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Abstract Assembling 2D materials such as MXenes into functional 3D aerogels using 3D printing technologies gains attention due to simplicity of fabrication, customized geometry and physical properties, and improved performance. Also, the establishment of straightforward electrode fabrication methods with the aim to hinder the restack and/or aggregation of electrode materials, which limits the performance of the electrode, is of great significant. In this study, unidirectional freeze casting and inkjetâbased 3D printing are combined to fabricate macroscopic porous aerogels with vertically aligned Ti3C2Txsheets. The fabrication method is developed to easily control the aerogel microstructure and alignment of the MXene sheets. The aerogels show excellent electromechanical performance so that they can withstand almost 50% compression before recovering to the original shape and maintain their electrical conductivities during continuous compression cycles. To enhance the electrochemical performance, an inkjetâprinted MXene current collector layer is added with horizontally aligned MXene sheets. This combines the superior electrical conductivity of the current collector layer with the improved ionic diffusion provided by the porous electrode. The cells fabricated with horizontal MXene sheets alignment as current collector with subsequent vertical MXene sheets alignment layers show the best electrochemical performance with thicknessâindependent capacitive behavior.
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Process signature-driven high spatio-temporal resolution alignment of multimodal data
We present HiRA-Pro, a novel procedure to align, at high spatio-temporal resolutions, multimodal signals from real-world processes and systems that exhibit diverse transient, nonlinear stochastic dynamics, such as manufacturing machines. It is based on discerning and synchronizing the process signatures of salient kinematic and dynamic events in these disparate signals. HiRA-Pro addresses the challenge of aligning data with sub-millisecond phenomena, where traditional timestamp, external trigger, or clock-based alignment methods fall short. The effectiveness of HiRA-Pro is demonstrated in a smart manufacturing context, where it aligns data from 13+ channels acquired during 3D-printing and milling operations on an Optomec-LENSÂŽ MTS 500 hybrid machine. The aligned data is then voxelized to generate 0.25 second aligned data chunks that correspond to physical voxels on the produced part. The superiority of HiRA-Pro is further showcased through case studies in additive manufacturing, demonstrating improved machine learning-based predictive performance due to precise multimodal data alignment. Specifically, testing classification accuracies improved by almost 35% with the application of HiRA-Pro, even with limited data, allowing for precise localization of artifacts. The paper also provides a comprehensive discussion on the proposed method, its applications, and comparative qualitative analysis with a few other alignment methods. HiRA-Pro achieves temporal-spatial resolutions of 10-1000 đs and 100 đm in order to generate datasets that register with physical voxels on the 3D-printed and milled part. These resolutions are at least an order of magnitude finer than the existing alignment methods that employ individual timestamps, statistical correlations, or common clocks, which achieve precision of hundreds of milliseconds.
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- Award ID(s):
- 1849085
- PAR ID:
- 10499081
- Publisher / Repository:
- ArXiv
- Date Published:
- Journal Name:
- arXivorg
- ISSN:
- 2331-8422
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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