Article Cite This: Macromolecules XXXX, XXX, XXX−XXX
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Block Copolymer Amphiphile Phase Diagrams by High-Throughput Transmission Electron Microscopy Mollie A. Touve,†,∥,¶ Daniel B. Wright,†,‡,§,∥,¶ Chen Mu,⊥ Hao Sun,†,‡,§,∥ Chiwoo Park,⊥ and Nathan C. Gianneschi*,†,‡,§,∥ †
Department of Chemistry, ‡Department of Materials Science and Engineering, §Department of Biomedical Engineering, and International Institute for Nanotechnology, Chemistry of Life Processes Institute, and Simpson Querrey Institute, Northwestern University, 2145 Sheridan Road, Evanston, Illinois 60208-3113, United States ⊥ Department of Industrial & Manufacturing Engineering, Florida State University, Tallahassee, Florida 32310, United States Downloaded via KEAN UNIV on July 19, 2019 at 12:02:25 (UTC). See https://pubs.acs.org/sharingguidelines for options on how to legitimately share published articles.
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S Supporting Information *
ABSTRACT: In this paper, we show the rapid generation of phase diagrams for block copolymer amphiphiles. We demonstrate the high-throughput approach for two separate types of amphiphilic block copolymers: One type consisting of poly(ethylene glycol)-b-poly(2-hydroxypropyl methacrylate) and the other consisting of poly(2-(dimethylamino)ethyl methacrylate)-bpoly(2-hydroxypropyl methacrylate), where each amphiphilic block copolymer was prepared by systematically varying the degree of polymerization of the hydrophobic block. These were each synthesized in 96-well plates using polymerization-induced self-assembly to rapidly generate a range of morphologies in aqueous solution. In this manner, 45 different assembled polymer samples were prepared at a time. These samples were sampled by automated picoliter volume liquid handling (piezoelectric robotic dispenser) and analyzed by automated transmission electron microscopy and automated image analysis to rapidly generate phase diagrams.
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INTRODUCTION Amphiphilic block copolymers form myriad phases when assembled in films and as discrete nano- and microscale structures.1−7 An understanding of which polymers, under what conditions, give rise to specific morphologies is critical to the predictability, reproducibility, and utility of this approach toward soft matter preparation.8−15 Many paths are available for controlling these assembly processes, with phase diagrams connecting the polymer composition with the nanoscale morphology.10,16−21 The process of establishing these conditions and converging on a desired microphase separated structure is time-consuming; it involves synthesis of multiple polymers, polymer characterization, assembly under various conditions, and analysis of the resulting structures by scattering and microscopy techniques.12,22−25 It should also be noted that an initial phase diagram often requires further refinement via additional experiments to establish the location of one or more phase boundaries with reasonable precision. While transmission electron microscopy (TEM) is a powerful and central technique for determining the phase, it remains a serious bottleneck in the process. This is true not only in terms of the © XXXX American Chemical Society
time to generate samples but also in the acquisition and analysis, where potentially hundreds of structures need to be examined over many months. A rapid approach to generating libraries of nanoscale morphologies in aqueous solution for a given set of monomers is polymerization-induced self-assembly (PISA).26 In this method, a block copolymer is synthesized with simultaneous assembly into nano- and microscale particles.14,27−34 PISA has been demonstrated for a range of polymerization methods, monomers, and initiation agents.15,26,28,35,36 Recently, PISA was shown to work at low volumes in 96-well plates under ultraviolet (UV) irradiation.37,38 We reasoned that this approach could have great utility in the preparation and characterization of a large number of samples if rapid verification of precise morphologies for each and every sample could be achieved. This would require high-throughput TEM for direct imaging of each particle, where rapid synthetic Received: March 19, 2019 Revised: June 5, 2019
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DOI: 10.1021/acs.macromol.9b00563 Macromolecules XXXX, XXX, XXX−XXX
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Figure 1. Schematic of the PISA systems explored. (a) PEG-b-PHPMA block copolymer and (b) PDMAEMA-b-PHPMA block copolymer synthesized via UV-initiated iniferter polymerization.
