Radiolytic redox interplay defines nanomaterial synthesis in liquids

AbstractThe creation of synthetic polymer nanoobjects with well-defined hierarchical structures is important for a wide range of applications such as nanomaterial synthesis, catalysis, and therapeutics. Inspired by the programmability and precise three-dimensional architectures of biomolecules, here we demonstrate the strategy of fabricating controlled hierarchical structures through self-assembly of folded synthetic polymers. Linear poly(2-hydroxyethyl methacrylate) of different lengths are folded into cyclic polymers and their self-assembly into hierarchical structures is elucidated by various experimental techniques and molecular dynamics simulations. Based on their structural similarity, macrocyclic brush polymers with amphiphilic block side chains are synthesized, which can self-assemble into wormlike and higher-ordered structures. Our work points out the vital role of polymer folding in macromolecular self-assembly and establishes a versatile approach for constructing biomimetic hierarchical assemblies.

Download Full-text

Two-step machine learning enables optimized nanoparticle synthesis

npj Computational Materials ◽

10.1038/s41524-021-00520-w ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Flore Mekki-Berrada ◽

Zekun Ren ◽

Tan Huang ◽

Wai Kuan Wong ◽

Fang Zheng ◽

...

Keyword(s):

Machine Learning ◽

Optical Properties ◽

Materials Science ◽

Nanoparticle Synthesis ◽

Bayesian Optimization ◽

Absorbance Spectrum ◽

Nanomaterial Synthesis ◽

Target Spectrum ◽

Colour Palette ◽

Algorithmic Framework

AbstractIn materials science, the discovery of recipes that yield nanomaterials with defined optical properties is costly and time-consuming. In this study, we present a two-step framework for a machine learning-driven high-throughput microfluidic platform to rapidly produce silver nanoparticles with the desired absorbance spectrum. Combining a Gaussian process-based Bayesian optimization (BO) with a deep neural network (DNN), the algorithmic framework is able to converge towards the target spectrum after sampling 120 conditions. Once the dataset is large enough to train the DNN with sufficient accuracy in the region of the target spectrum, the DNN is used to predict the colour palette accessible with the reaction synthesis. While remaining interpretable by humans, the proposed framework efficiently optimizes the nanomaterial synthesis and can extract fundamental knowledge of the relationship between chemical composition and optical properties, such as the role of each reactant on the shape and amplitude of the absorbance spectrum.

Download Full-text

Controlling nanomaterial synthesis, chemical reactions and self assembly in dynamic thin films

Chemical Society Reviews ◽

10.1039/c3cs60247h ◽

2014 ◽

Vol 43 (5) ◽

pp. 1387-1399 ◽

Cited By ~ 33

Author(s):

Xianjue Chen ◽

Nicole M. Smith ◽

K. Swaminathan Iyer ◽

Colin L. Raston

Keyword(s):

Thin Films ◽

Chemical Reactions ◽

Self Assembly ◽

Nanomaterial Synthesis

Download Full-text

Nanomaterial synthesis and characterization for toxicological studies: TiO2 case study

Mineralogical Magazine ◽

10.1180/minmag.2008.072.1.515 ◽

2008 ◽

Vol 72 (1) ◽

pp. 515-519 ◽

Cited By ~ 7

Author(s):

E. Valsami-Jones ◽

D. Berhanu ◽

A. Dybowska ◽

S. Misra ◽

A. R. Boccaccini ◽

...

Keyword(s):

Surface Modifications ◽

Model System ◽

Synthesis And Characterization ◽

The Novel ◽

Commercial Products ◽

Nanomaterial Synthesis ◽

Toxicological Studies

AbstractIn recent years it has become apparent that the novel properties of nanomaterials may predispose them to a hitherto unknown potential for toxicity. A number of recent toxicological studies of nanomaterials exist, but these appear to be fragmented and often contradictory. Such discrepancies may be, at least in part, due to poor description of the nanomaterial or incomplete characterization, including failure to recognise impurities, surface modifications or other important physicochemical aspects of the nanomaterial. Herew em ake a casef or the importance of good quality, well-characterized nanomaterials for future toxicological studies, combined with reliable synthesis protocols, and we present our efforts to generate such materials. The model system for which we present results is TiO2 nanoparticles, currently used in a variety of commercial products.

Download Full-text