The application of super-hydrophobic nanomaterials for synthesizing membranes with unique physiochemical properties has gained a lot of interest among researchers. The presence of super-hydrophobic materials inside the membrane matrix can play a vital role not only in the separation of toxins, but also to achieve higher water flux with lower fouling tendencies required for an efficient membrane distillation process. In this research, super-hydrophobic carbon nanomaterials (CNMs) were synthesized using powder activated carbon (PAC) as a precursor, whereby the growth was initiated using a bimetallic catalyst of iron (Fe) and molybdenum (Mo). Until recently, no research has been conducted for synthesis and to observe the catalytic influence of bimetallic catalysts on the physiochemical characteristics of the derived CNMs. The synthesis process was carried out using the chemical vapor deposition (CVD) process. The CVD process was optimized using Box–Behnken factorial design (BBD), whereby 15 experiments were carried out under different conditions. Three input variables, which were percentage composition of catalysts (percentage of Fe and Mo) and reaction time (tr), were optimized with respect to their impact on the desired percentage output of yield (CY) and contact angle (CA). Analysis of variance (ANOVA) testing was carried out. It was observed that the developed model was statistically significant. The highest CY (320%) and CA (172°) were obtained at the optimal loading of 5% Fe and 2% Mo, with a reaction time of 40 min. Surface morphological features were observed using field emission scanning electron microscopic (FESEM) and transmission electron microscopic (TEM) analysis. The images obtained from FESEM and TEM revealed the presence of two types of CNMs, including carbon nanofibers (CNFs) and multiwall carbon nanotubes (CNTs). Thermogravimetric analysis was carried out to observe the temperature degradation profile of the synthesized sample. Raman spectroscopic analysis was also used in order to have a better understanding regarding the proportion of ordered and disordered carbon content inside the synthesized sample.

中文翻译：

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双金属 Mo-Fe 共催化剂基纳米碳浸渍在 PAC 上以获得最佳超疏水性

超疏水纳米材料在合成具有独特理化性质的膜方面的应用引起了研究人员的极大兴趣。膜基质内超疏水材料的存在不仅可以在毒素分离方面发挥重要作用，而且还可以实现高效膜蒸馏过程所需的更高水通量和更低污染趋势。在这项研究中，使用粉末活性炭（PAC）作为前驱体合成了超疏水碳纳米材料（CNM），其中使用铁（Fe）和钼（Mo）的双金属催化剂开始生长。直到最近，还没有对合成和观察双金属催化剂对衍生 CNM 的理化特性的催化影响进行研究。使用化学气相沉积（CVD）工艺进行合成工艺。使用 Box-Behnken 因子设计 (BBD) 优化了 CVD 工艺，在不同条件下进行了 15 次实验。三个输入变量，即催化剂的百分比组成（Fe 和 Mo 的百分比）和反应时间（tr），针对它们对所需的产量百分比（CY）和接触角（CA）的影响进行了优化。进行了方差分析（ANOVA）测试。据观察，开发的模型具有统计学意义。最高的 CY (320%) 和 CA (172°) 是在 5% Fe 和 2% Mo 的最佳负载下获得的，反应时间为 40 分钟。使用场发射扫描电子显微镜 (FESEM) 和透射电子显微镜 (TEM) 分析观察表面形态特征。从 FESEM 和 TEM 获得的图像显示存在两种类型的 CNM，包括碳纳米纤维 (CNF) 和多壁碳纳米管 (CNT)。进行热重分析以观察合成样品的温度降解曲线。还使用拉曼光谱分析以更好地了解合成样品中有序和无序碳含量的比例。进行热重分析以观察合成样品的温度降解曲线。还使用拉曼光谱分析以更好地了解合成样品中有序和无序碳含量的比例。进行热重分析以观察合成样品的温度降解曲线。还使用拉曼光谱分析以更好地了解合成样品中有序和无序碳含量的比例。