journal article·2023-04-01Open access

Simple physical model with empirical formulas for solid-state sintering of CaCO3 for estimation of porosity

Kyuichi Yasui, Koichi Hamamoto

AIP Advances· Volume 13· Issue 4

Abstract

The porosity after solid-state sintering is theoretically estimated by using a simple physical model with empirical formulas as a function of applied pressure and initial particle size. The comparison with the experimental data has revealed that tight aggregation of CaCO3 nanoparticles strongly increases porosity in solid-state sintering compared to that predicted for isolated nanoparticles.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Calcium Carbonate Crystallization and Inhibition · Bone Tissue Engineering Materials · Advanced ceramic materials synthesis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

National Institute of Advanced Industrial Science and Technology (AIST) , Nagoya 463-8560, Japan

References (38)

Show source-provided references
1. Geologically-inspired strong bulk ceramics made with water at room temperature10.1038/ncomms14655 ↗
2. Agglomerate and particle size effects on sintering yttria-stabilized zirconia10.1111/j.1151-2916.1981.tb09552.x ↗
3. Feasibility of in situ de-agglomeration during powder consolidation10.1111/jace.15899 ↗
4. Comparison between cold sintering and dry pressing of CaCO3 at room temperature by numerical simulations10.1063/5.0087226 ↗
5. Machine learning in materials science10.1002/inf2.12028 ↗
6. Machine learning accelerates the discovery of light-absorbing materials for double perovskite solar cells10.1021/acs.jpcc.1c07262 ↗
7. Accelerating materials science with high-throughput computations and machine learning10.1016/j.commatsci.2019.01.013 ↗
8. Machine learning for predicting thermal transport properties of solids10.1016/j.mser.2021.100642 ↗
9. Innovative materials science via machine learning10.1002/adfm.202108044 ↗
10. Identification of advanced spin-driven thermoelectric materials via interpretable machine learning10.1038/s41524-019-0241-9 ↗
11. From DFT to machine learning: Recent approaches to materials science–a review10.1088/2515-7639/ab084b ↗
12. Machine learning for renewable energy materials10.1039/c9ta02356a ↗
13. Recent advances and applications of machine learning in solid-state materials science10.1038/s41524-019-0221-0 ↗
14. A critical review of machine learning of energy materials10.1002/aenm.201903242 ↗
15. Machine learning: Accelerating materials development for energy storage and conversion10.1002/inf2.12094 ↗
16. Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics10.1089/omi.2019.0151 ↗
17. Machine learning in polymer informatics10.1002/inf2.12167 ↗
18. Fabrication of single-grain-layered garnet-type electrolyte sheets by a precursor method10.1080/21870764.2021.1974782 ↗
19. Densification of garnet-type electrolyte thin sheets by cold sintering10.1246/cl.210374 ↗
20. Densification of a NASICON-type LATP electrolyte sheet by a cold-sintering process10.3390/ma14164737 ↗
21. Low-temperature synthesis of dense garnet-type Li6.5La3Zr1.5Ta0.5O12 layered structures using fluorite-type precursor oxide10.1016/j.jallcom.2020.158223 ↗
22. Effects of porosity on dielectric properties of BaTiO3 ceramics10.1143/jjap.34.1922 ↗
23. Quantitative dependence of the properties of Pb0.99(Zr0.95Ti0.05)0.98Nb0.02O3 ferroelectric ceramics on porosity10.1016/j.materresbull.2010.01.022 ↗
24. Effect of porosity on functional properties of lead-free piezoelectric BaZr0.15Ti0.85O3 porous ceramics10.3390/ma13153324 ↗
25. Sintering trajectories: Description on how density, surface area, and grain size change10.1007/s11837-015-1795-8 ↗
26. Sintering kinetics for the high density alumina process
27. Controlled grain growth
28. A few remarks on the kinetics of static grain growth in rocks10.1007/s005310000150 ↗
29. Grain growth in synthetic marbles with added mica and water10.1007/bf00373591 ↗
30. Grain growth of the lime and periclase phases in a synthetic doloma10.1111/j.1151-2916.1988.tb06404.x ↗
31. The normal grain growth behaviour of nominally pure calcitic aggregates10.1007/s004100050335 ↗
32. Modeling of the atomic diffusion coefficient in nanostructured materials10.3390/e20040252 ↗
33. Grain boundary sliding as a deformation mechanism during creep10.1080/14786437008220939 ↗
34. Iodate in calcite, aragonite and vaterite CaCO3: Insights from first-principles calculations and implications for the I/Ca geochemical proxy10.1016/j.gca.2018.02.017 ↗
35. Dislocation escape-related size effects in single-crystal micropillars under uniaxial compression10.1016/j.actamat.2006.10.021 ↗
36. Importance of dislocations in ultrasound-assisted sintering of silver nanoparticles10.1063/5.0063334 ↗
37. Aluminum speciation, vibrational entropy and short-range order in calcium aluminosilicate glasses10.1016/j.gca.2009.03.041 ↗
38. 2010