Digitizing Chemical Synthesis in 3D Printed Reactionware

Bubliauskas, Andrius; Blair, Daniel J.; Powell-Davies, Henry; Kitson, Philip J.; Burke, Martin D.; Cronin, Leroy

doi:10.1002/anie.202116108

Cited by 24 publications

(21 citation statements)

References 42 publications

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“…Particularly, the publication of full data sets from self-driving laboratories, as well as standardized data formats, have the potential to promote the publication of negative results in easily accessible databases. This is important because the current endemic publication bias is detrimental to ML approaches as “negative” results are as important as “positive” ones for training models. , Standardized digital procedures promise more straightforward transferability between laboratories, enabling delocalized and reproducible science. − …”

Section: Why Build a Self-driving Lab?mentioning

confidence: 99%

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

et al. 2022

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Conspectus We must accelerate the pace at which we make technological advancements to address climate change and disease risks worldwide. This swifter pace of discovery requires faster research and development cycles enabled by better integration between hypothesis generation, design, experimentation, and data analysis. Typical research cycles take months to years. However, data-driven automated laboratories, or self-driving laboratories, can significantly accelerate molecular and materials discovery. Recently, substantial advancements have been made in the areas of machine learning and optimization algorithms that have allowed researchers to extract valuable knowledge from multidimensional data sets. Machine learning models can be trained on large data sets from the literature or databases, but their performance can often be hampered by a lack of negative results or metadata. In contrast, data generated by self-driving laboratories can be information-rich, containing precise details of the experimental conditions and metadata. Consequently, much larger amounts of high-quality data are gathered in self-driving laboratories. When placed in open repositories, this data can be used by the research community to reproduce experiments, for more in-depth analysis, or as the basis for further investigation. Accordingly, high-quality open data sets will increase the accessibility and reproducibility of science, which is sorely needed. In this Account, we describe our efforts to build a self-driving lab for the development of a new class of materials: organic semiconductor lasers (OSLs). Since they have only recently been demonstrated, little is known about the molecular and material design rules for thin-film, electrically-pumped OSL devices as compared to other technologies such as organic light-emitting diodes or organic photovoltaics. To realize high-performing OSL materials, we are developing a flexible system for automated synthesis via iterative Suzuki–Miyaura cross-coupling reactions. This automated synthesis platform is directly coupled to the analysis and purification capabilities. Subsequently, the molecules of interest can be transferred to an optical characterization setup. We are currently limited to optical measurements of the OSL molecules in solution. However, material properties are ultimately most important in the solid state (e.g., as a thin-film device). To that end and for a different scientific goal, we are developing a self-driving lab for inorganic thin-film materials focused on the oxygen evolution reaction. While the future of self-driving laboratories is very promising, numerous challenges still need to be overcome. These challenges can be split into cognition and motor function. Generally, the cognitive challenges are related to optimization with constraints or unexpected outcomes for which general algorithmic solutions have yet to be developed. A more practical challenge that could be resolved in the near future is that of software control and integration because few instrument manufacturers d...

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Section: Why Build a Self-driving Lab?mentioning

confidence: 99%

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

et al. 2022

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“…This work has decreased negative effects from reactors, thereby avoiding frequent modification of chemical codes of the software for different reactions. 45 Tailoring specific reactors for different chemical reactions provides a new way of choosing suitable reactors for the hardware. For a mature AI-controlled system, the device customization module should be stronger.…”

Section: Hardware: Executor Of Ai-controlled Synthesismentioning

confidence: 99%

The way to AI-controlled synthesis: how far do we need to go?

et al. 2022

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“…These initial works highlighted concepts of extraordinary importance today, such as easy work up or to facilitate the dispensing of reagents by automated robotic systems connected to multimedia systems. Collectively, behind these studies is the idea of “democratizing” and digitizing the syntheses and their scale up in many laboratories [ 6 ]. This goal is even more feasible if we consider the considerable technological advancement of 3D printers in recent years [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

3D-Printing of Capsule Devices as Compartmentalization Tools for Supported Reagents in the Search of Antiproliferative Isatins

et al. 2023

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The application of high throughput synthesis methodologies in the generation of active pharmaceutical ingredients (APIs) currently requires the use of automated and easily scalable systems, easy dispensing of supported reagents in solution phase organic synthesis (SPOS), and elimination of purification and extraction steps. The recyclability and recoverability of supported reagents and/or catalysts in a rapid and individualized manner is a challenge in the pharmaceutical industry. This objective can be achieved through a suitable compartmentalization of these pulverulent reagents in suitable devices for it. This work deals with the use of customized polypropylene permeable-capsule devices manufactured by 3D printing, using the fused deposition modeling (FDM) technique, adaptable to any type of flask or reactor. The capsules fabricated in this work were easily loaded “in one step” with polymeric reagents for use as scavengers of isocyanides in the work-up process of Ugi multicomponent reactions or as compartmentalized and reusable catalysts in copper-catalyzed cycloadditions (CuAAC) or Heck palladium catalyzed cross-coupling reactions (PCCCRs). The reaction products are different series of diversely substituted isatins, which were tested in cancerous cervical HeLa and murine 3T3 Balb fibroblast cells, obtaining potent antiproliferative activity. This work demonstrates the applicability of 3D printing in chemical processes to obtain anticancer APIs.

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Digitizing Chemical Synthesis in 3D Printed Reactionware

Cited by 24 publications

References 42 publications

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

Autonomous Chemical Experiments: Challenges and Perspectives on Establishing a Self-Driving Lab

The way to AI-controlled synthesis: how far do we need to go?

3D-Printing of Capsule Devices as Compartmentalization Tools for Supported Reagents in the Search of Antiproliferative Isatins

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