Joanna Aizenberg

Version 2: Added missing files to sniffing_data.zip See GitHub repository for data processing functions and examples: https://github.com/soerenbrandt/sniffing-sensor Abstract:Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

Branching patterns can emerge when one fluid is injected into a more viscous one within a quasi-two-dimensional cavity. While these patterns have dazzled physicists for decades, modern engineering efforts have focused on suppressing, rather than leveraging, these flow instabilities. Here, by designing fluidic devices with calibrated geometries, liquid absorptivities, and rheology, we exploit the thermal sensitivity of the Saffman-Taylor instability to achieve thermoregulatory shading systems with self-adjustment capabilities. Our devices produce negative feedback branching patterns that reduce indoor solar heating when warm but increase it when cool. Moreover, compared to existing temperature-responsive shading approaches with fixed thermal behaviors, our system can switch its thermal sensitivity and indoor temperature setpoints on-demand by adjusting the rate that patterns are grown. Experiments and models reveal the energy savings and indoor climate control capabilities enabled by this thermoregulatory framework. Overall, our work provides a blueprint for designing materials with self-regulatory behaviors based on flow instabilities.

Thermotropic liquid crystals (LCs) underpin technologies from adaptive optics to responsive soft materials, yet their design is constrained by the difficulty of predicting two key phase transition temperatures—the melting temperature and clearing temperature—across structurally diverse subclasses. Here, we present the first unified, open-access LC prediction platform integrating large-scale data curation, LC/non-LC classification, and phase transition regression. Using a curated dataset spanning rod-like, discotic, and bent-core LCs, we trained an LC-favoring majority-vote ensemble model that maximizes recall, virtually eliminating false negatives in candidate screening. For melting temperature prediction, an ensemble of random forest and a graph neural network based on the message passing neural network architecture achieved the highest accuracy, while for clearing temperature, the graph neural network alone provided sufficient generalization. The precision of the framework reproduces even subtle odd–even effects and identifies structural motifs underlying high-error outliers, offering mechanistic insights into mesophase stability. Deployed as the Liquid Crystal Predictor web tool, this platform enables data-driven LC property prediction for community usage and establishes a scalable route toward data-driven discovery of advanced LC materials.

Real-Time Methane Monitoring An ear-tag–mounted electronic nose monitors enteric methane from cattle in real time. The illustration highlights a materials-engineered, dual-layer filter that selectively permits methane transport while suppressing interfering vapors, enabling robust sensing in complex farm air. This work demonstrates a scalable, non-invasive approach to precision livestock methane monitoring. More details can be found in Research Article e02508 by Haritosh Patel, Joanna Aizenberg and co-workers.

Natural filaments, such as proteins, plant tendrils, octopus tentacles, and elephant trunks, can transform into arbitrary three-dimensional shapes that carry out vital functions. Their shape-morphing behavior arises from intricate patterning of active and passive regions, which are difficult to replicate in synthetic matter. Here, we introduce a filament-centric strategy for programmable shape morphing in which intrinsic curvature and twist are directly encoded within multimaterial elastomeric filaments during fabrication. By harnessing rotational multimaterial 3D printing, we directly prescribe the filament’s natural curvature–twist field κ(s) through controlled material distribution and helical liquid crystal mesogen alignment. When heated above their nematic-to-isotropic transition temperature ( T NI ), the helically aligned liquid crystal elastomer regions contract along their local director field, while passive regions remain essentially unchanged. This approach enables independent control of bending and torsion at every cross-section along the filament centerline: the principal natural curvatures of the filament along two orthogonal axes as well as the local twist. Next, we printed architected lattices composed of unit cells formed by sinusoidal filaments that either reversibly contract, expand, or exhibit out-of-plane deformations. Discrete elastic rod simulations of Janus filaments with different natural curvatures and twist, which are interconnected within the printed lattices, allow accurate prediction of their observed shape-morphing behavior. By integrating active–passive elastomers, additive manufacturing, and computational modeling, we have created shape-morphing matter with complex programmable responses for applications that rely on adaptive, robotic, or deployable architectures.

