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The cold sintering process (CSP) is a low-temperature consolidation method used to fabricate materials and their composites by applying transient solvents and external pressure. In this mechano-chemical process, the local dissolution, solvent evaporation, and supersaturation of the solute lead to “solution-precipitation” for consolidating various materials to nearly full densification, mimicking the natural pressure solution creep. Because of the low processing temperature (<300°C), it can bridge the temperature gap between ceramics, metals, and polymers for co-sintering composites. Therefore, CSP provides a promising strategy of interface engineering to readily integrate high-processing temperature ceramic materials (e.g., active electrode materials, ceramic solid-state electrolytes) as “grains” and low-melting-point additives (e.g., polymer binders, lithium salts, or solid-state polymer electrolytes) as “grain boundaries.” In this minireview, the mechanisms of geomimetics CSP and energy dissipations are discussed and compared to other sintering technologies. Specifically, the sintering dynamics and various sintering aids/conditions methods are reviewed to assist the low energy consumption processes. We also discuss the CSP-enabled consolidation and interface engineering for composite electrodes, composite solid-state electrolytes, and multi-component laminated structure battery devices for high-performance solid-state batteries. We then conclude the present review with a perspective on future opportunities and challenges.
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A new neural network approach to density calculation on ceramic materials
The materials’ consolidation, especially ceramics, is very important in advanced research development and industrial technologies. Science of sintering with all incoming novelties is the base of all these processes. A very important question in all of this is how to get the more precise structure parameters within the morphology of different ceramic materials. In that sense, the advanced procedure in collecting precise data in submicro-processes is also in direction of advanced miniaturization. Our research, based on different electrophysical parameters, like relative capacitance, breakdown voltage, and [Formula: see text], has been used in neural networks and graph theory successful applications. We extended furthermore our neural network back propagation (BP) on sintering parameters’ data. Prognosed mapping we can succeed if we use the coefficients, implemented by the training procedure. In this paper, we continue to apply the novelty from the previous research, where the error is calculated as a difference between the designed and actual network output. So, the weight coefficients contribute in error generation. We used the experimental data of sintered materials’ density, measured and calculated in the bulk, and developed possibility to calculate the materials’ density inside of consolidated structures. The BP procedure here is like a tool to come down between the layers, with much more precise materials’ density, in the points on morphology, which are interesting for different microstructure developments and applications. We practically replaced the errors’ network by density values, from ceramic consolidation. Our neural networks’ application novelty is successfully applied within the experimental ceramic material density [Formula: see text] [kg/m 3 ], confirming the direction way to implement this procedure in other density cases. There are many different mathematical tools or tools from the field of artificial intelligence that can be used in such or similar applications. We choose to use artificial neural networks because of their simplicity and their self-improvement process, through BP error control. All of this contributes to the great improvement in the whole research and science of sintering technology, which is important for collecting more efficient and faster results.
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- Award ID(s):
- 2101041
- PAR ID:
- 10357254
- Date Published:
- Journal Name:
- Modern Physics Letters B
- Volume:
- 36
- Issue:
- 02
- ISSN:
- 0217-9849
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1142/S0217984921505497
