Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation

Knox, Stephen T.; Parkinson, Sam; Wilding, Clarissa Y. P.; Bourne, Richard A.; Warren, Nicholas J.

doi:10.1039/d2py00040g

Cited by 46 publications

(49 citation statements)

References 49 publications

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“…Both can be measured online in continuous flow, conversion via NMR spectroscopy, and average molecular weight via size exclusion chromatography (SEC). [8,17,24,34] Thanks to the miniaturization of NMR devices, coupling of flow reactors to benchtop models are made simple. [35] SEC is a little bit more tricky to couple online, as it relies on spaced injections into HPLC systems, and is hence non-continuous.…”

Section: Automated Polymer Screening Platformmentioning

confidence: 99%

Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC

et al. 2022

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Section: Automated Polymer Screening Platformmentioning

confidence: 99%

Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC

et al. 2022

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“…[26] This approach involves mathematical models with closed loops where the recorded information of an experiment influences the experimental conditions for the following ones. [41] The more extensively used algorithm for selfoptimisation is the Nelder-Mead Simplex Method, [42] but there are also examples of the use of others like a modified version known as Complex, [43] Mie, [4b] TSEMO [44] or SNOBFIT. [45] Figure 5 illustrates the general workflow for automated selfoptimising systems: Initial experimental conditions are set, the output of the reactor is recorded and according to the received signal new experimental conditions are chosen by the algorithm.…”

Section: Discussionmentioning

confidence: 99%

Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses

2022

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Digitalisation and industry 4.0 are set to profoundly change the way chemical and materials discovery and development work. The integration of multiple enabling technologies such as flow synthesis, automation, analytics, and real-time reaction control lead to highly efficient, productive, data-driven discovery and synthetic protocols. For instance, the development of flow chemistry enables the fine control and automation of process parameters such as flow rates, temperature, and pressure, which inherently enhances process efficiency. Flow chemistry presents a more sustainable means of manufacturing in terms of waste minimisation, as it enables the integration of synthetic processes with downstream processing. Furthermore, it allows the integration of analytical techniques to provide in situ process monitoring of large amounts of process and product data. The application of Artificial Intelligence (AI) and/or Machine Learning (ML) techniques allows rapid decision making that can optimise existing processes, and it has also been applied in the discovery of novel materials, synthetic pathways and chemicals. All this is contributing to an effective digitalisation of chemical and material synthetic processes from the laboratory to large-scale industrial deployment. This paper presents recent developments in the effective digitalisation of chemical synthetic processes which integrates continuous flow synthesis, analytics and artificial intelligence technologies. Specifically, this paper illustrates the emerging trend of process digitalisation through the advanced syntheses of materials with catalytic, optical and optoelectronic applications.

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“…Further, their approach is particularly effective for solvents in which SEC analysis may be challenging. 91 Recently, Knox et al 92 demonstrated multiobjective optimization of RAFT by applying Thompson sampling efficient multiobjective optimization algorithm to resolve the trade-off between dispersity and monomer conversion without any human intervention. This study relied heavily on inline analysis to ensure that their algorithm could be deployed effectively in real-time even if batch processes enable automated synthesis of a large numbers of polymers, bottlenecks will arise from downstream operations such as NMR and SEC analysis, polyplex formation, physical characterization, and sample preparation for biological testing.…”

Section: Polymer Libraries Via Reversible-deactivation Radical Polyme...mentioning

confidence: 99%

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

Kumar

2022

ACS Appl. Bio Mater.

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Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic−hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.

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Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation

Abstract: Application of artificial intelligence and machine learning for polymer discovery offers an opportunity to meet the drastic need for the next generation high performing and sustainable polymer materials. Here, these...

Cited by 46 publications

References 49 publications

Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC

Operator-independent high-throughput polymerization screening based on automated inline NMR and online SEC

Recent Developments in Process Digitalisation for Advanced Nanomaterial Syntheses

Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis?

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