Natural filaments, such as proteins, plant tendrils, octopus tentacles, and elephant trunks, can transform into arbitrary three-dimensional shapes that carry out vital functions. Their shape-morphing behavior arises from intricate patterning of active and passive regions, which are difficult to replicate in synthetic matter. Here, we introduce a filament-centric strategy for programmable shape morphing in which intrinsic curvature and twist are directly encoded within multimaterial elastomeric filaments during fabrication. By harnessing rotational multimaterial 3D printing (RM-3DP), we directly prescribe the filament's natural curvature--twist field $\mathbf{k}(s)$ through controlled material distribution and helical liquid crystal mesogen alignment. When heated above their nematic-to-isotropic transition temperature ($T_\mathrm{NI}$), the helically aligned LCE regions contract along their local director field, while passive regions remain essentially unchanged. This approach enables independent control of bending and torsion at every cross-section along the filament centerline: the principal natural curvatures of the filament along two orthogonal axes as well as the local twist. Next, we printed architected lattices composed of unit cells formed by sinusoidal filaments that either reversibly contract, expand, or exhibit out-of-plane deformations. Discrete elastic rod simulations of Janus filaments with different natural curvatures and twist, which are interconnected within the printed lattices, allow accurate prediction of their observed shape-morphing behavior. By integrating active-passive elastomers, additive manufacturing, and computational modeling, we have created shape-morphing matter with complex programmable responses for applications that rely on adaptive, robotic, or deployable architectures.

Natural filaments, such as proteins, plant tendrils, octopus tentacles, and elephant trunks, can transform into arbitrary three-dimensional shapes that carry out vital functions. Their shape-morphing behavior arises from intricate patterning of active and passive regions, which are difficult to replicate in synthetic matter. Here, we introduce a filament-centric strategy for programmable shape morphing in which intrinsic curvature and twist are directly encoded within multimaterial elastomeric filaments during fabrication. By harnessing rotational multimaterial 3D printing (RM-3DP), we directly prescribe the filament's natural curvature--twist field $\mathbf{k}(s)$ through controlled material distribution and helical liquid crystal mesogen alignment. When heated above their nematic-to-isotropic transition temperature ($T_\mathrm{NI}$), the helically aligned LCE regions contract along their local director field, while passive regions remain essentially unchanged. This approach enables independent control of bending and torsion at every cross-section along the filament centerline: the principal natural curvatures of the filament along two orthogonal axes as well as the local twist. Next, we printed architected lattices composed of unit cells formed by sinusoidal filaments that either reversibly contract, expand, or exhibit out-of-plane deformations. Discrete elastic rod simulations of Janus filaments with different natural curvatures and twist, which are interconnected within the printed lattices, allow accurate prediction of their observed shape-morphing behavior. By integrating active-passive elastomers, additive manufacturing, and computational modeling, we have created shape-morphing matter with complex programmable responses for applications that rely on adaptive, robotic, or deployable architectures.

Abstract: Living systems sense, respond to, and harvest energy from the changing environment by interweaving chemistry, mechanics, optics, and fluid dynamics across time and length scales. In this lecture, materials scientist Joanna Aizenberg gives a taste of how inspiration from nature teaches us to break barriers between these fields in the synthetic realm and leads to fascinating new concepts in materials design. She looks at a deep-sea sponge and envisions a green, illuminated skyscraper that harvests energy from the wind. The brittle star’s intricate skeleton inspires dynamic optical systems that can collect light. She presents cilia-inspired adaptive hairy surfaces that alter their wetting, optical, and adhesive behavior via reconfiguration of tiny nanostructures. Creating liquid-sensing “noses” from chemically patterned photonic crystals inspired by butterflies, or ultra-slippery, antifouling surfaces with self-tuning transparency inspired by pitcher plant and cacti are just the beginning of the multifunctional, dynamic materials possibilities waiting to be explored at the intersection of biology, engineering, chemistry, and physics.

