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Rolling bearings are the core transmission components of large-scale rotating machinery such as wind power gearboxes and aviation engines, so timely and effective monitoring and diagnosis of their status are crucial to ensure the stable operation of equipment, reduce maintenance costs, and improve production efficiency. However, the noise interference in the industrial field often hides the original characteristics of the bearing fault signal, leading to the deep learning-based fault diagnosis model’s lack of diagnostic reliability in the strong industrial noise background. To address this problem, this paper proposes a multi-domain collaborative denoising diagnostic model based on dynamic inter-domain attention mechanism and noise-aware loss function. First, the model extracts high-dimensional features of bearing fault signals from multiple domains, such as time and frequency domains, aiming to enhance the richness and diversity of high-dimensional features to effectively suppress noise interference on the diagnostic results. Second, the dynamic inter-domain attention mechanism (DIDAM) is proposed, aiming to distinguish the importance of information in different signal domains and flexibly integrate them to realize more efficient and accurate multi-domain information fusion. Finally, the noise-aware loss function (NALF) is designed to avoid the phenomenon of the conduction model being prone to making wrong decisions due to excessive noise. Experimental results on two publicly available datasets, CWRU and MFPT, show that even in the extreme noise environment with SNR = –10 dB, the proposed model still achieves 81.25% and 76.36% fault diagnosis accuracies, which are better than most existing mainstream denoising models. Overall, the proposed method can still perform well under substantial noise interference, providing a new idea for intelligent bearing fault diagnosis in real industrial scenarios.

Cu-based nanocatalysts are the cornerstone of various industrial catalytic processes. Synergistically strengthening the catalytic stability and activity of Cu-based nanocatalysts is an ongoing challenge. Herein, the high-entropy principle is applied to modify the structure of Cu-based nanocatalysts, and a PVP templated method is invented for generally synthesizing six-eleven dissimilar elements as high-entropy two-dimensional (2D) materials. Taking 2D Cu 2 Zn 1 Al 0.5 Ce 5 Zr 0.5 O x as an example, the high-entropy structure not only enhances the sintering resistance from 400 °C to 800 °C but also improves its CO 2 hydrogenation activity to a pure CO production rate of 417.2 mmol g −1 h −1 at 500 °C, 4 times higher than that of reported advanced catalysts. When 2D Cu 2 Zn 1 Al 0.5 Ce 5 Zr 0.5 O x are applied to the photothermal CO 2 hydrogenation, it exhibits a record photochemical energy conversion efficiency of 36.2%, with a CO generation rate of 248.5 mmol g −1 h −1 and 571 L of CO yield under ambient sunlight irradiation. The high-entropy 2D materials provide a new route to simultaneously achieve catalytic stability and activity, greatly expanding the application boundaries of photothermal catalysis. Synergistically enhancing catalytic stability and activity of Cu-based nanocatalysts is an ongoing challenge. Here the authors report Cu-based high-entropy two-dimensional oxide as stable and active catalyst for photothermal CO2 hydrogenation under ambient sunlight irradiation.

Blood clots (BCs) play a crucial biomechanical role in promoting osteogenesis and regulating mesenchymal stem cell (MSC) function and fate. This study shows that BC formation enhances MSC osteogenesis by activating Itgb1/Fak-mediated focal adhesion and subsequent Runx2-mediated bone regeneration. Notably, BC viscoelasticity regulates this effect by modulating Runx2 nuclear translocation. To mimic this property, a viscoelastic peptide bionic hydrogel named BCgel was developed, featuring a nanofiber network, Itgb1 binding affinity, BC-like viscoelasticity, and biosafety. The anticipated efficacy of BCgel is demonstrated by its ability to induce nuclear translocation of Runx2 and promote bone regeneration in both in vitro experiments and in vivo bone defect models with blood clot defect, conducted on rats as well as beagles. This study offers insights into the mechano-transduction mechanisms of MSCs during osteogenesis and presents potential guidelines for the design of viscoelastic hydrogels in bone regenerative medicine. This study shows that blood clot (BC) formation enhances MSC osteogenesis by activating Itgb1/Fak-mediated focal adhesion and subsequent Runx2-mediated bone regeneration. Further, the authors propose a viscoelastic peptide bionic hydrogel (termed BCgel), which features BC-like viscoelasticity and promotes bone regeneration.

