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Despite the broad catalytic relevance of metal–support interfaces, controlling their chemical nature, the interfacial contact perimeter (exposed to reactants), and consequently, their contributions to overall catalytic reactivity, remains challenging, as the nanoparticle and support characteristics are interdependent when catalysts are prepared by impregnation. Here, we decoupled both characteristics by using a raspberry-colloid-templating strategy that yields partially embedded PdAu nanoparticles within well-defined SiO₂or TiO₂supports, thereby increasing the metal–support interfacial contact compared to nonembedded catalysts that we prepared by attaching the same nanoparticles onto support surfaces. Between nonembedded PdAu/SiO₂and PdAu/TiO₂, we identified a support effect resulting in a 1.4-fold higher activity of PdAu/TiO₂than PdAu/SiO₂for benzaldehyde hydrogenation. Notably, partial nanoparticle embedding in the TiO₂raspberry-colloid-templated support increased the metal–support interfacial perimeter and consequently, the number of Au/TiO₂interfacial sites by 5.4-fold, which further enhanced the activity of PdAu/TiO₂by an additional 4.1-fold. Theoretical calculations and in situ surface-sensitive desorption analyses reveal facile benzaldehyde binding at the Au/TiO₂interface and at Pd ensembles on the nanoparticle surface, explaining the connection between the number of Au/TiO₂interfacial sites (via the metal–support interfacial perimeter) and catalytic activity. Our results demonstrate partial nanoparticle embedding as a synthetic strategy to produce thermocatalytically stable catalysts and increase the number of catalytically active Au/TiO₂interfacial sites to augment catalytic contributions arising from metal–support interfaces. 

The next-generation semiconductors and devices, such as halide perovskites and flexible electronics, are extremely sensitive to water, thus demanding highly effective protection that not only seals out water in all forms (vapor, droplet, and ice), but simultaneously provides mechanical flexibility, durability, transparency, and self-cleaning. Although various solid-state encapsulation methods have been developed, no strategy is available that can fully meet all the above requirements. Here, we report a bioinspired liquid-based encapsulation strategy that offers protection from water without sacrificing the operational properties of the encapsulated materials. Using halide perovskite as a model system, we show that damage to the perovskite from exposure to water is drastically reduced when it is coated by a polymer matrix with infused hydrophobic oil. With a combination of experimental and simulation studies, we elucidated the fundamental transport mechanisms of ultralow water transmission rate that stem from the ability of the infused liquid to fill-in and reduce defects in the coating layer, thus eliminating the low-energy diffusion pathways, and to cause water molecules to diffuse as clusters, which act together as an excellent water permeation barrier. Importantly, the presence of the liquid, as the central component in this encapsulation method provides a unique possibility of reversing the water transport direction; therefore, the lifetime of enclosed water-sensitive materials could be significantly extended via replenishing the hydrophobic oils regularly. We show that the liquid encapsulation platform presented here has high potential in providing not only water protection of the functional device but also flexibility, optical transparency, and self-healing of the coating layer, which are critical for a variety of applications, such as in perovskite solar cells and bioelectronics. 

Abstract Droplet microarray technology is of great interest in biology and chemistry as it allows for significant reactant savings and massive parallelization of experiments. Upon scaling down the footprint of each droplet in an array, it becomes increasingly challenging to produce the array drop‐by‐drop. Therefore, techniques for parallelized droplet production are developed, e.g., dip‐coating of biphilic substrates. However, it is in general difficult to tailor the characteristics of individual droplets, such as size and content, without updating the substrate. Here, the method of dip‐coating of uniformly patterned biphilic substrates in so‐called “acceleration‐mode” to produce droplet arrays featuring gradients in droplet height for fixed droplet footprint is developed. The results herein present this method applied to produce drops with base diameters varying over orders of magnitude, from as high as 6 mm to as small as 50 µm; importantly, the experimentally measured power‐law‐dependency of volume on capillary‐number matches analytical theory for droplet formation on heterogenous substrates though the precise quantitative values likely differ due to 2D substrate patterning. Gradient characteristics, including average droplet volume, steepness of the gradient, and its monotonicity, can all be tuned by changing the dip‐coating parameters, thus providing a robust method for high‐throughput screening applications and experiments. 

Although sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch, the development of artificial noses is significantly behind their biological counterparts. This largely stems from the sophistication of natural olfaction, which relies on both fluid dynamics within the nasal anatomy and the response patterns of hundreds to thousands of unique molecular-scale receptors. We designed a sensing approach to identify volatiles inspired by the fluid dynamics of the nose, allowing us to extract information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) rather than relying on a large sensor array. By accentuating differences in the nonequilibrium mass-transport dynamics of vapors and training a machine learning algorithm on the sensor output, we clearly identified polar and nonpolar volatile compounds, determined the mixing ratios of binary mixtures, and accurately predicted the boiling point, flash point, vapor pressure, and viscosity of a number of volatile liquids, including several that had not been used for training the model. We further implemented a bioinspired active sniffing approach, in which the analyte delivery was performed in well-controlled 'inhale-exhale' sequences, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. Our results outline a strategy to build accurate and rapid artificial noses for volatile compounds that can provide useful information such as the composition and physical properties of chemicals, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring. 

