Mechanically tunable metasurface with large gamut of color: Lateral hybrid system

Hybrid metasurfaces are made of metals and dielectrics in which dielectrics (metals) are sandwiched between metals (dielectrics) to control the reflection and transmission of light. The existing designs have low sensitivity, little color coverage, and a lack of flexibility. Here, a new structural color design is proposed in which metals and dielectric resonators are arranged spatially in 2D to form a lateral hybrid system, instead of being placed as layers. Such a design exhibits a high level of sensitivity to mechanical forces because it works via 2D optical coupling and light confinement between adjacent resonators. Our study shows that in-planar coupling of two dissimilar resonators can enhance sensitivity by an order of magnitude in comparison to stacking them. Metasurfaces with our design would have unprecedented mechanical tunability without compromising either the materials choice or processing. Using the proposed hybrid system, we demonstrate large tunability across the full range of colors with only a 10% change in the size of the lattice, which further proves its superiority over existing designs. This concept could find application in wearable devices that require high sensitivity to small mechanical fluctuations

Ultra-stretchable active metasurfaces for high-performance structural color

Metamaterials as artificially structural materials exhibit customized properties unattainable in nature. While dynamic response is highly desired, metamaterials are usually passive and cannot be tuned post-fabrication. A conventional active metamaterial consists of rigid resonators mounted on flexible substrates that permit a limited amount of mechanical tuning. Given that rigid resonators permanently deform or debond under large strains (above 30%), the range of flexibility that is possible with tunable metamaterials is limited. Here, we propose a kirigami-inspired geometry that overcomes this limitation. The proposed design enhances stretchability exceeding 100% when compared with the existing design. A high degree of flexibility is achieved through “stress engineering” at the interface between rigid resonators and flexible substrates. Our design shows that the resonance modes shift at a rate of 3.32 ± 0.1 nm for every 1% change in strain, which is the highest tunability reported thus far. We demonstrate how this new concept can be applied to structural color. Using a single design, we demonstrated the full range of colors for the first time. The novel concept of highly stretchable metamaterials may revolutionize the field and enable its use in applications such as wearable sensors, smart displays, and switchable devices requiring extremely dynamic properties

In-Materio Extreme Learning Machines

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as datasets get larger and more complex remains unclear. Extreme Learning Machines (ELMs) seek to exploit a randomly initialised single layer feed forward neural network by training the output layer only. An analogy for a physical ELM is produced by exploiting nanomaterial networks as material neurons within the hidden layer. Circuit simulations are used to eciently investigate diode-resistor networks which act as our material neurons. These in-Materio ELMs (iM-ELMs) outperform common classication methods and traditional articial ELMs of a similar hidden layer size. For iM-ELMs using the same number of hidden layer neurons, leveraging larger more complex material neuron topologies (with more nodes/electrodes) leads to better performance, showing that these larger materials have a better capability to process data. Finally, iM-ELMs using virtual material neurons, where a single material is re-used as several virtual neurons, were found to achieve comparable results to iM-ELMs which exploited several dierent materials. However, while these Virtual iM-ELMs provide signicant exibility, they sacrice the highly parallelised nature of physically implemented iM-ELMs

Highly Sensitive Flexible Pressure Sensor Based on PVDF-TrFE-BaTiO3 Piezoelectric Nanofibers

