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Superconductivity at the 2D limit is significant for advancing fundamental physics, leading to extensive research on monolayer two-dimensional materials. In particular, monolayer transition metal dichalcogenides such as NbSe2exhibit Ising superconductivity due to broken in-plane inversion symmetry and strong spin–orbit coupling, which has garnered significant attention. In this letter, we adopted an organic cation intercalation technique to modulate the interlayer interaction of NbSe2by expanding the interlayer distance, thereby making intercalated NbSe2behave similarly to monolayer NbSe2. The interlayer distances of NbSe2intercalated with THA+, CTA+, and TDA+cations are almost double or triple that of pristine NbSe2. The superconducting transition temperature (Tc) of THA-NbSe2is comparable to that of pristine NbSe2, while the charge density wave (CDW) transition temperature is higher. The Tcof intercalated NbSe2decreases with a reduced hole concentration, and the enhanced CDW is ascribed to the dimensional reduction. Notably, the in-plane upper critical field of intercalated NbSe2significantly exceeds the Pauli paramagnetic limit, which is similar to the Ising superconductivity observed in monolayer NbSe2. Our work demonstrates that the organic cations intercalated two-dimensional materials exhibit behavior similar to their monolayer counterparts, providing a convenient platform for exploring and modulating physical phenomena at the two-dimensional limit.

This article proposes a set of novel attitude estimation algorithms for a bioinspired integrated navigation system including an insect-inspired polarization sensor, an inertial measurement unit, and a global navigation satellite system (GNSS). In particular, the biomimetic polarization sensor introduced as the new type of navigation component has an orientation performance comparable to that of a magnetometer. Moreover, this small and light sensor is immune to magnetic interference, making it highly suitable for orientation in unmanned aerial vehicles (UAVs). However, inaccurate modeling, unknown parameters, and unknown statistical characteristics of noise are common problems with the orientation solution of the sensor. Therefore, we theoretically compare various adaptive and robust filtering algorithms, select the best error prediction covariance and noise covariance correction schemes, introduce Kalman smoothers and approximate inference methods, and then propose two outlier-robust algorithms. This approach can suppress multiple outliers from the two steps of prediction and estimation. The polarization sensor and GNSS update the state and error covariance according to the asynchronous fusion mode. The prediction error covariance, the polarization measurement noise covariance, and the GNSS measurement noise covariance are approximately estimated by the variational inference method. To validate the performance of our algorithms, we collected a set of multi-rotor UAV flight data for testing. Experimental results show that the proposed algorithms have better robustness performance than existing state-of-the-art algorithms. Note to Practitioners—This article was motivated by Bioinspired autonomous navigation systems suffering from inaccurate modeling, unknown parameters, and noise with unknown statistical properties. Subject to the large uncertainty in the flight of the UAV, the traditional filtering algorithm has slow convergence or even divergence when processing such actual data. In this article, we first establish a unified state estimation framework. Then, we mathematically describe correction strategies for prediction error covariance and measurement noise covariance. Considering the need for real-time estimation, we adopted the extended Kalman filter. However, this introduces a truncation error, then, Rauch-Tung-Striebel smoothing is introduced to improve the linearized reference point. However, the role of robust filtering is limited, and the non-Gaussian state still exists. Finally, the approximate inference method is used to estimate the state. The proposed method integrates adaptability, robustness, reasoning, and smoothing, and is suitable for dealing with multi-source interference problems in multi-source information fusion.

Rare earth nickelates are of significant interest due to their emerging physical properties such as high-temperature superconductivity, multiferroicity, metal–insulator transition, charge and spin order, etc. However, single-crystal growth of these nickelates remains a central challenge. Here, single crystals of the trilayer nickelate La4Ni3O10with dimensions up to 182 μm were successfully grown for the first time using K2CO3as a flux at ambient pressure. The as-grown crystals crystallize in the P21/aspace group at room temperature and exhibit a first-order metal-to-metal transition, as evidenced by resistivity, magnetic susceptibility, and DSC. The successful growth of La4Ni3O10single crystals at ambient pressure provides an alternative and easy-to-access method without the need of high pressure for the growth of other interesting rare earth nickelate single crystals, including the newly discovered high-temperature superconductor La3Ni2O7.

Precise and reliable autonomous navigation in a GPS-denied environment is critical to unmanned systems. The idea of combining SINS, the polarized navigation system (PNS), and the odometer (OD) inspired by desert ants has been proven to be effective for autonomous navigation. However, there are two major challenges for polarization navigation nowadays: inaccurate modeling and obtaining reliable heading information when some sensor channels are blocked. Aiming at these two problems, a tightly-coupled solution for SINS/PNS is proposed in this paper. To obtain a refined integrated navigation system model, the installation errors between the inertial units and PNS, and polarization angle calculation errors are analyzed and modeled for SINS/PNS. Then, to quickly gain the accurate state estimation, an improved iterative unscented filtering method is devised. In particular, the sigma-point updating step with the conditional distribution of high-dimensional Gaussian distribution random variables is developed, which employs partial states to sample in each iteration to reduce the calculation burden. Finally, a detection and elimination mechanism for the abnormal light channels is provided to enhance the reliability of the integration in the presence of a light blockage. The optical channels for navigation are chosen using this mechanism depending on the difference between the predicted and measured incident light intensities. The results in both simulations and outdoor experiments show that the proposed method provides a higher heading estimation accuracy than the traditional SINS/PNS navigation method. Note to Practitioners—This article is motivated by the inaccurate modeling and obtaining reliable heading in the presence of light occlusion for the polarization navigation. Various integrated navigation algorithms based on polarized skylight have been widely developed. However, the complex environment and inaccurate modeling limit the application of the polarization navigation. This article gives a novel accurate modeling method and reliable navigation algorithm. Tightly-coupled model with installation errors and the polarization angle error aims to improve the accuracy of the polarization navigation model. The improved iterative unscented Kalman filter is utilized to quickly obtain accurate state estimation. And the light detection and elimination mechanism is used to select normal light channels to navigate in the presence of light blockage. This navigation method can also extend the application of the polarization navigation.

Bioinspired polarization navigation technology has become a new autonomous navigation solution in GNSS-denied environments. In the case of point-source polarization sensors, clouds will affect the accuracy of the sensors. To enhance the adaptability of the polarization navigation system in complex weather conditions, this article presents an integrated navigation system using the multidirectional polarization sensors, sun sensor, and inertial measurement unit (IMU). The information fusion framework is established for attitude and heading determination. The proposed outlier detection method in multidirectional polarization sensors is used to select the optimal polarization sensors. Moreover, a chi-square-based Kalman filter (Chi-KF) is adopted to adapt variable measurement noises of the polarization sensors and sun sensor. The results of static ground test and unmanned aerial vehicle (UAV) flight tests demonstrate that the proposed method can improve the navigation accuracy in cloudy weather conditions.