Matter最新文献

Revealing the distribution of elastic deformations in anisotropic solids is of crucial importance for evaluating the mechanical performance of complex materials. However, elastic deformation fields (EDFs) need to be investigated under applied loads and in 3D, so they are often limited to planar approaches or simplifying assumptions. Here, we introduce 3D-RISM, a method by which the 3D spatial distribution of EDFs can be mapped in operando and with submicron resolution in laser-translucent materials. Taking examples of geological and biological ceramics, we visualize the 3D distribution and stepwise development of anisotropic EDFs under Hertzian contacts. We leverage our method to showcase how the anisotropy of elastic behavior regulates the distribution of induced plasticity and the direction of microcracking in crystals. 3D-RISM offers a promising platform for real-time mapping of deformation tensors in complex materials and devices, opening new avenues for better understanding the behavior of materials with azimuthal anisotropy in Poisson’s ratio.

Metallic glasses usually exhibit lower elastic moduli compared with their crystalline counterparts. Structural relaxation may impel the amorphous structures to lower energy states, which is so far the only way that may moderately elevate the elastic modulus of metallic glasses. In this study, we found that decomposition of a binary CuZr glass may substantially increase its elastic modulus. With intensive plastic straining followed by annealing, the as-quenched homogeneous glass decomposed into two finely spaced (below 10 nm) glassy phases with different chemical compositions. Young’s modulus of the decomposed glass is ∼139% that of the as-quenched one and comparable to the corresponding crystalline phases. The modulus elevation may be attributed to formation of more ordered amorphous phases with a high density of glass/glass interfaces.

Multi-wavelength, multi-channel, and multi-depth imaging techniques are essential in the research of three-dimensional reconstruction and comprehensive information representation of target objects. However, traditional techniques are generally limited by cumbersome combinations of costly and bulky components. In this preview, we highlight an excellent imaging technique named “stochastic photoluminescence and compressed encoding” or SPACE. By combining four randomly arrayed lanthanide transducers as photonic encoders with a compressed sensing reconstruction algorithm, the proposed technology enables efficient ultra-broadband, multi-wavelength compression imaging and information reconstruction.

The directed design and discovery of compounds with pre-determined properties is a long-standing challenge in materials research. We provide a perspective on progress toward achieving this goal using generative models for chemical compositions and crystal structures based on a set of powerful statistical techniques drawn from the artificial intelligence community. We introduce the central concepts underpinning generative models of crystalline materials. Coverage is provided of early implementations for inorganic crystals based on generative adversarial networks and variational autoencoders through to ongoing progress involving autoregressive and diffusion models. The influence of the choice of chemical representation and the generative architecture is discussed, along with metrics for quantifying the quality of the hypothetical compounds produced. While further developments are required to enable realistic predictions drawn from richer structure and property datasets, generative artificial intelligence is already proving to be complementary to traditional materials design strategies.

Leveraging the air-trapping features of the lotus leaf, recent advancements in pressure sensors have introduced a unique aero-elastic capacitive pressure sensor using a gas-liquid-liquid-solid multiphasic interface. This innovation significantly enhances pressure sensitivity and reliability in challenging liquid environments due to its minimal hysteresis, excellent linearity, and negligible threshold effects. It promises to enable sensitive and reliable intra-body monitoring and improve surgical precision where biomechanical pressure sensing is necessary.

The family of two-dimensional (2D) carbides and/or nitrides, also known as MXenes, has generated great excitement within the scientific community and has been proposed for a wide variety of applications since its discovery. Despite this attention, there have been only limited studies of the atomically resolved local electronic and physical structure of MXene surfaces strongly affecting the physicochemical properties of these materials. Here, we report local structural, spectroscopic, and chemical investigations of the surfaces of Ti₃C₂T_x flakes using scanning tunneling microscopy and spectroscopy, closely coupled to theoretical studies. Terminal groups are visualized and characterized, along with surface TiO₂ clusters formed upon exposure to air. Fundamental insight into the local electronic and chemical properties associated with different terminal groups on MXenes and their oxidation products is presented.

The editorial team at Matter is frequently asked about common reasons for desk rejections that can be potentially avoided. Here, we focus on four common archetype reasons papers are rejected without review, not because they reflect poor science, but because they succumb to common pitfalls that are prevalent across materials science papers.

Self-driving labs (SDLs) have recently emerged as one of the most significant technological developments in the chemical and materials sciences and hold the potential to revolutionize the research process. Herein, we discuss the structure of an SDL in terms of the hardware, the coordinator software, and the AI agent and examine how the selection of these elements affects the overall research capabilities. We further look to the application and accessibility of these platforms to better understand their future impact on synthetic and materials chemistry. With the rapid rise in the capabilities of these SDLs, and with the increasing democratization of the space, it is crucial to be aware of both the promises and pitfalls this technology holds.

Scent digitalization, interpreting scents in digital realms, promises transformative impacts on human life in various areas. However, it faces challenges due to the diversity of scent molecules and the complex physicochemical interactions between scents and sensing materials. To date, a myriad of scent-sensing technologies and methodologies has been developed, yet no single approach has succeeded in capturing the intricate compositions of scents. Here, we propose an integrated multimodal sensing strategy that leverages the strengths of multiple sensing modalities to obtain a more complete and accurate representation of scent compositions. In this perspective, we revisit representative scent sensing techniques and explore the underlying physicochemical properties captured by each technique. We then present examples of integrated multimodal sensing, discussing the unique capabilities and operational features of combined modalities. Based on current progress and limitations, we discuss several future research directions for the field and envision a promising future of scent digitalization.