利用机器人技术和高通量实验方法开发新型缓蚀剂

N. Obeyesekere, J. Wylde
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摘要

临界胶束浓度(CMC)是表面活性剂(如缓蚀剂)从两相体系(如油和水)中分离能力的已知指标。大多数缓蚀剂具有表面活性,在临界胶束浓度下,化学物质被分解到水中,吸附在金属表面,形成钢和水之间的物理屏障。当施加的化学物质浓度等于或高于CMC时,化学物质在水相中可用,从而防止腐蚀发生。因此,CMC可作为腐蚀控制的最佳化学剂量指标1。缓蚀剂产品的CMC越低,该化学品的腐蚀控制效果越好,因为该化学品在水相中的可用性增加,因此可以用较少的化学品实现腐蚀控制。在这项工作中,该物理性质(CMC)被用作区分缓蚀剂性能的指标。通过组合化学方法构建缓蚀剂配方,并利用高通量机器人2-4筛选化学配方阵列,以CMC为选择指南。为了验证这一概念,我们选择了几种已知的缓蚀剂配方来优化它们的效果。每个配方含有几种有效成分和一个溶剂包。这些原材料是随机混合的,但以控制的方式,使用组合方法。采用控制设计(DOE)方法来限制混合的数量,而不是利用机器人技术快速混合大量配方。一旦通过DOE方法生成配方,使用Design Expert软件可以有效地探索所需的空间。还开发了一种同样强大的预筛选分析。这是通过使用高通量筛选方法测量CMC来完成的。在使用Design Expert软件配制了大量的配方后,产品由CMC使用自动化表面张力工作站进行筛选。选择了几种CMC低于对照产品的配方。确定了选定的缓蚀剂配方,并进行了大规模的混合。通过旋转圆柱体电极(RCE)和旋转笼式高压灭菌器(RCA)等经典实验室测试方法对这些产品的防腐性能进行了测试。这些测试是针对原始参考缓蚀剂进行的。测试表明,几种缓蚀剂配方的性能优于原始混合物,从而验证了概念的证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of New Corrosion Inhibitors Using Robotics with High Throughput Experimentation Methods
Critical micelle concentration (CMC) is a known indicator for surfactants such as corrosion inhibitors ability to partition from two phase systems such as oil and water. Most corrosion inhibitors are surface active and at critical micelle concentration, the chemical is partitioned to water, physadsorb on metallic surfaces and form a physical barrier between steel and water. This protective barrier thus prevents corrosion from taking place on the metal surface When the applied chemical concentration is equal or higher than the CMC, the chemical is available in aqueous phase, thus preventing corrosion. Therefore, it was suggested that CMC can be used as an indicator of optimal chemical dose for corrosion control1. The lower the CMC of a corrosion inhibitor product, the better is this chemical for corrosion control as the availability of the chemical in the aqueous phase increase and therefore, can achieve corrosion control with less amount of chemical. In this work, this physical property (CMC) was used as an indicator to differentiate corrosion inhibitor performance. The corrosion inhibitor formulations were built out by using combinatorial chemical methods and the arrays of chemical formulations were screened by utilizing high throughput robotics 2-4, using CMC as the selection guide. To validate the concept, several known corrosion inhibitor formulas were selected to optimize their efficacy. Each formula contained several active ingredients and a solvent package. These raw materials were blended in random but in a control, manner using combinatorial methodologies. Instead of rapidly blending a large number of formulations using robotics, the design of control (DOE) methods were utilized to constrain the number of blends. Once the formulations were generated by DOE method, using Design Expert software that can effectively explore a desired space. The development of an equally robust prescreening analysis was also developed. This was done by using the measurements of CMC with a high-throughput screening methodology. After formulation of a vast array of formulation by using Design Expert software, the products were screened for by CMC using automated surface tension workstation. Several formulations with lower CMC than the reference products were selected. The selected corrosion inhibitor formulations were identified and blended in larger scales. The efficacy of these products was tested by classical laboratory testing methods such as rotating cylinder electrode (RCE) and rotating cage autoclave (RCA) to determine their performance as anti-corrosion agents. These tests were performed against the original reference corrosion inhibitor. The testing indicated that several corrosion inhibitor formulations outperform the original blend thus validating the proof of concept.
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