With the rapid advancement of wearable electronics, there is continuing demand to explore flexible sensors with high sensitivity to detect even the subtlest of mechanical stimuli enabling accurate and real-time monitoring for various applications. Among piezoelectric organic materials, poly(vinylidene fluoride-co-trifluoroethylene) ( PVDF-TrFE) stands out as a good candidate for the fabrication of flexible and wearable devices with stability and biocompatibility. This letter presents the development and characterization of PVDF-TrFE barium titanate (BaTiO 3 ) (PVDF-TrFE-BaTiO 3 ) composite nanofibers fabricated by electrospinning, demonstrating that the addition of piezoelectric inorganic material BaTiO 3 enhances the crystallinity, amount of the β -phase, and the piezoelectric response. A conformal PVDF-TrFE-BaTiO 3 nanofiber-based piezoelectric sensor was further developed and tested under low pressure/vibration for potential wearable applications. The sensor exhibits an enhanced pressure sensitivity of 0.21 V/kPa at a pressure range from 6.4 to 16 kPa at a fixed frequency of 7 Hz, and a frequency sensitivity of 1.72 V/Hz within a frequency range from 2 to 5 Hz at a fixed pressure of 6.4 kPa. This means that the PVDF-BaTiO 3 -based flexible sensor is particularly sensitive to low-regime mechanical movement holding great potential application in human motion monitoring and wearable devices, such as heartbeat, pulse, and respiration monitoring, for sports performance tracking and healthcare

Nanostructured Channel for Improving Emission Efficiency of Hybrid Light-Emitting Field-Effect Transistors

We report on the mechanism of enhancing the luminance and external quantum efficiency (EQE) by developing nanostructured channels in hybrid (organic/inorganic) light-emitting transistors (HLETs) that combine a solution-processed oxide and a polymer heterostructure. The heterostructure comprised two parts: (i) the zinc tin oxide/zinc oxide (ZTO/ZnO), with and without ZnO nanowires (NWs) grown on the top of the ZTO/ZnO stack, as the charge transport layer and (ii) a polymer Super Yellow (SY, also known as PDY-132) layer as the light-emitting layer. Device characterization shows that using NWs significantly improves luminance and EQE (≈1.1% @ 5000 cd m–2) compared to previously reported similar HLET devices that show EQE < 1%. The size and shape of the NWs were controlled through solution concentration and growth time, which also render NWs to have higher crystallinity. Notably, the size of the NWs was found to provide higher escape efficiency for emitted photons while offering lower contact resistance for charge injection, which resulted in the improved optical performance of HLETs. These results represent a significant step forward in enabling efficient and all-solution-processed HLET technology for lighting and display applications

Controlling the growth of single crystal ZnO nanowires by tuning the atomic layer deposition parameters of the ZnO seed layer

Semiconducting nanowires (NWs) offer exciting prospects for a wide range of technological applications. The translation of NW science into technology requires reliable high quality large volume production. This study provides an in-depth investigation of the parameters using an atomic layer deposition system to grow zinc oxide (ZnO) seed layers followed by the chemical bath deposition (CBD) of ZnO NWs to demonstrate the low-cost production of uniform single crystal wurtzite phase ZnO NWs that is scalable to large area substrates. The seed layer texture and the morphology of the NWs grown were systematically investigated using atomic force microscopy as a function of the seed layer deposition parameters. It is shown that the NWs growth orientation can be controlled by tuning the seed layer deposition parameters while maintaining the same CBD conditions. Likewise, the diameters and the surface densities of the NWs varied from 23 to 56 nm and 40 to 327 NWs μm−2, respectively. Significantly, the relationship between the seed layer structure and the NW density indicates a clear correlation between the density of seed layer surface features and the resulting surface NW density of NWs grown

Enhanced Methods for Evolution in-Materio Processors

Evolution-in-Materio (EiM) is an unconventional computing paradigm, which uses an Evolutionary Algorithm (EA) to configure a material's parameters so that it can perform a computational task. While EiM processors show promise, slow manufacturing and physical experimentation hinder their development. Simulations based on a physical model were used to efficiently investigate three specific enhancements to EiM processors which operate as classifiers. Firstly, an adapted Differential Evolution algorithm that includes batching and a validation dataset. This allows more generational updates and a validation metric which could tune hyper-parameters. Secondly, the introduction of Binary Cross Entropy as an objective function for the EA, a continuous fitness metric with several advantages over the commonly used classification error objective function. Finally, the use of regression to quickly assess the material processor's output states and produce an optimal readout layer, a significant improvement over fixed or evolved interpretation schemes which can ‘hide’ the true performance of a material processor. Together these enhancements provide guidance on the production of more flexible, better performing, and robust EiM processors