The solution processibility of perovskites provides a cost-effective and high-throughput route for fabricating state-of-the-art solar cells. However, the fast kinetics of precursor-to-perovskite transformation is susceptible to processing conditions, resulting in an uncontrollable variance in device performance. Here, we demonstrate a supramolecule confined approach to reproducibly fabricate perovskite films with an ultrasmooth, electronically homogeneous surface. The assembly of a calixarene capping layer on precursor surface can induce host–guest interactions with solvent molecules to tailor the desolvation kinetics, and initiate the perovskite crystallization from the sharp molecule–precursor interface. These combined effects significantly reduced the spatial variance and extended the processing window of perovskite films. As a result, the standard efficiency deviations of device-to-device and batch-to-batch devices were reduced from 0.64–0.26% to 0.67–0.23%, respectively. In addition, the perovskite films with ultrasmooth top surfaces exhibited photoluminescence quantum yield > 10% and surface recombination velocities < 100 cm s⁻¹ for both interfaces that yielded p-i-n structured solar cells with power conversion efficiency over 25%.

As the number of wireless applications and devices grows, higher standards for the quality of service and navigation performance of mobile networks are required. Numerous critical applications, including unmanned aerial vehicles, internet of things, digital twin, and military systems, require reliable communication and accurate navigation services. To meet these requirements, the development of the sixth generation (6G) network is necessary. 6G networks provide seamless 3-dimensional coverage in space–air–ground–sea area, as well as deep coupling of communication, sensing, and computation. 6G networks will be combined with low Earth orbit (LEO) satellites to construct a universal and intelligent integrated system of communication, sensing, and computing by utilizing the benefits of LEO satellites, such as miniaturization, modularity, large bandwidth, low latency, and wide area coverage. One of the critical construction tasks in this system is the integration of communication and navigation (ICAN), which can break the limitations of the global navigation satellite system, provide high-precision, robust navigation capability, and enable high- quality communication services.

Family health history is more than just a story—it's a roadmap. As genetics and technology converge, understanding our DNA is becoming the key to building healthier families and preventing inherited conditions before they occur. Genetic testing offers a window into that future. By analyzing an individual's DNA, carrier screening can identify couples who carry recessive genetic variants linked to serious diseases such as cystic fibrosis, spinal muscular atrophy, or thalassemia.

Anthocyanins shape the vivid colors of ornamental flowers, yet their hormonal regulation varies substantially across species. 

Preclinical studies indicate that cavity-resident macrophages in the peritoneal and pleural spaces contribute to immunosuppression and cancer progression. While these macrophages typically accumulate on organ surfaces rather than deeply infiltrating into tissues, their behavior and function in tumors remains unclear. Using dual recombinase-mediated genetic lineage tracing in a B16F10 melanoma lung metastasis model, this study demonstrated that pleural cavity macrophages (GATA6⁺CD45⁺) infiltrate lung tumors and contribute to a significant proportion of tumor-associated macrophage. Genetic ablation or sequestration of these macrophages markedly suppressed tumor growth, underscoring their pro-tumorigenic function.

Drought threatens tea production by damaging root systems and disrupting metabolic balance in tea plants.

Fruit crops often suffer severe losses due to fungal and bacterial infections, yet the molecular mechanisms regulating immunity in fleshy fruits remain poorly understood.

Space manipulators play an important role in the on-orbit services and planetary surface operation whose reliability is a key issue. In the extreme environment of space, space manipulators are susceptible to a variety of unknown disturbances. Since it is difficult for the manipulator to be repaired immediately, once it fails, it will mean that it cannot complete the mission as expected, which may cause serious losses and dangers. How to have a resilient guarantee, i.e., the manipulator’s ability to resiliently recover and continue to complete tasks, in failure or disturbance is the core capability of its future development. The motion planning unit is used as the computing terminal of the manipulator’s joint motion trajectory. Compared with traditional motion planning, learning-based motion planning has gradually become a hot spot in current research. However, no matter what kind of research ideas, the single robotic manipulator is studied as an independent agent, making it unable to provide sufficient flexibility under conditions such as external force disturbance, observation noise, and mechanical failure.

The gravitational wave is a prediction of the general theory of relativity that can be detected by measuring the relative distance between two test masses (TM) along the geodesics. The TM is fixed by the cage and vent mechanism to avoid collision with the spacecraft cavity during launch. Once the drag-free spacecraft reaches its target orbit, the TM must be released by the grabbing positioning and release mechanism (GPRM) to the cage center of the inertial sensor in a limited amount of time. The capture of the TM by means of the electrostatic suspensions is crucial for the mission. However, the GPRM inevitably generates large release errors and the low electrostatic force makes the TM capture control a difficult task. Moreover, the experimental results of the LISA Pathfinder mission have shown that the TM release velocities were well higher than the ones expected. The previous robust sliding mode controller needs to be optimized.

This study created a highly accurate machine learning model to predict acute exacerbations of COPD (AECOPD) using data from 878 patients. By integrating biochemical, demographic, and pulmonary function parameters with stepwise Cox regression and random survival forest algorithms, the model outperformed traditional methods, demonstrating excellent predictive performance and stability.