The task of multiple-tiny-object detection from diverse perspectives in unmanned aerial vehicles (UAVs) using onboard edge devices is a significant and complex challenge within computer vision. In order to address this challenge, we propose a lightweight and efficient tiny-object-detection algorithm named LE-YOLO, based on the YOLOv8n architecture. To improve the detection performance and optimize the model efficiency, we present the LHGNet backbone, a more extensive feature extraction network, integrating depth-wise separable convolution and channel shuffle modules. This integration facilitates a thorough exploration of the inherent features within the network at deeper layers, promoting the fusion of local detail information and channel characteristics. Furthermore, we introduce the LGS bottleneck and LGSCSP fusion module incorporated into the neck, aiming to decrease the computational complexity while preserving the detector’s accuracy. Additionally, we enhance the detection accuracy by modifying its structure and the size of the feature maps. These improvements significantly enhance the model’s capability to capture tiny objects. The proposed LE-YOLO detector is examined in ablation and comparative experiments on the VisDrone2019 dataset. In contrast to YOLOv8n, the proposed LE-YOLO model achieved a 30.0% reduction in the parameter count, accompanied by a 15.9% increase in the mAP(0.5). These comprehensive experiments indicate that our approach can significantly enhance the detection accuracy and optimize the model efficiency through the organic combination of our suggested enhancements.

Electrochemical carbon monoxide (CO) reduction to high-energy-density fuels provides a potential way for chemical production and intermittent energy storage. As a valuable C 3 species, n-propanol still suffers from a relatively low Faradaic efficiency (FE), sluggish conversion rate and poor stability. Herein, we introduce an “atomic size misfit” strategy to modulate active sites, and report a facile synthesis of a Pb-doped Cu catalyst with numerous atomic Pb-concentrated grain boundaries. Operando spectroscopy studies demonstrate that these Pb-rich Cu-grain boundary sites exhibit stable low coordination and can achieve a stronger CO adsorption for a higher surface CO coverage. Using this Pb-Cu catalyst, we achieve a CO-to-n-propanol FE (FE propanol ) of 47 ± 3% and a half-cell energy conversion efficiency (EE) of 25% in a flow cell. When applied in a membrane electrode assembly (MEA) device, a stable FE propanol above 30% and the corresponding full-cell EE of over 16% are maintained for over 100 h with the n-propanol partial current above 300 mA (5 cm 2 electrode). Furthermore, operando X-ray absorption spectroscopy and theoretical studies reveal that the structurally-flexible Pb-Cu surface can adaptively stabilize the key intermediates, which strengthens the *CO binding while maintaining the C–C coupling ability, thus promoting the CO-to-n-propanol conversion. CO electroreduction to valuable high-energy content fuels is desired yet improving multicarbon C3 selectivity remains challenging. Here, authors enhance the n-propanol formation on a Cu-based electrocatalyst by introducing Pb atoms into the Cu lattice to induce Pb-rich Cu grain boundary sites.

The synthesis of multi-carbon (C 2+ ) fuels via electrocatalytic reduction of CO, H 2 O using renewable electricity, represents a significant stride in sustainable energy storage and carbon recycling. The foremost challenge in this field is the production of extended-chain carbon compounds (C n , n ≥ 3), wherein elevated * CO coverage (θ co ) and its subsequent multiple-step coupling are both critical. Notwithstanding, there exists a “seesaw” dynamic between intensifying * CO adsorption to augment θ co and surmounting the C-C coupling barrier, which have not been simultaneously realized within a singular catalyst yet. Here, we introduce a facilely synthesized lattice-strain-stabilized nitrogen-doped Cu (LSN-Cu) with abundant defect sites and robust nitrogen integration. The low-coordination sites enhance θ co and concurrently, the compressive strain substantially fortifies nitrogen dopants on the catalyst surface, promoting C-C coupling activity. The n-propanol formation on the LSN-Cu electrode exhibits a 54% faradaic efficiency and a 29% half-cell energy efficiency. Moreover, within a membrane electrode assembly setup, a stable n-propanol electrosynthesis over 180 h at a total current density of 300 mA cm −2 is obtained. The transformation of CO and H 2 O into C 2+ fuels using renewable electricity represents a significant stride in carbon recycling. Here, the authors introduce a plasma-treated Cu catalyst, achieving high CO coverage and promoted C-C coupling ability for efficient n-propanol formation.

As deep learning technologies gradually penetrate various industries, the issue of data scarcity has become a key factor restricting their widespread application and further development. The existing image classification models typically use the Generative Adversarial Network (GAN) to expand the amount of data. However, the GAN focuses solely on generating spatial domain features, overlooking the complementary role of frequency domain information in image representation. In addition, these models assign the same loss weight to both real and generated samples, failing to effectively reflect the contribution differences of these samples during model training. To address these issues, this paper proposes a fully supervised image classification model (D2S-DiffGAN) under limited labeled samples. First, a dual-domain synchronous GAN (DDSGAN) is constructed that constrains the generator from both the spatial and frequency domains. This ensures that the generated samples satisfy both the visual realism of RGB images and the consistency of frequency domain energy distribution, resulting in more diversity and realism. Second, a multi-branch feature extraction network (MBFE) is designed to capture the local texture features, global semantic features, and cross-channel correlation features of the samples. Meanwhile, an attention module is introduced to dynamically fuse multi-dimensional features, further enhancing the channel feature representation relevant to the task. Finally, a differentiated loss function (DIFF) is proposed, setting different loss weights based on the characteristics of generated samples and real images, thereby more reasonably handling the differences between generated and real samples and optimizing the model training process. Extensive experiments on the SVHN and CIFAR-10 datasets show that the proposed model can still achieve good classification accuracy under limited labeled samples, fully validating its effectiveness.

