Ingenieurwissenschaften und zugeordnete Tätigkeiten
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While the synthesis of Metal-Organic Framework (MOF) particles can be as easy as adding two solutions together, reproducibly obtaining the same particles, time and time again, is a lot harder. As laboratory-independent reproducibility is a cornerstone of the scientific method, we must put effort into finding and controlling all necessary parameters to achieve this.
An open-source Python/EPICS-controlled robotic platform (see picture) was adapted to systematically explore this for a 20 ml MOF synthesis of the Zeolitic Imidazole Framework-8 (ZIF-8) chemistry in methanol. Parameters that were explored included: 1) addition sequence, 2) addition speeds, 3) reaction times, 4) source chemicals, 5) stirring speeds, 6) stirring bar choice, 7) starting concentrations, and 8) workup methodologies. It was found that, by controlling these parameters, highly reproducible syntheses are obtained. Secondly, the variation of these parameters alone led to a dramatic difference in volume-weighted particle size means, which exceeds an order of magnitude as investigated by our in-house X-ray scattering instrument [1].
The syntheses are thoroughly documented in an automated fashion, and the synthesis libraries as well as analyses libraries will become available in batches soon. With this library, it will be possible to extract previously unknown correlations, and other laboratories can produce specific particles by following the exact procedures of the particles of their choice.
Following points are presented and discussed: i) nPSize as an EMPIR project on the nanoparticle size and shape distribution of (more) complex particles including sample preparation and machine learning approaches; ii) parallel development of ISO 21363 (PSSD by TEM), ISO 19749 (PSSD by SEM), ISO 22292 (3D TEM), ISO 52408 (NP Prep for AFM and EM), iii) VAMAS and pre-standardisation, TWA 34 „Nanoparticle Populations“ with two nPSize ILC projects P15 & P16 – results to be published and integrated in ISO 21363 (PSSD by TEM); iv) Remaining challenges: platelets, 2D materials (TiO2 and GR2M); v) Reference data sets as part of a complete Characterisation Workflow.