The measurement of thin film mechanical properties free from substrate influence remains one of the outstanding challenges in nanomechanics. Here, a technique based on indentation of a supported film with a flat punch whose diameter is many times the initial film thickness is introduced. This geometry generates a state of confined uniaxial strain for material beneath the punch, allowing direct access to intrinsic stress versus strain response. For simple elastic–plastic materials, this enables material parameters such as elastic modulus, bulk modulus, Poisson's ratio, and yield stress to be simultaneously determined from a single loading curve. The phenomenon of confined plastic yield has not been previously observed in thin films or homogeneous materials, which we demonstrate here for 170 -470 nm thick polystyrene (PS), polymethyl-methacrylate (PMMA) and amorphous Selenium films on silicon. As well as performing full elastic -plastic parameter extraction for these materials at room temperature, we used the technique to study the variation of yield stress in PS to temperatures above the nominal glass transition of 100 °C.

This work demonstrates a double-step method, a simple chemical bath deposition and an in situ polymerization process, to synthesize the stable structure of a composite of Polyaniline/BiVO4/cellulose aerogel (PBC) in wastewater treatment. The poor stability of the carrier catalyst was improved significantly by forming a dense film of polyaniline (PANI) through polymerization on BiVO4/cellulose aerogel (BC). The developed three-dimensional porous structure enhanced photocatalytic stability. For instance, photocatalytic degradation of a dye, methylene blue, reached to 91.67% under the eight times successive irradiation of the visible light. The resulted fine performance could be owed to the strong adsorption of cellulose aerogel, uniform spreading of BiVO4, and the speedy electron separation efficiency of PBC. Moreover, the photocatalytic mechanisms including the role of the free radicals (•OH and •O2−) of the developed PBC were also discussed. The novel structure may present a new insight into the development of the carrier catalyst.

The field of nonlinear optics is a well-established discipline that relies on macroscopic media and employs propagation distances longer than a wavelength of light. Recent progress with electromagnetic metamaterials has allowed for the expansion of this field into new directions of new phenomena and novel functionalities. In particular, nonlinear effects in thin, artificially structured materials such as metasurfaces do not rely on phase-matching conditions and symmetry-related selection rules of natural materials; they may be substantially enhanced by strong local and collective resonances of fields inside the metasurface nanostructures. Consequently, nonlinear processes may extend beyond simple harmonic generation and spectral broadening due to electronic nonlinearities. This article provides a brief review of basic concepts and recent results in the field of nonlinear optical metasurfaces.

Miniaturization is a strong demand of modern scientific technology. However, conventional optical components based on refraction suffer from functional degradation as the device size decreases. Metasurfaces consisting of subwavelength optical antenna arrays have emerged as planar optical devices that enable many promising applications in lenses, holograms, and optical cloaks. During recent decades, metasurfaces have been developed for their specific functionalities by exploiting new materials and design algorithms. In this issue of MRS Bulletin, progress in metasurfaces is discussed to provide a comprehensive understanding of metasurfaces and their novel applications in optics and photonics.

To address critical energy issues in civic structures, we have developed a novel concept of optical thermal insulation (OTI) without relying on a conventional thermal intervention medium, such as air or argon, as often used in conventional window systems. We have synthesized the photothermal (PT) materials, such as the Fe3O4 and Fe3O4@Cu2−xS nanoparticles, that exhibit strong UV and near-infrared (NIR) absorptions but with good visible transparency. Upon coating the inner surface of the window glass with a PT film, under solar irradiation, the inner surface temperature rises due to the PT effect. Subsequently, the temperature difference, ΔT, is reduced between the single pane and room interior. This leads to lower the thermal loss through a window, reflected by the U-factor, resulting in considerable energy saving without double- or triple-glazing. Comparing with the Fe3O4 coatings, Fe3O4@Cu2−xS is spectrally characterized with a much stronger NIR absorbance, contributing to an increased PT efficiency under simulated solar irradiation (0.1 W/cm2). PT experiments are carried out via both white light and monochromic NIR irradiations (785 nm). The parameters associated with the thermal performance of the PT films are calculated, including PT conversion efficiency, specific absorption rate (SAR), and U-factor. Based on the concept of OTI, we have reached an optimum U-factor of 1.46 W/m2 K for a single pane, which is satisfactory to the DOE requirement (<1.7 W/m2 K).

The past decade has witnessed the advent of nanophotonics, where light–matter interaction is shaped, almost at will, with human-made designed nanostructures. However, the design process for these nanostructures has remained complex, often relying on the intuition and expertise of the designer, ultimately limiting the reach and penetration of this groundbreaking approach. Recently, there has been an increasing number of studies in applying machine learning techniques for the design of nanostructures. Most of these studies engage deep learning techniques, which entail training a deep neural network (DNN) to approximate the highly nonlinear function of the underlying physical process of the interaction between light and the nanostructures. At the end of the training, the DNN allows for on-demand design of nanostructures (i.e., the model can infer nanostructure geometries for desired light spectra). In this article, we review previous studies for designing nanostructures, including recent advances where a DNN is trained to generate a two-dimensional image of the designed nanostructure, which is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry. This allows for better generalization, with higher applicability for real-world design problems.

Metasurfaces are thin-film electromagnetic devices with subwavelength-scale geometric structuring. They can be tailored to produce a broad range of optical functions due to the strong relationship between electromagnetic response and geometric shape. An open challenge has been understanding how to produce an ideal metasurface design when presented with a desired electromagnetic response. This article discusses the use of topology optimization as a design platform for high-performance, freeform metasurfaces. Two types of topology optimizers are covered—local gradient-based optimizers that leverage the adjoint variables method, and global population-based optimizers that reframe the optimization process as the training of a generative neural network. It is anticipated that these inverse design concepts will push metasurface performance to the physical limits of structured media and enable new functionalities in electromagnetic systems.