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Developing stable and efficient electrocatalysts is vital for boosting oxygen evolution reaction (OER) rates in sustainable hydrogen production. High-entropy oxides (HEOs) consist of five or more metal cations, providing opportunities to tune their catalytic properties toward high OER efficiency. This work combines theoretical and experimental studies to scrutinize the OER activity and stability for spinel-type HEOs. Density functional theory confirms that randomly mixed metal sites show thermodynamic stability, with intermediate adsorption energies displaying wider distributions due to mixing-induced equatorial strain in active metal-oxygen bonds. The rapid sol-flame method is employed to synthesize HEO, comprising five 3d-transition metal cations, which exhibits superior OER activity and durability under alkaline conditions, outperforming lower-entropy oxides, even with partial surface oxidations. The study highlights that the enhanced activity of HEO is primarily attributed to the mixing of multiple elements, leading to strain effects near the active site, as well as surface composition and coverage.

 

The past decade has witnessed a rapid growth of graphene plasmonics and their applications in different fields. Compared with conventional plasmonic materials, graphene enables highly confined plasmons with much longer lifetimes. Moreover, graphene plasmons work in an extended wavelength range, i.e., mid-infrared and terahertz regime, overlapping with the fingerprints of most organic and biomolecules, and have broadened their applications towards plasmonic biological and chemical sensors. In this review, we discuss intrinsic plasmonic properties of graphene and strategies both for tuning graphene plasmons as well as achieving higher performance by integrating graphene with plasmonic nanostructures. Next, we survey applications of graphene and graphene-hybrid materials in biosensors, chemical sensors, optical sensors, and sensors in other fields. Lastly, we conclude this review by providing a brief outlook and challenges of the field. Through this review, we aim to provide an overall picture of graphene plasmonic sensing and to suggest future trends of development of graphene plasmonics.

 

Data‐driven, machine learning (ML)‐assisted approaches have been used to study structure‐property relationships at the atomic scale, which have greatly accelerated the screening process and new material discovery. However, such approaches are not easily applicable to modulating material properties of a soft material in a laboratory with specific ingredients. Moreover, it is desirable to relate material properties directly to the experimental recipes. Herein, a data‐driven approach to tailoring mechanical properties of a soft material is demonstrated using ML‐assisted predictions of mechanical properties based on experimental synthetic recipes. Polyurethane (PU) elastomer is used as a model soft material to demonstrate the approach and experimentally varied mechanical properties of the PU elastomer by modulating the mixing ratio between components of the elastomer. Twenty‐five experimental conditions are selected based on the design of experiment and use those data points to train a linear regression model. The resulting model takes desired mechanical properties as input and returns synthetic recipes of a soft material, which is subsequently validated by experiments. Lastly, the prediction accuracies of different machine learning algorithms is compared. It is believed that the approach is widely applicable to other material systems to establish experimental conditions and material property relationships for soft materials.

 

Abstract Hierarchical heterostructures of two-dimensional (2D) nanomaterials are versatile platforms for nanoscale optoelectronics. Further coupling of these 2D materials with plasmonic nanostructures, especially in non-close-packed morphologies, imparts new metastructural properties such as increased photosensitivity as well as spectral selectivity and range. However, the integration of plasmonic nanoparticles with 2D materials has largely been limited to lithographic patterning and/or undefined deposition of metallic structures. Here we show that colloidally synthesized zero-dimensional (0D) gold nanoparticles of various sizes can be deterministically self-assembled in highly-ordered, anisotropic, non-close-packed, multi-scale morphologies with templates designed from instability-driven, deformed 2D nanomaterials. The anisotropic plasmonic coupling of the particle arrays exhibits emergent polarization-dependent absorbance in the visible to near-IR regions. Additionally, controllable metasurface arrays of nanoparticles by functionalization with varying polymer brushes modulate the plasmonic coupling between polarization dependent and independent assemblies. This self-assembly method shows potential for bottom-up nanomanufacturing of diverse optoelectronic components and can potentially be adapted to a wide array of nanoscale 0D, 1D, and 2D materials. 

Thermoelectrics operating at high temperature can cost-effectively convert waste heat and compete with other zero-carbon technologies. Among different high-temperature thermoelectrics materials, silicon nanowires possess the combined attributes of cost effectiveness and mature manufacturing infrastructures. Despite significant breakthroughs in silicon nanowires based thermoelectrics for waste heat conversion, the figure of merit (ZT) or operating temperature has remained low. Here, we report the synthesis of large-area, wafer-scale arrays of porous silicon nanowires with ultra-thin Si crystallite size of ~4 nm. Concurrent measurements of thermal conductivity (κ), electrical conductivity (σ), and Seebeck coefficient (S) on the same nanowire show aZTof 0.71 at 700 K, which is more than ~18 times higher than bulk Si. ThisZTvalue is more than two times higher than any nanostructured Si-based thermoelectrics reported in the literature at 700 K. Experimental data and theoretical modeling demonstrate that this work has the potential to achieve aZTof ~1 at 1000 K.

 

The emergence of two-dimensional (2D) materials as functional surfaces for sensing, electronics, mechanics, and other myriad applications underscores the importance of understanding 2D material–liquid interactions. The thinness and environmental sensitivity of 2D materials induce novel surface forces that drive liquid interactions. This complexity makes fundamental 2D material–liquid interactions variable. In this review, we discuss the (1) wettability, (2) electrical double layer (EDL) structure, and (3) frictional interactions originating from 2D material–liquid interactions. While many 2D materials are inherently hydrophilic, their wettability is perturbed by their substrate and contaminants, which can shift the contact angle. This modulation of the wetting behavior enables templating, filtration, and actuation. Similarly, the inherent EDL at 2D material–liquid interfaces is easily perturbed. This EDL modulation partially explains the wettability modulation and enables distinctive electrofluidic systems, including supercapacitors, energy harvesters, microfluidic sensors, and nanojunction gating devices. Furthermore, nanoconfinement of liquid molecules at 2D material surfaces arising from a perturbed liquid structure results in distinctive hydrofrictional behavior, influencing the use of 2D materials in microchannels. We expect 2D material–liquid interactions to inform future fields of study, including modulation of the chemical reactivity of 2D materials via tuning 2D material–liquid interactions. Overall, 2D material–liquid interactions are a rich area for research that enables the unique tuning of surface properties, electrical and mechanical interactions, and chemistry.