Electrolyte Thermodynamics: A Crystallization Tool for Engineering Materials From the Nanoscale to the Microscale Richard E. Riman and Eugene Zlotnikov Department of Materials Engineering Rutgers, The State University of New Jersey 607 Taylor Road Piscataway, NJ 08854-8065
Sponsors Office of Naval Research Defense Advanced Projects Research Agency National Science Foundation New Jersey Commission on Science and Technology PPG Industries, Inc. Ceramare Corporation
Outline Hydrothermal-derived ceramic materials Rational approach to low temperature hydrothermal synthesis Thermochemistry-engineering the reaction medium for ceramic materials synthesis
Current Research Areas Processing Science Hydrothermal/Solvothermal Crystallization Mixing Assembly Functional Areas Biomedical(tissue engineering) Electronic (piezoelectric, dielectric) Optical (amplifiers, lasers, taggants, chameleon optics) Structural (corrosion, ferroelastics)
Hydrothermal Synthesis Chemical precursors are heterogeneous slurries, gel and or homogeneous solutions, acid or base mineralizer required Aqueous, mixed solvent or solvothermal solution medium Focus is on mild reaction conditions (T<300 o C, P<250 atm) Anhydrous oxides form in a single process step P-T-H 2 O interaction => unique phase equilibria Solution-mediated reaction => labile reaction kinetics relative to solid state reaction Controlled nucleation, growth and aging => controlled size and morphology Inexpensive processes
Hydrothermal Reactor Heat Heat Outer Thickness Density Conductivity 5 Capacity 5 Mass Surface (m) (kg/m 3 ) Wt/(m deg) (kj /(kg deg) (kg) (m 2) Stainless steel 304 0.008 7900 16.0 0.502 3.27 0.04 Teflon 0.011 2120 0.26 0.350 0.44 0.02 Parr 4748 Autoclave, 125 ml, < 240 C
Batch Hydrothermal Crystallizers Parr Parr Instrument Company: Model Model 4530 4530 Hastalloy C276 C276 alloy alloy Temperatures < 350 C 350 C Stirring Speed < 1700 1700 rpm rpm
Rational Approach to Low Temperature Hydrothermal Synthesis Compute thermodynamic equilibria as a function of the processing variables for phase of interest Generate equilibrium diagrams to map processing variable space for phase of interest Design hydrothermal experiments to test and validate computed diagrams Utilize processing variable space maps to explore opportunities for control of reaction and crystallization kinetics
Equilibria of Ca(OH) 2 -H 3 PO 4 -NH 4 OH-HNO 3 -H 2 O System 1. H 2 O = H +1 + OH -1-3 2. HP 2 O 7 = H +1-4 + P 2 O 7-2 3. H 2 P 2 O 7 = H +1-3 + HP 2 O 7-1 4. H 3 P 2 O 7 = H +1-2 + H 2 P 2 O 7 5. H 4 P 2 O 7 (aq) = H +1-1 + H 3 P 2 O 7-2 6. HPO 4 = H +1-3 + PO 4-1 7. H 2 PO 4 = H +1-2 + HPO 4-1 -2 8. 2 H 2 PO 4 = (H 2 PO 4 ) 2 9. H 3 PO 4 (aq) = H +1-1 + H 2 PO 4 10. HNO 3 (aq) = H +1-1 + NO 3 +1 11. NH 3 (aq) + H 2 O = NH 4 + OH -1 +1-1 12. NH 4 NO 3 (aq) = NH 4 + NO 3 +1 13. CaH 2 PO 4 = Ca +2-1 + H 2 PO 4 +1 14. CaNO 3 = Ca +2-1 + NO 3 15. CaOH +1 = Ca +2 + OH -1-1 16. CaPO 4 = Ca +2-3 + PO 4 17. CaHPO 4 (aq) = Ca +2-2 + HPO 4 18. Ca(OH) 2 (aq) = Ca +2 + 2OH -1 19. Ca(NO 3 ) 2 (aq) = Ca +2-1 + 2NO 3 20. Ca 5 (OH)(PO 4 ) 3 s = 5Ca +2 + OH -1-3 + 3PO 4 21. CaHPO 4 (s) = Ca +2-2 + HPO 4 22. CaHPO 4.2 H 2 O (s) = Ca +2-2 + HPO 4 + 2H 2 O 23. Ca 3 (PO 4 ) 2 (s) = 3Ca +2-3 + 2PO 4 24. Ca(H 2 PO 4 ) 2 H 2 O (s) = Ca +2-1 + 2H 2 