Wei Li

Decoupling ionic conductivity from polymer segmental relaxation while maintaining interfacial stability remains a pivotal challenge for solid polymer electrolytes (SPEs). In this study, we construct an ionic‐ligand‐assisted transport framework by copolymerizing a fluorinated acrylate polymer with a poly(ionic liquid) (PIL) and optimizing the lithium bis(fluorosulfonyl)imide (LiFSI) salt concentration. Density functional theory and molecular dynamics simulations reveal that cationic PIL moieties strongly anchor FSI ⁻ anions, creating anion‐rich, spatially correlated coordination pathways that facilitate efficient Li ⁺ hopping. The optimized electrolyte exhibits a high ionic conductivity (0.38 mS cm ⁻¹ ) and an exceptional Li ⁺ transference number (0.77) at 30°C. The fluorinated backbone and PIL synergistically expand the oxidative stability window to 4.94 V and promote an inorganic‐rich solid electrolyte interphase (SEI) on the Li‐metal anode. Consequently, Li||Cu cells cycle stably for over 400 cycles with 99.1% average Coulombic efficiency. Solid‐state Li||LiNi _0.9 Co _0.05 Mn _0.05 O ₂ (NCM90) cells retain ≈81.0% capacity after 600 cycles at 4.3 V, and ≈83.1% capacity after 200 cycles at 4.5 V. Moreover, a high‐energy‐density (515 Wh kg ⁻¹ ) solid‐state pouch cell with an ultrathin Li anode (50 µm) and a high‐loading NCM90 cathode (≈56 mg cm ⁻² ) delivers stable cycling and exceptional safety. This ionic‐ligand regulation paradigm provides critical mechanistic insights for designing high‐performance and high‐safety solid‐state batteries.

Interface-confined carbon nanotubes (CNTs) form non-percolating microcapacitors that enhance charge storage while suppressing humidity-induced leakage in melt-processable triboelectric polymer films.

Data-driven methodologies have transformed the discovery and prediction of hard materials with well-defined atomic structures by leveraging standardized datasets, enabling accurate property predictions and facilitating efficient exploration of design spaces^1,2,3. However, their application to soft materials remains challenging because of complex, multiscale structure–property relationships^4,5,6. Here we present a data-driven approach that integrates data mining, experimentation and machine learning to design high-performance adhesive hydrogels from scratch, tailored for demanding underwater environments. By leveraging protein databases, we developed a descriptor strategy to statistically replicate protein sequence patterns in polymer strands by ideal random copolymerization, enabling targeted hydrogel design and dataset construction. Using machine learning, we optimized hydrogel formulations from an initial dataset of 180 bioinspired hydrogels, achieving remarkable improvements in adhesive strength, with a maximum value exceeding 1 MPa. These super-adhesive hydrogels hold immense potential across diverse applications, from biomedical engineering to deep-sea exploration, marking a notable advancement in data-driven innovation for soft materials.

The isomerization of pendant groups plays a crucial role in modulating helical structures and biological activity in both biomacromolecule and dynamic helical conjugated polymers. While the effects of solvents, pH, and temperature on pendant isomerization and helix preference have been extensively studied, the combined influence of pendant isomerization and enantiomeric excess remains largely unexplored. Herein, we report a dynamic helical poly­(phenylacetylene) (PPA) system, poly-[D _r -co-L _(1–r)], in which the helix preference is dictated and nonlinearly amplified via the “majority rule” mechanism at low temperatures. Remarkably, temperature-triggered pendant isomerization can override this helix preference, leading to an “abnormal majority rule” behavior. By harnessing these dual effects, precise helix regulation is achieved, enabling helix inversion at high enantiomeric excess and chiral sustenance at low enantiomeric excess. A bifurcation point in helix preference facilitates the orthogonal control of polymer helicity, paving the way for programmable circularly polarized luminescence microarrays, with potential applications in information encoding and anticounterfeiting technologies. These findings provide a strategy for fine-tuning dynamic helical polymer structures.

The scission of chemical bonds in materials can lead to catastrophic failure, with weak bonds typically undermining the materials’ strength. Here we demonstrate how weak bonds can be leveraged to achieve self-strengthening in polymer network materials. These weak sacrificial bonds trigger mechanochemical reactions, forming new networks rapidly enough to reinforce the material during deformation and significantly improve crack resistance. This rapid strengthening exhibits strong rate dependence, dictated by the interplay between bond breaking and the kinetics of force-induced network formation. As the network formation is generally applicable to diverse monomers and crosslinkers with different kinetics, a wide range of mechanical properties can be obtained. These findings may inspire the design of tough polymer materials with on-demand, rate-dependent mechanical behaviours through mechanochemistry, broadening their applications across various fields.

Data-driven methodologies have revolutionized the discovery and prediction of new hard materials, such as crystal structures and high-entropy alloys1-5. However, their application to soft materials remains challenging due to the inherent complexity of their structure–property relationships6-8. Here, we present a comprehensive data-driven approach that integrates data mining, experimentation, and machine learning to develop high-performance adhesive hydrogels from scratch, tailored for demanding underwater environments. By leveraging protein databases, we devised a descriptor strategy to statistically replicate protein sequence patterns via ideal random copolymerization, enabling targeted hydrogel design and dataset construction. Using machine learning, we optimized hydrogel formulations even with a small dataset, achieving unprecedented adhesive performance. These super-adhesive hydrogels demonstrate immense potential across diverse applications, from biomedical engineering to deep-sea exploration, marking a significant advancement in the data-driven innovation for soft materials.

Mechanically robust and tough polymeric materials are in high demand for applications ranging from flexible electronics to aerospace. However, achieving both high toughness and strength in polymers remains a significant challenge due to their inherently contradictory nature. Here, a universal strategy for enhancing the toughness and strength of polymer blends using ligand‐modulated metal–organic framework (MOF) nanoparticles is presented, which are engineered to have adjustable hydrophilicity and lipophilicity by varying the types and ratios of ligands. Molecular dynamics (MD) simulations demonstrate that these nanoparticles can effectively regulate the interfaces between chemically distinct polymers based on their amphiphilicity. Remarkably, a mere 0.1 wt.% of MOF nanoparticles with optimized amphiphilicity (ML‐MOF_(5:5)) delivered ≈1.1‐ and ≈34.1‐fold increase in strength and toughness of poly (lactic acid) (PLA)/poly (butylene succinate) (PBS) blend, respectively. Moreover, these amphiphilicity‐tailorable MOF nanoparticles universally enhance the mechanical properties of various polymer blends, such as polypropylene (PP)/polyethylene (PE), PP/polystyrene (PS), PLA/poly (butylene adipate‐co‐terephthalate) (PBAT), and PLA/polycaprolactone (PCL)/PBS. This simple universal method offers significant potential for strengthening and toughening various polymer blends.

Hydrophobic hydrogels with various copolymer sequences and network structures are prepared by tuning the solvent condition of the reaction solution, which exhibit different appearance and properties under the same monomer composition.