Synthesis and Characterization of Nano-Aerogels
Psat Vs. Temperature Table
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- Appendix 6. Block Panel of LabView VI’s. Block panel of pressure control
- Appendix 8. Synthesis and Characterization of the Ti-Acetate Single Crystals
- Synthesis of Ti-acetate crystals.
- Figure A1. The molecular structure of Ti 6 O 6 (OPr i ) 6 (OAc) 6 (2).
- 1H NMR
- Appendix 9. Description of the SIMPLISMA Modeling
- Appendix 10. Statistical Results of the SIMPLISMA Modeling
- Statistics Table Notes
- Appendix 11. Calculation of Solubility Parameters Using Group Contribution Method
- Calculation of the solubility parameter of the macromolecule with a repeating unit of -(Ti 6 O 12 (OAc) 6 )
- Curriculum Vitae Name Post-secondary
Psat Vs. Temperature Table
Equilibration options: Equilibration interval: 5 secs Min. equil. delay at P/Po ≥ 0.995: 600 secs Sample backfill options: Backfill at start of analysis: Yes Backfill at end of analysis: Yes Backfill Gas: Analysis gas
Appendix 7. Instrumentation and Configuration of the Temperature and Pressure Control
Appendix 8. Synthesis and Characterization of the Ti-Acetate Single Crystals The information in this section is reproduced from a report by the author: Hexakis(μ-acetato-κ 2O,O') hexaisopropoxyhexa -μ 3-oxo-hexatitanium, with permission from Acta Crystallographica Section E, (2006), 2, m373-375, IUCr's Copyright (http://journals.iucr.org/). Synthesis of Ti-acetate crystals. 0.273 g of acetic acid (99.7%, Aldrich) and 1 g of titanium isopropoxide (97%, Aldrich) were quickly put in the 10 ml view cell, followed by addition of CO2 (99.99%, BOC Canada) to 6000 psig at 40 ºC under mixing with a magnetic stirrer. The stirring was stopped at reaction time of 8 hours. A light yellow granular crystal was formed after about 7 days and a colorless plate crystal was formed after about 15 days in the view cell. The crystals were washed by 200 ml of CO2 at 6000 psig and 40 ºC with a flow rate of 0.5 ml/min, followed by venting of CO2. Single Crystal XRD. The single crystal XRD was operated on Nonius KappaCCD X-ray diffractometer. Two types of crystals were produced, yellow blocks and colorless plates. The yellow blocks were found to be the structure Ti6O4(OPri)8(OAc)8 3 (rutilane shape), and the colorless plates to be the structure Ti6O6(OPri)6(OAc)6 2 (hexaprismane shape) (Figure A1).234 
Figure A1. The molecular structure of Ti6O6(OPri)6(OAc)6 (2).234 Ti: green, O: red, and C: grey. FTIR (KBr, Bruker IFS55, cm-1). Structure 2: 2972 (m), 2933 (m), 2875 (m), 1604 (s), 1543 (s), 1458 (s), 1375 (w), 1361 (w), 1348 (w), 1327 (w), 1165 (m), 1132 (s), 1016 (s), 858 (m), 727 (s), 658 (s), 627 (s), 611 (s). Structure 3: 2970 (m), 2931 (m), 2866 (w), 1581 (s), 1560 (s), 1452 (s), 1410 (s), 1360 (w), 1338 (w); 1165 (m), 1122 (m); 1018 (m), 991 (m), 951 (w), 852 (w), 835 (w), 800 (s), 692 (m), 660 (s), 633 (s), 615 (s), 592 (m), 542 (m). 1H NMR (chloroform, Inova 400 MHz). Structure 2: δ 7.22 (s), δ 5.07 (sept., JH-H = 6.2 Hz, OCHMe2), δ 1.97 (s, O2CCH3), δ 1.35 (d, JH-H = 6.2 Hz, OCHMe2), δ . 13C NMR (chloroform, 101 MHz): δ 177.9, δ 82.0, δ 77.2, δ 24.9, δ 23.1. Appendix 9. Description of the SIMPLISMA Modeling In the system with a pure material, the IR spectrum in the range 600-4000 cm-1 with a scanning step of 1 cm-1 can be written as a matrix with one row and 3401 columns: Bpure = [b600, b601, b602 … bj…, b3999, b4000] (A8.1) Where, Bpure is the matrix of a pure material IR spectrum, bj denotes the IR absorbance at wavenumber j. In a reaction system with n components, a set of in situ IR spectra collected at different reaction times can be written as a matrix, D. As D is a function of the concentration and the absorbance (Bpure) of each component, it can be expressed as: D = C ×B (A8.2) or:
