Carbon nitrides constitute a class of earth‐abundant polymeric semiconductors, which have high potential for tunability on a molecular level, despite their high chemical and thermal inertness. Here the first postsynthetic modification of the 2D carbon nitride poly(heptazine imide) (PHI) is reported, which is decorated with terminal melamine (Mel) moieties by a functional group interconversion. The covalent attachment of this group is verified based with a suite of spectroscopic and microscopic techniques supported by quantum–chemical calculations. Using triethanolamine as a sacrificial electron donor, Mel‐PHI outperforms most other carbon nitrides in terms of hydrogen evolution rate (5570 µmol h−1 g−1), while maintaining the intrinsic light storing properties of PHI. The origin of the observed superior photocatalytic performance is traced back to a modified surface electronic structure and enhanced interfacial interactions with the amphiphile triethanolamine, which imparts improved colloidal stability to the catalyst particles especially in contrast to methanol used as donor. However, this high activity can be limited by oxidation products of donor reversibly building up at the surface, thus blocking active centers. The findings lay out the importance of surface functionalization to engineer the catalyst–solution interface, an underappreciated tuning parameter in photocatalytic reaction design.
Bioinspired elastomeric structural adhesives can provide reversible and controllable adhesion on dry/wet and synthetic/biological surfaces for a broad range of commercial applications. Shape complexity and performance of the existing structural adhesives are limited by the used specific fabrication technique, such as molding. To overcome these limitations by proposing complex 3D microstructured adhesive designs, a 3D elastomeric microstructure fabrication approach is implemented using two‐photon‐polymerization‐based 3D printing. A custom aliphatic urethane‐acrylate‐based elastomer is used as the 3D printing material. Two designs are demonstrated with two combined biological inspirations to show the advanced capabilities enabled by the proposed fabrication approach and custom elastomer. The first design focuses on springtail‐ and gecko‐inspired hybrid microfiber adhesive, which has the multifunctionalities of side‐surface liquid super‐repellency, top‐surface liquid super‐repellency, and strong reversible adhesion features in a single fiber array. The second design primarily centers on octopus‐ and gecko‐inspired hybrid adhesive, which exhibits the benefits of both octopus‐ and gecko‐inspired microstructured adhesives for strong reversible adhesion on both wet and dry surfaces, such as skin. This fabrication approach could be used to produce many other 3D complex elastomeric structural adhesives for future real‐world applications.
Gecko adhesive performance increases as relative humidity increases. Two primary mechanisms can explain this result: capillary adhesion and increased contact area via material softening. Both hypotheses consider variable relative humidity, but neither fully explains the interactive effects of temperature and relative humidity on live gecko adhesion. In this study, we used live tokay geckos (Gekko gecko) and a gecko-inspired synthetic adhesive to investigate the roles of capillary adhesion and material softening on gecko adhesive performance. The results of our study suggest that both capillary adhesion and material softening contribute to overall gecko adhesion, but the relative contribution of each depends on the environmental context. Specifically, capillary adhesion dominates on hydrophilic substrates, and material softening dominates on hydrophobic substrates. At low temperature (12 °C), both capillary adhesion and material softening likely produce high adhesion across a range of relative humidity values. At high temperature (32 °C), material softening plays a dominant role in adhesive performance at an intermediate relative humidity (i.e., 70% RH).
A novel approach for high-performance gecko-inspired adhesives for strong and reversible adhesion to smooth surfaces is proposed. The composite patterns comprising elastomeric mushroom-shaped microfibers decorated with an extremely soft and thin terminal layer of pressure sensitive adhesive. Through the optimal tip shape and improved load sharing, the adhesion performance was greatly enhanced. A high adhesion strength of 300 kPa together with superior durability on smooth surfaces are achieved, outperforming monolithic fibers by 35 times. Our concept of composite microfibrillar adhesives provides significant benefits for real world applications including wearable medical devices, transfer printing systems, and robotic manipulation.
A 3D‐printed pneumatically actuated soft suction gripper with an elastomer film is proposed. Suction in such gripper is actively controlled by applying a negative pressure behind the film. The elastomeric gripper body is 3D‐printed, making it easy to customize and integrate into future robotic gripping systems. The gripper can pick a wide variety of objects, such as delicate fruits, small parts, and parts with uneven loads, with high pull‐off forces (over 7.4 N with ∅ 20 mm/55 kPa). The achieved pull‐off forces are significantly higher than the previously reported suction cup grippers with films and more comparable with commercial vacuum grippers. The pull‐off forces show no significant differences with surfaces of varying roughness (up to root‐mean‐square roughness of 5.66 μm) and the gripper is able to pick and release target objects repeatedly. The gripper is also compared with a commercial vacuum gripper with comparable dimensions. It outperforms the commercial gripper in the case of fragile objects, objects smaller than the gripper diameter, and objects with uneven loads. It can apply high pull‐off forces while having controllable release, and is suitable for gripping a wide variety of real‐world objects, including heavy, rough, small, thin, and fragile ones.
Atomic layer deposition (ALD) is an enabling technology for encapsulating sensitive materials owing to its high-quality, conformal coating capability. Finding the optimum deposition parameters is vital to achieving defect-free layers; however, the high dimensionality of the parameter space makes a systematic study on the improvement of the protective properties of ALD films challenging. Machine-learning (ML) methods are gaining credibility in materials science applications by efficiently addressing these challenges and outperforming conventional techniques. Accordingly, this study reports the ML-based minimization of defects in an ALD-Al 2 O 3 passivation layer for the corrosion protection of metallic copper using Bayesian optimization (BO). In all experiments, BO consistently minimizes the layer defect density by finding the optimum deposition parameters in less than three trials. Electrochemical tests show that the optimized layers have virtually zero film porosity and achieve five orders of magnitude reduction in corrosion current as compared to control samples. Optimized parameters of surface pretreatment using Ar/H 2 plasma, the deposition temperature above 200 °C, and 60 ms pulse time quadruple the corrosion resistance. The significant optimization of ALD layers presented in this study demonstrates the effectiveness of BO and its potential outreach to a broader audience, focusing on different materials and processes in materials science applications.
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