Mechanism and Parameters Controlling the Decomposition Kinetics of Na2SiF6 Powder to SiF4
Mechanism and Parameters Controlling the Decomposition Kinetics of Na2SiF6 Powder to SiF4
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DOI：
10.1002/kin.20999
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发表时间：
2016-07
影响因子：
1.5
通讯作者：
N. Soltani;M. Pech-Canul;L. A. González;A. Bahrami
中科院分区：
文献类型：
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作者：
N. Soltani;M. Pech-Canul;L. A. González;A. Bahrami
以六氟硅酸钠（Na2SiF6）粉末为硅源，采用混合前驱体体系-化学气相沉积（HYSY - CVD）方法制备了si3n4涂层。采用标准田口实验设计和方差分析，研究了处理时间、温度、气体流量和工艺气氛（n2和N2:5% NH3）对na2sif6分解过程中分数失重的定量影响。应用收缩核模型，对Na2SiF6(s)在550 ~ 650℃范围内的分解动力学进行了理论和实验研究。结果表明，无论何种气氛类型，反应阶数均为≈0.12，由化学反应和边界层气体传递组成的两阶段混合机制控制了分解速率。测定的na2sif6在氮气气氛中分解时的失重分数比在N2:NH3中大1.05 ~ 1.5个数量级。两种气氛下，当流量为20、60和100 cm3/min时，N2的解离活化能分别为121、109和94 kJ/mol， N2:NH3的解离活化能分别为140、120和115 kJ/mol。通过将实验减重数据与模型预测结果进行比较，发现两者吻合较好。
Sodium hexafluorosilicate (Na2SiF6) powder has been used as a silicon source for formation of Si3N4coatings by the hybrid precursor system‐chemical vapor deposition (HYSY‐CVD) route. The quantitative effect of processing time, temperature, gas flow rate, and process atmosphere (N2and N2:5% NH3) upon the fractional weight loss during the decomposition of Na2SiF6was studied using a standard L9Taguchi experimental design and analysis of variance. The decomposition kinetics of Na2SiF6(s) was studied theoretically and experimentally in the temperature range of 550–650ºC by applying the shrinking core model. It was found that regardless of atmosphere type, the reaction order isn≈ 0.12 and that a two‐stage mixed mechanism consisting of chemical reaction and boundary layer gas transfer controls the decomposition rate. The determined fractional weight loss during Na2SiF6decomposition in nitrogen atmosphere is about 1.05–1.5 orders of magnitude greater than that in N2:NH3. The gas flow rate affects the dissociation activation energy, being of 121, 109, and 94 kJ/mol in N2and of 140, 120, and 115 kJ/mol in N2:NH3, for the flow rates of 20, 60, and 100 cm3/min, respectively, in both atmosphere types. A good agreement is observed by comparing experimental weight loss data with model predictions.