Abstract Directed self‐assembly of block copolymers is a key enabler for nanofabrication of devices with sub‐10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self‐assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self‐assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.
Graft-through ring-opening metathesis polymerization (ROMP) of norbornene-terminated macromonomers (MMs) prepared using various polymerization methods has been extensively used for the synthesis of bottlebrush (co)polymers, yet the potential of ROMP for the synthesis of MMs that can subsequently be polymerized by graft-through ROMP to produce new bottlebrush compositions remains untapped. Here, we report an efficient "ROMP-of-ROMP" method that involves the synthesis of norbornene-terminated poly(norbornene imide) (PNI)-based MMs that, following ROMP, provide new families of bottlebrush (co)polymers and "brush-on-brush" hierarchical architectures. In the bulk state, the organization of the PNI pendants drives bottlebrush backbone extension to enable rapid assembly of asymmetric lamellar morphologies with large asymmetry factors. Overall, this work expands the scope of complex macromolecular architectures and provides insights into the interplay of backbone rigidity and self-assembly that will guide future nanolithography applications.
To tackle the challenging secrecy communication problem in energy harvesting cognitive radio networks, this paper considers an overlay system with one energy harvesting secondary user (SU) to assist primary transmission under the assumption that the primary channel at primary receiver is worse than the eavesdropper. Under such scenario, we optimize the secrecy rate of the PU transmitter by jointly investigating energy harvesting slot, cooperative transmission slot and so on. Given the transmission rate requirement between SUs, the optimization problem is formulated as a mixed integer non-linear (MINLP) program. Due to the special features, we design a polynomial time algorithm SRMA to optimally solve this problem. The algorithm computes the lower bound and upper bound of the transmission power in a secondary transmitter, which are relative with the QoS requirement and energy harvesting parameters. Then SRMA determines its optimal transmission power by iteratively searching between two bounds. Numerical results demonstrate that the primary secrecy rate grows with the increasing energy save ratio and optimal energy save ratio is inversely proportional to the energy harvesting rate.
Block copolymer self-assembly is a powerful tool for 2D nanofabrication; however, its extension to complex 3D network structures, which would be useful for a range of applications, is limited. Here, we report a simple method to generate unprecedented 3D mesh morphologies through intrinsic molecular confinement self-assembly. We designed triblock bottlebrush polymers with two Janus domains: one perpendicular and one parallel to the polymer backbone. The former enforces a lamellar superstructure that intrinsically confines the intra-layer self-assembly of the latter, giving rise to a mesh-like monoclinic M15 network substructure with excellent long-range order. Dissipative particle dynamics simulations show that the spatial constraints exerted on the polymer backbone drive the emergence of M15, as well as a tetragonal T131 in the strong segregation regime. This work demonstrates intrinsic molecular confinement as a path to bottom-up assembly of new geometrical phases of soft matter, extending the capabilities of block copolymer nanofabrication.
The initial emission rights allocation is the key measure to achieve the goal of total amount control and deepen the emission trading system. Although many studies have focused on the modeling of initial emission rights allocation, such as using game theory and multi-objective optimization methods, few studies have observed the hierarchical relationship of mutual interference and restriction between watershed management agency and local governments in each subarea during allocation. This relationship directly affects the rationality of the results of regional emission rights allocation. In this study, a leader-follower hierarchical decision model (LFHDM) for allocating initial emission rights in a basin is developed. Based on the bilevel programming approach, the model simulates the interactive decision-making process between the watershed management agency of the upper-level model (LFHDM-U) and the local government of the lower-level model (LFHDM-L) in the allocation under total amount control. A case study of China’s Yellow River Basin is conducted to demonstrate the feasibility and practicality of the model. Findings reveal that, compared with the single-level model, the developed LFHDM has higher satisfaction with the allocation scheme. Under different scenarios, the overall satisfaction of the configuration schemes of COD and NH3-N in each province and autonomous region remains above 0.9. In addition, the allocation volumes of COD and NH3-N in each province of the Yellow River Basin in planning year increase with the enhancement of allowable assimilative capacity of water bodies, but the interval gap of satisfaction with allocation schemes gradually narrows. It shows that when the allowable assimilation capacity of a water body is low, the decision-making of the allocation scheme needs to be more cautious. Moreover, for the Yellow River Basin, apart from Qinghai and Sichuan, the task of reducing water pollutants in other provinces in the next few years is very arduous. The average reduction of total COD and NH3-N in the basin is about 48% and 46%, respectively.
Diblock Janus-type "A-branch-B" bottlebrush copolymers (di-JBBCPs) consist of a backbone with alternating A and B side chains, in contrast to the side chain arrangement of conventional bottlebrush copolymers. As a result, A and B blocks of di-JBBCPs can microphase-separate perpendicular to the backbone, which is located at the interface between the two blocks. A reparametrized dissipative particle dynamics (DPD) model is used to theoretically investigate the self-assembly of di-JBBCPs and to compare with the experimental results of a range of polystyrene-branch-polydimethylsiloxane di-JBBCPs. The experimentally formed cylinder, gyroid, and lamellar morphologies showed good correspondence with the model phase diagram, and the effect of changing volume fraction and backbone length is revealed. The DPD model predicts a bulk-stable perforated lamella morphology together with two unconventional spherical phases, the Frank-Kasper A15 spheres and the hexagonally close-packed spheres, indicating the diversity of morphologies available from complex BCP molecular architectures.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)