Molecule configurations modulate packing array and charge transfer in cocrystal engineering

Organic cocrystal as an emerging assembly strategy has received increasing attention in constructing multiple functional materials,through continually screening suitable constituent monomers or changing their stoichiometry ratios.However,the role of molecule configuration in the cocrystal field is rarely explored despite the fascinating potential in regulating the packing mode.In this study,the N,N′-diphenyl-5,10-dihydrophenazine derivatives(DPPs) with the flexible scaffold and different methyl substitutions are selected as the donors bearing various molecular configurations.In a simple collaborative way,a series of cocrystals based on DPPs and OFN(octafluoronaphthalene) are fabricated,exhibiting two self-adaptive molecular stacking arrays.X-ray crystallographic analysis and theoretical calculation unveil their different π···π interactions and charge transfer characters,leading to the significant emission redshift up to 125 nm compared with individual DPPs.As a result,controllable molecule stacking structure,tunable emission,and charge transfer properties can be achieved.The study provides a new perspective to reveal the structure-property relationships at the molecular level by controlling the molecule configuration in cocrystal engineering,contributing to the development of cocrystal theoretical physics and organic functional materials.

Machine learning assisted prediction of charge transfer properties in organic solar cells by using morphology-related descriptors

Charge transfer and transport properties are crucial in the photophysical process of exciton dissociation and recombination at the donor/acceptor(D/A)interface.Herein,machine learning(ML)is applied to predict the charge transfer state energy(ECT)and identify the relationship between ECT and intermolecular packing structures sampled from molecular dynamics(MD)simulations on fullerene-and non-fullerene-based systems with different D/A ratios(RDA),oligomer sizes,and D/A pairs.The gradient boosting regression(GBR)exhibits satisfactory performance(r=0.96)in predicting ECT withπ-packing related features,aggregation extent,backbone of donor,and energy levels of frontier molecular orbitals.The charge transport property affected byπ-packing with different RDA has also been investigated by space-charge-limited current(SCLC)measurement and MD simulations.The SCLC results indicate an improved hole transport of non-fullerene system PM6/Y6 with RDA of 1.2:1 in comparison with the 1:1 counterpart,which is mainly attributed to the bridge role of donor unit in Y6.The reduced energetic disorder is correlated with the improved miscibility of polymer with RDA increased from 1:1 to 1.2:1.The morphology-related features are also applicable to other complicated systems,such as perovskite solar cells,to bridge the gap between device performance and microscopic packing structures.

In-situ evolution process understanding from a salan-ligated manganese cluster to supercapacitive application

The goal of material chemistry is to study the relationship among hierarchical structure,chemical reaction and precision preparation for materials,yet tracking pyrolysis process on multi-dimensional scale is still at primary stage.Here we propose packing mode analysis to understand evolution process in high temperature reaction.As a proof of concept,we first design a salan-ligated Mn3(Mn3(3-MeOsalophen)_(2)(Cl)_(2))cluster and pyrolyze it under an inert atmosphere directly to a mixed valence MnOx embedded in a porous N-doped carbon skeleton(MnOx/C).Meanwhile,combining thermogravimetry-mass spectrometry(TG-MS)with other characterization techniques,its pyrolysis process is precisely tracked real-time and Mn^(2+)/Mn^(3+)ratios in the resulting materials are deduced,ensuring excellent electrochemical advantages.As a result,the as-preferred MnOVC-900 sample reaches 943 F/g at 1 A/g,maintaining good durability under 5,000 cycles with 90%retention.The highlight of packing mode analysis strategy in this work would provide a favorable approach to explore the potential relationship between structure and performance in the future.