Block Copolymer Packing Limits and Interfacial Reconfigurability in the Assembly of Periodic Mesoporous Organosilicas

Abstract

wileyonlinelibrary.comwith mesoscale dimensions are of particular interest; they can be introduced by embedding molecular or polymeric porogens within the host material and then processing the composite to create mesopores. [17][18][19][20][21][22][23][24][25][26][27][28] Elucidating design rules for how mesopore dimensions, shape, spatial arrangement, and defect structure dictate properties relies on access to well-controlled, ordered architectures for a wider range of overall porosity than has been possible previously. [ 5,23,29,30 ] To advance the science of mesoscale assembly, a careful revaluation of the factors governing porogen packing and shape persistence before and after processing is needed.We describe here a robust framework to understand the fundamental packing limits for spherical block copoly mer (BCP) micellar porogens during the assembly and thermal processing of periodic mesoporous organosilicas (PMOs), shown schematically in Figure 1 . For this work, we developed and applied a new family of block copolymer (BCP) architecture-directing agents based on a poly(styrene)-blockpoly( N,N-dimethylacrylamide) (PS-b -PDMA) platform. [ 31,32 ] Under the acidic conditions used to hydrolyze 1,1,3,3,5,5-hexaethoxy-1,3,5-trisilacyclohexane (HETSCH), our PMO matrix precursor, we show that the HCl ( aq ) adsorbs to the corona of the PS-b -PDMA micelles. The electrostatic interactions that result, both between micelles, and with hydrolyzed HETSCH molecules, effi ciently direct the periodic arrangements of porogens in the composite fi lms for total porogen volume fractions spanning 26%-78%. A simple spin-on procedure was able to produce uniform fi lms with tunable thicknesses between 50 and 1000 nm across 5 cm × 5 cm Si substrates, and controlled thermal processing yielded structurally sound PMOs for all fi lms. Our implementation of PS-b -PDMA BCPs as PMO architecture-directing agents allows us to independently defi ne a specifi c pore size and shape across a wide range of porosities. As such, we can provide here a more comprehensive portrait into the nonequilibrium structures and phenomena yielding PMOs than has been possible previously. [33][34][35][36][37] One notable and unexpected outcome of our work includes the observation that the degree of order as determined by ( N,N -dimethylacrylamide)-block -poly(styrene) block copolymer micelles (BCPs) are advanced and applied to assemble periodic mesoporous organosilicas (PMOs) with noncylindrical pores. Using these BCP micelles, it is found that pore dimensions (11-23 nm), wall thicknesses (5-9 nm), and overall porosities (26%-78%) are independently programable, depending only on relative inputs for BCP and matrix former. Notably, the degree of order in all fi lms improves as BCP loading approaches a packing limit of 63 vol%. Beyond this limit and regardless of pore dimensions, both porogen packing in the fi lm and pore structure after thermal processing show signifi cant deviations away from spherical close-packed lattices. The surprising absence of fi lm colla...

Facile Synthesis of Yolk-Shell-Structured Triple-Hybridized Periodic Mesoporous Organosilica Nanoparticles for Biomedicine

“…However, the Si‐O‐Si frameworks of mesoporous silica still have some limitations and drawbacks in the biobehaviors and performance, and their medical translation is still a big challenge. The periodic mesoporous organosilicas (PMOs) prepared by using bridged silsesquioxane with the type of (R′O) 3 Si‐R‐Si(R′O) 3 as precursors provide organic groups homogeneously incorporated frameworks . The organic groups incorporated in the frameworks can greatly change the properties of mesoporous siliceous nanoparticles and endow them with better performance in biomedical applications .…”

Tunable depletion potentials driven by shape variation of surfactant micelles

“…In this case, the screening length should be ignored, and the "bare" rod length, L, and rod width, D, should be used in the depletion potential analysis. However, considerable evidence exists to support the notion that ethylene oxide groups of the C 12 E 6 surfactant micelles can acquire charge in the presence of salt [71][72][73]. In this case, dressed dimensions that incorporate the screening length should be used in the analysis.…”

Convolutional neural networks at the interface of physical and digital data

“…We will continue developing these software tools for the recognition of the scientific data collected at imaging facilities, including other modalities and materials [34], seeking to better categorize geometrical motifs present in images. The task of extracting morphological structures and representing them through features maps will have similarities to traditional pattern recognition, and we intend to build analogies and extend concepts to 3D imaging modalities.…”

“…Some of the experimental data generated at DOE facilities are in the form of images, which are particularly well-suited 1 U.S. Government work not protected by U.S. copyright for convolutional neural networks (CNN). CNN goes beyond conventional computer vision approaches [3,4,5] to address the semantic gap between signal acquisition and image interpretation [6]. The major challenge in using CNN is to come up with appropriate labels and descriptors for existing data that have been processed, and to construct CNNs that learn from these labeled datasets to make intelligent predictions and characterization of new data to be collected in future experiments.…”