ABSTRACT Accurate quantification of enteric methane emissions is essential for mitigating greenhouse gas outputs from livestock. However, existing methods such as respiration chambers and tracer gas systems remain costly, invasive, and impractical for large‐scale deployment. Here, we present a modular electronic nose (e‐nose) platform that introduces a materials‐informed sensor–filter framework for selective methane detection in complex gas environments relevant to livestock monitoring. The system integrates three modules: an environmental unit for temperature, humidity, and carbon dioxide tracking; a volatile organic compound module; and a methane‐selective unit comprising a metal oxide sensor with a dual‐layer adsorbent filter made of activated carbon spheres and a polyethylene terephthalate (PET) membrane. This hybrid, dual‐layer adsorptive filter enables a sensor–filter codesign strategy that suppresses both polar and non‐polar interferents while preserving methane transport, shifting the dominant selectivity mechanism from surface oxygen competitive depletion to diffusion‐mediated filtering. The methane‐selective sensor exhibited a strong signal‐to‐noise ratio (33.9 dB), low detection limit (7.8 ppm), and excellent linearity (R 2 = 0.999). In methane–pentane mixtures (20–1000 ppm), the filtered sensor maintained low error (∼13%), over 100‐fold lower than unfiltered counterparts. Controlled interferent studies elucidated how filters influence selectivity, offering a framework for designing sensor–filter pairs tailored to complex gas environments. Integrated with environmental compensation, this materials‐informed, ear‐tag‐shaped e‐nose establishes a deployment‐oriented framework for scalable, non‐invasive methane monitoring in livestock‐relevant environments.

Spherulites are polycrystalline architectures defined by their radial internal alignment, often forming spherical morphologies. Unlike faceted single crystals, their nonfaceted growth enables a decoupling of the external morphology from the internal crystallographic symmetry, offering a promising route to complex and radially aligned crystal shapes. However, the growth of compact spherulites remains uncommon, poorly understood, and largely limited to spherical shapes. Here, we precipitate strontium sulfate (SrSO 4 ) in the presence of silica into compact hemispherical spherulites from solution and steer their morphology into nontrivial geometries. Our mechanistic studies reveal that after a nucleation burst, growth occurs via a diffusion-limited process and can be paused and resumed seamlessly due to the tunability of solution conditions. This tunability allows for size and morphology control, which we demonstrate with the templated fabrication of complex quarter-sphere, acorn, and interconnected network spherulitic shapes. These findings establish spherulitic growth as a versatile tool for building complex architectures, offering structural control beyond archetypical spherical forms and the limits imposed by crystallographic symmetry.

Abstract Stimuli‐responsive materials typically contain responsive molecular units that couple an external trigger to a defined macroscale response. Ongoing efforts to boost the versatility and complexity of these responses increasingly focus on multi‐stimuli‐responsive molecular units and crosslinkers, as these bear the potential to impart self‐regulatory behaviors building on cooperative effects and feedback mechanisms. Herein, such a stimuli‐responsive platform consisting of polyacrylamide‐based hydrogels with spiropyrans covalently bound as pendant groups or ‘non‐innocent’ crosslinkers is studied. Surprisingly, as compared to methylenebisacrylamide‐crosslinked poly(acrylamide‐ co ‐acrylic acid) gels incorporating monofunctional appended spiropyran, gels with bifunctional spiropyran as a secondary crosslinker swell up to twofold larger despite their increased crosslinking density. To uncover the origin of this unexpected behavior, we employ nanoindentation, swelling analysis, and UV–vis spectroscopy to study changes in mechanical properties and in spiropyran isomer equilibrium distribution as a function of solution pH, co‐monomer chemistry, and swelling‐induced polymer strain. The osmotic counterion pressures as a function of spiropyran isomer distribution were estimated but found to be insufficient to explain the observed behavior. The combined effects of (i) charge complexation, (ii) cooperativity between the hydrogel's mechanics and chemistry, and (iii) formation of aggregates may all be invoked to explain features of the observed ‘non‐innocence’ of spiropyran crosslinkers. Taken together, these insights aid rational implementation of such responsive crosslinkers in materials design and extend the functionality of existing polymeric materials toward more complex tunable behaviors, useful in soft robotics, microfluidics, drug delivery, or self‐regulated materials.

