Search for: All records

Direct ink writing (DIW) is an extrusion-based additive manufacturing technology. It has gained wide attentions in both industry and research because of its simple design and versatile platform. In electric-field-assisted Direct Ink Writing (eDIW) processes, an external electric field is added between the nozzle and the printing substrate to manipulate the ink-substrate wetting dynamics and therefore optimize the ink printability. eDIW was found effective in printing liquids that are typically difficult to print in the conventional DIW processes. In this paper, an eDIW process modeling system based on machine learning (ML) algorithms is developed. The system is found effective in predicting eDIW printing geometry under varied process parameter settings. Image processing approaches to collect experiment data are developed. Accuracies of different machine learning algorithms for predicting printing results and trace width are compared and discussed. The capabilities, applications and limitations of the presented machine learning-based modeling approach are presented.

Proper distribution of thermally conductive nanomaterials in polymer batteries offers new opportunities to mitigate performance degradations associated with local hot spots and safety concerns in batteries. Herein, a direct ink writing (DIW) method is utilized to fabricate polyethylene oxide (PEO) composite polymers electrolytes (CPE) embedded with silane‐treated hexagonal boron nitride (S‐hBN) platelets and free of any volatile organic solvents. It is observed that the S‐hBN platelets are well aligned in the printed CPE during the DIW process. The in‐plane thermal conductivity of the printed CPE with the aligned S‐hBN platelets is 1.031 W⁻¹K⁻¹, which is about 1.7 times that of the pristine CPE with the randomly dispersed S‐hBN platelets (0.612 W⁻¹K⁻¹). Thermal imaging shows that the peak temperature (°C) of the printed electrolytes is 24.2% lower than that of the CPE without S‐hBN, and 10.6% lower than that of the CPE with the randomly dispersed S‐hBN, indicating a superior thermal transport property. Lithium‐ion half‐cells made with the printed CPE and LiFePO₄cathode displayed high specific discharge capacity of 146.0 mAh g⁻¹and stable Coulombic efficiency of 91% for 100 cycles at room temperature. This work facilitates the development of printable thermally‐conductive polymers for safer battery operations.

Developing intelligent wearable energy storage devices that can endure harsh conditions is of interest for emerging applications in next‐generation electronics. Despite recent success in exploring functional materials for sophisticated self‐adaptivity in energy storage devices, it remains challenging to obtain both high reliability and superior performance. Herein, a novel method for fabricating micropatterned wearable thermoresponsive supercapacitors via direct ink writing (DIW) technique is reported. Thermal runaway of typical electrochemical storage devices with high power delivery capability can cause serious safety problems. The proposed temperature‐dependent structure works as self‐protection against the common thermal runaway issues of electrochemical energy storage devices. Such construction provides an automatic adjustment as high as 8 F g⁻¹in specific capacitance, resulting in an overall heat reduction by up to 40%. The printing resolution of the electrodes (175 µm) is among the best in recently reported planar carbon‐based energy storage devices by DIW technique. Manufacturing‐related parameters such as time‐dependent printing speed and curing temperature are also investigated to fabricate this integrated design with varied materials and accuracy. This strategy shows tremendous promise for future intelligent energy storage devices.