Assessing the possibilities of designing a unified multistep continuous flow synthesis platform.

The multistep flow synthesis of complex molecules has gained momentum over the last few years. A wide range of reaction types and conditions have been integrated seamlessly on a single platform including in-line separation as well as monitoring. Beyond merely getting considered as 'flow version' of conventional 'one-pot synthesis', multistep flow synthesis has become the next generation tool for creating libraries of new molecules. Here we give a more 'engineering' look at the possibility of developing a 'unified multistep flow synthesis platform'. A detailed analysis of various scenarios is presented considering 4 different classes of drugs already reported in the literature. The possible complexities that an automated and controlled platform needs to handle are also discussed in detail. Three different design approaches are proposed: (i) one molecule at a time, (ii) many molecules at a time and (iii) cybernetic approach. Each approach would lead to the effortless integration of different synthesis stages and also at different synthesis scales. While one may expect such a platform to operate like a 'driverless car' or a 'robo chemist' or a 'transformer', in reality, such an envisaged system would be much more complex than these examples.

Automating multistep flow synthesis: approach and challenges in integrating chemistry, machines and logic.

The implementation of automation in the multistep flow synthesis is essential for transforming laboratory-scale chemistry into a reliable industrial process. In this review, we briefly introduce the role of automation based on its application in synthesis viz. auto sampling and inline monitoring, optimization and process control. Subsequently, we have critically reviewed a few multistep flow synthesis and suggested a possible control strategy to be implemented so that it helps to reliably transfer the laboratory-scale synthesis strategy to a pilot scale at its optimum conditions. Due to the vast literature in multistep synthesis, we have classified the literature and have identified the case studies based on few criteria viz. type of reaction, heating methods, processes involving in-line separation units, telescopic synthesis, processes involving in-line quenching and process with the smallest time scale of operation. This classification will cover the broader range in the multistep synthesis literature.

Feasibility and Advantages of Continuous Synthesis of Bioinspired Silica Using CO2 as an Acidifying Agent.

In this work, we present a method for the continuous synthesis of bioinspired porous silica (BIS) particles using carbon dioxide (CO2) as an acidifying agent. Typical BIS synthesis uses strong mineral acids (e.g., HCl) to initiate the hydrolysis and subsequent condensation reactions. The use of strong acids leads to challenges in controlling the reaction pH. The synthesis approach proposed in this work offers for the first time CO2 as an attractive alternative for the synthesis of BIS and demonstrates the continuous process. The developed method leverages the mild acidic and the self-buffering nature of the CO2 combined with additional options for controlling mass transfer rates to facilitate enhanced control of pH, which is crucial for controlling the properties of synthesized BIS. Proof of concept experiments conducted in continuous mode demonstrated a yield of over 70% and a surface area exceeding 500 m2/g. These results indicate the successful synthesis of BIS using CO2 with properties in the desired range. The enhanced pH control offered by this CO2-based process will facilitate the implementation of a sustainable and robust continuous process for BIS synthesis.

Novel Machine Learning-Based Method for Estimation of the Surface Area of Porous Silica Particles.

This work reports a novel and quick method to estimate the surface area of porous materials. Conventionally, surface area measurement requires the BET method/N2 adsorption experiment which is time-consuming. In this work, we developed a method based on machine learning (ML) and the adsorption of a conductive dye on porous materials. The rate and quantity of dye adsorption, which is characterized by dynamic measurement of conductivity, provide an indirect measure of surface area and zeta potential. An ML-based soft sensor is developed to relate the measured conductivity profiles with surface area and zeta potential. A phenomenological model on dye adsorption is also developed, validated, and used to augment experimental data for training the soft sensor. The developed method was tested for porous silica particles with a range of surface areas (250-1100 m2/g) and zeta potential (-17 mV: -29 mV). The developed soft sensor was able to estimate the surface area and zeta potential quite well. The developed approach and method reduce overall measurement time for surface area from several hours to a few minutes. The method can potentially be implemented in continuous plants producing porous materials like silica.