. Consequently, such interconnected material properties cannot enable systematic investigations whereby individual NP or support properties are independently tuned to elucidate the catalytic role(s) of each structural or chemical descriptor or their combination thereof, especially if their contributions exert orthogonal effects on the catalytic performance.To address this gap, we devised a raspberry-colloid-templating (RCT) strategy. In this Account, we outline the RCT synthetic methodology and highlight two key design features: partial NP embedding into the support (which enhances catalytic stability against NP sintering while maintaining high reactant accessibility) and synthetic modularity (which enables independent combinatorial variations of the catalyst's building blocks and their spatial organization). These two features yield thermomechanically stable RCT catalysts with multiple degrees of freedom at different length scales to isolate and independently tune potential catalytic descriptors, thereby deriving unambiguous structure-property relationships to guide future catalyst designs.We describe how we leveraged these two key design features to employ the RCT strategy as a well-defined and synthetically robust model thermocatalytic platform to deconvolute the individual effects of traditionally coupled structural descriptors and elucidate important insights into catalyst design that cannot be easily achieved using conventional catalyst preparative methods. We highlight our recent investigations into three structural features found in all NP-supported catalysts: individual NP properties, properties of NP ensembles as a collective entity, and NP-support interfaces. First, we show how using preformed colloidal NPs in the RCT method decouples the NP and support formation steps to facilitate systematic evaluations of individual NP properties. We exemplify this point through separate studies into the nanoscale effects of Pd ensemble sizes on the surfaces of PdAu alloyed NPs on reactant adsorption energetics, and the sintering behavior of Pt and Pd NP diesel oxidation catalysts. Second, we demonstrate how the colloidal templating steps in the RCT strategy control the NP spatial localization to tune NP proximity, a collective NP ensemble property, at a fixed NP size, to induce local enrichment of reaction intermediates within the pore structure to direct catalytic selectivity. Third, we illustrate how partial embedding of NPs in RCT catalysts not only accentuates catalytic contributions arising from NP-support interfacial sites but also reveals nanoscale wetting effects at the interface that we exploited to direct bimetallic catalyst synthesis. Interspersed throughout this Account, we also describe the advanced characterization and modeling tools that we adapted to probe the RCT catalyst structure and establish structural insights underpinning some of our main results. Finally, we provide an outlook on the RCT catalyst platform and speculate on its future opportunities, challenges, and practical applications.

The performance of bimetallic dilute alloy catalysts is largely determined by the size of minority metal ensembles on the nanoparticle surface. By analyzing the synthesis of catalysts comprising Pd8Au92 nanoparticles supported on silica using surface-sensitive techniques, we report that whether Pd overgrowth occurs before or after Au nanoparticle deposition onto the support controls the surface Pd ensemble size and abundance. These differences in Pd ensembles influence catalytic reactivity in H2–D2 isotope exchange and benzaldehyde hydrogenation, which, in correlation with theoretical calculations, is used to elucidate the active site(s) in each reaction. To clarify how the synthetic sequence controls the formation of Pd ensembles, we combine numerical wetting calculations and molecular dynamics simulations (with a machine-learned force field) to visualize Pd deposition and migration on the nanoparticle surface, respectively. Our results suggest that the nanoparticle–support interface restricts nanoparticle accessibility to Pd deposition, which consequently controls the Pd ensemble size, illustrating the critical role of nanoscale wetting phenomena during bimetallic catalyst preparation. The catalytic performance of dilute Pd-in-Au alloys depends on the Pd ensemble size on the bimetallic nanoparticle surface. Here the authors reveal how Pd ensemble formation on Au nanoparticles depends on the deposition sequence and nanoparticle–support wetting interactions, consequently affecting reactivity.

This work describes a microfluidic high-throughput fabrication method for untethered soft microactuators which, while initially unspecific, develop distinct shapes, surface textures, and actuation modes based on various environmental cues. Analogous to the core concept of cell differentiation, the central idea of this technique is to apply controlled mechanical and chemical stimuli to a deformable hydrogel fiber and transmit the induced geometrical and textural changes to embedded droplets. Using liquid crystal (LC) monomer droplets as a core allows us to orthogonally program the geometric, textural, and molecular architecture of the resulting microactuators upon droplet polymerization. Fine-tuning of the microfluidic parameters yields microdroplets that dry and transform into microparticles with a variety of shapes, including spindle, rod, pancake, dumbbell, pyramid, and worm-like assemblies with a range of aspect ratios. Leveraging mechanical instability via rapid dehydration of hydrogel fibers allows us to generate and impart stable 3D patterns to the core, resulting in microparticles that vary both in global shape and surface texture. After polymerizing these precursor droplets in a magnetic field to encode the mesogenic orientation, LCE microactuators are realized with a rich library of shapes, surface patterns, and molecular structures, each displaying distinct deformations upon heating, validated via finite element analysis.

