We report a flexible and wearable pressure sensor based on the giant piezocapacitive effect of a three-dimensional (3-D) microporous dielectric elastomer, which is capable of highly sensitive and stable pressure sensing over a large tactile pressure range. Due to the presence of micropores within the elastomeric dielectric layer, our piezocapacitive pressure sensor is highly deformable by even very small amounts of pressure, leading to a dramatic increase in its sensitivity. Moreover, the gradual closure of micropores under compression increases the effective dielectric constant, thereby further enhancing the sensitivity of the sensor. The 3-D microporous dielectric layer with serially stacked springs of elastomer bridges can cover a much wider pressure range than those of previously reported micro-/nanostructured sensing materials. We also investigate the applicability of our sensor to wearable pressure-sensing devices as an electronic pressure-sensing skin in robotic fingers as well as a bandage-type pressure-sensing device for pulse monitoring at the human wrist. Finally, we demonstrate a pressure sensor array pad for the recognition of spatially distributed pressure information on a plane. Our sensor, with its excellent pressure-sensing performance, marks the realization of a true tactile pressure sensor presenting highly sensitive responses to the entire tactile pressure range, from ultralow-force detection to high weights generated by human activity.

Wearable electronics represent a significant paradigm shift in consumer electronics since they eliminate the necessity for separate carriage of devices. In particular, integration of flexible electronic devices with clothes, glasses, watches, and skin will bring new opportunities beyond what can be imagined by current inflexible counterparts. Although considerable progresses have been seen for wearable electronics, lithium rechargeable batteries, the power sources of the devices, do not keep pace with such progresses due to tenuous mechanical stabilities, causing them to remain as the limiting elements in the entire technology. Herein, we revisit the key components of the battery (current collector, binder, and separator) and replace them with the materials that support robust mechanical endurance of the battery. The final full-cells in the forms of clothes and watchstraps exhibited comparable electrochemical performance to those of conventional metal foil-based cells even under severe folding-unfolding motions simulating actual wearing conditions. Furthermore, the wearable textile battery was integrated with flexible and lightweight solar cells on the battery pouch to enable convenient solar-charging capabilities.

Conjugation of mussel-inspired catechol groups to various polymer backbones results in materials suitable as silicon anode binders. The unique wetness-resistant adhesion provided by the catechol groups allows the silicon nanoparticle electrodes to maintain their structure throughout the repeated volume expansion and shrinkage during lithiation cycling, thus facilitating substantially improved specific capacities and cycle lives of lithium-ion batteries.

Direct measurement of the adhesion energy of monolayer graphene as-grown on metal substrates is important to better understand its bonding mechanism and control the mechanical release of the graphene from the substrates, but it has not been reported yet. We report the adhesion energy of large-area monolayer graphene synthesized on copper measured by double cantilever beam fracture mechanics testing. The adhesion energy of 0.72 ± 0.07 J m(-2) was found. Knowing the directly measured value, we further demonstrate the etching-free renewable transfer process of monolayer graphene that utilizes the repetition of the mechanical delamination followed by the regrowth of monolayer graphene on a copper substrate.

RGO flakes are homogeneously dispersed in a Cu matrix through a molecular-level mixing process. This novel fabrication process prevents the agglomeration of the RGO and enhances adhesion between the RGO and the Cu. The yield strength of the 2.5 vol% RGO/Cu nanocomposite is 1.8 times higher than that of pure Cu. The strengthening mechanism of the RGO is investigated by a double cantilever beam test using the graphene/Cu model structure.

Figure 8 . Demonstration of a bendable touch screen on a non-fl at surface. Letters "ANTS" were written.

While the regioregularity (RR) of conjugated polymers is known to have a strong influence on their inherent properties, systematic study of the RR effect has been limited due to the lack of a synthetic methodology. Herein, we successfully produced a series of poly(3-hexylthiophene)s (P3HTs) having a wide range of RR from 64 to 98%. Incorporation of controlled amounts of head-to-head (H−H) coupled dimer in modified Grignard metathesis polymerization allows a facile tuning of the RR of the P3HTs with comparable molecular weight and low polydispersity. Then, we investigated the effect of RR on structural, electrical, and mechanical properties of P3HTs in which a higher content of H−H regio-defects, namely lower RR, systematically lowered the degree of crystallinity. Although high RR P3HT (98%) had higher charge carrier mobility (1.81 × 10 −1 cm 2 V −1 s −1 ), its strong crystallinity induced high brittleness and stiffness, resulting in device failure under a very small strain, as shown in tensile and bending tests. The tensile modulus was reduced significantly from 287 MPa (RR 98%) to 13 MPa (RR 64%), and also the RR 64% P3HT film had much better mechanical resilience with an order of magnitude higher elongation at break than that of the RR 98% polymer. Our findings suggest that the mechanical and electrical properties of conjugated polymers can be systematically tuned by controlling the RR to meet the purposes of various organic electronic applications, i.e., flexible portable devices vs high-performance panels.

While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity.