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The grand challenge in the development of atomically dispersed metallic catalysts is their low metal-atom loading density, uncontrollable localization and ambiguous interactions with supports, posing difficulty in maximizing their catalytic performance. Here, we achieve an interface catalyst consisting of atomic cobalt array covalently bound to distorted 1T MoS 2 nanosheets (SA Co-D 1T MoS 2 ). The phase of MoS 2 transforming from 2H to D-1T, induced by strain from lattice mismatch and formation of Co-S covalent bond between Co and MoS 2 during the assembly, is found to be essential to form the highly active single-atom array catalyst. SA Co-D 1T MoS 2 achieves Pt-like activity toward HER and high long-term stability. Active-site blocking experiment together with density functional theory (DFT) calculations reveal that the superior catalytic behaviour is associated with an ensemble effect via the synergy of Co adatom and S of the D-1T MoS 2 support by tuning hydrogen binding mode at the interface. While single-atom catalysis offers an efficient materials usage, the ambiguous interactions with supports poses a difficulty in understanding catalytic performances. Here, authors show an ensemble effect via synergy of Co adatoms and the S of MoS 2 supports to boost hydrogen evolution activities.

Accurate ultra-short-term prediction of UT1-UTC is crucial for real-time applications in high-precision reference frame conversions. Presently, traditional LS + AR and LS + MAR hybrid methods are commonly employed for UT1-UTC prediction. However, inherent unmodeled errors in fitting residuals of these methods often compromise the prediction performance. Thus, mitigating these common unmodeled errors presents an opportunity to enhance UT1-UTC prediction performance. Consequently, we propose a novel hybrid difference method for UT1-UTC ultra-short-term prediction by integrating LOD prediction and the prediction of the sum of the LOD and the first-order-difference UT1-UTC. The evaluation demonstrated promising results: (1) The mean absolute errors (MAEs) of the proposed method range from 21 to 869 µs in 1–10-day UT1-UTC predictions. (2) Comparative analysis against zero-/first-/second-order-difference LS + AR and zero-/first-order-difference LS + MAR hybrid method reveals a substantial reduction in MAEs by an average of 54/64/44 µs, and 47/20 µs, respectively, with the proposed method. (3) Correspondingly, the proposed method achieves average improvement percentages of 17%/18%/15%, and 13%/3% in 1–10-day UT1-UTC predictions.

Frictional vibration often occurs during sliding, commonly referred to as the stick-slip phenomenon. It is more likely to occur in the range of the Stribeck curve, where friction and velocity have negative gradient characteristics. In this study, ultrasonic vibration is applied to the metal/metal sliding friction pair which reduce both static and kinetic friction forces. The inhibition mechanism of ultrasonic vibration on the stick-slip phenomenon is investigated for a sliding pair moving at low velocities (0.05–1.0 mm/s) under the condition of dry friction and oil lubrication. Under the condition of dry friction, ultrasonic vibration reduces the slider’s friction force by up to 89%, and the displacement fluctuation by up to 61%, effectively inhibiting the stick-slip phenomenon. Under the condition of oil lubrication, the friction force fluctuates when driving at a constant velocity, and the displacement fluctuation also fluctuates with the change of driving velocities. After ultrasonic vibration is applied, the friction reduction of the slider changes greatly with the driving velocity, so that the inhibition effect of ultrasonic vibration on stick-slip phenomenon under oil lubrication condition is unstable.

Global Navigation Satellite System (GNSS) provides accurate positioning data for vehicular navigation in open outdoor environment. In an indoor environment, Light Detection and Ranging (LIDAR) Simultaneous Localization and Mapping (SLAM) establishes a two-dimensional map and provides positioning data. However, LIDAR can only provide relative positioning data and it cannot directly provide the latitude and longitude of the current position. As a consequence, GNSS/Inertial Navigation System (INS) integrated navigation could be employed in outdoors, while the indoors part makes use of INS/LIDAR integrated navigation and the corresponding switching navigation will make the indoor and outdoor positioning consistent. In addition, when the vehicle enters the garage, the GNSS signal will be blurred for a while and then disappeared. Ambiguous GNSS satellite signals will lead to the continuous distortion or overall drift of the positioning trajectory in the indoor condition. Therefore, an INS/LIDAR seamless integrated navigation algorithm and a switching algorithm based on vehicle navigation system are designed. According to the experimental data, the positioning accuracy of the INS/LIDAR navigation algorithm in the simulated environmental experiment is 50% higher than that of the Dead Reckoning (DR) algorithm. Besides, the switching algorithm developed based on the INS/LIDAR integrated navigation algorithm can achieve 80% success rate in navigation mode switching.

