Abstract
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 pKa) 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.Impact statementTannic 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.Graphic abstract
Highlights
Tannic acid (TA) is an abundant and versatile bio-based material, which readily affords synthetic pathways for the isolation of its elementary building blocks
We focused on the ratio of particle surface area to its volume: surface-based chemical processes underpin many technological applications, so maximizing surface area per volume (A/V) complements particle morphology control as an important design objective
The purpose of this work was to evaluate the predictive power of Gaussian process regression (GPR) on a small experimental data set; we deliberately constrained the dimensionality of the problem, which produced interpretable surrogate models
Summary
Tannic acid (TA) is an abundant and versatile bio-based material, which readily affords synthetic pathways for the isolation of its elementary building blocks. MACHINE LEARNING AS A TOOL TO ENGINEER MICROSTRUCTURES of compounds with higher molecular weight and thereby decreases the solubility of the substance.[4] In this form, OTA can interact with different molecules and serve as coatings,[3,5] surface modifiers[1,6] and emulsion stabilizers,[1,3,6,7] or act as Bayesian optimization.[31–33]. Tannic acid has recently been capable of good data interpolation, allowing us to build good quality surrogate models with relatively few data points They produce smooth and continuous landscapes that reflect the shown to suppress SARS-CoV-2 as a dual inhibitor of the viral main protease.[13]. All these favorable aspects of OTA and other continuous chemical process underpinning the data, and can account for experimental uncertainties as data noise. All of phenolic particles have fueled research into a wide spectrum of applications.[14] these characteristics makes GPR well suited to experimental applications
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