Networking chemical robots for reaction multitasking

The development of the internet of things has led to an explosion in the number of networked devices capable of control and computing. However, whilst common place in remote sensing, these approaches have not impacted chemistry due to difficulty in developing systems flexible enough for experimental data collection. Herein we present a simple and affordable (<$500) chemistry capable robot built with a standard set of hardware and software protocols that can be networked to coordinate many chemical experiments in real time. We demonstrate how multiple processes can be done with two internet connected robots collaboratively, exploring a set of azo-coupling reactions in a fraction of time needed for a single robot, as well as encoding and decoding information into a network of oscillating reactions. The system can also be used to assess the reproducibility of chemical reactions and discover new reaction outcomes using game playing to explore a chemical space

A nanomaterials discovery robot for the Darwinian evolution of shape programmable gold nanoparticles

The fabrication of nanomaterials from the top-down gives precise structures but it is costly, whereas bottom-up assembly methods are found by trial and error. Nature evolves materials discovery by refining and transmitting the blueprints using DNA mutations autonomously. Genetically inspired optimisation has been used in a range of applications, from catalysis to light emitting materials, but these are not autonomous, and do not use physical mutations. Here we present an autonomously driven materials-evolution robotic platform that can reliably optimise the conditions to produce gold-nanoparticles over many cycles, discovering new synthetic conditions for known nanoparticle shapes using the opto-electronic properties as a driver. Not only can we reliably discover a method, encoded digitally to synthesise these materials, we can seed in materials from preceding generations to engineer more sophisticated architectures. Over three independent cycles of evolution we show our autonomous system can produce spherical nanoparticles, rods, and finally octahedral nanoparticles by using our optimized rods as seeds

Silver Ion Biocide Delivery System for Water Disinfection

U.S. space exploration missions have long considered returning to the Moon and exploration of Mars that challenge life support systems. For these long duration missions, there is interest in replacing the iodine water treatment system with ionic silver, a proven biocide. For long duration exploration missions, it is imperative that an effective biocide be used that prevents microbial growth, biofilm formation, and microbially induced corrosion in the water storage and distribution systems while minimizing logistical supply requirements associated with the biocide delivery system. Two biocide delivery systems have been developed that electrochemically produce silver ions for disinfecting water throughout the water storage and distribution system. One system uses a newly developed hybrid micro-filtration and ion-exchange membrane to produce an abundance of silver ions at the 1000 ppb level upstream in the water distribution system to prevent biofilm growth. This is followed by a downstream collection module that electrochemically removes these silver ions before the water is discharged. Another approach uses a membraneless reactor to produce a 1000 ppb silver ion concentration level that also has a mechanically driven electrode cleaning mechanism that removes oxide films ensuring long life operation. By maintaining a sufficiently high level of silver ions throughout the water storage and distribution system, biofilm formation is suppressed. This approach overcomes present concerns where spurious silver deposition occurs on the container and flow line surfaces thus lowering the silver ion concentration to unsatisfactory disinfection levels

A modular programmable inorganic cluster discovery robot for the discovery and synthesis of polyoxometalates

The exploration of complex multicomponent chemical reactions leading to new clusters, where discovery requires both molecular self-assembly and crystallization, is a major challenge. This is because the systematic approach required for an experimental search is limited when the number of parameters in a chemical space becomes too large, restricting both exploration and reproducibility. Herein, we present a synthetic strategy to systematically search a very large set of potential reactions, using an inexpensive, high-throughput platform that is modular in terms of both hardware and software and is capable of running multiple reactions with in-line analysis, for the automation of inorganic and materials chemistry. The platform has been used to explore several inorganic chemical spaces to discover new and reproduce known tungsten-based, mixed transition-metal polyoxometalate clusters, giving a digital code that allows the easy repeat synthesis of the clusters. Among the many species identified in this work, the most significant is the discovery of a novel, purely inorganic W24FeIII–superoxide cluster formed under ambient conditions. The modular wheel platform was employed to undertake two chemical space explorations, producing compounds 1–4: (C2H8N)10Na2[H6Fe(O2)W24O82] (1, {W24Fe}), (C2H8N)72Na16[H16Co8W200O660(H2O)40] (2, {W200Co8}), (C2H8N)72Na16[H16Ni8W200O660(H2O)40] (3, {W200Ni8}), and (C2H8N)14[H26W34V4O130] (4, {W34V4}), along with many other known species, such as simple Keggin clusters and 1D {W11M2+} chains