Lithium metal is considered the ultimate anode material for future rechargeable batteries1,2, but the development of Li metal-based rechargeable batteries has achieved only limited success due to uncontrollable Li dendrite growth3–7. In a broad class of all-solid-state Li batteries, one approach to suppress Li dendrite growth has been the use of mechanically stiff solid electrolytes8,9. However, Li dendrites still grow through them10,11. Resolving this issue requires a fundamental understanding of the growth and associated electro-chemo-mechanical behaviour of Li dendrites. Here, we report in situ growth observation and stress measurement of individual Li whiskers, the primary Li dendrite morphologies12. We combine an atomic force microscope with an environmental transmission electron microscope in a novel experimental set-up. At room temperature, a submicrometre whisker grows under an applied voltage (overpotential) against the atomic force microscope tip, generating a growth stress up to 130 MPa; this value is substantially higher than the stresses previously reported for bulk13 and micrometre-sized Li14. The measured yield strength of Li whiskers under pure mechanical loading reaches as high as 244 MPa. Our results provide quantitative benchmarks for the design of Li dendrite growth suppression strategies in all-solid-state batteries.Lithium whisker growth and mechanical properties can be studied in situ using a combination of two microscopies.

The tunable properties of halide perovskite/two dimensional (2D) semiconductor mixed-dimensional van der Waals heterostructures offer high flexibility for innovating optoelectronic and photonic devices. However, the general and robust growth of high-quality monocrystalline halide perovskite/2D semiconductor heterostructures with attractive optical properties has remained challenging. Here, we demonstrate a universal van der Waals heteroepitaxy strategy to synthesize a library of facet-specific single-crystalline halide perovskite/2D semiconductor (multi)heterostructures. The obtained heterostructures can be broadly tailored by selecting the coupling layer of interest, and can include perovskites varying from all-inorganic to organic-inorganic hybrid counterparts, individual transition metal dichalcogenides or 2D heterojunctions. The CsPbI 2 Br/WSe 2 heterostructures demonstrate ultrahigh optical gain coefficient, reduced gain threshold and prolonged gain lifetime, which are attributed to the reduced energetic disorder. Accordingly, the self-organized halide perovskite/2D semiconductor heterostructure lasers show highly reproducible single-mode lasing with largely reduced lasing threshold and improved stability. Our findings provide a high-quality and versatile material platform for probing unique optoelectronic and photonic physics and developing further electrically driven on-chip lasers, nanophotonic devices and electronic-photonic integrated systems. Halide perovskite/2D transition metal dichalcogenides (TMD) heterostructures hold promise for photonic/optoelectronic applications, but their bottom-up growth remains challenging. Here, the authors report a van der Waals heteroepitaxy strategy to synthesize various halide perovskite/TMD heterostructures with enhanced lasing performance.

Liver sinusoidal endothelial cells (LSECs) lose their characteristic fenestrations and become capillarized during the progression of liver fibrosis. Mesenchymal stem cell (MSC) transplantation can reverse this capillarization and reduce fibrosis, but MSC therapy has practical limitations that hinder its clinical use. Here, with the help of artificial intelligence (AI), we show that MSCs secrete a microRNA (miR-325-3p) that helps restore LSEC fenestrations (tiny pores) by modulating their cytoskeleton, effectively reversing capillarization. We further develop a spherical nucleic acid (SNA) nanoparticle carrying miR-325-3p as an alternative to MSC therapy. This SNA specifically enters fibrotic LSECs via the scavenger receptor A (Scara). In three mouse models of liver fibrosis, the SNA treatment restores LSEC fenestrations, reverses capillarization, and significantly reduces fibrosis without adverse effects. Our findings highlight the potential of SNA-based therapy for liver fibrosis, paving the way for targeted nucleic acid treatments directed at LSECs and offering hope for patients. RNAi therapies have potential to treat a range of diseases including liver fibrosis. Here, the authors report on the delivery of miR-325-3p in nucleic acid spheres, which can enter the liver sinusoidal endothelial cells, reversing their capillarisation, treating liver fibrosis in mice.