Polymer synthesis and self-assembly behavior and dynamics
Electrolytes
Development and characterization of solid-state polymer
electrolytes
Mechanism
Investigation of ion transport and phase transition in solid-state
electrolytes
AI/ML
Integrate AI/ML into material science
Polymer
Polymer architecture and self-assembly are
of fundamental importance for us to
investigate in order to design new functional materials. Helical macromolecules have been widely recognized as self-assembly templates to develop more functional materials.
As we know, double helix structure is exceedingly rare in synthetic polymers.
We had initially discovered a double helix conformation in an artificially synthesized lyotropic liquid crystal poly-2,2′-disulfonyl-4,4′-benzidine terephthalamide (PBDT), which can be prepared by simple and robust condensation polymerization.
This double helix macromolecule represents one of the most rigid simple molecular structures, exhibiting an extremely high axial persistence length (~1 micrometer). This liquid crystalline polymer shows great potential as template material for next-generation electrolyte materials,
which require tunable nano-scale molecular architecture.
An X-ray diffraction pattern for 20 wt% PBDT aqueous solution. The main diffractions are labeled A, B, C, D, E, and F and the helix tilt angle is θ. b The simulated X-ray diffraction pattern based on the HELIX software package (see also Supplementary Figure 1). The layer lines 1, 2, and 3 in the simulated results clearly mimic the experimental results. c The chemical repeat unit of PBDT includes one set of –SO3− groups (two sulfonate groups from one biphenyl unit) and two –NHCO– groups, each of which are mutually connected by one benzene ring. d The second PDBT strand is shifted 8.4 Å (B = P/4) away from the first strand along the helix axis. Numerous intermolecular interactions between chains (notably hydrogen bonding, dipole–dipole, and/or ion–dipole interactions between –SO3− and –NHCO– groups—shown as green dashed lines) and the rotation of each subunit contribute to the double helical conformation.
Panel a shows concentration-dependent 23Na quadrupole spectra for PBDT solutions at 298 K. Panels b–d show configurations of PBDT self-assembly behavior with increasing concentration, and the critical geometric parameters at the null (isotropically averaged) point where Na+ shows isotropic dynamics are displayed in c. Intra-helical Na+ interactions dominate in b, the null point is at c and inter-helical Na+ interactions dominate in d. Panel e shows SAXS results for PBDT aqueous solutions with CPBDT =5%, 10%, and 20%.
Electrolytes
Building on the newly developed ionic liquid-based polymer electrolyte obtained in step 1, we also successfully fabricated an organic-inorganic composite polymer electrolyte as shown in step 2 with a nanocrystalline and 3D network structure, which shows incredible high conductivity.
The Li ion conductivity of this material can reach 1 mS/cm2 , which is 1000 times higher compared to a traditional solid state PEO based polymer electrolytes.
By introducing a liquid crystal-oriented polymer structure, the material is nano-reinforced, which greatly improves the material's modulus (~ GPa), inhibits the growth of lithium dendrites at the electrode/electrolyte interface, thus guarantee the safety of the solid-state batteries.
In addition, the material greatly reduces the interface resistance of the material to 32 Ω·cm2 , which is ~100 times lower than that of the traditional solid polymer electrolytes, indicating the importance of polymer in reducing the interfacial resistance for solid state batteries. Through multi-array characterizations, we investigated the ion conduction, activation energy, phase transition, nano-scale structure and
related electrochemical properties in materials.
a, Step 1 shows fabrication of the RMIC(Raw molecular ionic composite). We obtain this material based on an interfacial ion exchange between a water-soluble IL (for example, C2mimBF4) and an aqueous rigid-rod polyelectrolyte solution (Li-form PBDT in H2O). The photograph shows the sliced transparent RMIC sample. b, Step 2 shows the second ion-exchange process wherein we immerse a sliced section of the RMIC into the ILE (C3mpyrFSI with 50 mol% LiFSI). The photograph shows the sliced iridescent LiMIC sample. c,d, SEM images for RMIC-5 (c) and RMIC-15 (d). Higher magnification images are shown in the upper right insets. The scale bar for the insets is 1 μm. The interfaces between individual PBDT grains form the grain boundaries (darker regions). Both the aligned PBDT grains and the grain boundaries contain C2mimBF4
c,d) The long-term cycling performance and galvanostatic charge/discharge voltage profiles of Li||LiFePO4 full cells at 5 C (2.54 mA cm−2), 10 C (4.59 mA cm−2) and 15 C (7.46 mA cm−2) at RT. e) The high-rate and long-term cycling performance of PLMBs using SPEs in this study compared with other polymer electrolytes reported in the field (capacity retention limited above 80%), with the cathode loadings displayed as the bubble size. f) Compare the stable cycling number and current density of Li metal symmetric cells based on SPEs to other reported polymer electrolytes.