Hydrocephalus, a neurological condition characterized by an excessive buildup of cerebrospinal fluid (CSF) in the brain, affects millions worldwide and leads to severe consequences. Current treatments, such as ventriculoperitoneal shunts, divert excess CSF from the brain but often face complications, mainly due to shunt obstructions caused by biological matter accumulation. While previous shunt designs aimed to improve fluid flow and reduce occlusion, they often lacked the precision needed for real-world applications due to simplified simulation models that did not fully capture the dynamics of the cerebral ventricular system. Here, we introduce BrainFlow, a computational model that integrates detailed anatomical and physiological features to simulate CSF dynamics in the presence of shunt implants. BrainFlow incorporates patient-specific medical imaging data, pulsatile flow to mimic cardiac cycles, adjustable parameters for various hydrocephalus conditions, and a biomolecule tracking feature to evaluate the long-term risk of shunt occlusion due to flow-mediated biomolecular transport. This model provides a more nuanced understanding of the factors contributing to shunt obstruction, offering insights into optimal shunt placement, design, and materials choice. Through validation against four-dimensional MRI flow data, BrainFlow demonstrates robust accuracy across multiple flow metrics. Our work lays the groundwork for the development of next-generation shunts tailored to individual patient anatomy and pathology, ultimately aiming to improve hydrocephalus treatment through informed, patient-specific design strategies.

High Resolution Image Download MS PowerPoint Slide Coral reefs are undergoing unprecedented degradation due to rising ocean temperatures, acidification, overfishing, and coastal pollution. Despite conservation efforts, including marine protected areas and sustainable fishing practices, the magnitude of these challenges calls for innovative approaches to repair and restore coral reefs. In this study, we explore the application of bioinspired materials to address the challenge of algal competition, a key bottleneck for effective restoration approaches. We develop and optimize slippery liquid-infused porous surfaces (SLIPS), as a fouling-prevention coating tailored for coral reef restoration and engineering. Through aquarium experiments and in situ trials on O’ahu, Hawai’i, we assess the effectiveness of these coatings in mitigating algal competition and facilitating coral growth. Our results demonstrate that PDMS-based SLIPS coatings significantly reduce algal coverage compared to commercial aragonite-based surfaces, with up to 70% reduction observed over a 12-week deployment period in situ . We also develop coral-guards, which are slippery substrates customized for coral fragment outplanting. Coral-guards facilitate tissue growth of Stylophora pistillata fragments, without competitive turf algal growth. These approaches hold promise for advancing restoration efforts, including the engineering of hybrid reefs and targeted coral gardening approaches.

Supported Pd–Au bimetallic nanoparticles make up a promising class of catalysts used for hydrogenation and oxidation reactions. Recently, the role of dynamic restructuring of Pd regions at and near the nanoparticle surface in response to modulating gas (H 2 and O 2 ) concentrations was highlighted for controlling the surface Pd oxide stoichiometry. Here, we investigate the mechanism of formation and decomposition of Pd hydride (PdH x ) at and near the bimetallic nanoparticle surfaces, a key species for controlling the activity, selectivity, and stability of Pd catalysts in many hydrogenation reactions. We employ modulation excitation X-ray absorption spectroscopy (ME-XAS) to directly observe the time scale of PdH x formation and decomposition on the surface of Pd–Au nanoparticles. Density functional theory (DFT) calculations provide additional insights into the stability and energetics of PdH x formation under varying H fractions and Pd substructures. Our results reveal a complex interplay between Pd ensemble size, surface structure, and hydrogen environment in determining the kinetics and thermodynamics of PdH x formation. By elucidating the mechanisms underlying surface PdH x formation and decomposition, the rational design of dynamic catalysts with controlled Pd hydride stoichiometries can become possible.

