Jin Soo-Ah, Kämäräinen Tero, Rinke Patrick, Rojas Orlando J, Todorović Milica
Department of Chemical & Biomolecular Engineering, North Carolina State University, Raleigh, NC 27695 USA.
Department of Bioproducts and Biosystems, Aalto University, Vuorimiehentie 1, P.O. Box 16300, 00076 Espoo, Aalto, Finland.
MRS Bull. 2022;47(1):29-37. doi: 10.1557/s43577-021-00183-4. Epub 2022 Feb 28.
Oxidized tannic acid (OTA) is a useful biomolecule with a strong tendency to form complexes with metals and proteins. In this study we open the possibility to further the application of OTA when assembled as supramolecular systems, which typically exhibit functions that correlate with shape and associated morphological features. We used machine learning (ML) to selectively engineer OTA into particles encompassing one-dimensional to three-dimensional constructs. We employed Bayesian regression to correlate colloidal suspension conditions (pH and p ) with the size and shape of the assembled colloidal particles. Fewer than 20 experiments were found to be sufficient to build surrogate model landscapes of OTA morphology in the experimental design space, which were chemically interpretable and endowed predictive power on data. We produced multiple property landscapes from the experimental data, helping us to infer solutions that would satisfy, simultaneously, multiple design objectives. The balance between data efficiency and the depth of information delivered by ML approaches testify to their potential to engineer particles, opening new prospects in the emerging field of particle morphogenesis, impacting bioactivity, adhesion, interfacial stabilization, and other functions inherent to OTA.
Tannic acid is a versatile bio-derived material employed in coatings, surface modifiers, and emulsion and growth stabilizers, which also imparts mild anti-viral health benefits. Our recent work on the crystallization of oxidized tannic acid (OTA) colloids opens the route toward further valuable applications, but here the functional properties tend to depend strongly on particle morphology. In this study, we eschew trial-and-error morphology exploration of OTA particles in favor of a data-driven approach. We digitalized the experimental observations and input them into a Gaussian process regression algorithm to generate morphology surrogate models. These help us to visualize particle morphology in the design space of material processing conditions, and thus determine how to selectively engineer one-dimensional or three-dimensional particles with targeted functionalities. We extend this approach to visualize other experimental outcomes, including particle yield and particle surface-to-volume ratio, which are useful for the design of products based on OTA particles. Our findings demonstrate the use of data-efficient surrogate models for general materials engineering purposes and facilitate the development of next-generation OTA-based applications.
The online version contains supplementary material available at 10.1557/s43577-021-00183-4.
氧化单宁酸（OTA）是一种有用的生物分子，具有与金属和蛋白质形成络合物的强烈倾向。在本研究中，我们开启了将OTA组装成超分子体系时进一步应用的可能性，超分子体系通常表现出与形状及相关形态特征相关的功能。我们使用机器学习（ML）将OTA选择性地设计成包含一维到三维结构的颗粒。我们采用贝叶斯回归将胶体悬浮条件（pH值和p）与组装的胶体颗粒的尺寸和形状关联起来。发现在实验设计空间中，少于20次实验就足以构建OTA形态的替代模型景观，这些景观具有化学可解释性且对数据具有预测能力。我们从实验数据中生成了多个性质景观，帮助我们推断出能同时满足多个设计目标的解决方案。ML方法在数据效率和所提供信息深度之间的平衡证明了它们在设计颗粒方面的潜力，为颗粒形态发生这一新兴领域开辟了新前景，影响了生物活性、粘附性、界面稳定性以及OTA固有的其他功能。
单宁酸是一种用途广泛的生物衍生材料，用于涂料、表面改性剂、乳液和生长稳定剂，还具有温和的抗病毒健康益处。我们最近关于氧化单宁酸（OTA）胶体结晶的工作开启了进一步有价值应用的途径，但在此功能特性往往强烈依赖于颗粒形态。在本研究中，我们摒弃了对OTA颗粒形态的试错探索，转而采用数据驱动的方法。我们将实验观察数字化并输入到高斯过程回归算法中以生成形态替代模型。这些模型帮助我们在材料加工条件的设计空间中可视化颗粒形态，从而确定如何选择性地设计具有目标功能的一维或三维颗粒。我们扩展此方法以可视化其他实验结果，包括颗粒产率和颗粒表面体积比，这对于基于OTA颗粒的产品设计很有用。我们的发现证明了使用数据高效的替代模型用于一般材料工程目的，并促进了下一代基于OTA的应用的开发。
在线版本包含可在10.1557/s43577 - 021 - 00183 - 4获取的补充材料。
