Periodic Reporting for period 4 - GROWMOF (Modelling of MOF self-assembly, crystal growth and thin film formation)
Reporting period: 2020-02-01 to 2020-07-31
1. the self-assembly process of MOFs under synthesis conditions
2. the formation of nanoparticles
3. the performance of MOF including nanoparticles and thin films
* When a MOF is synthesised, its pores are typically filled with solvent which has to be removed before a MOF can be used. However, during this so-called activation process MOFs often collapse. We studied in detail the role that the solvent plays and why certain solvents are easier to remove without collapsing the framework.
* For many applications (e.g. drug delivery), MOF nanoparticles are needed. Here flexible MOFs, i.e. MOFs that change their pore sizes and opening when exposed to guest molecules or a higher temperature are of particular interest as this switch can be exploited to tailor the uptake and release process very precisely. Together with an experimental group we studied in detail how the flexibility behaviour changes when the size of the crystals is reduced from bulk crystals to nanoparticles. We discovered that functionalising the surface with different organic molecules is a promising new way to tailor the exact point when the MOF switches.
* Related to MOF nanoparticles are MOF nanosheets which experimentally show better separation performance than the bulk material. We could elucidate the separation mechanism and show that defect free nanosheets are essential for good separation performance.
* We developed a simulation protocol that allows us to describe the uptake and release of an anti-cancer drug in MOFs. We could show that the solvent plays an important role in the exact behaviour, a factor that has so far mainly been neglected in the simulation literature. We also studied the role defects (e.g. missing linkers) and different organic groups on the linkers play and to what extend these can be used to control the uptake and release. Building on this we screen several different MOFs for their suitability for the release of the cancer drug and revealed a complex interplay of different factors that determine if a MOF is a promising material for drug delivery or not.
* The sheer endless possibilities how organic linkers and metal nodes can be combined is one of the most attractive features of MOFs. However, this wide variety of existing MOFs and MOFs build on the computer also poses a challenge when it comes to identifying promising materials for a particular application. Here machine learning presents a promising approach. However, most approaches in the literature so far rely on carrying out molecular simulations for many tens of thousands of individual structures and then using a large proportion of these as a training set to fit a model to which is then validated using the remaining structures. We developed a different approach where we use so-called active learning which develops the model "on the fly" and allows us to find the most promising structures by conducting just a few hundreds of simulations making screening for more complex applications feasible.
* development of a multiscale simulation method to describe the assembly of MOFs from their building block approach under synthesis conditions
* development of a screening approach based on active machine learning to reduce the effort required to identify promising materials for a particular application by several orders of magnitude
* discovered a promising route to tailor the behaviour of flexible MOF nanoparticles by functionalising the surface
* studied the role of defects for applications such as drug delivery and separation and identified factors that so far in the simulation literature had been overlooked