(A8.3)
where D is the known r  w matrix transformed from in situ IR spectra, C is the to-be-determined rn matrix representing concentration coefficients, and B is the to-be-determined nw matrix representing n pure component spectra; dij, cij and bij are the element of the matrix D, C and B, respectively, at i-th row and j-th column.286
In the SIMPLISMA method, first, the mean value of dij is calculated from the matrix D using Equation A8.4; then, the standard deviation is calculated using Equation A8.5.  (A8.4)
 (A8.5)
Where, dij is the element of the matrix D in the i-th row and j-th column, Μ = [μ1, μ2, ... μj,…, μw] (A8.6) Similarly, the calculation using Equation A8.5 results in a standard deviation matrix Σ: Σ = [σ1, σ 2, ... σ j,…, σ w] (A8.7) After calculations of matrices Μ and Σ, the “purity”, qj, is calculated as:  (A8.8)
where, α is an empirical parameter called offset, which is added to the mean value in order to suppress the noise in the region where the absorbance is close to zero. Typical values for α range from 1-5 % of the maximum μj for the conventional method; for the second-derivative method as described later, however, α is normally selected as 20 %.276, 287 A pure variable is defined as the wavenumber where only one component contributes to the absorbance in the whole column of matrix D. The calculation using Equation A8.8 results in a purity matrix Q: Q = [q1, q2, ... q j,…, q w] (A8.9) where, Q is the purity matrix. The first ‘pure variable’ is selected as the wavenumber that has the highest qj value. Then the second pure variable is selected, which is independent of the first variable. Theoretically, totally n independent variables should be select as there are n components. The absorbance of the pure variables from all components in matrix D can be used to calculate matrix C using the linear correlation of Beer-Lambert law. With the known matrices D and C, Matrix B can be calculated with a least-squares method.272, 274, 276, 286
where, E is the square of the residual matrix. If the pure variables do not exist for all components, the conventional SIMPLISMA method described above may fail. To solve this problem, in the revised SIMPLISMA method the raw spectral data were replaced by their second derivatives. In the beginning of this revised method, the conventional SIMPLISMA method was used to calculate matrices C and B; then the calculated matrix B was used to recalculate a new C denoted as C’ using the least square approximation. Thus the matrix C’ provides the second-derivative concentrations. Using matrices D and C’, the second-derivative pure component spectra B’ can be obtained.
Note: as one more pure variable is selected, either the component coefficient is negative, or a negative concentration profile is obtained, indicating the new variable being redundant. Table A10.2. Statistics results of the SIMPLISMA modeling in the region of 1400-1650 cm-1: the IR spectra were collected during the reaction of 1.10 M TIP reacting with 6.05 M HOAc in scCO2, at reaction time of 10-610 minutes and at 4500 psig and 60 ºC.
Note: as one more pure variable is selected, either the component coefficient is negative, or a negative concentration profile is obtained, indicating the new variable being redundant. Table A10.2. Statistics results of the SIMPLISMA modeling in the region of 1300-1650 cm-1: the IR spectra were collected during the reaction of 0.547 M ZBO reacting with 1.76 M HOAc in scCO2, at reaction time of 10-3720 minutes and at 4500 psig and 40 ºC.