Abstract Nanoparticle‐supported Pt and Pd catalysts are employed industrially to convert CO and hydrocarbon residue from incomplete diesel fuel combustion into more environmentally‐benign products. However, these catalysts deactivate over time due to sintering, especially for Pt nanoparticles which readily generate volatile species under high operating temperatures. Here, we turned the detrimental vapor‐mediated sintering of Pt into an advantage by using a physical mixture of Pt and Pd catalysts prepared using a raspberry‐colloid‐templating (RCT) method. The RCT method produced Pt/Al 2 O 3 and Pd/Al 2 O 3 catalysts with partially embedded NPs to inhibit surface‐mediated sintering pathways. As validated using an industry‐defined emission control test protocol, aging a physical mixture of Pt/Al 2 O 3 and Pd/Al 2 O 3 at high temperature produced an alloyed PtPd/Al 2 O 3 catalyst that outperformed the fresh catalyst mixture and both individual catalysts for hydrocarbon conversion, while exhibiting high catalytic stability and resistance to sintering and to SO 2 poisoning. X‐ray photoelectron spectroscopy revealed that in the aged catalyst mixture, half of the Pd content existed in the more active metallic state, compared to the less active oxide forms in the fresh mixture and both individual catalysts, explaining the unusual activity enhancement. Our results represent a practical approach to producing active and stable PtPd/Al 2 O 3 diesel oxidation catalysts for emission control applications.

Living organisms absorb external nutrients to grow, changing their macroscopic shapes to meet various challenges through mass transport and integration. While several strategies have been developed to create dynamic polymers that allow for mainchain remodelings to mimic the growing ability of living organisms, most are limited to simple homogeneous growth without complex control of global geometric transformation during growth. Herein, we report an approach to design controlled, growth-induced shape transformation in synthetic materials, in which significant mass transport within the materials is induced by spatially controlled polymerization leading to reshaping the materials. This method is demonstrated using silicone systems made through anionic ring-opening polymerization (anionic ROP) of octamethylcyclotetrasiloxane (D4) with a strong base as the catalyst. We show that a flat square sample can be transformed into a sphere through growth without the need for remolding and preprogramming. By varying the composition of the monomer mixture provided to the samples, and the modes of triggering and shutting down polymerization, we achieve exquisite control over growing polymeric objects into various sizes and shapes, modulating their mechanical properties, self-healing ability, and availability of active sites for further growth from a desired location. We envision this strategy opening an innovative direction in preparing soft materials with specific shapes or surface morphologies. Shape-changing polymers are attractive in a range of applications, but achieving non-homogeneous growth is challenging. Here, the authors report the development of a method for controlled, growth-induced shape transformations in synthetic materials on the macroscale.

Electronic Noses Unlocking “universal smell” requires integrating key principles of natural olfactory systems: modulation of gas transport, utilization of non-selective sensing elements, chemical filters akin to the biological mucus layer to separate compounds, refined context-specific signal integration, and more. These components work in harmony to improve the accuracy, sensitivity, and adaptability of electronic noses whilst mitigating common failure modes. More details can be found in article number 2412669 by Haritosh Patel, Joanna Aizenberg, and co-workers.

Natural olfactory systems possess remarkable sensitivity and precision beyond what is currently achievable by engineered gas sensors. Unlike their artificial counterparts, noses are capable of distinguishing scents associated with mixtures of volatile molecules in complex, typically fluctuating environments and can adapt to changes. This perspective examines the multifaceted biological principles that provide olfactory systems their discriminatory prowess, and how these ideas can be ported to the design of electronic noses for substantial improvements in performance across metrics such as sensitivity and ability to speciate chemical mixtures. The topics examined herein include the fluid dynamics of odorants in natural channels; specificity and kinetics of odorant interactions with olfactory receptors and mucus linings; complex signal processing that spatiotemporally encodes physicochemical properties of odorants; active sampling techniques, like biological sniffing and nose repositioning; biological priming; and molecular chaperoning. Each of these components of natural olfactory systems are systmatically investigated, as to how they have been or can be applied to electronic noses. While not all artificial sensors can employ these strategies simultaneously, integrating a subset of bioinspired principles can address issues like sensitivity, drift, and poor selectivity, offering advancements in many sectors such as environmental monitoring, industrial safety, and disease diagnostics.

