Research Insight: Desktop SEM Applications in Battery and Energy Storage

February 15, 2026 • Research Insight • 6 min read

Desktop scanning electron microscopes are becoming essential tools in battery and energy storage research, enabling rapid morphological characterization of cathode powders, recycled materials, electrode surfaces, and photovoltaic nanostructures — directly within the research lab rather than through a shared imaging center. These five peer-reviewed studies demonstrate how SEC desktop SEMs are supporting advances across the energy storage landscape, from lithium-ion battery recycling to next-generation solar cells.

AI-Driven Analysis of Recycled Lithium-Ion Battery Materials

This study combined artificial intelligence with electron microscopy to identify previously unknown crystal structures emerging from lithium-ion battery recycling processes. The research team used the SNE-Alpha desktop SEM to image recycled material at multiple magnifications, revealing a novel malate crystal structure that had not been previously documented in battery recycling literature. The SEM micrographs provided the morphological data that, when paired with AI-driven crystallographic analysis, enabled definitive identification of this new phase. This work highlights a growing trend: pairing desktop SEM with computational methods to accelerate materials discovery from waste streams.

Microwave Recovery of NMC Battery Black Mass

As the volume of spent lithium-ion batteries grows, efficient recovery of cathode materials becomes both an economic and environmental priority. This paper investigated microwave heating as a route to recover nickel-manganese-cobalt (NMC) black mass from end-of-life batteries. SEM imaging on the SNE-Alpha was used to track particle morphology changes through each stage of the microwave treatment process. The micrographs revealed how microwave heating affected particle agglomeration, surface texture, and phase composition, providing the visual evidence needed to optimize processing temperatures and durations. Having a desktop SEM in the recycling laboratory allowed the team to run dozens of imaging sessions in parallel with processing trials.

CNN-Based Cathode Composition Prediction from SEM Images

This innovative study trained convolutional neural networks to predict cathode composition and degradation state directly from SEM images, potentially eliminating the need for time-consuming compositional analysis in quality control settings. The researchers used the SNE-4500M Plus to generate a large, consistent training dataset of cathode particle images across varying compositions and cycling states. The success of the approach depended on image consistency — the desktop SEM provided repeatable imaging conditions that are difficult to maintain when collecting training data across multiple instruments or facilities. The CNN achieved high prediction accuracy, demonstrating that morphological features captured by desktop SEM carry enough compositional information for automated classification.

Silver-Decorated Nanoparticles for Solid-State Solar Cells

Dye-sensitized solar cells offer a low-cost alternative to silicon photovoltaics, but efficiency improvements require precise control of nanostructured components. This research developed silver-decorated silica nanoparticles to enhance light harvesting in fiber-shaped solid-state solar cells. The SNE-4500M Plus provided the surface imaging needed to confirm successful silver decoration on the silica nanoparticle surfaces and to characterize the uniformity of nanoparticle coatings on the fiber electrode. SEM imaging at multiple magnifications verified that the Ag-SiO2 composite particles maintained their intended morphology after integration into the photoanode structure, correlating directly with the observed efficiency improvements.

Green Laser Welding of Cylindrical Battery Cells

Battery pack assembly depends on reliable, high-quality welds between cell tabs and bus bars. This study evaluated green laser welding for cylindrical secondary battery cell connections, a process increasingly adopted by EV battery manufacturers. The SNE-4500M desktop SEM was used to examine weld cross-sections, heat-affected zones, and surface morphology of the welded joints. SEM characterization revealed the microstructural changes that occur at the weld interface under different laser parameters, helping the team identify optimal welding conditions that minimize defects while ensuring mechanical strength. The compact desktop SEM format proved particularly practical for this manufacturing-oriented research, where welding trials generated large numbers of specimens requiring rapid screening.

Why Desktop SEM Fits Energy Storage Research

Battery and energy storage research moves fast. Cell chemistries evolve annually, recycling processes need rapid iteration, and manufacturing quality control demands consistent, repeatable imaging. A desktop SEM installed in the battery lab shortens the distance between experiment and characterization to a few steps rather than a building transfer. For research groups producing the volume of samples typical in battery R&D — multiple cathode formulations, recycling process variants, or welded joint specimens per week — immediate access to electron microscopy is not a luxury but a practical necessity for maintaining publication velocity.