Various forms of ecological monitoring and disease diagnosis rely upon the detection of amphiphiles, including lipids, lipopolysaccharides, and lipoproteins, at ultralow concentrations in small droplets. Although assays based on droplets’ wettability provide promising options in some cases, their reliance on the measurements of surface and bulk properties of whole droplets (e.g., contact angles, surface tensions) makes it difficult to monitor trace amounts of these amphiphiles within small-volume samples. Here, we report a design principle in which self-assembled monolayer–functionalized microstructured surfaces coated with silicone oil create locally disordered regions within a droplet’s contact lines to effectively concentrate amphiphiles within the areas that dominate the droplet static friction. Remarkably, such surfaces enable the ultrasensitive, naked-eye detection of amphiphiles through changes in the droplets’ sliding angles, even when the concentration is four to five orders of magnitude below their critical micelle concentration. We develop a thermodynamic model to explain the partitioning of amphiphiles at the contact line by their cooperative association within the disordered, loosely packed regions of the self-assembled monolayer. Based on this local analyte concentrating effect, we showcase laboratory-on-a-chip surfaces with positionally dependent pinning forces capable of both detecting industrially and biologically relevant amphiphiles (e.g., bacterial endotoxins), as well as sorting aqueous droplets into discrete groups based on their amphiphile concentrations. Furthermore, we demonstrate that the sliding behavior of amphiphile-laden aqueous droplets provides insight into the amphiphile’s effective length, thereby allowing these surfaces to discriminate between analytes with highly disparate molecular sizes. 

Implantable tubes, shunts, and other medical conduits are crucial for treating a wide range of conditions from ears and eyes to brain and liver but often impose serious risks of device infection, obstruction, migration, unreliable function, and tissue damage. Efforts to alleviate these complications remain at an impasse because of fundamentally conflicting design requirements: Millimeter-scale size is required to minimize invasiveness but exacerbates occlusion and malfunction. Here, we present a rational design strategy that reconciles these trade-offs in an implantable tube that is even smaller than the current standard of care. Using tympanostomy tubes (ear tubes) as an exemplary case, we developed an iterative screening algorithm and show how unique curved lumen geometries of the liquid-infused conduit can be designed to co-optimize drug delivery, effusion drainage, water resistance, and biocontamination/ingrowth prevention in a single subcapillary–length-scale device. Through extensive in vitro studies, we demonstrate that the engineered tubes enabled selective uni- and bidirectional fluid transport; nearly eliminated adhesion and growth of common pathogenic bacteria, blood, and cells; and prevented tissue ingrowth. The engineered tubes also enabled complete eardrum healing and hearing preservation and exhibited more efficient and rapid antibiotic delivery to the middle ear in healthy chinchillas compared with current tympanostomy tubes, without resulting in ototoxicity at up to 24 weeks. The design principle and optimization algorithm presented here may enable tubes to be customized for a wide range of patient needs. 

paired with distinct reverse arcs1,2. Efforts to mimic such dynamics synthetically rely on multimaterial designs but face limits to programming arbitrary motions or diverse behaviours in one structure3–8. Here we show how diverse, complex, non-reciprocal, stroke-like trajectories emerge in a single-material system through self-regulation. When a micropost composed of photoresponsive liquid crystal elastomer with mesogens aligned oblique to the structure axis is exposed to a static light source, dynamic dances evolve as light initiates a travelling order-to-disorder transition front, transiently turning the structure into a complex evolving bimorph that twists and bends via multilevel opto-chemo-mechanical feedback. As captured by our theoretical model, the travelling front continuously reorients the molecular, geometric and illumination axes relative to each other, yielding pathways composed from series of twisting, bending, photophobic and phototropic motions. Guided by the model, here we choreograph a wide range of trajectories by tailoring parameters, including illumination angle, light intensity, molecular anisotropy, microstructure geometry, temperature and irradiation intervals and duration. We further show how this opto-chemo-mechanical self-regulation serves as a foundation for creating self-organizing deformation patterns in closely spaced microstructure arrays via light-mediated interpost communication, as well as complex motions of jointed microstructures, with broad implications for autonomous multimodal actuators in areas such as soft robotics7,9,10, biomedical devices11,12 and energy transductionmaterials13, and for fundamental understanding of self-regulated systems14,15