Wearable sensors that offer significant adaptability and comfort demonstrate substantial potential for discrete, seamless integration into garments, enabling diverse applications for remote monitoring. The transformation of rigid electronic devices into flexible and stretchable sensors, achieved by integrating conductive materials with compliant dielectrics, has enabled the development of robust sensors. However, several factors must be addressed to enable widespread adoption, including garment integration, wireless communication, data pipelines, signal processing, multimodal fusion, privacy and security concerns. Addressing these challenges is essential for establishing the technological and social infrastructure necessary for the widespread adoption of wearable technologies. This review highlights innovations in material and mechanical design strategies that have shaped the current landscape of wearables, and points out some remaining challenges. Stretchable wearable sensors enable comfortable garment integration for remote monitoring, but advances in connectivity, data processing, privacy, and security are needed for widespread adoption. Here, the authors highlight innovations in materials and mechanical design and identify key challenges for wearable technologies.
For automated analysis of diffractograms, it seems straight forward to use machine-learning models developed for images, which can indeed generate highly accurate predictions. However, the fundamental difference between images and diffractograms can introduce hidden vulnerabilities. As a case study, we analyze $$\upalpha$$- and $$\upbeta$$-phase fractions in Ti–6Al–4V using two independent synchrotron 2D X-ray diffractogram datasets. We analyze the performance of two neural networks: “IE,” pre-trained only on ImageNet (a database of natural images), and “ISE,” additionally trained on a large corpus of simulated X-ray diffractograms. Subsequently fine-tuned with just 250 experimental diffractograms, both models achieve similarly high predictive accuracy. However, robustness tests and attention mapping reveal fundamentally different internal logic. IE suffers from “surface-texture bias,” predicting phase fractions from irrelevant background and pixel noise. ISE, in contrast, evaluates physically relevant features – Bragg reflections – in the diffractograms. Accordingly, prediction accuracy alone does not validate machine-learning models. Trustworthy models for diffractogram analysis require physics-driven pre-training. Machine-learning models can accurately predict outcomes from diffractograms, but differences between images and diffractograms may introduce vulnerabilities. Here, the authors compare neural networks trained on natural images and simulated diffractograms, revealing that physics-driven pre-training enhances model robustness by focusing on relevant features, underscoring the need for domain-specific training in scientific applications.
Flexoelectricity offers a universal route to electromechanical coupling in centrosymmetric materials, but its macroscopic output is often constrained by the weak strain gradients attainable in planar thin films. Here, we introduce a femtosecond-laser strategy to sculpt hollow nanoblister arrays in anatase TiO2 thin films, creating high-curvature architectures that amplify flexoelectric response. The blister morphology can be continuously tuned from isolated domes to line-shaped structures through laser-parameter control. Cross-sectional analysis reveals that nanoblister formation arises from localized TiO2/Pt interfacial delamination followed by shell buckling. Piezoresponse force microscopy confirms enhanced local polarization within the hollow blisters and their mechanical and electrical tunability. By coupling nanoscale blister curvature with macroscopic substrate bending, the device achieves multiscale strain-gradient amplification, delivering an output voltage of 0.516 V and a 21.5-fold enhancement over planar TiO2 films. Finite-element simulations corroborate the geometry-driven mechanism, establishing hollow-structure engineering as a general strategy for high-performance flexible flexoelectric devices. This study uses a femtosecond-laser to sculpt hollow nanoblister arrays in TiO2 thin films, creating high-curvature architectures that amplify flexoelectric response. By coupling this with macroscopic substrate bending, devices achieve an output voltage of 0.516 V and a 21.5-fold enhancement over planar TiO2 films.
Chemical vapor deposition (CVD) is a thriving bottom-up synthesis technique that serves as an alternative to the conventional methods involving the etching of layered MAX phases. CVD is particularly advantageous in terms of large-scale synthesis and atomic economy, time and energy savings, as well as the capability to produce bare MXenes without surface terminations. However, its widespread employment remains limited as the growth generally only occurs in specific immiscible alloy systems consisting of transition metals (such as Mo) and low melting point catalytic metals (such as Cu). Here, we propose a surface diffusion liquid metal CVD (SDLM-CVD) method for synthesizing bare few-layer 2D Nb2C MXenes. Controlling the Nb/C ratio and its distribution on the Cu liquid surface is the key factor. Single crystalline bare Nb2C flakes with a trigonal structure were obtained in a specific region where the appropriate Nb/C ratio is satisfied. Additionally, a continuous 2D Nb2C film is formed by leveraging the self-assembling process that observed during the growth. The growth mechanism and the self-assembling mechanism were further elucidated through density functional theory calculations. The SDLM-CVD method provides a novel approach for the direct and large-scale synthesis of materials in the MXenes family. This paper reports a surface diffusion liquid metal chemical vapor deposition method for synthesizing bare few-layer 2D Nb2C MXenes. Single crystalline Nb2C flakes with a trigonal structure are obtained in a specific region, and a continuous 2D film can be formed by leveraging the self-assembling process observed during growth.
Silicon (Si) photonics is a solid candidate to address the challenges of emerging communication systems with an increasing number of interconnected devices. However, the properties of Si prevent on-chip integration of key functions including light amplification. The increasing demand for efficient photonic materials compatible with silicon platforms, at low economic and environmental costs, opened the possibility to the integration of rare-earth doped oxides for the development of integrated optical amplifiers. In this context, this paper reports on an heterogeneously integrated photonic device based on erbium-doped yttria-stabilized zirconia on silicon nitride (SiNx) waveguides. Average erbium concentration of 1.06×1021 cm−3 was implemented in a 220 nm thick oxide layer. We demonstrated on-chip signal amplification with a measured signal enhancement of 7.45 dB for 3 mm long waveguide, as well as material losses of 10.8 dB·cm⁻¹ and modal gain with a value as high as 32.5 dB·cm⁻¹ for 1.1 mm long waveguide at a wavelength of 1530 nm. Silicon photonics is promising for developing increasingly complex communication systems but faces challenges in integrating key functionalities such as light amplification. Here, the authors demonstrate a heterogeneously integrated photonic device using erbium-doped yttria-stabilized zirconia on silicon nitride waveguides, achieving significant on-chip signal amplification, which advances efficient photonic materials compatible with silicon platforms.
