통계학적 실험계획법 해석을 통한 MOF-235 합성 최적화

정민지; 유계상; Mingee Chung; Kye Sang Yoo

Applied Chemistry for Engineering, Vol.30, No.5, 615-619, October, 2019

DOI10.14478/ace.2019.1066 Export Citation

통계학적 실험계획법 해석을 통한 MOF-235 합성 최적화

Optimization of MOF-235 Synthesis by Analysis of Statistical Design of Experiment

정민지, 유계상 ^†

서울과학기술대학교 화공생명공학과

Mingee Chung, and Kye Sang Yoo^†

Department of Chemical & Biomolecular Engineering, Seoul National University of Science & Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Korea

E-mail:

초록

통계학적 실험계획법을 이용하여 다공성 구조체인 MOF-235 합성 공정 최적화를 수행하였다. 합성에 사용되는 주성분인 terephthalic acid (TPA), Iron (III) chloride hexahydrate, N,N-dimethylformamide (DMF) 및 ethanol의 농도가 MOF-235의 결정구조를 형성하는데 중요한 요소가 되었다. 다양한 농도의 4가지 성분을 이용하여 MOF-235를 합성한 후 XRD를 이용하여 결정도를 측정하였다. 16가지 실험조건을 통해 합성한 MOF-235의 결정도 결과를 통계학적 해석을 통해 주성분의 조성이 입자의 합성에 미치는 영향을 분석하였다. F 검정법을 이용한 분산분석에서 에탄올의 농도가 입자의 결정도에 가장 큰 영향을 미치고 TPA가 가장 영향력이 작은 것으로 분석되었다. 결정도를 예측할 수 있는 회귀모델을 도출하였고 2가지 합성변수에 대한 예측결과를 등고선도를 이용하여 제시하였다. 마지막으로 혼합물법을 이용하여 3가지 합성인자가 미치는 결정도를 예측하여 제시하였다.

Statistical design of experiments was performed to optimize MOF-235 synthesis process. Concentrations of terephthalic acid (TPA), iron (III) chloride hexahydrate, N,N-dimethylformamide (DMF) and ethanol were important factors to develop the crystal structure of MOF-235. MOF-235 was synthesized with various concentrations of the listed chemicals above and the crystallinity was measured by XRD. The effect of the composition on the synthesis of MOF-235 was evaluated using a statistical analysis. For the variance analysis using F-test, the concentration of ethanol showed the greatest effect on the crystallinity and TPA the least influential. A regression model for predicting the crystallinity of MOF-235 was derived and the prediction results for two synthetic variables were presented using contour plots. Finally, the crystallinity was predicted by a mixture method with FeCl3, ethanol and DMF.

Keywords:MOF-235;Design of experiment;Statistical optimization

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[2] Stock N, Biswas S, Chem. Rev., 1122, 933 (2012)

[3] Wang Z, Cohen SM, Chem. Soc. Rev., 38, 1315 (2009)

[4] Song YF, Cronin L, Angew. Chem.-Int. Edit., 47, 4635 (2008)

[5] Li X, Zhang J, Li W, J. Ind. Eng. Chem., 44, 146 (2016)

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[7] Kim J, Kim SN, Jang HG, Seo G, Ahn WS, Appl. Catal. A: Gen., 453, 175 (2013)

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[9] Adil K, Belmabkhout Y, Pillai RS, Cadiau A, Bhatt PM, Assen AH, Maurin G, Eddaoudi M, Chem. Soc. Rev., 46, 3402 (2017)

[10] Lee YR, Cho SM, Ahn SW, Korean J. Chem. Eng., 35(7), 1542 (2018)

[11] Jiang N, Deng Z, Liu S, Tang C, Wang G, Korean J. Chem. Eng., 33(9), 2747 (2016)

[12] Sudik AC, Cote AP, Yaghi OM, Inorg. Chem., 44(9), 2998 (2005)

[13] Anbia M, Hoseini V, Sheykhi S, J. Ind. Eng. Chem., 18(3), 1149 (2012)

[14] Haque E, Jun JW, Jhung SH, J. Hazard. Mater., 185, 507 (2011)

[15] Tran NT, Kim D, Yoo KS, Kim J, Bull. Korean Chem. Soc., 40, 112 (2019)

[16] Tao X, Sun C, Han Y, Huang L, Xu D, Cryst. Eng. Comm., 21, 2541 (2019)

[17] Walpole RE, Ye KE, Raymond, Myers H, Myers SL, Probability and Statistics for Engineers and Scientists, 9th ed., 639-652, Prentice Hall, Boston, USA (2012).

[18] Mason RL, Gunst RF, Hess JL, Statistical Design and Analysis of Experiments, with Applications to Engineering and Science, 2nd ed., 568-597, Wiley-Interscience, New Jersey, USA (2003).