Revisiting El-Sayed Synthesis: Bayesian Optimization for Revealing New Insights during the Growth of Gold Nanorods

TL;DR

This paper uses Bayesian optimization to discover new synthesis conditions for gold nanorods, revealing insights into their growth process.

Contribution

The study introduces a workflow using Bayesian optimization to uncover synergistic effects in AuNR synthesis conditions.

Findings

Elevated temperatures and high ascorbic acid concentrations enable viable and accelerated AuNR synthesis.

Ascorbic acid and temperature can counteract each other's negative effects on AuNR growth.

Interpretable ML algorithms helped revise the hierarchical relationships in El-Sayed-based AuNR growth.

Abstract

In diverse fields, machine learning (ML) has sparked
transformative
changes, primarily driven by the wealth of big data. However, an alternative
approach seeks to mine insights from “precious data”, offering the possibility to reveal missed knowledge and
escape potential knowledge traps. In this context, Bayesian optimization
(BO) protocols have emerged as crucial tools for optimizing the synthesis
and discovery of a broad spectrum of compounds including nanoparticles.
In our work, we aimed to go beyond the commonly explored experimental
conditions and showcase a workflow capable of unearthing fresh insights,
even in well-studied research domains. The growth of AuNRs is a nonequilibrium
process that remains poorly understood despite the presence of well-established
seeded growth protocols. Traditional research aimed at understanding
the mechanism of AuNR growth has primarily relied on altering one
reaction condition at a time. While these studies are undeniably valuable,
they often fail to capture the synergies between different reaction
conditions, thus constraining the depth of insights they can offer.
In the present study, we exploit BO, to identify diverse experimental
conditions yielding AuNRs with similar spectroscopic characteristics.
Notably, we identify viable and accelerated synthesis conditions involving
elevated temperatures (36–40 °C) as well as high ascorbic
acid concentrations. More importantly, we note that ascorbic acid
and temperature can modulate each other’s undesirable influences
on the growth of AuNRs. Finally, by harnessing the power of interpretable
ML algorithms, complemented by our deep chemical understanding, we
revisited the established hierarchical relationships among reaction
conditions that impact the El-Sayed-based growth of AuNRs.

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