Because elevated levels of caffeine intake can cause many health complications, it is necessary to develop an accurate, simple, rapid, and cost-effective methodology to quantify caffeine in commonly consumed products. This article discusses electrochemical methods to synthesize platinum-graphene hybrid nanosheets (Pt-GR), and how these methods can be utilized to create a new modified electrode, the platinum-graphene nanohybrid glass carbon electrode (Pt-GR/GCE). The electrochemical behavior of caffeine on Pt-GR/GCE was studied by differential pulse voltammetry (DPV). The results showed that a sensitive oxidation peak was observed at 1.336 V in 0.01 mol L−1 H2SO4 buffer solution, indicating that the Pt-GR/GCE exhibited a good electrooxidation activity towards caffeine. The detection limit is 1.129 × 10−7 mol L−1. The modified electrode was applied to the determination of caffeine in real samples with satisfactory electrocatalytic results.

Enhancing the regeneration of cartilage defects remains challenging owing to limited innate self-healing as well as acute inflammation arising from the overexpression of reactive oxygen species (ROS) in post-traumatic microenvironments. Recently, stem cell-derived exosomes (Exos) have been developed as potential cell-free therapy for cartilage regeneration. Although this approach promotes chondrogenesis, it neglects the emerging inflammatory microenvironment. In this study, a smart bilayer-hydrogel dual-loaded with sodium diclofenac (DC), an anti-inflammatory drug, and Exos from bone marrow-derived mesenchymal stem cells was developed to mitigate initial-stage inflammation and promote late-stage stem-cell recruitment and chondrogenic differentiation. First, the upper-hydrogel composed of phenylboronic-acid-crosslinked polyvinyl alcohol degrades in response to elevated levels of ROS to release DC, which mitigates oxidative stress, thus reprogramming macrophages to the pro-healing state. Subsequently, Exos are slowly released from the lower-hydrogel composed of hyaluronic acid into an optimal microenvironment for the stimulation of chondrogenesis. Both in vitro and in vivo assays confirmed that the dual-loaded bilayer-hydrogel reduced post-traumatic inflammation and enhanced cartilage regeneration by effectively scavenging ROS and reprogramming macrophages. The proposed platform provides multi-staged therapy, which allows for the optimal harnessing of Exos as a therapeutic for cartilage regeneration. [Display omitted] •A bilayer-hydrogel scaffold loaded with DC and Exos integrates the inflammatory phase and proliferative phase.•Upper-hydrogel with ROS-scavenging and macrophage-reprogramming properties improves the inflammatory environment.•Exos function optimally in the favorable \"umbrella\" environment created by the upper-hydrogel, promoting cartilage repair.

