A stretchable resistive pressure sensor is achieved by coating a compressible substrate with a highly stretchable electrode. The substrate contains an array of microscale pyramidal features, and the electrode comprises a polymer composite. When the pressure-induced geometrical change experienced by the electrode is maximized at 40% elongation, a sensitivity of 10.3 kPa(-1) is achieved.

Conductive electrodes and electric circuits that can remain active and electrically stable under large mechanical deformations are highly desirable for applications such as flexible displays, field-effect transistors, energy-related devices, smart clothing and actuators. However, high conductivity and stretchability seem to be mutually exclusive parameters. The most promising solution to this problem has been to use one-dimensional nanostructures such as carbon nanotubes and metal nanowires coated on a stretchable fabric, metal stripes with a wavy geometry, composite elastomers embedding conductive fillers and interpenetrating networks of a liquid metal and rubber. At present, the conductivity values at large strains remain too low to satisfy requirements for practical applications. Moreover, the ability to make arbitrary patterns over large areas is also desirable. Here, we introduce a conductive composite mat of silver nanoparticles and rubber fibres that allows the formation of highly stretchable circuits through a fabrication process that is compatible with any substrate and scalable for large-area applications. A silver nanoparticle precursor is absorbed in electrospun poly (styrene-block-butadiene-block-styrene) (SBS) rubber fibres and then converted into silver nanoparticles directly in the fibre mat. Percolation of the silver nanoparticles inside the fibres leads to a high bulk conductivity, which is preserved at large deformations (σ ≈ 2,200 S cm(-1) at 100% strain for a 150-µm-thick mat). We design electric circuits directly on the electrospun fibre mat by nozzle printing, inkjet printing and spray printing of the precursor solution and fabricate a highly stretchable antenna, a strain sensor and a highly stretchable light-emitting diode as examples of applications.

We present machine learning models for the prediction of thermal and mechanical properties of polymers based on the graph convolutional network (GCN). GCN-based models provide reliable prediction performances for the glass transition temperature (T g ), melting temperature (T m ), density (ρ), and elastic modulus (E) with substantial dependence on the dataset, which is the best for T g (R 2 ∼ 0.9) and worst for E (R 2 ∼ 0.5). It is found that the GCN representations for polymers provide prediction performances of their properties comparable to the popular extended-connectivity circular fingerprint (ECFP) representation. Notably, the GCN combined with the neural network regression (GCN-NN) slightly outperforms the ECFP. It is investigated how the GCN captures important structural features of polymers to learn their properties. Using the dimensionality reduction, we demonstrate that the polymers are organized in the principal subspace of the GCN representation spaces with respect to the backbone rigidity. The organization in the representation space adaptively changes with the training and through the NN layers, which might facilitate a subsequent prediction of target properties based on the relationships between the structure and the property. The GCN models are found to provide an advantage to automatically extract a backbone rigidity, strongly correlated with T g , as well as a potential transferability to predict other properties associated with a backbone rigidity. Our results indicate both the capability and limitations of the GCN in learning to describe polymer systems depending on the property.

Recently, achieving flexible and highly efficient light‐emitting elements is the most noticeable demand for lighting or displays. Here, fully flexible gallium nitride (GaN) light‐emitting diodes (LEDs) are demonstrated based on a unique transfer method. The LED structure consisting of GaN pyramid arrays are first fabricated on an amorphous glass‐based template with a low‐temperature gallium nitride/titanium (LT‐GaN/Ti) hetero‐interface, then released and embedded into a flexible or stretchable substrate using a specialized interface control. Nanovoids created during thermal annealing render the hetero‐interface weaker than the other interfaces. This interface is further weakened by a post‐mechanical treatment for gentle release of the GaN pyramid arrays from the interface during a transfer process. The LEDs typically have a total thickness of }70 lm and exhibit stable surface‐emitting electroluminescence even at a bending radius of }2 mm with exceptionally high luminance values of 595 and 175 cd/m2 at peak wavelengths of 514 and 483 nm, respectively. The results suggest a route to high brightness, large, flexible/stretchable blue or green lighting or displays.

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