MOF-688(Mn), while the difference is within the error range.
This observation indicates that changing the type of center metal
ions is unlikely to significantly affect ionic conductivity. In addition,
the discrepancy of the solvation structure between the two model materials
is minor (Figure a,b, Figure S7) and attributed to a slightly weaker
interaction between Li+ and MnMo6 (Table S2). It is further found that the self-diffusion
coefficients of tethered and solvated Li+ are similar (Figure S10). Considering that only 7% Li+ are fully detached from the POMs in both MOFs and solvated
(Figure a), the diffusion
of solvated Li+ is excluded from the major conduction mechanism.
Figure 1
Conduction
mechanism of MOF-688. The coordination number of (a)
Li+-O(POM), and (b) Li+-O(PC) in MOF-688(Al)
and MOF-688(Mn). (c) Ionic conductivities of MOF-688(Mn) and MOF-688(Al)
from experimental measurements, and theoretical calculations using
Green–Kubo relations (GK), Nernst–Einstein equation
(NE), and simple hopping model (hopping). (d) Scheme of solvent-assisted
hopping. The evolution of Li+-O(POM) and Li+-O(PC) coordination numbers before and after hopping in (e) MOF-688(Mn)
and (f) MOF-688(Al). The light-colored area denotes the extent of
standard deviation.
Conduction
mechanism of MOF-688. The coordination number of (a)
Li+-O(POM), and (b) Li+-O(PC) in MOF-688(Al)
and MOF-688(Mn). (c) Ionic conductivities of MOF-688(Mn) and MOF-688(Al)
from experimental measurements, and theoretical calculations using
Green–Kubo relations (GK), Nernst–Einstein equation
(NE), and simple hopping model (hopping). (d) Scheme of solvent-assisted
hopping. The evolution of Li+-O(POM) and Li+-O(PC) coordination numbers before and after hopping in (e) MOF-688(Mn)
and (f) MOF-688(Al). The light-colored area denotes the extent of
standard deviation.Next, we calculated the
ionic conductivity assuming that the ionic
conduction is mostly contributed by uncorrelated Li+ (self-)diffusion,
and the intrinsically anionic frameworks are treated as fixed. With
this assumption, the ionic conduction can be correlated to the self-diffusion
coefficient of Li+ using the Nernst–Einstein (NE)
equation. The computed NE conductivity is in fair agreement with the
GK conductivity. Additionally, the concerted/correlated ion diffusion
observed in all-solid-state electrolytes (e.g., NASICON[25,26]) is insignificant in the MOF-based QSSE, as analyzed using the Onsager
transport theory (SI Section 5). Hence,
the underlying assumption arguably holds that the conductivity is
mostly contributed by Li+ self-diffusion, in agreement
with the measured transference number (tLi+= 0.87).[12]Finally, we calculated
the ionic conductivity with a simple hopping
model. The uncorrelated individual ion hopping can be described by
a random-walk model.[27] By incorporating
the hopping diffusion coefficient into the NE equation, we obtain
the hopping conductivity. While the calculation utilizes a simplified
model, it yielded fair agreement with the other two models. On average,
the hopping conductivity contributes to 100% and 77% of the GK conductivity
of MOF-688(Mn) and MOF-688(Al), respectively, suggesting that Li+ hopping between POM clusters dominates Li+ diffusion.The evolution of Li+ solvation sheath during Li+ hopping further reveals the solvent effect on the process.
We observed discrete changes of average coordination numbers of both
O(PC) and O(POM) before and after each hopping event (set to 0 ps)
(Figure e,f). During
hopping, the coordination number between Li+ and O(POM)
decreases as Li+ no longer binds to previous binding sites
and has not reestablished binding with another POM. Simultaneously,
the average coordination number of O(PC) increases from 3 to 3.5 for
both MOF-688(Mn) and MOF-688(Al). In the following 500–1000
ps after hopping, when Li+ is gradually tethered to the
framework and the excess PC leaves the Li+ solvation sheath,
the coordination numbers of O(PC) and O(POM) are restored to the bulk
average. The direct involvement of the excess PC suggests that the
primary mechanism of Li+ conduction is solvent-assisted
hopping between POM clusters (Figure d), while the short residence time further
indicates that the excess PC plays a temporary role rather than forming
solvent-separated Li+ solvation structures that diffuse
freely.We find that the local charge distribution on the POM
surface largely
determines the interaction between Li+ and the framework.
The electrostatic potential (ESP) surface of MnMo6 (Figure a,b) and AlMo6 (Figure S13) was calculated as
a measure of the Coulombic interaction between POM and Li+. The ESP distribution of the two clusters is nearly identical, indicating
that center metal ions with the same valency exhibit minor influence
on the surface charge distribution of POM. The Li+ density
distribution around MnMo6 (Figure c,d) coincides well with the ESP distribution,
where the highest Li+ density is found on sites between
two adjacent MoO4 moieties with the lowest electrostatic
potential. This observation indicates that the electrostatic term
is dominant in the interaction between Li+ and MnMo6, which further determines the most probable Li+ binding sites. In addition, Li+ can occasionally be monodentate-coordinated
by one O as observed in the density plot.
Figure 2
Charge and Li+ distribution on MnMo6 surface.
(a) Front and (b) top view of the electrostatic potential (ESP) surface
of MnMo6. Carbon, hydrogen, oxygen, nitrogen, and molybdenum
atoms are represented by gray, white, red, blue, and cyan, respectively.
The color bar shows the electrostatic potential in volts. (c) Front
and (d) top view of the Li+ density plot on MnMo6 surface. White balls denote the oxygens in MnMo6. The
color bar shows Li+ number count from 100,000 randomly
sampled coordinates.
Charge and Li+ distribution on MnMo6 surface.
(a) Front and (b) top view of the electrostatic potential (ESP) surface
of MnMo6. Carbon, hydrogen, oxygen, nitrogen, and molybdenum
atoms are represented by gray, white, red, blue, and cyan, respectively.
The color bar shows the electrostatic potential in volts. (c) Front
and (d) top view of the Li+ density plot on MnMo6 surface. White balls denote the oxygens in MnMo6. The
color bar shows Li+ number count from 100,000 randomly
sampled coordinates.The revealed correlation
between the charge distribution and Li+ distribution has
important implications for the rational
design of MOF-based QSSEs. Strongly localized charges that are immobilized
on POM are expected to produce a tightly confined Li+ distribution.
The overbinding between Li+ and the framework may lead
to less favorable Li+ hopping with decreased mobility and
ionic conductivity. Therefore, we surmise that more distributed local
charges on POM are expected to facilitate weaker binding with Li+ and to enhance Li+ motion.[7]The solvent is another important factor regulating Coulombic
interaction.
In MOF-based QSSEs, the solvent screens Li+ from the anionic
frameworks, an aspect that results in less correlation between the
Li+ motion and the framework.[28] The apparent and effective Li+ concentrations in MOF-688(Mn)
are calculated to be 2.0 and 2.6 mol L–1, respectively
(SI Section 10).[29] The substantial Li+/PC ratio (1:3.54) and corresponding
Li+ concentration suggest a high ionic strength in the
material. The low PC content is mainly due to the limited pore volume
resulting from the interpenetrating frameworks (Figure a). Therefore, we propose that reducing the
degree of interpenetration could be an effective approach to increase
the pore volume and accordingly the amount of solvent in pores, thereby
reducing the viscosity and ionic strength.
Figure 3
MOF-688(one-fold) design.
Schemes of MOF-688 QSSEs with (a) three-fold
and (b) proposed one-fold interpenetrating structures. (c) Coordination
number of Li+-O(POM) and Li+-O(PC) in MOF-688(one-fold).
(d) Comparison of ionic conductivities of MOF-688(Mn), MOF-688(one-fold),
and the conductivity range of liquid carbonate electrolytes (highlighted).
MOF-688(one-fold) design.
Schemes of MOF-688 QSSEs with (a) three-fold
and (b) proposed one-fold interpenetrating structures. (c) Coordination
number of Li+-O(POM) and Li+-O(PC) in MOF-688(one-fold).
(d) Comparison of ionic conductivities of MOF-688(Mn), MOF-688(one-fold),
and the conductivity range of liquid carbonate electrolytes (highlighted).To corroborate the hypothesized design, we modeled
a noninterpenetrating
MOF-688 denoted as MOF-688(one-fold) (Figure b). After the same insertion process, a POM/PC
ratio of 8:355 was obtained (Figure S6).
The apparent and effective Li+ concentrations are both
0.7 mol L–1, comparable to the usual concentration
(1.0 mol L–1) of conventional liquid electrolytes.
Further analysis of solvation structure reveals that more than 60%
Li+ are fully solvated by PC (Figure c, Figure S7).
The boost in solvent-separated Li+ is especially favorable
for the uncorrelated Li+ diffusion in electrolytes.[6] Using the same methods, the GK and NE conductivities
of MOF-688(one-fold) are calculated to be 1.58 and 1.74 mS cm–1, respectively. The theoretical prediction (Figure d) is almost an order
of magnitude higher than the conductivity of MOF-688(Mn) and other
anionic MOF-based electrolyte,[9,10] and significantly narrows
the gap with typical liquid electrolyte conductivities (5–10
mS cm–1).[30,31]Furthermore,
specific contributions from the tethered and solvated
Li+ to the total ionic conductivity were calculated using
the NE equation and Li+ self-diffusion coefficients (Figure S11), in which the solvated Li+ contributed to 64% of the total ionic conductivity. Therefore, the
ionic conduction in MOF-688(one-fold) can be mainly attributed to
the solvated Li+ diffusion, providing a different ionic
conduction mechanism with significantly improved conductivity. Moreover,
unlike polymers which can become solvated and lose their mechanical
strength after mixing with organic electrolyte,[32] mechanical properties of MOFs can even be enhanced when
solvent fills the pores.[33,34] The bulk modulus (Voigt
average) of MOF-688(one-fold) is calculated to be 1.1 and 2.4 GPa
before and after adding PC, respectively. This is 1 to 2 orders of
magnitude higher than that of poly(ethylene oxide) gel polymer electrolytes
(a common QSSE)[35−37] and retained about 1/3 of the 3-fold interpenetrating
MOF-688(Mn) (SI Section 7).To actually
synthesize MOF-688(one-fold) with a much larger pore
size than MOF-688(Mn), large and appropriately matching template guests
or cations are required to support the reticulation of organic and
inorganic building blocks. We envision that screening methods based
on first-principles calculations[38] and
machine learning of suitable synthetic pathways[39,40] may help identify template candidates.
Authors: Allison L Dzubak; Li-Chiang Lin; Jihan Kim; Joseph A Swisher; Roberta Poloni; Sergey N Maximoff; Berend Smit; Laura Gagliardi Journal: Nat Chem Date: 2012-08-19 Impact factor: 24.427
Authors: Andrew S Rosen; M Rasel Mian; Timur Islamoglu; Haoyuan Chen; Omar K Farha; Justin M Notestein; Randall Q Snurr Journal: J Am Chem Soc Date: 2020-02-20 Impact factor: 15.419
Authors: Kara D Fong; Julian Self; Kyle M Diederichsen; Brandon M Wood; Bryan D McCloskey; Kristin A Persson Journal: ACS Cent Sci Date: 2019-06-14 Impact factor: 14.553