An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials

We present an autonomous chemical synthesis robot for the exploration, discovery, and optimization of nanostructures driven by real-time spectroscopic feedback, theory, and machine learning algorithms that control the reaction conditions and allow the selective templating of reactions. This approach allows the transfer of materials as seeds between cycles of exploration, opening the search space like gene transfer in biology. The open-ended exploration of the seed-mediated multistep synthesis of gold nanoparticles (AuNPs) via in-line ultraviolet-visible characterization led to the discovery of five categories of nanoparticles by only performing ca. 1000 experiments in three hierarchically linked chemical spaces. The platform optimized nanostructures with desired optical properties by combining experiments and extinction spectrum simulations to achieve a yield of up to 95%. The synthetic procedure is outputted in a universal format using the chemical description language (χDL) with analytical data to produce a unique digital signature to enable the reproducibility of the synthesis

Robotic stepwise synthesis of hetero-multinuclear metal oxo clusters as single-molecule magnets

An efficient stepwise synthesis method for discovering new heteromultinuclear metal clusters using a robotic workflow is developed where numerous reaction conditions for constructing heteromultinuclear metal oxo clusters in polyoxometalates (POMs) were explored using a custom-built automated platform. As a result, new nonanuclear tetrametallic oxo clusters {FeMn4}Lu2A2 in TBA5[(A-α-SiW9O34)2FeMn4O2{Lu(acac)2}2A2] (IIA; A = Ag, Na, K; TBA = tetra-n-butylammonium; acac = acetylacetonate) were discovered by the installation of diamagnetic metal cations A+ into a paramagnetic {FeMn4}Lu2 unit in TBA7[(A-α-SiW9O34)2FeMn4O2{Lu(acac)2}2] (I). POMs IIA exhibited single-molecule magnet properties with the higher energy barriers for magnetization reversal (IIAg, 40.0 K; IINa, 40.3 K; IIK, 26.7 K) compared with that of the parent I (19.7 K). Importantly, these clusters with unique properties were constructed as designed by a step of the predictable sequential multistep reactions with the time-efficient platform

Algorithm-driven robotic discovery of polyoxometalate-scaffolding metal-organic frameworks

The experimental exploration of the chemical space of crystalline materials, especially metal–organic frameworks (MOFs), requires multiparameter control of a large set of reactions, which is unavoidably time-consuming and labor-intensive when performed manually. To accelerate the rate of material discovery while maintaining high reproducibility, we developed a machine learning algorithm integrated with a robotic synthesis platform for closed-loop exploration of the chemical space for polyoxometalate-scaffolding metal–organic frameworks (POMOFs). The eXtreme Gradient Boosting (XGBoost) model was optimized by using updating data obtained from the uncertainty feedback experiments and a multiclass classification extension based on the POMOF classification from their chemical constitution. The digital signatures for the robotic synthesis of POMOFs were represented by the universal chemical description language (χDL) to precisely record the synthetic steps and enhance the reproducibility. Nine novel POMOFs including one with mixed ligands derived from individual ligands through the imidization reaction of POM amine derivatives with various aldehydes have been discovered with a good repeatability. In addition, chemical space maps were plotted based on the XGBoost models whose F1 scores are above 0.8. Furthermore, the electrochemical properties of the synthesized POMOFs indicate superior electron transfer compared to the molecular POMs and the direct effect of the ratio of Zn, the type of ligands used, and the topology structures in POMOFs for modulating electron transfer abilities

Margarita de Sossa, Sixteenth-Century Puebla de los Ángeles, New Spain (Mexico)