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 SiO2 or TiO2 supports, 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/SiO2 and PdAu/TiO2, we identified a support effect resulting in a 1.4-fold higher activity of PdAu/TiO2 than PdAu/SiO2 for benzaldehyde hydrogenation. Notably, partial nanoparticle embedding in the TiO2 raspberry-colloid-templated support increased the metal-support interfacial perimeter and consequently, the number of Au/TiO2 interfacial sites by 5.4-fold, which further enhanced the activity of PdAu/TiO2 by an additional 4.1-fold. Theoretical calculations and in situ surface-sensitive desorption analyses reveal facile benzaldehyde binding at the Au/TiO2 interface and at Pd ensembles on the nanoparticle surface, explaining the connection between the number of Au/TiO2 interfacial 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/TiO2 interfacial sites to augment catalytic contributions arising from metal-support interfaces.

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 SiO 2 or TiO 2 supports, 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/SiO 2 and PdAu/TiO 2 , we identified a support effect resulting in a 1.4-fold higher activity of PdAu/TiO 2 than PdAu/SiO 2 for benzaldehyde hydrogenation. Notably, partial nanoparticle embedding in the TiO 2 raspberry-colloid-templated support increased the metal–support interfacial perimeter and consequently, the number of Au/TiO 2 interfacial sites by 5.4-fold, which further enhanced the activity of PdAu/TiO 2 by an additional 4.1-fold. Theoretical calculations and in situ surface-sensitive desorption analyses reveal facile benzaldehyde binding at the Au/TiO 2 interface and at Pd ensembles on the nanoparticle surface, explaining the connection between the number of Au/TiO 2 interfacial 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/TiO 2 interfacial sites to augment catalytic contributions arising from metal–support interfaces.

The Pd ensemble size in dilute and single atom alloy PdAu catalysts controls competitive adsorption between reactants and consequently directs selectivity in the one-pot hydrogenation and reductive amination between nitroarenes and aldehydes.

We develop a quantitative Raman microscopy approach to study erythrocyte biochemistry at the sub-cellular level. To model Raman microscopy images, we review theory of Raman tensors and derive expressions for Raman responses suitable to compute Raman micro-images accounting effects of radial and vertical deformations of cellular envelopes. In application to membrane components, we extend the approach to a "counted per rotation" fast imaging protocol: once having Raman tensors for a molecule, precomputed expressions of molecular distributions can be used to construct Raman images of the modelled membrane envelope and its Raman spectra under any polarisation setting instantly. Using the theory, we review sub-cellular distributions of oxy-, deoxy- and methaemoglobins, as measured in experiment, considering their role in oxygen transport and oxidative stress mechanisms in the cytosol and when next to a membrane. We discuss possible applications of the approach in membrane specific studies, and its potential for combination with phase-sensitive and confocal fluorescence microscopy for advancing health care diagnostics.

Ambidirectionality, which is the ability of structural elements to move beyond a reference state in two opposite directions, is common in nature. However, conventional soft materials are typically limited to a single, unidirectional deformation unless complex hybrid constructs are used. We exploited the combination of mesogen self-assembly, polymer chain elasticity, and polymerization-induced stress to design liquid crystalline elastomers that exhibit two mesophases: chevron smectic C (cSmC) and smectic A (SmA). Inducing the cSmC-SmA-isotropic phase transition led to an unusual inversion of the strain field in the microstructure, resulting in opposite deformation modes (e.g., consecutive shrinkage or expansion and right-handed or left-handed twisting and tilting in opposite directions) and high-frequency nonmonotonic oscillations. This ambidirectional movement is scalable and can be used to generate Gaussian transformations at the macroscale.

High Resolution Image Download MS PowerPoint Slide Sequential self-organization can be used to design the hierarchy and complexity of materials beyond what is possible with single-step synthesis. However, such sequential approaches introduce additional challenges in maintaining control over the process. To guide the position and orientation of newly nucleated material, we propose the use of self-assembled nanocrystals (SANCs). We test the potential of SANCs in BaCO 3 |SiO 2 nanocomposites, also termed silica biomorphs, to direct the formation of nascent microscopic crystals. We find that SANCs can direct the location and crystallographic orientation of microcrystals at the nucleation stage, while the material, polymorph, and growth behavior of the crystal can be tuned largely independently. Using ion exchange reactions, we show that structures can be unified into a single material of interest in subsequent steps. This level of control over material position, orientation, and chemical composition allows for the retrosynthetic design of complex hierarchical structures.

