The electrochemical reduction of CO₂ (CO₂RR) facilitates the sustainable synthesis of fuels and chemicals. Grain-boundary-rich nanoparticles show superior CO₂RR performance, however, the nature of active sites remains elusive. Here, we utilize in situ attenuated total reflectance surface-enhanced infrared absorption spectroscopy to identify the active sites and achieve structure-activity correlations, by taking advantage of synthesizing colloidal nanoparticles with well-defined shapes. It indicates that grain boundaries selectively convert CO₂ to CO during the CO₂RR, and the terrace sites on Cu(100) facets are the active sites for the conversion of CO to C₂₊ products. Density functional theory calculations show that the optimal adsorption energies of CO₂ and CO on grain boundaries facilitate the conversion of CO₂ into CO, while the terrace sites on Cu(100) facets selectively convert CO to C₂₊ products by lowering the activation energy of C-C coupling process. This work provides experimental evidences for the rational design of highly selective catalysts to produce C₂₊ products via the CO₂RR.

Despite the increasing prevalence of wearable flexible sensors for human joint biomechanics, challenges, such as limited intelligence capacity, insufficient sensitivity, and power constraints, limit their practical application. To address these issues, this work presents a low-power and low-latency edge computing algorithm that incorporates interpretable machine learning to guide dimensionality reduction for direct on-sensor signal processing. Compared to traditional wireless transmission methods, the deployed tiny machine learning (tinyML) model achieves a prediction latency of only 9 ms and reduces power consumption by 75.6%. Furthermore, utilizing a triboelectric sensor based on MXene and featuring a micro-conical structure demonstrates excellent self-powered sensing capability, with output voltage and charge increased by 27.4% and 52.9%, respectively, and a high-sensitivity monitoring performance of 16 mV/Pa. The synergy between the efficient algorithm and the high-performance sensor is validated in knee joint biomechanics scenarios, showing advantages over conventional approaches in power consumption, cost, response speed, size, and accuracy. These combined strengths indicate broad application prospects in portable intelligent healthcare.

Nanoscale confinement environments often affect the transport mechanisms of nanofluids. Understanding the dynamic behavior of molecules in two-dimensional (2D) confined channels is of great importance in the areas of sensing, catalysis and energy storage. As a popular candidate for a new type of gas sensing material, MXenes have the problem of nonselectivity towards polar gases with slow responses, which severely limits their applications. Here, we report a study on regulating the confinement effect of 2D channels between MXene layers through annealing treatment and ion (Na⁺) intercalation for high-performance ammonia (NH₃) sensing. Firstly, the annealing treatment accurately modulates the size of the 2D channels to effectively block the entry of large-size gas molecules and improve the selectivity for NH₃. Ab initio molecular dynamics (AIMD) also confirms that the modulated channel size has a special "nano-pumping effect", which can accelerate the dynamic behavior of NH₃ molecules in the 2D confined space. Moreover, the intercalation of Na⁺ ions increases the adsorption capacity of NH₃. Therefore, the "nano-pumping effect" and theintercalation of Na⁺ ions effectively enhance the response speed and sensitivity of MXene to NH₃, respectively. The experimental results show that the modified Ti₃C₂ exhibits high sensitivity (0.17), rapid response (181 s), excellent selectivity and stability towards NH₃.

The detection of multiple trace analytes using single sensors is often impeded by the limited sensitivity of material and the interference form overlapping signals in complex mixtures. Here, we introduce an efficient and durable heterostructured high-entropy alloy (HEA) material, where non-noble HEA nanoparticles are used to disperse and stabilize Pt clusters (denoted as HEA@Pt). The HEA@Pt exhibits high sensitivity to three trace analytes (dopamine, uric acid, and paracetamol) during the electrochemical detection process, leveraging its multifunctional catalytic sensing capabilities for diverse mixtures. Additionally, to address the challenge of signal overlap, we integrate a recurrent neural network into multimodal sensing, combined with machine learning (ML) algorithms to accurately identify multiple analytes in mixtures. After five-fold cross-validation, the prediction accuracy deviations for dopamine, uric acid, and paracetamol were 3.20, 9.18 and 3.84, respectively, with goodness-of-fit values of 0.984, 0.992 and 0.990. The model achieved a prediction accuracy of 96.67% for unknown mixture samples, demonstrating robust generalization performance. This approach of multifunctional HEA combined with ML algorithms effectively overcomes detection errors caused by the complex detection of multiple chemical substances and the overlap of multiple response signals, thereby enabling accurate and reliable identification of multi-target analytes.

Two-dimensional (2D) materials have attracted extensive attention from aerospace, integrated circuits, precision sensors, and flexible electronics due to their unique layered structure and excellent physicochemical properties. In practice applications, the components of functional nanodevices are subjected to mechanical stress, which can affect the robust performance and structural reliability of these devices. Therefore, it is imperative to explore the mechanical properties and underlying mechanisms of 2D materials. However, researchers have an inadequate understanding of the accuracy of various in situ microscopy techniques and neglect the significance of high-quality, clean transfer techniques, resulting in deviated measurement results. There is now an urgent need to develop guidelines that allow researchers to select appropriate material transfer techniques and mechanical testing strategies based on the specific properties of 2D materials. Furthermore, the mechanical mechanism of 2D materials lacks systematic and comprehensive studies, which hinders researchers from deeply understanding the relationship between the material structure and the device performance. This work reviews the latest progress in the mechanics of 2D materials, focusing on the challenges of various transfer techniques and in situ microscopy techniques in mechanical testing, and provides effective guidance for the formulation of experimental schemes for mechanical testing. In addition, we offer detailed mechanistic insights into the fracture behavior, geometric dimension effects, edge defects, and interlayer bonding effects of 2D materials. This work is expected to advance the field development of 2D material mechanics.

