Search for: All records

Side chain alkyl groups have become the standard for incorporating solubilizing groups into conjugated polymers. However, the variety of alkyl groups available and their location on the polymer’s backbone can contribute to the packing of the polymer chains in many different ways, resulting in many different morphologies in the polymer that can affect its properties and performances. In this paper, we investigate the effects on the conductivity of nine phenothiazine-containing polyaniline derivatives (P1−P9) with alkyl or aryl side chains on the phenothiazine core while also varying the number of methyl groups on the p-phenylenediamine unit. 1H nuclear magnetic resonance spectroscopy, ultraviolet−visible spectroscopy, differential scanning calorimetry, scanning electron microscopy, atomic force microscopy, and wide-angle X-ray scattering (WAXS) were all used to study the polymers’ structures, physical and thermal properties, and morphologies. The t-butylphenyl substituent on the phenothiazine core seems to provide more rigidity in the polymer’s backbone resulting in higher Tg for series 3, while series 2 containing the 2-hexyldecyl-substituted polymers had the lowest Tg, which is attributed to the large volume of the side chain, that limits interchain interactions. Consequently, series 2 had the lowest conductivity. However, the strongest effect on the conductivity was seen from the tetramethyl groups on the PPDA unit, which resulted in the lowest conductivity in each series due to torsional strain (twisting) in the polymer’s backbone. The WAXS data suggest mostly amorphous films; thus, the conductivity in these materials seems to be dominated by a multiscale charge transport phenomenon that occurs in amorphous conjugated materials. Our results will aid in the understanding of side chain engineering of PANI derivatives for their optimum performances. 

Nanocluster-based photoresists enable 3D printing of polymer nanocomposites with enhanced mechanical strength and stability. 

Optical and confocal microscopy is used to image the self-assembly of microscale colloidal particles. The density and size of self-assembled structures is typically quantified by hand, but this is extremely tedious. Here, we investigate whether machine learning can be used to improve the speed and accuracy of identification. This method is applied to confocal images of dense arrays of two-photon lithographed colloidal cones. RetinaNet, a deep learning implementation that uses a convolutional neural network, is used to identify self-assembled stacks of cones. Synthetic data is generated using Blender to supplement experimental training data for the machine learning model. This synthetic data captures key characteristics of confocal images, including slicing in the z-direction and Gaussian noise. We find that the best performance is achieved with a model trained on a mixture of synthetic data and experimental data. This model achieves a mean Average Precision (mAP) of ∼85%, and accurately measures the degree of assembly and distribution of self-assembled stack sizes for different cone diameters. Minor discrepancies between machine learning and hand labeled data is discussed in terms of the quality of synthetic data, and differences in cones of different sizes. 

Polymeric particles with complex shapes are required for biomedical therapies, colloidal self‐assembly, and micro‐robotics. It has been challenging to synthesize particles beyond simple shapes (e.g., spheres, cubes) with high structural accuracy using existing methods. Here, a method for fabricating polymeric microparticles of complex 3D shapes is reported using two‐photon lithography, and dispersing the particles in an aqueous solution on a glass substrate. The fabrication of polyhedrons (e.g., tetrahedron, pyramid), polypods (e.g., tetrapod, hexapod), and other shapes of 5–10 µm in size is demonstrated. Confocal microscopy is used to track the motion of the sphere, tetrahedron, tetrapod, and screw‐shaped particles near the substrate, and determine their translational diffusion coefficients. HYDRO++ is used to simulate the motion of the particles far from the substrate. The influence of particle size and substrate effects on diffusion in the spherical particles is determined and finds that the non‐spherical particles have increased hindrance at the substrate compared to the spherical particles.