Unveiling the Synthesis Patterns of Nanomaterials: A Text Mining and Meta-Analysis Approach with ZIF-8 as a Case Study

09 June 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


With the continuously growing number of scientific articles on synthesis of nanomaterials, it becomes impossible for researchers to grasp and comprehend the landscape of synthetic protocols available for a particular material. The aim of this study is to explore the feasibility of extracting the collective knowledge on synthesis of a particular material accumulated over the years from the published corpus of articles and organizing it in a systematic manner. Accordingly, we developed methods to perform detailed text mining on a single nanomaterial target for the purposes of methodology optimisation. Taking the common material ZIF-8 as a case study, we analysed 1600 synthesis protocols to identify trends in parameters, such as reagents, concentrations, and reaction time/temperature. We used this information to find the distribution of synthesis parameters and their relationships to one another, identifying the limits of common reaction parameters and revealing subtle details, such as insolubility of metal acetate reagents in alcoholic solvents, or the occurrence of amorphous oxides at low stoichiometric ratios. We then clustered similar synthesis protocols together, using their relative popularity to identify promising regions of the synthesis phase space for optimisation, reducing the need for brute force synthesis optimisation. The techniques developed here are a general tool accelerating the synthesis development of a wide range of nanomaterials by aggregating existing research trends, averting the need for laborious manual comparison of existing synthesis protocols or repetition of previously-developed techniques.


text mining
synthesis data
digital manufacturing

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