Micro-supercapacitors (MSCs) face significant limitations due to low energy density despite their high power density and long cycle life. In this study, single-layer Ti₃C₂T_x nanosheets were employed to fabricate a MXene-hydroxylated nanocellulose-carbon nanotube (MHC) composite ink, which was used to fabricate high-energy flexible MSCs via direct ink writing three-dimensional (3D) printing technology. The introduction of the rheological modifier hydroxylated nanocellulose (HNC) not only constructs interlayer spacers to inhibit nanosheet restacking but also optimizes the rheological properties and 3D printability of the composite ink. Meanwhile, the synergistic effect of carbon nanotubes (CNTs) as conductive agents enhances interlayer electron transport and electrochemical performance. Benefiting from the rational design of the ink and printing process, the fabricated MSCs exhibit high-precision structures (electrode width of 250 μm and electrode area of 0.2625 cm²) and outstanding energy storage properties, achieving an areal capacitance of 543 mF·cm⁻², an energy density of 27.15 μWh·cm⁻², and a power density of 6 mW·cm⁻², significantly surpassing previously reported MXene-based MSCs. Moreover, the flexible all-solid-state MSCs demonstrate excellent performance stability under mechanical bending, series/parallel module integration, and long-term cycling tests, providing a customizable energy storage solution for flexible wearable microelectronic systems.

Dopamine plays a crucial role in regulating various brain functions, making the development of highly sensitive detection methods and precise quantitative analysis techniques of great significance. However, realizing highly selective and sensitive detection of dopamine in complex biological environments remains a challenge. Here, we prepared three-dimensional (3D) crumpled Ti₃C₂T_x structures loaded with Pt nanoparticles (Pt/Na-Ti₃C₂T_x) by wet chemical reduction and ion intercalation. The synergistic coupling between Pt nanoparticles and MXene support facilitates efficient electron transfer between dopamine and the electrode surface, thereby improving the sensing performance of dopamine. Furthermore, this wrinkled structure not only enhances the specific surface area by inhibiting the stacking of layered Ti₃C₂T_x nanosheets, but also effectively prevents the agglomeration of nanoparticles. The experimental results showed that Pt/Na-Ti₃C₂T_x possessed a wide linear range (0.1–100 μM), a low detection limit (0.029 μM), and a high sensitivity (0.556 μA·μM⁻¹·cm⁻²). This work proposes an innovative strategy for achieving highly sensitive dopamine detection while advancing the utilization of MXene-based nanocomposites in electrochemical sensor development.

Flexible strain sensors are essential in fields such as medicine, sports, robotics, and virtual reality but face challenges in achieving excellent sensing performance and accurate multi-directional detection simultaneously. To address this issue, we have developed a spider-web structured multi-directional flexible strain sensor using Ti₃C₂T_x (MXene) conductive ink and three-dimensional (3D) printing technology. Combined with a multi-class, multi-output neural network model algorithm, the sensor achieves signal decoupling from the sensor array, allowing for precise detection of strain direction and intensity. It exhibits good sensitivity (gauge factor ~ 26.3), a moderate sensing range (0%–10%), and high reliability (1000 stretching cycles). Using neural network algorithms, a four-unit spider-web sensor array achieves approximately 97% accuracy in identifying strain intensity and direction within the 0%–10% strain range under various surface stimuli. Additionally, it can track complex human motions, demonstrating significant potential in applications such as motion monitoring and human–machine interaction.

Single-atom catalysts (SACs) attract widespread attention in heterogeneous catalysis due to their maximum atomic utilization efficiency and unique physical and chemical properties. However, their applications in chemical sensing keep huge potential but remain unclear. Herein, a Ni-N₄-C SAC was synthesized for the trace detection of dopamine (DA) and uric acid (UA). The Ni-N₄-C SAC exhibited superior sensing performance compared to the Ni clusters. The detection range for DA and UA were 0.05–75 µM and 5–90 µM with detection limits of 0.027 and 0.82 µM, respectively. Density functional theory (DFT) computations indicate that Ni-N₄-C has a lower reaction barrier during electrochemical process, indicating that the atomic Ni sites possess higher intrinsic activity than Ni clusters. Moreover, DA and UA show strong potential dependency on the Ni-N₄-C catalyst, indicating its applicability for their concurrent detection. This work extends the application of SACs in chemical sensing.

Herein we proposed a data-driven high-throughput principle to screen high-performance single-atom materials for hydrogen evolution reaction (HER) and hydrogen sensing by combing the theoretical computations and a topology-based multi-scale convolution kernel machine learning algorithm. After the rational training by 25 groups of data and prediction of all 168 groups of single-atom materials for HER and sensing, respectively, a high prediction accuracy (> 0.931 R² score) was achieved by our model. Results show that the promising HER catalysts include Pt atoms in C₄ and Sc atoms in C₁N₃ coordination environment. Moreover, Y atoms in C₄ coordination environment and Cd atoms in C₂N₂-ortho coordination environment were predicted with great potential as hydrogen sensing materials. This method provides a way to accelerate the discovery of innovative materials by avoiding the time-consuming empirical principles in experiments.