Inflammatory responses play a central role in coordinating biomaterial‐mediated tissue regeneration. However, precise modulation of dynamic variations in microenvironmental inflammation post‐implantation remains challenging. In this study, the traditional β‐tricalcium phosphate‐based scaffold is remodeled via ultrathin MXene‐Ti3C2 decoration and Zn2+/Sr2+ ion‐substitution, endowing the scaffold with excellent reactive oxygen species‐scavenging ability, near‐infrared responsivity, and enhanced mechanical properties. The induction of mild hyperthermia around the implant via periodic near‐infrared irradiation facilitates spatiotemporal regulation of inflammatory cytokines secreted by a spectrum of macrophage phenotypes. The process initially amplifies the pro‐inflammatory response, then accelerates M1‐to‐M2 macrophage polarization transition, yielding a satisfactory pattern of osteo‐immunomodulation during the natural bone healing process. Later, sustained release of Zn2+/Sr2+ ions with gradual degradation of the 3D scaffold maintains the favorable reparative M2‐dominated immunological microenvironment that supports new bone mineralization. Precise temporal immunoregulation of the bone healing process by the intelligent 3D scaffold enhances bone regeneration in a rat cranial defect model. This strategy paves the way for the application of β‐tricalcium phosphate‐based materials to guide the dynamic inflammatory and bone tissue responses toward a favorable outcome, making clinical treatment more predictable and durable. The findings also demonstrate that near‐infrared irradiation‐derived mild hyperthermia is a promising method of immunomodulation. The natural bone healing process is modulated by sequential immunological responses, including the pro‐inflammatory initiation stage and later anti‐inflammatory remodeling. Focusing on spatiotemporal osteoimmunomodulation, the study develops a 3D scaffold capable of dynamically coordinating the inflammatory and bone tissue responses by regulating the initiation stage, accelerating the shift from a pro‐ to an anti‐inflammatory, and enhancing the anti‐inflammatory remodeling stage.

Tunnel Boring Machines (TBMs) have been the main equipment for tunneling and underground construction due to their high safety performance and tunneling efficiency. However, the unknown and changing geological conditions during construction pose a challenge to TBM construction. As one of the essential parameters of rock properties, accurate acquisition of uniaxial compressive strength (UCS) is crucial for TBMs to adapt to changing ground conditions in a timely manner. Therefore, this study proposes a Catboost intelligent model based on Bayesian Optimization to predict UCS. Rock mass are velocity information and key TBM operational parameters are used as model input variables. The Gaussian data augmentation method is used to compensate for the difficulty of obtaining field data in large quantities. The Zhujiang Delta Water Resources Allocation Engineering field data are used in the model, and the obtained evaluation indicators MAPE, RMSE, VAF and a20-index are obtained as 9.91%, 499.38 MPa, 90.7% and 0.95, respectively. In addition, another project is selected to verify the applicability of the model. The validation results also confirm that the model is valid and reliable when applied to practical engineering.

Back-analysis can be regarded as the reverse process of forwarding analysis. The forward analysis in geotechnical engineering is to utilize the geometric dimensions, constitutive relations, material parameters, and boundary load conditions determined by the rock mass medium to solve the physical quantity information (strain, stress, Displacement) process. In this paper, a novel back analysis program based on BP neural network is proposed, which can realize automatic correction and adjustment of parameters and adapt to most tunnel projects. Subsequently, the analysis program is applied to the front and back joint modeling of the Nagasaki tunnel project. The mechanical parameters and initial stresses of the back analysis are utilized to conduct finite element calculations, and the displacements of all monitoring points of the multi-point displacement measurement are calculated. The results are in good agreement with the measured values. The proposed back-analysis method in this paper can also be applied to the determination of other tunnel rock mass parameters.

Highlights Ionic polymers can directly serve as high-performance ion-selective membranes when it was physically confined within submillimeter-sized cylindrical pore. The universality of this strategy is demonstrated in preparing cation/anion-selective membrane. With real seawater and river water, the output power density of a three-chamber cell on behalf of repeat unit of reverse electrodialysis system can reach up to 8.99 W m −2 . Harvesting the immense and renewable osmotic energy with reverse electrodialysis (RED) technology shows great promise in dealing with the ever-growing energy crisis. One key challenge is to improve the output power density with improved trade-off between membrane permeability and selectivity. Herein, polyelectrolyte hydrogels (channel width, 2.2 nm) with inherent high ion conductivity have been demonstrated to enable excellent selective ion transfer when confined in cylindrical anodized aluminum pore with lateral size even up to the submillimeter scale (radius, 0.1 mm). The membrane permeability of the anti-swelling hydrogel can also be further increased with cellulose nanofibers. With real seawater and river water, the output power density of a three-chamber cell on behalf of repeat unit of RED system can reach up to 8.99 W m −2 (per unit total membrane area), much better than state-of-the-art membranes. This work provides a new strategy for the preparation of polyelectrolyte hydrogel-based ion-selective membranes, owning broad application prospects in the fields of osmotic energy collection, electrodialysis, flow battery and so on.