Mechanism
Through the nano-confinement theory, electrolyte materials with ultra-high conductivity can be prepared by architecting the atomic structure of molecules and composites. If the conduction mechanism of solid electrolytes is introduced into polymer electrolyte materials, it will be an important breakthrough for the development of solid-state electrolytes.
Theoretical research is especially important for material design.
Through the nano-confinement theory, electrolyte materials with ultra-high conductivity can be prepared by architecting the atomic structure of molecules and composites. If the conduction mechanism of solid electrolytes is introduced into polymer electrolyte materials, it will be an important breakthrough for the development of solid-state electrolytes. At current stage, the understanding of the conduction mechanism of lithium ions in solid electrolytes still needs further investigation.
Experimental evidence associated with theoretical calculations and computer simulation will reveal the ion conduction mechanism underneath the solid-state electrolytes. It is also worth noting that the phase transition in solid electrolyte materials is also a critical point to break through the bottleneck in finding the suitable phase with high ionic conductivity.
a, Powder X-ray diffraction pattern for the RMIC. b, In the RMIC, PBDT LC grains and grain boundaries, indicated with black solid and dashed lines, respectively, are filled with amorphous IL as a result of Step 1 of the fabrication process. c, X-ray diffraction pattern for the LiMIC. d, In the LiMIC, there exists an in-situ-formed and highly defective nanocrystalline structure between PBDT LC grains, indicated by the green shapes and black dots.
Long-term cycling performance of Li|SPEs|LiFePO4 cells. a) The discharge capacity and coulombic efficiency of the Li||LiFePO4 full cell (commercial cathode with high loading of 11 mg cm−2) using OEs, ILEs, and SPEs at room temperature at 1 C. b) The galvanostatic charge/discharge voltage profiles of a). c,d) The long-term cycling performance and galvanostatic charge/discharge voltage profiles of Li||LiFePO4 full cells at 5 C (2.54 mA cm−2), 10 C (4.59 mA cm−2) and 15 C (7.46 mA cm−2) at RT.
AI/ML
In recent decades, theoretical computation and AI have received great attention in material science and chemical engineering.
It is important for us to narrow down the discovery scope. AI is also widely used to predict the corresponding electrochemical property and life cycle of battery materials.
Thus, a complete database of the system is expected to be constructed through the experimental parameters of the whole battery high-throughput experimental design.
The unsupervised learning of AI, for example k-means classification, PCA, can help us classify and label materials, while the supervised learning of AI and various theories including random forest, support vector machine, gradient boosting and neural network can help predict the electrochemical properties of the material, such as conductivity, interface resistance, electrochemical window, capacitance, etc. These high-throughput parameters can help narrow the searching scope thus improve the efficiency to discover new applicable materials.
Through reasonable and ingenious experimental design and intelligent analysis, AI can be truly applied to scientific research base on a high-accuracy data sample, combined with the supervised learning and unsupervised learning. After reasonable comparison among various models, we can selectively find the best model to predict the properties of materials. Looking back to the development of materials science, we start from the traditional trial and error phase to computer simulation and now we are approaching to the domain of AI.
Of course, there are still many challenges, such as data incompleteness and inaccuracy, which requires enough high-throughput experimental results and systematic database design and development. In the future, we will be actively participating in the construction of a material database, so that the material genome engineering project can achieve a long-term development and enrich the collaborations with computational science projects.
Four calculation conditions investigated in this research, (a) “ion + gas”; (b) “ion + sol”; (c) “ion pair + gas”; and (d) “ion pair + sol”, where “ion” refers to isolated ions, “ion pair” refers to cation–anion pair, “gas” represents gas phase, and “sol” means liquid phase.
The permutation of 74 cations and 30 anions forms an IL pool containing 2220 unique ILs. Employing RDKit, Psi4, and PyG to generate the molecular descriptors for the raw dataset. Unsupervised learning contains boxplots, pair plots, and hierarchical clustering, which are essential analytical methods for investigating the structure and correlations of variables in the dataset. Supervised learning leverages both regression and classification. The IL pool will initially be classified as a solid or liquid group.Finally, ECW>4V and σ≥5 mS cm−1 at room temperature is the final screening criterion for the final recommendation list of potential ILs.