Thickness Limit for Alignment of Block Copolymer Films Using Solvent Vapor Annealing with Shear

Solvent-assisted self-assembly of block copolymer thin films

Solvent-assisted block copolymer self-assembly is a compelling method for processing and advancing practical applications of these materials due to the exceptional level of the control of BCP morphology and significant acceleration of ordering kinetics. Despite substantial experimental and theoretical efforts devoted to understanding of solvent-assisted BCP film ordering, the development of a universal BCP patterning protocol remains elusive; possibly due to a multitude of factors which dictate the self-assembly scenario. The aim of this review is to aggregate both seminal reports and the latest progress in solvent-assisted directed self-assembly and to provide the reader with theoretical background, including the outline of BCP ordering thermodynamics and kinetics phenomena. We also indicate significant BCP research areas and emerging high-tech applications where solvent-assisted processing might play a dominant role.

Developing Anisotropy in Self-Assembled Block Copolymers: Methods, Properties, and Applications

Block copolymers (BCPs) self-assembly has continually attracted interest as a means to provide bottom-up control over nanostructures. While various methods have been demonstrated for efficiently ordering BCP nanodomains, most of them do not generically afford control of nanostructural orientation. For many applications of BCPs, such as energy storage, microelectronics, and separation membranes, alignment of nanodomains is a key requirement for enabling their practical use or enhancing materials performance. This review focuses on summarizing research progress on the development of anisotropy in BCP systems, covering a variety of topics from established aligning techniques, resultant material properties, and the associated applications. Specifically, the significance of aligning nanostructures and the anisotropic properties of BCPs is discussed and highlighted by demonstrating a few promising applications. Finally, the challenges and outlook are presented to further implement aligned BCPs into practical nanotechnological applications, where exciting opportunities exist.

Solvent Vapor Annealing, Defect Analysis, and Optimization of Self-Assembly of Block Copolymers Using Machine Learning Approaches

Self-assembly of block copolymers (BCPs) is an alternative patterning technique that promises high resolution and density multiplication with lower costs. The defectivity of the resulting nanopatterns remains too high for many applications in microelectronics and is exacerbated by small variations of processing parameters, such as film thickness, and fluctuations of solvent vapor pressure and temperature, among others. In this work, a solvent vapor annealing (SVA) flow-controlled system is combined with design of experiments (DOE) and machine learning (ML) approaches. The SVA flow-controlled system enables precise optimization of the conditions of self-assembly of the high Flory-Huggins interaction parameter (χ) hexagonal dot-array forming BCP, poly(styrene-b-dimethylsiloxane) (PS-b-PDMS). The defects within the resulting patterns at various length scales are then characterized and quantified. The results show that the defectivity of the resulting nanopatterned surfaces is highly dependent upon very small variations of the initial film thicknesses of the BCP, as well as the degree of swelling under the SVA conditions. These parameters also significantly contribute to the quality of the resulting pattern with respect to grain coarsening, as well as the formation of different macroscale phases (single and double layers and wetting layers). The results of qualitative and quantitative defect analyses are then compiled into a single figure of merit (FOM) and are mapped across the experimental parameter space using ML approaches, which enable the identification of the narrow region of optimum conditions for SVA for a given BCP. The result of these analyses is a faster and less resource intensive route toward the production of low-defectivity BCP dot arrays via rational determination of the ideal combination of processing factors. The DOE and machine learning-enabled approach is generalizable to the scale-up of self-assembly-based nanopatterning for applications in electronic microfabrication.

