Atroposelective Formal [2 + 5] Macrocyclization Synthesis for a Novel All-Hydrocarbon Cyclo[7] Meta-Benzene Macrocycle.

A novel axially chiral all-hydrocarbon cyclo[7] (1,3-(4,6-dimethyl)benzene (CDMB-7) was designed and synthesized using atroposelective[2 + 5] cyclization through Suzuki-Miyaura coupling. CDMB-7 adopts an irregular bowl-like shape with C2 symmetry and exhibits two diastereoisomers in its crystallographic structure. The conformational isomers of CDMB-7 racemates remain stable at high temperatures (393 K). High-performance liquid chromatography (HPLC) confirmed that a single chiral isomer will spontaneously undergo racemization within 30 min at room temperature. This finding opens up possibilities for achieving adaptive chirality in all-hydrocarbon cyclo[7] m-benzene macrocycles.

Crystallization-Inspired Design and Modeling of Self-Assembly Lattice-Formation Swarm Robotics.

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model's accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability.

Luminescence thermometry driven by a support vector machine: a strategy toward precise thermal sensing.

Luminescence thermometry is a promising non-contact temperature measurement technique, but improving the precision and reliability of this method remains a challenge. Herein, we propose a thermal sensing strategy based on a machine learning. By using Gd3Ga5O12: Er3+-Yb3+ as the sensing medium, a support vector machine (SVM) is preliminarily adopted to establish the relationship between temperature and upconversion emission spectra, and the sensing properties are discussed through the comparison with luminescence intensity ratio (LIR) and multiple linear regression (MLR) methods. Within a wide operating temperature range (303-853 K), the maximum and the mean measurement errors actualized by the SVM are just about 0.38 and 0.12 K, respectively, much better than the other two methods (3.75 and 1.37 K for LIR and 1.82 and 0.43 K for MLR). Besides, the luminescence thermometry driven by the SVM presents a high robustness, although the spectral profiles are distorted by the interferences within the testing environment, where, however, LIR and MLR approaches become ineffective. Results demonstrate that the SVM would be a powerful tool to be applied on the luminescence thermometry for achieving a high sensing performance.