RESUMEN
An advanced scanning probe microscopy system enhanced with artificial intelligence (AI-SPM) designed for self-driving atomic-scale measurements is presented. This system expertly identifies and manipulates atomic positions with high precision, autonomously performing tasks such as spectroscopic data acquisition and atomic adjustment. An outstanding feature of AI-SPM is its ability to detect and adapt to surface defects, targeting or avoiding them as necessary. It is also designed to overcome typical challenges such as positional drift and tip apex atomic variations due to the thermal effects, ensuring accurate, site-specific surface analysis. The tests under the demanding conditions of room temperature have demonstrated the robustness of the system, successfully navigating thermal drift and tip fluctuations. During these tests on the Si(111)-(7 × 7) surface, AI-SPM autonomously identified defect-free regions and performed a large number of current-voltage spectroscopy measurements at different adatom sites, while autonomously compensating for thermal drift and monitoring probe health. These experiments produce extensive data sets that are critical for reliable materials characterization and demonstrate the potential of AI-SPM to significantly improve data acquisition. The integration of AI into SPM technologies represents a step toward more effective, precise and reliable atomic-level surface analysis, revolutionizing materials characterization methods.
RESUMEN
Light-flavour Baijiu is a type of Chinese liquor with a pure and mild flavour produced by traditional spontaneous solid-state fermentation. The flavour of this liquor has been found to vary in the different periods of annual production. To explore the factors affecting flavour, the microbiota of the surrounding environment, starter and fermentation process in different periods were investigated. Results showed that the ester content and acidity of light-flavour Baijiu were significantly lower when annual production was resumed after a summer break. HCA plot of volatile flavour profile and bacterial PCoA results indicated that the differences occurred at later stages, mainly due to different structures of Lactobacillus. Correlation analysis by O2PLS indicated that Lactobacillus positively correlated with esters. Species-level analysis showed that the lack of L. acetotolerans on the surface of the jar might cause a lag in fermentation and lower ester content. Thereafter, L. acetotolerans was revived during fermentation and enriched on the surface of the jar, which promoted ester formation. As important sources of L. acetotolerans, the air and fermentation jars played a critical role during fermentation. Therefore, this systematic study on environmental microbial ecology is valuable for quality control and to explore environmental microbiota functions during spontaneous fermentation.
