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In recent years, researchers have devoted considerable effort to the study of high crystalline titanium‐oxo clusters (TOCs), because TOCs are considered to be the molecular analogs of TiO2 nanoparticles and have the potential to be utilized as effective cleaning and catalytic materials. Two new tartrate‐anchored tetranuclear TOCs functionalized with chromophore ligand of p‐nitrobenzoate (NB)/p‐toluenesulfonate (TS), namely, [Ti4(TA)(NB)2(OiPr)10] (1, H4TA = tartaric acid) and [Ti4(TA)(TS)2(OiPr)10] (2), were synthesized and their structures were characterized by various methods. The molecular structure analyses indicated that 1 and 2 are all tetranuclear {Ti4} TOCs, in which tartrate appears as a hexadentate μ4‐bridging ligand while p‐nitrobenzoate/p‐toluenesulfonate acts as a bidentate μ2‐bridging ligand. TOCs 1 and 2 are of good thermal and solvent stability. Furthermore, their photocurrent responses and photocatalytic degradation activities towards methylene blue (MB) were also investigated. In the presence of H2O2, the photocatalytic degradation efficiencies of MB by 1 and 2 are about 84.43% and 71.00%, respectively, at room temperature under visible light irradiation for 60 min. These findings shed light on designing efficient polycarboxylate‐based TOCs photocatalysts. [ABSTRACT FROM AUTHOR]

Distillation columns are vital for substance separation and purification in various industries, where malfunctions can lead to equipment damage, compromised product quality, production interruptions, and environmental harm. Early fault detection using AI-driven methods like deep learning can mitigate downtime and safety risks. This study employed a lab-scale distillation column to collect passive acoustic signals under normal conditions and three potential faults: flooding, dry tray, and leakage. Signal processing techniques were used to extract acoustic features from low signal-to-noise ratios and weak time-domain characteristics. A deep learning-based passive acoustic feature recognition method was then applied, achieving an average accuracy of 99.03% on Mel-frequency cepstral coefficient (MFCC) spectrogram datasets. This method demonstrated robust performance across different fault types and limited data scenarios, effectively predicting and detecting potential faults in distillation columns. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) is the most frequent and disabling disease in developed countries. The progressive degeneration of articular cartilage characterized as thinner and erosive. Inflammation is well-known to be involved in OA development. However, there are no effective therapeutic strategies to cure it. Xanthohumol (XH) is a natural prenylflavonoid isolated from hops and beer. The protective activity of XH against OA chondrocytes inflammation and ECM degradation is unclear. In this article, we found that XH significantly inhibited inflammatory responses, attenuated catabolic enzymes expression, and ameliorated ECM degradation, as showed by decreased production of NO, PGE2, TNFα, and IL-6, decreased expression of MMP-3/-13 and ADAMTS-4/-5, and increased expression of collagen-II and aggrecan. In addition, XH activated HO-1 signaling and attenuated IL-1β-induced C/EBPβ. XH promoted the interaction between HO-1 and C/EBPβ, inhibiting the nuclear translocation of C/EBPβ. HO-1 knockdown could abrogate the protective effects of XH in IL-1β-treated chondrocytes. Collectively, XH attenuated inflammatory responses and ECM degradation by mediating HO-1 and C/EBPβ signaling pathways in osteoarthritis chondrocytes. [ABSTRACT FROM AUTHOR]