Note: as one more pure variable is selected, either the component coefficient is negative, or a negative concentration profile is obtained, indicating the new variable being redundant. Statistics Table Notes: No.: ordinal number of the component (pure variable). Pure Var units: displays the pure variables. TSS: total sum integral intensity of the purity-corrected standard deviation spectrum corresponding to pure variable. TSS indicates the percent amount of residual variance (unaccounted information) in the data. The initial sum of standard deviations (before the first pure variable effect was removed) is taken as 100%. For the first (significant) n components it rapidly decreases and then shows saturation at values close to zero, as the n approaches the actual size of the model. TSSR: relative TSS. Ratio of TSS of the current component to TSS of the next one. Coefficient: SIMPLISMA component coefficient. Component share in the total spectral intensity (TSI, plot of integral intensities of the raw spectral Vs their numbers in series) approximation. A negative value of the coefficient for any of currently accepted n is a strong indication of improperly chosen number of components (insufficient or excessive). RRSSQ: square root of the sum-of-squares difference between TSI and LSQ (residual sum of squares) divided by the square root of the sum of squares of the TSI. Thus, RRSSQ is a relative RSSQ and varies between 0 and 1. RRSSQ shows saturation after the actual number of components. RSD: residual standard deviation. The standard deviation of the residual matrix versus the component number. IE: imbedded error. IND: indicator function. The IND value exhibits a minimum at a proper n. Variance: variance in the data modeled by a single component. %C-Variance: cumulative %variance. Percent data variance explained by the model. %R-Variance: residual %variance. Percent variance not explained by the model. %R-Variance gets saturated after n exceeding the internal dimensionality of the data.
Δe(Ti): Using the data of Ti(OPri)4 in Table 8.1,  =17×1000 cal/mol − 1.987(cal/mol.K) ×298 K = 16.41 kcal/mol
(A10.1)
Using the data in Table 8.2, for Ti(OPri)4: ∑Δei = Δe(Ti) + 4×Δe(O) + 4×Δe(CH) + 8×Δe(CH3) = Δe(Ti) + 4×800 cal/mol + 4×820 cal/mol + 8×1125 cal/mol = Δe(Ti) + 15.48 kcal/mol. (A10.2) Since Δe(Ti) = 16.41( Similarly, using the data of Ti(OBun)4 in Table 8.1 and Table 8.2, Δe(Ti)΄ denotes the energy of vaporization calculated from Ti(OBun)4 and can be calculated as: =22 ×1000 cal/mol − 1.987(cal/mol.K) ×298 K = 21.41 kcal/mol = ∑Δei = Δe(Ti)΄+ 4×Δe(O) + 12×Δe(CH2) + 4×Δe(CH3) = Δe(Ti)΄+ 4×800 cal/mol + 12×1180 cal/mol + 4×1125 cal/mol = Δe(Ti)΄ + 21.86 kcal/mol, (A10.5)
and Δe(Ti)΄ = 0.1 kcal/mol. (A10.6) Thus, the average Δe(Ti) = (Δe(Ti) + Δe(Ti)΄)/2 = 0.5 kcal/mol (A10.7)
Using the data of Ti(OPri)4 in Table 8.1 and Table 8.2, ∑Δυi = 296 cm3/mol = Δυ (Ti) + 4×Δυ (O) + 4×Δυ (CH) + 8×Δυ (CH3) = Δυ (Ti) + 4×3.8 cm3/mol + 4× (-1) cm3/mol (CH) + 8× 33.5 cm3/mol = Δυ (Ti) + 279 (A10.8) So, Δυ (Ti) = 16.8 cm3/mol. (A10.9) Using the data of Ti(OBun)4 in Table 8.1 and Table 8.2, ∑Δυi΄= 340 cm3/mol = Δυ (Ti)΄+ 4×Δυ (O) + 12×Δυ (CH2) + 4×Δυ (CH3) = Δυ (Ti)΄+ 4×3.8 cm3/mol + 12×16.1 cm3/mol + 4×33.5 cm3/mol = Δυ (Ti)΄+ 342.4 cm3/mol. (A10.10) So, Δυ (Ti)΄= -2.4 cm3/mol. (A10.11) Thus, the average of Ti’s mole volume is:  = 7.2 cm3/mol. (A10.12)
