Cu₃(BTC)₂, a common type of metal organic framework (MOF), was synthesized through electrochemical route for CO₂ capture and its separation from N₂. Taguchi method was employed for optimization of key parameters affecting the synthesis of Cu₃(BTC)₂. The results indicated that the optimum synthesis conditions with the highest CO₂ selectivity can be obtained using 1 g of ligand, applied voltage of 25 V, synthesis time of 2 h, and electrode length of 3 cm. The single gas sorption capacity of the synthetized microstructure Cu₃(BTC)₂ for CO₂ (at 298 K and 1 bar) was a considerable value of 4.40 mmol · g⁻¹. The isosteric heat of adsorption of both gases was calculated by inserting temperature-dependent form of Langmuir isotherm model in the Clausius-Clapeyron equation. The adsorption of CO₂/N₂ binary mixture with a concentration ratio of 15/85 vol-% was also studied experimentally and the result was in a good agreement with the predicted value of IAST method. Moreover, Cu₃(BTC)₂ showed no considerable loss in CO₂ adsorption after six sequential cycles. In addition, artificial neural networks (ANNs) were also applied to predict the separation behavior of CO₂/N₂ mixture by MOFs and the results revealed that ANNs could serve as an appropriate tool to predict the adsorptive selectivity of the binary gas mixture in the absence of experimental data.

Cu ₃（BTC）₂是一种常见的金属有机骨架（MOF），是通过电化学途径合成的，用于CO _2的捕获及其与N _2的分离。Taguchi方法用于优化影响Cu ₃（BTC）₂合成的关键参数。结果表明，使用1 g的配体，25 V的施加电压，2 h的合成时间和3 cm的电极长度，可以获得具有最高CO ₂选择性的最佳合成条件。合成的微结构Cu ₃（BTC）₂对CO ₂的单气体吸附能力（在298K和1bar下）的可观值是4.40mmol·g ^-1。通过将温度相关形式的Langmuir等温线模型插入Clausius-Clapeyron方程中，可以计算两种气体的等吸收热。还通过实验研究了浓度比为15/85 vol-％的CO ₂ / N ₂二元混合物的吸附，结果与IAST法的预测值吻合良好。此外，Cu ₃（BTC）₂在六个连续循环后显示出对CO ₂吸附的明显损失。此外，人工神经网络（ANN）也被用于预测CO ₂ / N ₂的分离行为 结果表明，在没有实验数据的情况下，人工神经网络可以作为预测二元混合气体吸附选择性的合适工具。