Figure 2. Polymer sample preparation and dispensing. (a) General scheme of well plate with the location of each polymer composition, 1−45, shown. (b) Block lengths and solids concentrations for HPMA core block polymerization with either PEG-macroCTA or PDMAEMA-macroCTA. (c) Schematic of fiducial markings on a TEM grid. (d) Low-magnification micrograph, acquired by a scanning transmission electron microscope in the secondary electron mode, of an array of 320 pL sample droplets on a TEM grid. (e) Target locations on a grid for the dispensing of 45 samples by sciTEM.
and previously demonstrated under standard polymerization conditions.39 The known model system first explored was UVinitiated iniferter PISA of a poly(ethylene glycol) (PEG) water-soluble stabilizer block and a poly(2-hydroxypropyl methacrylate) core block (Figure 1). A water-soluble PEG macro-chain-transfer agent (PEG-macroCTA) was synthesized, with a block degree of polymerization of 113. Fortyfive wells of a 96-well plate were loaded with macroCTA, degassed water, and degassed 2-hydroxypropyl methacrylate in a glovebox under a nitrogen atmosphere and sealed. In each case, either the solid concentration or the targeted poly(2hydroxypropyl methacrylate) block length was altered to provide a variation of compositions for the polymer phase diagram (Figure 2). Once sealed, the 96-well plate was illuminated with UV light (365 nm) for 1 h. Then, the polymerization was opened to air and illumination was stopped, and aliquots were taken for analysis by size exclusion chromatography (SEC) (Table S1). High weight percent solutions (compositions with 20−30% solids concentrations
approaches are paired directly with rapid characterization. Without a high-throughput sample handling and imaging approach, the production of a phase diagram would remain either very time expensive or largely incomplete. Herein, we combine PISA of block copolymer amphiphilebased nanostructures on small scales in a 96-well plate format, with sampling of every well by automated methods: automated picoliter volume liquid handling (piezoelectric robotic dispenser), automated TEM (SerialEM), and automated image analysis for the rapid generation of phase diagrams. We propose this approach as a generalizable method for handling large numbers of samples, amenable to other kinds of nanomaterials where a high-throughput synthesis is possible.
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RESULTS AND DISCUSSION 96-Well Plate Polymerization and Automated TEM Sample Preparation. A UV-initiated iniferter polymerization by PISA can be utilized to form a range of polymer nanoparticle morphologies in one pot.35 The system is robust B
DOI: 10.1021/acs.macromol.9b00563 Macromolecules XXXX, XXX, XXX−XXX
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Figure 3. TEM analysis of the nanostructures for PEG-b-PHPMA block copolymer synthesized via UV-initiated iniferter PISA. (a) Images acquired of various samples formed from various polymer compositions, with the observed morphology denoted by m (spherical micelle), w (wormlike micelle), or v (vesicle). Inset numbers correspond to composition. White scale bars = 20 nm. Black scale bars = 1 μm. (b) Zoom-ins of selected morphologies. Arrows point to different particle types. Inset is of the boxed region of the same image. Inset scale bar = 100 nm. (c) Phase diagram generated for PEG-b-PHPMA nanostructures. Numbered compositions refer to those generated in a 96-well plate and analyzed via single TEM grid analysis.
Morphology Screening by TEM on a Single Grid. After automated dispensing, the grid was first imaged manually by TEM (Figure 3). Here, a TEM operator identified the sample locations on the grid, adjusted the sample eucentric height and focus, and acquired images at various magnifications. Surprisingly, it was found that manual imaging of 45 samples on a single grid was much faster than typical single-sample grids due, in large part, to elimination of grid exchange between each sample screen. Additionally, having all samples on the same grid meant that eucentric height and focus were the same or very close for all samples. TEM analysis of the single grid highlighted that the vast range of poly(ethylene glycol)-b-poly(2-hydroxypropyl methacrylate) (PEG-bPHPMA) nanostructures formed via the PISA process could be analyzed on a single grid without the need for a staining agent (Figure 3a). By altering the solids concentration of the
and compositions 41 and 42) were diluted in water to decrease the viscosity and concentration for piezoelectric dispensing and TEM analysis. Otherwise, the polymerization solutions could be sampled directly without dilution. The 96-well plate was loaded into the sciTEM droplet dispensing system, and 250 pL of each sample was individually sampled and deposited onto a TEM grid into a predefined array (Figures S1 and S2).39,40 Grid hydrophilicity and droplet volumes were adjusted for optimal sample spreading, while ensuring integrity of the carbon film of the grid upon drying. Enough sample was present in each location for a thorough morphology screening and each droplet was sufficiently separated to prevent sample mixing and overlapping. (Figures 2d and S2). Up to 45 samples could be loaded onto a single grid with a droplet resolution sufficient for TEM analysis (Figure 2c−e) in 75 min. C
DOI: 10.1021/acs.macromol.9b00563 Macromolecules XXXX, XXX, XXX−XXX
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Figure 4. TEM analysis of the nanostructures for PDMAEMA-b-PHPMA block copolymer synthesized via UV-initiated iniferter polymerization. (a) Images acquired of samples formed from various polymer compositions, with the observed morphology denoted by m (spherical micelle), w (worm), v (vesicle), or LCM. Inset numbers correspond to composition. White scale bars = 20 nm. Black scale bars = 1 μm. (b) Zoom-ins of selected morphologies. Arrows point to different particle types. Inset scale bar = 100 nm. (c) Phase diagram generated for PDMAEMA-b-PHPMA nanostructures. Numbered compositions refer to those generated in a 96-well plate and analyzed via single TEM grid analysis.