Correlating Nanostructure, Optical and Electronic Properties of Nanogranular Silver Layers during Polymer-Template-Assisted Sputter Deposition

Tailoring the optical and electronic properties of nanostructured polymer-metal composites demonstrates great potential for efficient fabrication of modern organic optical and electronic devices such as flexible sensors, transistors, diodes, or photovoltaics. Self-assembled polymer-metal nanocomposites offer an excellent perspective for creating hierarchical nanostructures on macroscopic scales by simple bottom-up processes. We investigate the growth processes of nanogranular silver (Ag) layers on diblock copolymer thin film templates during sputter deposition. The Ag growth is strongly driven by self-assembly and selective wetting on the lamella structure of polystyrene-block-poly(methyl methacrylate). We correlate the emerging nanoscale morphologies with collective optical and electronic properties and quantify the difference in Ag growth on the corresponding homopolymer thin films. Thus, we are able to determine the influence of the respective polymer template and observe substrate effects on the Ag cluster percolation threshold, which affects the insulator-to-metal transition (IMT). Optical spectroscopy in the UV-vis regime reveals localized surface plasmon resonance for the metal-polymer composite. Their maximum absorption is observed around the IMT due to the subsequent long-range electron conduction in percolated nanogranular Ag layers. Using X-ray photoelectron spectroscopy and Fourier-transform infrared spectroscopy, we identify the oxidation of Ag at the acrylate side chains as an essential influencing factor driving the selective wetting behavior in the early growth stages. The results of polymer-templated cluster growth are corroborated by atomic force microscopy and field emission scanning electron microscopy.

Synthesis and Characterization of Graphene Oxide/Chitosan Composite Aerogels with High Mechanical Performance

Chitosan, a semi-crystalline biomolecule, has attracted wide attention due to its high synthesis flexibility. In this study, to improve the mechanical properties of chitosan aerogels (CSAs), graphene oxide (GO) was extracted and introduced into chitosan aerogels as fillers. The porous CSAs/GO composite aerogels were fabricated by an environmentally friendly freeze-drying process with different GO contents (0, 0.5, 1.0, 1.5, wt.%). The characteristics of the CSAs/GO were investigated by scanning electron microscopy (SEM), mechanical measurements and mercury porosimeter. The crystallinity of samples was characterized by X-ray diffraction (XRD). The mechanism of the effect of graphene oxide on chitosan was studied by Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). The results show that the microstructure of the samples is developed in the network structure. The porosity of CSAs/GO aerogels is as high as 87.6%, and the tensile strength of the films increased from 6.60 MPa to 10.56 MPa with the recombination of graphene oxide. The crystallinity (CrI) of composite aerogels increased from 27% to 81%, which indicates that graphene oxide improves the mechanical properties of chitosan by chemical crosslinking.

Highly Conductive Ti3 C2 Tx MXene Hybrid Fibers for Flexible and Elastic Fiber-Shaped Supercapacitors

Fiber-shaped supercapacitors (FSCs) are promising energy storage solutions for powering miniaturized or wearable electronics. However, the scalable fabrication of fiber electrodes with high electrical conductivity and excellent energy storage performance for use in FSCs remains a challenge. Here, an easily scalable one-step wet-spinning approach is reported to fabricate highly conductive fibers using hybrid formulations of Ti₃ C₂ T_x MXene nanosheets and poly(3,4-ethylenedioxythiophene):polystyrene sulfonate. This approach produces fibers with a record conductivity of ≈1489 S cm^-1 , which is about five times higher than other reported Ti₃ C₂ T_x MXene-based fibers (up to ≈290 S cm^-1 ). The hybrid fiber at ≈70 wt% MXene shows a high volumetric capacitance (≈614.5 F cm^-3 at 5 mV s^-1 ) and an excellent rate performance (≈375.2 F cm^-3 at 1000 mV s^-1 ). When assembled into a free-standing FSC, the energy and power densities of the device reach ≈7.13 Wh cm^-3 and ≈8249 mW cm^-3 , respectively. The excellent strength and flexibility of the hybrid fibers allow them to be wrapped on a silicone elastomer fiber to achieve an elastic FSC with 96% capacitance retention when cyclically stretched to 100% strain. This work demonstrates the potential of MXene-based fiber electrodes and their scalable production for fiber-based energy storage applications.