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Abstract Women have made significant contributions to applied physics research and development, and their participation is vital to continued progress. Recognizing these contributions is important for encouraging increased involvement and creating an equitable environment in which women can thrive. This Roadmap on Women in Applied Physics, written by women scientists and engineers, is intended to celebrate women’s accomplishments, highlight established and early career researchers enlarging the boundaries in their respective fields, and promote increased visibility for the impact women have on applied physics research. Perspectives cover the topics of plasma materials processing and propulsion, super-resolution microscopy, bioelectronics, spintronics, superconducting quantum interference device technology, quantum materials, 2D materials, catalysis and surface science, fuel cells, batteries, photovoltaics, neuromorphic computing and devices, nanophotonics and nanophononics, and nanomagnetism. Our intent is to inspire more women to enter these fields and encourage an atmosphere of inclusion within the scientific community. 

In database and large-scale data analytics, recursive aggregate processing plays an important role, which is generally implemented under a framework of incremental compuping and executed synchronously and/or asynchronously. We identify three barriers in existing recursive aggregate data processing. First, the processing scope is largely limited to monotonic programs. Second, checking on conditions for monotonicity and correctness for async processing is sophisticated and manually done. Third, execution engines may be suboptimal due to separation of sync and async execution.In this paper, we lay an analytical foundation for conditions to check if a recursive aggregate program that is mono-tonic or even non-monotonic can be executed incrementally and asynchronously with its correct result. We design and implement a condition verification tool that can automatically check if a given program satisfies the conditions. We further propose a unified sync-async engine to execute these pro-grams for high performance. To integrate all these effective methods together, we have developed a distributed Datalog system, called PowerLog. Our evaluation shows that PowerLog can outperform three representative Datalog systems on both monotonic and non-monotonic recursive programs. 

Inspired by protein polymerizations, much progress has been made in making “polymer‐like” supramolecular structures from small synthetic subunits through non‐covalent bonds. A few regulation mechanisms have also been explored in synthetic platforms to create supramolecular polymers and materials with dynamic properties. Herein, a type of reactive regulator that facilitates the dimerization of the monomer precursors through dynamic bonds to trigger the supramolecular assembly from small molecules in an aqueous solution is described. The supramolecular structures are crystalline in nature and the reaction coupled assembly strategy can be extended to a supramolecular assembly of aromatic amide derivatives formed in‐situ. The method may be instructive for the development of supramolecular nanocrystalline materials with desired physical properties.

 

Thermoset shape memory polymers (SMPs) have a series of advantages in practical applications when compared with thermoplastic SMPs. However, the reprocessing and reshaping of thermosets are difficult due to the covalent crosslinking which severely limits the reconfiguration of polymer networks even under high temperature. Here, hinered urea bond (HUB), a urea bearing a bulky substituent on its nitrogen atom that can reversibly dissociate to the corresponding bulky amine and isocyanate, is integrated into the poly(urea‐urethane) (PUU) crosslinked networks, enabling the networks to topologically reconfigure to other structures. Instead of having to cure the thermoset in a mold, the incorporation of HUBs can decouple the synthesis and shape‐forming steps, which is a huge advantage for the processing of thermoset materials compared to conventional thermosets. This new PUU thermoset shows a broad glass transition behavior and exhibits excellent triple‐shape‐memory performance. With the incorporation of this dynamic urea bonds, permanent shapes can be flexibly tuned via the network reconfiguration, which is often neglected but significant to the practical application of SMPs.

 

Non‐volatile resistive switching (NVRS) is a widely available effect in transitional metal oxides, colloquially known as memristors, and of broad interest for memory technology and neuromorphic computing. Until recently, NVRS was not known in other transitional metal dichalcogenides (TMDs), an important material class owing to their atomic thinness enabling the ultimate dimensional scaling. Here, various monolayer or few‐layer 2D materials are presented in the conventional vertical structure that exhibit NVRS, including TMDs (MX₂, M=transitional metal, e.g., Mo, W, Re, Sn, or Pt; X=chalcogen, e.g., S, Se, or Te), TMD heterostructure (WS₂/MoS₂), and an atomically thin insulator (h‐BN). These results indicate the universality of the phenomenon in 2D non‐conductive materials, and feature low switching voltage, large ON/OFF ratio, and forming‐free characteristic. A dissociation–diffusion–adsorption model is proposed, attributing the enhanced conductance to metal atoms/ions adsorption into intrinsic vacancies, a conductive‐point mechanism supported by first‐principle calculations and scanning tunneling microscopy characterizations. The results motivate further research in the understanding and applications of defects in 2D materials.

 

2D materials have attracted much interest over the past decade in nanoelectronics. However, it was believed that the atomically thin layered materials are not able to show memristive effect in vertically stacked structure, until the recent discovery of monolayer transition metal dichalcogenide (TMD) atomristors, overcoming the scaling limit to sub‐nanometer. Herein, the nonvolatile resistance switching (NVRS) phenomenon in monolayer hexagonal boron nitride (h‐BN), a typical 2D insulator, is reported. The h‐BN atomristors are studied using different electrodes and structures, featuring forming‐free switching in both unipolar and bipolar operations, with large on/off ratio (up to 10⁷). Moreover, fast switching speed (<15 ns) is demonstrated via pulse operation. Compared with monolayer TMDs, the one‐atom‐thin h‐BN sheet reduces the vertical scaling to ≈0.33 nm, representing a record thickness for memory materials. Simulation results based on ab‐initio method reveal that substitution of metal ions into h‐BN vacancies during electrical switching is a likely mechanism. The existence of NVRS in monolayer h‐BN indicates fruitful interactions between defects, metal ions and interfaces, and can advance emerging applications on ultrathin flexible memory, printed electronics, neuromorphic computing, and radio frequency switches.