poly(2-(dimethylamino) ethyl methacrylate) (PDMAEMA) water-soluble stabilizer block was synthesized, where the PDMAEMA had a block degree of polymerization of 25 (Tables S2 and S3) and used further with the HPMA monomer to synthesize diblock copolymers and generate nanostructures in a 96-well plate. Initial screens were undertaken to confirm whether this system was amenable to generate nanostructures via PISA. To do so, polymer compositions 2−5 (Figure 2b) were synthesized to target the spherical micelle phase and then analyzed by standard dry-state TEM. Surprisingly, all compositions resulted in a mixed spherical micelle and worm phase, which highlights how specific polymer phases are at first targeted with an educated guess but can be incorrect. 41 Knowing this, several compositions at 5 wt % were generated, and all possessed spherical micelle morphologies, as determined by the standard dry-state TEM analysis. Then, similar to the previous PEG-b-
targeted block degree of polymerization of HPMA, transitions from spherical micelles to wormlike micelles and vesicles were observed, and various structural intermediates to produce a well-populated phase diagram (Figure 3b) were also observed. Morphologies were assigned generally as spherical micelles, wormlike micelles, vesicular, or mixtures thereof, and phase boundaries are consistent with those reported previously on similar PEG-b-PHPMA PISA assemblies.35,39 The general phases screened by this high-throughput methodology can be more thoroughly investigated under larger-scale preparations and complementary analytical techniques, such as scattering and cryogenic electron microscopy, to determine the exact, and perhaps more specific, phases of a given polymer system (e.g., branched worms, lamella, multilamellar vesicles). Given the success of the known model system, we applied our high-throughput TEM analysis to an unknown polymer system to rapidly screen and produce a phase diagram. A D
DOI: 10.1021/acs.macromol.9b00563 Macromolecules XXXX, XXX, XXX−XXX
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grid was sufficient for identifying sample locations for acquiring higher resolution images, which was useful when most of the grid area contained samples for screening (Figure 5a−d). Another method of SerialEM navigation we employed was to generate a montage of overlapping frames across the sample regions, where the location registry was more accurate because of the higher magnification acquisition. Image alignment was difficult because of the low-contrast nature and size of the nanoparticles, but sufficient location registry could be determined even for montages containing over 1000 images (Figure 5e). By knowing where the sample is located on the grid, acquiring montage navigator maps is a relatively straightforward method of automated image acquisition, capable of generating hundreds of images of each sample for a more robust and reliable method of sample screening. Given the capability to generate so many images, it now became obvious that analyzing the large number of micrographs and identifying the morphology of each sample was a limiting factor in phase diagram production. Attributing a phase to a given sample by eye was especially difficult when the sample contained mixed phases, for example, spherical and wormlike micelles. Automated Shape Classification. To streamline the process of assigning a phase to a given sample and making analysis less subjective, we employed automated image analysis and subsequent statistical shape classification to automatically identify the overall phase (spherical micelle, wormlike micelle, and vesicle), and in the case of mixed phases, to identify the relative percent of each phase present in the sample (Tables S4 and S5). We first run our robust image binarization method48 to extract micelle images from the image background, which gives the outlines and interior images of each micelle identified, as shown in the bottom panel of Figure 6. Then, an elastic curve-based shape-clustering algorithm49 was applied on the outline data to classify the outlines by their shapes, either spherical or wormlike. The clustering method is an unsupervised method of grouping the outlines by their shapes into these two distinct groups, so no training data is required. Once the clustering is finished, the operator can manually label the groups as either spherical or nonspherical and nonconvex. For each spherical outline, the interior area of the outline was analyzed to further classify by its interior intensity uniformity into spherical and uniform versus spherical and nonuniform (vesicle). Specifically, the radial intensity profile of each detected micelle was extracted, where the radial intensity profile is a plot of the image intensity versus the distance from the micelle centroid. The degree of uniformity was defined by the variance of the intensity profile, which is compared with a preset threshold