PO 4 + H 2 O 25. Ca(H 2 PO 4 ) 2 (s) = Ca +2-1 + 2H 2 PO 4 26. Ca 4 O(PO 4 ) 2 (s) + H 2 O = 4Ca +2 + 2OH -1-3 + 2PO 4 27. Ca 10 O(PO 4 ) 6 (s) + H 2 O = 10Ca +2 + 2OH -1-3 + 6PO 4 28. Ca 4 H(PO 4 ) 3 (s) = 4Ca +2-2 -3 + HPO 4 + 2PO 4 29. Ca 8 H 2 (PO 4 ) 6.5 H 2 O (s) = 8Ca +2-2 -3 + 2HPO 4 + 4PO 4 + 5H 2 O 30. Ca(NO 3 ) 2.3 H 2 O (s) = Ca +2-1 + 2NO 3 + 3H 2 O 31. Ca(NO 3 ) 2.4 H 2 O (s) = Ca +2-1 + 2NO 3 + 4H 2 O 32. Ca(NO 3 ) 2 (s) = Ca +2-1 + 2NO 3 33. Ca(OH) 2 (s) = Ca +2 + 2OH -1 +1-2 34. (NH 4 ) 2 HPO 4.2H 2 O (s) = 2NH 4 + HPO 4 + 2H 2 O +1-2 35. (NH 4 ) 2 HPO 4 (s) = 2NH 4 + HPO 4 +1-3 36. (NH 4 ) 3 PO 4.3 3H 2 O (s) = 3NH 4 + PO 4 + 3H 2 O +1-1 37. (NH 4 )H 2 PO 4 (s) = NH 4 + H 2 PO 4 +1-1 38. (NH 4 )NO 3 (s) = NH 4 + NO 3 39. H 2 O (v) = H 2 O 40. NH 3 (v) = NH 3 (aq) 41. HNO 3 (v) = HNO 3 (aq)
Calculated Solubility of Various Calcium Phosphates
Ca(OH) 2 has Limited Retrograde Solubility
Thermochemical Validation: Alkaline Earth Titanate Perovskites
Minimum Mineralizer Concentrations
Increasing Pb/Ti Reduces PT Processing Space
Use of EDTA to Eliminate Phase Heterogeneities No EDTA EDTA
Control of Phase Space Using EDTA No EDTA EDTA
Acmite Pourbaix Diagram NaOH + 2SiO 2 + Fe + H 2 O = NaFeSi 2 O 6 + (3/2)H 2 (g)
Na 2 SiO 3 Concentration Effects [m] - Concentration Soluble Species Fe(OH)4 X 2.0E-05 1.6E-05 1.2E-05 8.0E-06 4.0E-06 0.0E+00 0 mol/l 1 mol/l 2 mol/l 20 40 60 80 100 120 Temperature [ C]
Reaction Rate Maximization 5 [X]*[Silicate] 10-5 *m 2 4 3 2 1 T1, C1 0 T2, C2 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Na 2 SiO 3 [m]
Na 2 SiO 3 Concentration Increases Acmite Thickness 1.4 1.2 1.0 R 0.8 0.6 0.4 R = ( I I 310) ( I ) 221 + 110 Fe Acmite 0.2 0.0 0 0.2 0.4 0.6 Na 2 SiO 3 [mole/kg] Temperature 210 C, Fe(NO 3 ) 3 0.124 mole/kg, 11.5 h
Thermochemistry Breakthrough: Instant Hydrothermal 30 s BaTiO 3 Uses same precursors as conventional solid state reaction Reactions in open vessels Phase pure powder Controlled size distribution
Setting Nucleation Targets
Effect of Ethanol on ph ph vs. EtOH added to 28.8% w solution of NH4OH 12.3 12 ph 11.7 11.4 11.1 0 4 8 12 16 [EtOH] m
Non-ideality of Ethanol-Water-Ammonia Mixtures K 1 (1) NH 3 +H 2 O NH 4 + + OH - K 2 (2) NH 3 + nc 2 H 5 OH [NH 3 (C 2 H 5 OH) n ] (3) [NH 3 ] 0 =[NH 3 ] + [NH 4 + ] + [NH 3 (C 2 H 5 OH) n ] (4) {[NH 3 ] 0 [OH - ] [OH - ] 2 /K 1 } / [OH - ] 2 /K 1 = K 2 (C 2 H 5 OH) n
Ethanol-Ammonia Interaction Parameters Ln-Ln Linearization for Water-Ethyl Alcohol -NH4OH 2.6 Ln(F([OH], [NH4OH]initial) 2.4 2.2 2 R 2 = 0.9714 n=0.13 K 2 =2.14 0 2 4 Ln ([Ethyl Alcohol])
HA coated Titanium
In-Situ HA Coating/Synthesis Non-Isothermal Phosphate Kinetcs Conversion 1 0.8 0.6 0.4 0.2 200 160 120 80 Temperature C 0 40 0 50 100 150 Time [min] Conversion Temperature
HA & Ca-titanate: temperature scan
HA & Ca-Titanate: phosphate slow supply
Summary Design of materials Phase pure materials Optimization of formulations Design of processes Optimization of processes Process insight Assessment of parametric sensitivity Process monitoring Design of experiments Feasible ranges of processing variables Phase diagrams validation Go-Not Go study
Conclusion Thermochemical modeling is an effective design tool for engineering phase assemblage, precursor and reaction kinetics
Questions?