Emerging atomic force microscopy (AFM) technologies are poised to revolutionize the research and development of next-generation batteries, particularly solid-state and lithium-metal systems. These advanced techniques enable unprecedented nanoscale characterization, offering insights into interfacial phenomena, mechanical properties, and degradation mechanisms critical for improving battery performance and safety. High-speed AFM, multi-modal imaging, and AI-assisted analysis represent the forefront of this evolution, each contributing unique capabilities to accelerate innovation in energy storage.

High-speed AFM addresses a key limitation of conventional AFM by drastically reducing imaging times while maintaining nanoscale resolution. Traditional AFM scans can take minutes to hours, making real-time observation of dynamic processes impractical. High-speed variants achieve frame rates exceeding 10 frames per second, enabling researchers to capture transient phenomena such as lithium dendrite nucleation and growth during cycling. This capability is invaluable for studying solid-state electrolytes, where interfacial instabilities and dendrite penetration remain major challenges. By observing these processes in situ, researchers can identify failure modes and evaluate mitigation strategies, such as engineered interlayers or pressure application, with direct experimental validation.

Multi-modal AFM integrates complementary techniques like Kelvin probe force microscopy (KPFM), conductive AFM (C-AFM), and mechanical property mapping into a single platform. This approach provides correlated datasets that reveal structure-function relationships at the nanoscale. For example, KPFM maps surface potentials to track lithium-ion distribution, while C-AFM measures local conductivity variations in solid electrolytes. Simultaneous mechanical mapping quantifies Young’s modulus and stiffness, critical for understanding how mechanical properties influence dendrite suppression. In lithium-metal anodes, multi-modal AFM can correlate topological changes with electrochemical activity, identifying regions prone to inhomogeneous plating. Such insights guide the design of textured substrates or artificial SEI layers to promote uniform lithium deposition.

AI-assisted AFM analysis leverages machine learning to process large datasets, extract hidden patterns, and predict material behavior. Training algorithms on high-resolution AFM images of electrode-electrolyte interfaces can automate defect detection, classify degradation modes, and even suggest optimal material combinations. For solid-state batteries, AI models trained on mechanical and electrical property maps may predict interfacial stability under operational stresses. In lithium-metal systems, AI can analyze dendrite morphology evolution and correlate it with cycling conditions, enabling data-driven optimization of charging protocols. The integration of AI reduces human bias, accelerates analysis, and uncovers correlations that might otherwise remain unnoticed.

The impact of these AFM advancements extends to key challenges in next-gen batteries. For solid-state electrolytes, high-speed AFM can monitor crack propagation under mechanical stress, informing the development of fracture-resistant materials. Multi-modal imaging helps characterize grain boundaries and amorphous-crystalline phase distributions, which govern ion transport. AI-assisted analysis of these datasets can identify microstructural features that maximize ionic conductivity while minimizing electronic leakage. In lithium-metal anodes, high-speed AFM provides dynamic views of SEI formation, revealing how electrolyte additives or pressure application influence its homogeneity. Multi-modal techniques quantify the electronic insulation and mechanical robustness of the SEI, both critical for preventing dendrites.

Emerging AFM technologies also enable precise measurements at extreme conditions, such as elevated temperatures or under applied potentials. This is particularly relevant for solid-state batteries, where interfacial reactions often intensify at higher voltages or temperatures. High-speed AFM can track morphological changes during thermal runaway scenarios, while multi-modal tools map concurrent shifts in electrical and mechanical properties. Such experiments provide actionable data for designing thermally stable interfaces. For lithium-metal batteries, operando AFM under realistic current densities helps bridge the gap between lab-scale tests and commercial performance requirements.

The future of AFM in battery research will likely see further integration with other characterization tools, such as X-ray tomography or Raman spectroscopy, to build comprehensive multi-scale models. Automated AFM systems coupled with AI-driven experimental design could enable high-throughput screening of novel materials, accelerating the discovery of optimal compositions for interfaces or electrolytes. As these technologies mature, their adoption by industry will be essential for translating academic insights into manufacturable solutions.

In summary, advanced AFM techniques are transforming the study of next-generation batteries by providing dynamic, multi-parametric, and data-rich nanoscale analysis. Their application to solid-state and lithium-metal systems addresses fundamental challenges in interfacial engineering, mechanical stability, and degradation prevention. By enabling real-time observation, correlated property mapping, and intelligent data analysis, these tools will play a pivotal role in unlocking the full potential of future energy storage technologies.