Operando atomic force microscopy (AFM) has emerged as a powerful tool for investigating interfacial phenomena in batteries, providing real-time insights into solid-electrolyte interphase (SEI) formation, electrode morphology changes, and mechanical-electrical property evolution. By integrating AFM with electrochemical cells, researchers can directly observe dynamic processes at electrode-electrolyte interfaces under operating conditions, bridging the gap between macroscopic performance and nanoscale mechanisms.
AFM operates by scanning a sharp probe across a surface to measure topography and interactions at the nanoscale. In battery studies, several specialized modes are employed. Conductive AFM (C-AFM) maps local conductivity by applying a voltage between the tip and sample, revealing heterogeneities in SEI electronic transport. Electrochemical AFM (EC-AFM) combines traditional imaging with potentiostatic or galvanostatic control, enabling observation of structural changes during cycling. PeakForce tapping mode minimizes sample damage while quantifying mechanical properties like modulus and adhesion, critical for understanding SEI mechanical stability.
The experimental setup for operando AFM requires careful design to maintain electrochemical relevance. A sealed fluid cell prevents electrolyte evaporation while allowing electrical connections for cycling. The working electrode, often a thin-film model system or a single particle, is mounted to ensure flatness for stable imaging. Counter and reference electrodes complete the electrochemical circuit, with the AFM probe positioned to minimize interference. Environmental control is critical, as oxygen and moisture can alter interfacial chemistry. Some systems incorporate inert gas gloveboxes or vacuum chambers to eliminate contamination.
One key application is studying SEI formation on anode materials like graphite or lithium metal. Operando AFM captures the nucleation and growth of SEI components as nanoscale aggregates, which coalesce into a passivating layer. C-AFM reveals that the SEI is electrically insulating but exhibits localized conductive pathways, explaining its role in permitting Li+ transport while blocking electrons. Mechanical property mapping shows that the SEI’s modulus evolves with cycling, initially soft due to organic-rich components before inorganic phases dominate, increasing stiffness. These observations correlate with electrochemical impedance spectroscopy data, linking nanoscale structure to cell performance.
Electrode morphology changes are another focus. For silicon anodes, operando AFM tracks volume expansion and fracture during lithiation, revealing strain gradients that drive particle cracking. PeakForce measurements quantify the loss of mechanical integrity with cycling, informing strategies to mitigate degradation. In lithium metal batteries, the technique visualizes dendrite propagation and dead lithium formation, highlighting how inhomogeneous current distribution accelerates failure.
Challenges remain in translating operando AFM data to practical battery systems. Scan speeds are typically slower than interfacial dynamics, necessitating trade-offs between resolution and temporal fidelity. Fast-scanning modes and advanced data processing help mitigate this, but real-time imaging of rapid processes like lithium plating remains difficult. Environmental control is another hurdle, as even trace contaminants can skew results. Additionally, the confined geometry of operando cells may not fully replicate commercial battery conditions, requiring careful interpretation of data.
Despite these limitations, operando AFM has provided critical insights. For example, it has shown that SEI growth is not linear but occurs in bursts during specific potential windows, informing targeted electrolyte additive design. In cathodes, it has resolved surface reconstructions and phase segregations that contribute to capacity fade. By correlating mechanical properties with cycle life, the technique aids in developing more robust electrodes.
Future advancements in high-speed AFM, multimodal integration (e.g., combining AFM with Raman spectroscopy), and machine learning for data analysis will expand operando capabilities. These improvements will deepen understanding of interfacial phenomena, accelerating the development of safer, higher-performance batteries.
The following table summarizes key AFM modes and their applications in battery research:
| AFM Mode | Measured Properties | Battery Applications |
|-------------------|------------------------------|-----------------------------------------------|
| Conductive AFM | Local conductivity, current | SEI electronic structure, charge transport |
| Electrochemical AFM | Topography under bias | Morphology changes during cycling |
| PeakForce Tapping | Modulus, adhesion, deformation | Mechanical degradation, SEI stability |
| Kelvin Probe AFM | Surface potential | Li+ ion distribution, interfacial potentials |
By leveraging these techniques, operando AFM continues to unravel the complexities of battery interfaces, guiding material and engineering solutions for next-generation energy storage.