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Abstract A comprehensive understanding of the solid‐electrolyte interphase (SEI) in lithium‐ion batteries is crucial for improving energy efficiency, battery performance, and safety. In this study, a transformer‐based instance segmentation framework, integrating deep convolutional neural networks is introduced with a feature pyramid network (FPN), to quantitatively analyze High‐Resolution Transmission Electron Microscopy (HRTEM) images and explain the complex microstructural features of the SEI. The model is trained on a dataset of simulated HRTEM images generated using Density Functional Theory (DFT)‐optimized grain boundary (GB) structures and calibrated with experimental microscope parameters. The model achieves robust segmentation performance, with training and validation mean intersection over union (mIOU) values of 0.98 and 0.96, respectively. On unseen test data, the model attains mean area match (AM) scores of 91.4% for GBs, 92.3% for Li₂CO₃, 91.7% for LiF, 88.7% for LiOH, and 88.6% for Li₂O. These quantitative results highlight the model's high fidelity and its ability to capture subtle variations in crystallographic orientations and material contrasts. By enabling detailed, statistically grounded segmentation of SEI components, the approach offers valuable insights into ion transport and degradation mechanisms, paving the way for more resilient and efficient energy storage solutions. 

Dendritic growth of lithium (Li) is hindering potential applications of Li-metal batteries, and new approaches are needed to address this challenge. The confinement effect of two-dimensional materials triggered by strong molecular interactions between parallelly-aligned graphene oxide (GO) at Li metal interface is proposed here as a new strategy to suppress the dendritic growth of Li. The effectiveness of aligned GO for Li-metal cells is shown for two different polymer separator cells:liquid electrolytes with porous propylene (PP) separators and solid polyethylene oxide (PEO) electrolytes. For the case of liquid electrolytes, PP separators were modified with plasma treatment to induce the alignment of GO layers. The Li‖Li cells with aligned GO illustrate a stable Li platting/stripping (up to 1000 cycles). The Li‖lithium iron phosphate (LFP) battery cells with aligned GO could cycle at 5C for 1000 cycles (∼90% capacity retention). For solid polymer electrolyte (SPE) cells, GO–Li confinement effect is also effective in Li dendrites suppression enhancing the stability and lifespan of Li-metal batteries. The Li‖LFP cell with the GO-modified SPE showed ∼85% capacity retention after 200 cycles at 1C. Such combined high rate capability and number of cycles exceeds the previously reported performances for both liquid and SPE-based Li‖LFP cells. This points to a new opportunity for utilizing the confinement effect of two-dimensional materials for the development of next generation, fast rate rechargeable Li batteries. 

Real-time TEM images of evolution of the liposomes formed via self-assembly of phosphatidylcholine lipids in liquid pockets of GLC shows three stages of fast initial growth, slow growth and stabilization, and formation of stable liposomes. 

Abstract Rapidly growing flexible and wearable electronics highly demand the development of flexible energy storage devices. Yet, these devices are susceptible to extreme, repeated mechanical deformations under working circumstances. Herein, the design and fabrication of a smart, flexible Li‐ion battery with shape memory function, which has the ability to restore its shape against severe mechanical deformations, bending, twisting, rolling or elongation, is reported. The shape memory function is induced by the integration of a shape‐adjustable solid polymer electrolyte. This Li‐ion battery delivers a specific discharge capacity of≈140 mAh g^‐1at 0.2 C charge/discharge rate with≈92% capacity retention after 100 cycles and≈99.85% Coulombic efficiency, at 20°C. Besides recovery from mechanical deformations, it is visually demonstrated that the shape of this smart battery can be programmed to adjust itself in response to an internal/external heat stimulus for task‐specific and advanced applications. Considering the vast range of available shape memory polymers with tunable chemistry, physical, and mechanical characteristics, this study offers a promising approach for engineering smart batteries responsive to unfavorable internal or external stimulus, with potential to have a broad impact on other energy storage technologies in different sizes and shapes. 

Abstract Developing promising solid‐state Li batteries with capabilities of high current densities have been a major challenge partly due to large interfacial resistance across the electrode/electrolyte interfaces. This work represents an integrated network of self‐standing polymer electrolyte and active electrode materials with in situ UV cross‐linking. This method provides a uniform morphology of composite polymer electrolyte with low thickness of 20–40 μm. This modification leads to promising cycling results with 85% specific capacity retention in Li||LiFePO₄cell over 100 cycles at high current densities of 170 mA g⁻¹(~25 μA cm⁻², 1 C)_.By applying this method, the interfacial resistance decreases as high as seven folds compared to noncross‐linked interfaces. The following work introduce a facile and cost‐effective method in developing fast‐charging self‐standing polymer batteries with enhanced electrochemical properties. image