An official website of the United States government
Electrically tunable optical devices present diverse functionalities for manipulating electromagnetic waves by leveraging elements capable of reversibly switching between different optical states. This adaptability in adjusting their responses to electromagnetic waves after fabrication is crucial for developing more efficient and compact optical systems for a broad range of applications, including sensing, imaging, telecommunications, and data storage. Chalcogenide‐based phase‐change materials (PCMs) have shown great promise due to their stable, nonvolatile phase transition between amorphous and crystalline states. Nonetheless, optimizing the switching parameters of PCM devices and maintaining their stable operation over thousands of cycles with minimal variation can be challenging. Herein, the critical role of PCM pattern as well as electrical pulse form in achieving reliable and stable switching is reported on, extending the operational lifetime of the device beyond 13000 switching events. To achieve this, a computer‐aided algorithm that monitors optical changes in the device and adjusts the applied voltage in accordance with the phase transformation process is developed, thereby significantly enhancing the lifetime of these reconfigurable devices. The findings reveal that patterned PCM structures show significantly higher endurance compared to blanket PCM thin films.
more »
« less
Learning to learn by using nonequilibrium training protocols for adaptable materials
Evolution in time-varying environments naturally leads to adaptable biological systems that can easily switch functionalities. Advances in the synthesis of environmentally responsive materials therefore open up the possibility of creating a wide range of synthetic materials which can also be trained for adaptability. We consider high-dimensional inverse problems for materials where any particular functionality can be realized by numerous equivalent choices of design parameters. By periodically switching targets in a given design algorithm, we can teach a material to perform incompatible functionalities with minimal changes in design parameters. We exhibit this learning strategy for adaptability in two simulated settings: elastic networks that are designed to switch deformation modes with minimal bond changes and heteropolymers whose folding pathway selections are controlled by a minimal set of monomer affinities. The resulting designs can reveal physical principles, such as nucleation-controlled folding, that enable such adaptability.
more »
« less
- PAR ID:
- 10427366
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 120
- Issue:
- 27
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Harnessing instabilities of multicomponent multistable structural assemblies can potentially lead to scalable and reversible functionalities, which can be enhanced by exploring frustration. For instance, standard Kresling origami cells exhibit nontunable intrinsic energy landscapes determined by their geometry and material properties, limiting their adaptability after fabrication. To overcome this limitation, we introduce frustration to enable fine-tuning of the energy landscape and resulting deformation states. By prestressing the Kresling cell by means of special springs with individual control, we induce either global or localized (i.e., crease level) frustration, which allows changing the energy barrier (cell or assembly). We investigate the mechanical behavior of frustrated Kresling assemblies, both theoretically and experimentally, under various loading and boundary conditions. Our findings reveal that changing the frustration state leads to precise control of folding sequences, enabling previously inaccessible folding paths. The proposed concept paves the way for applications in mechanical metamaterials and other fields requiring highly programmable and reconfigurable systems – e.g., prosthetic limbs.more » « less
-
Reconfigurable microrobots promise advancements in microsurgical tools, self‐healing materials, and environmental remediation by enabling precise, adaptive functionalities at small scales. However, predicting their behaviors a priori remains a significant challenge, limiting the pace of design and discovery. To address this, a Monte Carlo simulation framework is presented for predicting the folding behavior of self‐assembled, sequence‐encoded microrobot chains composed of magnetic particles, enabling efficient exploration of their large design space. This computational framework employs metrics of radius of gyration, tortuosity, and symmetry score to map the design space, identify functional sequences, and predict likely folding behaviors before fabrication. The framework through experiments to demonstrate accuracy in capturing folding behaviors is validated. Statistical analysis reveals adherence to self‐avoiding walk principles from polymer theory, providing a foundation for understanding how input sequences drive folding capabilities. Moreover, the simulation surpasses current experimental capabilities, enabling exploration of novel microrobot designs, such as sequences incorporating mixtures of cubes and triangular prism subunits, which exhibit distinct compressive behaviors. Beyond the sequence‐encoded microrobots investigated in this study, this framework offers broad utility for the design of reconfigurable microscale systems by reducing reliance on experimental prototyping and accelerating discovery of new functional microrobots for use in biomedicine, materials engineering, and sustainability.more » « less
-
Abstract Architected materials exhibit unique properties and functionalities based on the geometric arrangement of their constituent materials. In most cases, these parameters are fixed, requiring that the system be redesigned and reconstructed if different properties are desired. Both stimuli‐responsive materials and modular designs have been used to enable re‐programmable properties in the past, but often have limitations, such as the need for a continuous application of external stimuli or power, or unwanted global morphing. In this study, a locally stable anti‐tetra chiral (LSAT) metamaterial is introduced consisting of independently multistable units that can deform and change state without inducing changes in the global morphology. Adjacent cells are only weakly coupled, allowing the collective metamaterial to be switched between many different possible states. Local bistability enables re‐programmable heterogeneity, such as the snapping of cells along an edge or diagonally within the architected material. Utilizing finite element analysis (FEA), the influence of key geometric parameters on the re‐programmability of the metamaterials is systematically investigated. The effect of these parameters on properties such as shear stiffness, Poisson's ratio, and vibration are also investigated using experimental prototypes. This re‐programmable metamaterial promises to expand the design space for mechanical systems, with potential applications in non‐traditional computation, robotic actuation, and adaptive structures.more » « less
-
Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations—whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico–chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2219558120