Margarita de Sossa’s freedom journey was defiant and entrepreneurial. In her early twenties, still enslaved in Portugal, she took possession of her body; after refusing to endure her owner’s sexual demands, he sold her, and she was transported to Mexico. There, she purchased her freedom with money earned as a healer and then conducted an enviable business as an innkeeper. Sossa’s biography provides striking insights into how she conceptualized freedom in terms that included – but was not limited to – legal manumission. Her transatlantic biography offers a rare insight into the life of a free black woman (and former slave) in late sixteenth-century Puebla, who sought to establish various degrees of freedom for herself. Whether she was refusing to acquiesce to an abusive owner, embracing entrepreneurship, marrying, purchasing her own slave property, or later using the courts to petition for divorce. Sossa continued to advocate on her own behalf. Her biography shows that obtaining legal manumission was not always equivalent to independence and autonomy, particularly if married to an abusive husband, or if financial successes inspired the envy of neighbors

Automated modular platforms for the exploration and discovery of inorganic materials

The work presented in this thesis focuses on the development and use of automated systems for the synthesis, discovery, and study of inorganic materials, specifically polyoxometalates and gold nanoparticles. The introduction of automated systems in chemistry laboratories has had a profound impact in many areas, increasing productivity whilst reducing menial tasks. However, for many reasons the wide scale adoption of automated systems has been historically slow. Despite having received much attention in recent years, commercially available automated solutions to everyday laboratory activities still suffer from a combination of the two major drawbacks. As Chapter 1 details, the first drawback is that many systems are prohibitively expensive and the second is a lack of adaptability beyond the initial purpose of a given unit, due to a lack of modularity in the design of hardware and/or software. Two of the three results chapters of this work demonstrate the creation of a singular modular architecture for chemical synthesis, that can range from its base unit providing simple liquid handling capabilities at a fraction the cost of commercially available alternates, to that same unit being the epicentre of a closed-loop workflow that can perform environmentally controlled reactions, obtain and learn from reaction data in order to navigate and optimise difficult synthesis. The former unit was used to combinatorically explore polyoxometalate chemistry in which it discovered new and novel species and independently reproduced known compounds. The latter was used to optimise several seed mediated syntheses of gold nanoparticle shapes using a genetic algorithm. The modular hardware and accompanying software can perform multiple reactions in parallel whilst incorporating sample removal for in-line analysis, probe-based feedback, reaction-to-reaction transfer and other capabilities. The remaining results chapter involved the development of robotic platforms capable of collaborating over shared chemical tasks. Two such units were used to study the crystallisation process of a known polyoxometalate and optimise the specific conditions for the formation of crystals. This project was a proof of concept to help envision a future where laboratories could possess interlinked reaction apparatus for the sharing of results to combat irreproducibility of published data. Combined the three results chapters of this work utilise three different types of automation for inorganic synthesis using custom designed architectures: a combinatorial approach (chapter two), a collaborative approach (chapter 3) and an intelligent, algorithm driven approach (chapter 4). We believe that automation of reaction and data recording processes have a significant part to play in combatting the growing reproducibility problem in chemistry as well as science generally. One fundamental goal and common theme seen throughout this work is attempts to us the systems developed here to increase the reliability and reproducibility of our published work

Robotic Modules for the Programmable Chemputation of Molecules and Materials

Before big data methods like machine learning and artificial intelligence methods can be used in chemistry, the digitization of chemistry and materials requires the development of a universal standard that is both affordable and broadly applicable. This has parallels with the foundations of the digital revolution which required standard architectures with a well-defined specification. Recently, we have developed automated platforms for the chemical artificial intelligence driven discovery, synthesis of molecules, materials, nanomaterials, and formulations. To avoid the use of highly expensive systems we focussed on the design and construction of standard hardware and software modules creating a road map for the digitization of chemistry across different fields. Our platforms can be divided into four different categories depending on their application: i) discovery systems for the search of chemical space and new reactivity, ii) synthesis and manufacture of fine chemicals, iii) formulation discovery and exploration, and iv) materials discovery and synthesis. We describe the evolution and convergence of these platforms in terms of, common hardware, firmware, and software along with the development of a programming language for chemical and material systems. This programming approach is not only useful for reliable synthesis, but for design of experiments, discovery, optimisation and providing new standards for collaboration. This approach is also vital for the verification of findings published in the literature, databases, to increase the reliability of experimental outcomes, and to allow collaboration across different research laboratory settings as well as the sharing of failed experiments