For the repeating unit -(Ti6O12(OAc)6)-, Δeir = 6×Δe(Ti) + 12×Δe(O) + 6×Δe(COO) + 6×Δe(CH3) = 6×0.5 ×1000 cal/mol + 12×800 cal/mol + 6×4300 cal/mol + 6×1125 cal/mol = 45.2 ×1000 cal/mol. (A10.13) Δυir = 6×Δυ (Ti) + 12×Δυ (O) + 6×Δυ (COO) + 6×Δυ (CH3) = 6×7.2 cm3/mol + 12×3.8 cm3/mol + 6×18 cm3/mol + 6×33.5 cm3/mol = 397.8 cm3/mol. (A10.14)
Curriculum Vitae
Publications: (1) Sui, R.; Rizkalla, A. S; Charpentier, P. A. Kinetics Study on Direct Sol-Gel Reactions in ScCO2 by Using In Situ ATR-FTIR. In preparation (2) Sui, R.; Rizkalla, A. S; Charpentier, P. A. Stabilization of the Colloidal Particles in ScCO2. In preparation (3) Sui, R.; Liu, S.; Lajoie, G. A; Rizkalla, A. S; Charpentier, P. A. Synthesis of TiO2 Monolithic Aerogel in the Liquid Chromatography Columns in scCO2. In preparation (4) Khaled, S. M.; Sui, R.; Rizkalla, A. S.; Charpentier, P. A. Synthesis of Titanate Nanotubes from Amorphous Aerogels in Aqueous KOH. In preparation (5) Lucky, R.; Sui, R.; Charpentier, P. A.; Jennings, M. C. Synthesis of Ti-Zr-Acetate Complex in Supercritical CO2. In preparation (6) Hojjati, B.; Sui, R.; Charpentier, P. A. Synthesis of TiO2/PAA Nanocomposite by RAFT Polymerization. Polymer (Under revision) (7) Khaled, S. M.; Sui, R.; Rizkalla, A. S.; Charpentier, P. A. (2007) Synthesis of TiO2-PMMA Nanocomposite: Using Methacrylic Acid as a Coupling Agent. Langmuir 23 (7) 3988-3995 (8) Sui, R.; Rizkalla, A. S.; Charpentier, P. A. (2006) FTIR Study on Formation of TiO2 Nanostructures in Supercritical CO2. J. Phys. Chem. B. 110 (33) 16212-16218. (9) Sui, R.; Rizkalla, A. S; Charpentier, P. A. (2006) Direct Synthesis of Zirconia Aerogel Nanoarchitecture in Supercritical CO2. Langmuir 22 (9) 4390-4396. (10) Sui, R.; Charpentier, P. A; Rizkalla, A. S; Jennings, M. C, (2006) Hexakis ( -acetato- 2O,O') hexaisopropoxyhexa- 3-oxo-hexatitanium. Acta Crystallographica E62 (2) m373-m375. (11) Sui, R.; Rizkalla, A. S; Charpentier, P. A. (2005) Formation of Titania Nanofibers: A Direct Sol-Gel Route in Supercritical CO2, Langmuir 21 (14) 6150-6153. (12) Sui, R.; Rizkalla, A. S; Charpentier, P. A. (2004) Synthesis and Formation of Silica Aerogel Particles by a Novel Sol-Gel Route in Supercritical Carbon Dioxide. J. Phys. Chem. B. 108 (32), 11886-11892. Conference Proceedings: (1) Sui, R.; Rizkalla, A. S; Charpentier, P. A. (2005) Synthesis of Metal Oxide Nanoaerogels via a Sol-Gel Route in Supercritical CO2. AIChE Annual Meeting, Cincinnati, OH. (2) Sui, R.; Rizkalla, A. S.; Charpentier, P. A. (2005) Synthesis and Characterization of Titania and Zirconia Aerogel via a Novel Sol-Gel Route in Supercritical Carbon Dioxide. International Symposium on Supercritical Fluids, Orlando, FL. (3) Sui, R.; Li, X.; Rizkalla, A. S; Charpentier, P. A. (2004) Synthesis of Silica Aerogel Particles and Polyethylene Nanocomposites by a Novel Sol-Gel Route in Supercritical Carbon Dioxide. 227th ACS National Meeting, Anaheim, CA. (4) Sui, R.; Rizkalla, A. S.; Charpentier, P. A. (2003) Particle Synthesis and Composite Formation Utilizing Supercritical CO2. The 53rd CSChE Conference, Hamilton, ON. Yüklə 0,88 Mb. Dostları ilə paylaş:
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= 
× 
is the mean value of dij in the j column of the matrix D, 
is the standard deviation of dij, and r is the number of matrix rows (spectra). As there are w 
1.6) kcal/mol −15.48 kcal/mol = 0.9 kcal/mol. (A10.4)
= [45.2 ×1000 cal/mol /397.8 cm3/mol]0.5 = 10.7 cal0.5cm-1.5. (A10.15)