Genetic Algorithm‐Guided Discovery of Additive Combinations That Direct Quantum Dot Assembly

Abstract

these techniques are limited by the requirement for complex enzymatic reactions. As an alternative strategy that bypasses the need for genetic engineering, combinatorial methods can be employed. These can be used to explore tens to hundreds of reaction conditions, where the most promising or "lead" conditions may be selected based on the structures or properties of the resultant material. [ 17,18 ] Lead conditions can then be used to narrow the reaction landscape in successive screening rounds. Surprisingly, although combinatorial methods are often used in solid-state chemistry to explore, for example, different reagent compositions, [ 18 ] we are not aware of their use in identifying soluble additives capable of directing mineralization.In this article we demonstrate how combinatorial methods can be used in conjunction with effi cient screening processes to rapidly identify combinations of small organic molecules that are capable of directing the formation of photoluminescent quantum dot minerals in aqueous solution and at room temperature. Indeed, a key feature of biomineralization processes is that control over mineral formation is achieved using many soluble additives that operate in concert. That this feature has seldom been addressed in bioinspired methods is almost certainly due to the vast number of potential variables, which renders a full, systematic exploration intractable. As a solution to this challenge, we here utilize a genetic algorithm as a bioinspired heuristic that mimics natural evolution. Genetic algorithms use selection, recombination, and mutation strategies to rapidly identify and optimize the combination of conditions (here, soluble additives), which gives rise to materials with target properties. [ 19 ] Using a pipetting robot to prepare reaction sets and a UV-light table to rapidly assess the reactions for the formation of photoluminescent minerals, we are able to rapidly identify the key additives that promote the formation of quantum dot superstructures from one-pot aqueous reactions.Our initial library of organic mediators included 23 components, of which 17 were amino acids and 6 were surfactants. Stock solutions of the amino acids were prepared to initial concentrations of 100 × 10 −3 M , and explored at concentrations ranging from 0.01 to 50 × 10 −3 M , while surfactants were prepared to near their solubility limits in water. Surfactants were included as potential structure-directing agents to drive hierarchical assembly in aqueous solution. The overall screening approach used to identify the key additive set is summarized in Figure 1 . First, library amino acids and surfactants were randomly mixed in 48 wells of a multi-well plate, such that each well contained between 1-6 amino acids and 1-3 surfactants (Figure 1 A). Cadmium chloride and thioacetic acid (as a sulfur source) [ 20 ] were then added to a concentration of 1 × 10 −3 M in all wells, as precursors for CdS. After 3 d the plate was viewed under UV illumination (Figure 1 A) and with a fl uorimetric Biomineralization, whi...

Enzymatically-controlled biomimetic synthesis of titania/protein hybrid thin films

“…[13][14][15] A range of biomimetic approaches have been explored to direct the formation of inorganic crystals and glasses from aqueous solution, [16][17][18] where the use of soluble organic additives has received the most attention. Biomolecules including small molecules, 19,20 , short polypeptides, 21 adhesive proteins, 22 , and carbohydrates 23 have all been shown to be active in controlling mineralization processes, enabling, for example, synthesis of patterned assemblies of inorganic nanoparticles, 20 , and mineralization under conditions where it does not typically occur. 14,19 There are also few examples of the use of enzymes in controlling mineralization processes, where the best example is arguably provided by the enzyme silicatein.…”

Rapid Screening of Calcium Carbonate Precipitation in the Presence of Amino Acids: Kinetics, Structure, and Composition

“…Indeed, the large number of potential variables in additive directed crystallization experiments typically precludes the identification of reaction conditions which deliver the target product. 32 While the current study focused on the control of calcium carbonate growth and properties using amino acids additives, our experimental design is undoubtedly quite general and could be readily adapted to a range of crystalline materials. Indeed, well plates are available that are resistant to many organic solvents, high acid/basic conditions, and temperatures of at least 200 °C.…”

“…Due to limitations in our understanding of crystal nucleation and growth processes, however, this remains impossible in most cases and identification of reaction conditions and additives that give the desired product is typically achieved by performing a huge number of experiments comprising different solution conditions, concentrations and even combinations of additives. 32 In the work described here, we address this issue by developing a high throughput method to study the influence of organic additives on the precipitation of inorganic crystals. Such combinatorial approaches enable the rapid evaluation of a wide reaction space and are well suited to crystallization studies.…”

Combinatorial Evolution of Biomimetic Magnetite Nanoparticles

“…[15] The first generation of experiments was performed in the absence and presence of the EKA polypeptides. 24 unique, copolymer-free experiments were carried out by preparing aqueous mixtures of FeCl2, KOH and KNO3, and subsequently exposing these to air, under mechanical mixing, for 24 hours at 22 °C.…”

“…[15] In the present study, we apply genetic algorithms to biomimetic mineralization, selecting as our goal the challenge of synthesizing phase-pure, monodisperse magnetite nanoparticles in aqueous solutions. Studying the precipitation of magnetite in the presence of different polypeptides and reaction conditions, the work described here shows how combinatorial methods, led by genetic algorithms, can provide an effective strategy for identifying the key conditions under which the organic additives are most effective for this mineralization system.…”