to classify it into uniform or nonuniform. Additional information on the shape detection algorithm can be found in the Supporting Information. Using this method, we were able to automatically identify phases from the images of both PEG-b-PHPMA (Figures 6a− d, S3, and Table S3) and PDMAEMA-b-PHPMA (Table S4) polymer systems. For the PEG-b-PHPMA system, 45 images were analyzed, and more than 90% of particles in the images were detected by the method for phase classification, based on our manual validation. The number of detected micelles was 374 per image on average, with the counts ranging between 19 and 934. For the PDMAEMA-b-PHPMA system, 45 images were analyzed to detect 455 micelles per image on average, with the counts ranging between 1 and 1390. In some cases, such as compositions 42 and 43 for the PDMAEMA-b-
PHPMA system, with minor adjustments to the polymer compositions, 45 wells of a 96-well plate were loaded with PDMAEMA-macroCTA, degassed water, and degassed hydroxypropyl methacrylate monomer, sealed under a nitrogen atmosphere, and illuminated with UV light (365 nm) for 1 h. The sciTEM droplet-dispensing system was utilized to dispense the 45 samples onto a single TEM grid. From manual TEM image acquisition, all classical PISA nanostructures (spherical micelles, wormlike micelles, and vesicles) as well as large compound micelles (LCMs) were accessed, and a phase diagram was produced (Figure 4). Automated Image Acquisition. SerialEM is a powerful tool typically used for automated low-dose imaging of cryogenic electron microscopy samples and automated electron tomography.42−47 The automated image acquisition software is routinely used to generate high-resolution montages of large-area samples, such as biological cells, wherein highresolution images are acquired sequentially across a sample and then stitched together into a montage. We were interested in the utility of SerialEM Navigator to generate single-image or montage maps to register the sample location on a grid and then acquire higher magnification images of the sample to identify the phase in an automated fashion (Figure 5).46 Using single-image mapping, a low-magnification image of the sample
Figure 5. Automated image acquisition for block copolymer nanostructures. (a) Low-magnification image of a grid containing droplets of PEG-b-PHPMA nanostructures. (b) Higher-magnification image of the area boxed in (a), which served as a single-image Navigator map in SerialEM with a point of interest highlighted in orange. (c) Higher-magnification image acquired at the region marked in (b) acquired by SerialEM. (d) High-magnification image acquired to clearly analyze the phase of the sample. (e) 40 × 40 micrograph montage generated as a Navigator map in SerialEM at ×2000 magnification. White circles denote droplet regions on the grid. E
DOI: 10.1021/acs.macromol.9b00563 Macromolecules XXXX, XXX, XXX−XXX
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Figure 6. Automated image analysis using statistical shape theory. (a−c) Original TEM micrograph (top panel) and applied shape classification (bottom panel) of a spherical micelle phase (a), mixed spherical micelle and wormlike micelle phase (b), and mixed vesicular and spherical micelle phase (c) of the PEG-b-PHPMA polymer system. Detected shapes are outlined in red. Relative percentages of the spherical micelle (m), wormlike micelle (w), and vesicular (v) phase assigned through shape classification are shown (d) by application of a shape detection algorithm to a micrograph containing spherical (yellow), wormlike micelles (red), and vesicles (blue).
PHPMA system, a vesicle phase was misclassified as a mixed spherical and wormlike micelle phase (Figure 4a and Table S4). We believe these mistakes were mainly because of errors in image binarization, when the background (deep dark lines around the vesicular structures) is darker than the foreground, in contrast to the other images which do not show vesicular structures. However, manual classification can easily be performed to correct these results. Selection of a Phase and Scale-Up Verification. For a given application involving copolymer assemblies, particular phases must be targeted. Therefore, we reasoned that our highthroughput methodology would be easily extended toward screening for a phase. Vesicular phases are generally elusive but desirable targets, and thus rapid, thorough screening would be a beneficial proof-of-concept. To confirm that the vesicular structures which we identified using our method are translatable to larger-scale polymerizations, we prepared each polymer which had displayed uniform vesicular morphologies in the 45-sample TEM grid analysis on the 250 mg of solids scale. These three polymers were composition 42 for the PDMAEMA-b-PHPMA system and composition 43 for both PEG-b-PHPMA and PDMAEMA-b-PHPMA polymer systems, all of which were originally verified as synthesized in a 96-well plate at