Long chain per‐ and polyfluorinated chemical compounds, commonly referred to as PFAS, historically offered superior breathable and flexible water‐repellent textile finishes on clothing. Now notoriously deemed “forever chemicals” for their environmental persistence and ability to bioaccumulate, they can cause a range of human health impacts including cancer and reproductive harm, which has highlighted the need for less harmful but equally superhydrophobic coatings. Inspired by the microscopically bumpy water‐repellent surfaces of lotus leaves, this work introduces a PFAS‐free hierarchical nanocoating on and within textile substrates using a multistep synthetic approach, involving fabric pretreatment to facilitate a robust particle‐to‐substrate attachment of subsequently infiltrated silica nanoparticles (NPs), followed by surface functionalization of textile‐infiltrated NPs with long‐alkyl‐chain silanes to impart PFAS‐free water repellency. The system is subjected to rigorous wash testing and found consistent superhydrophobic performance after 65 wash cycles. Furthermore, the system can be adapted as a flexible platform technology for a variety of design opportunities by changing the substrate, NP composition, and surface chemistry to suit a variety of applications. Herein, a robust, flexible, and breathable interface between fabric and water that offers a next‐generation PFAS‐free water‐repellent textile coating solution is developed.

Restructuring of metal components on bimetallic nanoparticle surfaces in response to the changes in reactive environment is a ubiquitous phenomenon whose potential for the design of tunable catalysts is underexplored. The main challenge is the lack of knowledge of the structure, composition, and evolution of species on the nanoparticle surfaces during reaction. We apply a modulation excitation approach to the X-ray absorption spectroscopy of the 30 atomic % Pd in Au supported nanocatalysts via the gas (H2 and O2) concentration modulation. For interpreting restructuring kinetics, we correlate the phase-sensitive detection with the time-domain analysis aided by a denoising algorithm. Here we show that the surface and near-surface species such as Pd oxides and atomically dispersed Pd restructured periodically, featuring different time delays. We propose a model that Pd oxide formation is preceded by the build-up of Pd regions caused by oxygen-driven segregation of Pd atoms towards the surface. During the H2 pulse, rapid reduction and dissolution of Pd follows an induction period which we attribute to H2 dissociation. Periodic perturbations of nanocatalysts by gases can, therefore, enable variations in the stoichiometry of the surface and near-surface oxides and dynamically tune the degree of oxidation/reduction of metals at/near the catalyst surface. Nanocatalysts can restructure during reactions. Here, the authors dynamically varied the stoichiometry of the surface species in 30 % Pd-in-Au nanoparticles by modulating H2 and O2 gases and quantified the formation kinetics of Pd regions and oxides.

Conventional catalyst preparative methods commonly entail the impregnation, precipitation, and/or immobilization of nanoparticles on their supports. While convenient, such methods do not readily afford the ability to control collective ensemble-like nanoparticle properties, such as nanoparticle proximity, placement, and compartmentalization. In this Perspective, we illustrate how incorporating colloidal templating into catalyst design for thermocatalysis confers synthetic advantages to facilitate new catalytic investigations and augment catalytic performance, focusing on three colloid-templated catalyst structures: 3D macroporous structures, hierarchical macro-mesoporous structures, and discrete hollow nanoreactors. We outline how colloidal templating decouples the nanoparticle and support formation steps to devise modular catalyst platforms that can be flexibly tuned at different length scales. Of particular interest is the raspberry colloid templating (RCT) method which confers high thermomechanical stability by partially embedding nanoparticles within its support, while retaining high levels of reactant accessibility. We illustrate how the high modularity of the RCT approach allows one to independently control collective nanoparticle properties, such as nanoparticle proximity and localization, without concomitant changes to other catalytic descriptors that would otherwise confound analyses of their catalytic performance. We next discuss how colloidal templating can be employed to achieve spatially disparate active site functionalization while directing reactant transport within the catalyst structure to enhance selectivity in multistep catalytic cascades. Throughout this Perspective, we highlight developments in advanced characterization that interrogate transport phenomena and/or derive new insights into these catalyst structures. Finally, we offer our outlook on the future roles, applications, and challenges of colloidal templating in catalyst design for thermocatalysis.