pHbot: Self-Driven Robot for pH Adjustment of Viscous Formulations via Physics-informed-ML**

IF 6.1 Q1 CHEMISTRY, MULTIDISCIPLINARY Chemistry methods : new approaches to solving problems in chemistry Pub Date : 2023-12-13 DOI:10.1002/cmtd.202300043
Aniket Chitre, Dr. Jayce Cheng, Sarfaraz Ahamed, Robert C. M. Querimit, Dr. Benchuan Zhu, Dr. Ke Wang, Dr. Long Wang, Prof. Kedar Hippalgaonkar, Prof. Alexei A. Lapkin
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Abstract

pH adjustment is crucial for many industrial products, yet this step is typically performed by manual trial-and-error. A particularly industrially relevant yet challenging titration is that of adjusting viscous liquid formulations using weak, polyprotic titrants (usually citric acid). Handling of viscous, non-Newtonian formulations, with such polyprotic acids preferred for their chelation and buffering effects make a robotic solution challenging. We present a self-driving pH robot integrated with physics-informed learning; this hybrid physical-ML model enables automated titration with weak-strong acid/base pairs. To deal with the high viscosities of these formulations, we developed specific automated mixing and cleaning protocols. We hit the target pH within two to five iterations over 250 distinct formulations in lab-scale small-batch (~10 mL and 12 samples) titrations. In the interest of scaling up to match industrial processes, we also demonstrate that our hybrid algorithm works at ~25× scale-up. The method is general, and we open-source our algorithm and designs.

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pHbot:通过物理信息ML**实现粘性配方 pH 值调节的自驱动机器人
pH 值调节对许多工业产品都至关重要,但这一步骤通常都是通过人工试错来完成的。与工业相关但又极具挑战性的一种滴定是使用弱聚丙烯酸滴定剂(通常是柠檬酸)调节粘性液体配方。处理粘性、非牛顿流体配方时,这类聚丙酸因其螯合和缓冲作用而备受青睐,因此机器人解决方案具有挑战性。我们介绍了一种集成了物理信息学习的自驱动 pH 值机器人;这种混合物理-化学模型可实现弱-强酸/碱对的自动滴定。为了应对这些配方的高粘度,我们开发了特定的自动混合和清洁方案。在实验室小批量(约 10 mL 和 12 个样品)滴定中,我们对 250 种不同的配方进行了两到五次迭代,最终达到了目标 pH 值。为了扩大规模以适应工业流程,我们还证明了我们的混合算法在扩大约 25 倍的规模时也能发挥作用。该方法具有通用性,我们对算法和设计进行了开源。
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