RESUMEN
Natural phosphatases featuring paired metal sites inspire various advanced nanozymes with phosphatase-like activity as alternatives in practical applications. Numerous efforts to create point defects show limited metal site pairs, further resulting in insufficient activity. However, it remains a grand challenge to accurately engineer abundant metal site pairs in nanozymes. Herein, we report a grain-boundary-rich ceria metallene nanozyme (GB-CeO2) with phosphatase-like activity. Grain boundaries acting as the line or interfacial defects can effectively increase the content of Ce4+/Ce3+ site pairs to 72.28%, achieving a 49.28-fold enhancement in activity. Furthermore, abundant grain boundaries optimize the band structure to assist the photoelectron transfer under irradiation, which further increases the content of metal site pairs to 88.96% and finally realizes a 114.39-fold enhanced activity over that of CeO2 without irradiation. Given the different inhibition effects of pesticides on catalysts with and without irradiation, GB-CeO2 was successfully applied to recognize mixed toxic pesticides.
Asunto(s)
Cerio , Cerio/química , Catálisis , Monoéster Fosfórico Hidrolasas/química , Monoéster Fosfórico Hidrolasas/metabolismo , Nanoestructuras/química , Plaguicidas/químicaRESUMEN
The design of heterojunctions that mimic natural photosynthetic systems holds great promise for enhancing photoelectric response. However, the limited interfacial space charge layer (SCL) often fails to provide sufficient driving force for the directional migration of inner charge carriers. Drawing inspiration from the electron transport chain (ETC) in natural photosynthesis system, we developed a novel anisotropic dual S-scheme heterojunction artificial photosynthetic system composed of Bi2O3-BiOBr-AgI for the first time, with Bi2O3 and AgI selectively distributed along the bicrystal facets of BiOBr. Compared to traditional semiconductors, the anisotropic carrier migration in BiOBr overcomes the recombination resulting from thermodynamic diffusion, thereby establishing a potential ETC for the directional migration of inner charge carriers. Importantly, this pioneering bioinspired design overcomes the limitations imposed by the limited distribution of SCL in heterojunctions, resulting in a remarkable 55-fold enhancement in photoelectric performance. Leveraging the etching of thiols on Ag-based materials, this dual S-scheme heterojunction is further employed in the construction of photoelectrochemical sensors for the detection of acetylcholinesterase and organophosphorus pesticides.
RESUMEN
In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2= 0.952 and a cross-validation value of Q2= 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity.
RESUMEN
BACKGROUND: Colorectal cancer has a low 5-year survival rate and high mortality. Human ß-defensin-1 (hBD-1) may play an integral function in the innate immune system, contributing to the recognition and destruction of cancer cells. Long non-coding RNAs (lncRNAs) are involved in the process of cell differentiation and growth. AIM: To investigate the effect of hBD-1 on the mammalian target of rapamycin (mTOR) pathway and autophagy in human colon cancer SW620 cells. METHODS: CCK8 assay was utilized for the detection of cell proliferation and determination of the optimal drug concentration. Colony formation assay was employed to assess the effect of hBD-1 on SW620 cell proliferation. Bioinformatics was used to screen potentially biologically significant lncRNAs related to the mTOR pathway. Additionally, p-mTOR (Ser2448), Beclin1, and LC3II/I expression levels in SW620 cells were assessed through Western blot analysis. RESULTS: hBD-1 inhibited the proliferative ability of SW620 cells, as evidenced by the reduction in the colony formation capacity of SW620 cells upon exposure to hBD-1. hBD-1 decreased the expression of p-mTOR (Ser2448) protein and increased the expression of Beclin1 and LC3II/I protein. Furthermore, bioinformatics analysis identified seven lncRNAs (2 upregulated and 5 downregulated) related to the mTOR pathway. The lncRNA TCONS_00014506 was ultimately selected. Following the inhibition of the lncRNA TCONS_00014506, exposure to hBD-1 inhibited p-mTOR (Ser2448) and promoted Beclin1 and LC3II/I protein expression. CONCLUSION: hBD-1 inhibits the mTOR pathway and promotes autophagy by upregulating the expression of the lncRNA TCONS_00014506 in SW620 cells.
RESUMEN
Control of the angular momentum of light at the nanoscale is critical for many applications of subwavelength photonics, such as high-capacity optical communications devices, super-resolution imaging and optical trapping. However, conventional approaches to generate optical vortices suffer from either low efficiency or relatively large device footprints. Here we show a new strategy for vortex generation at the nanoscale that surpasses single-pixel phase control. We reveal that interaction between neighbouring nanopillars of a meta-quadrumer can tailor both the intensity and phase of the transmitted light. Consequently, a subwavelength nanopillar quadrumer is sufficient to cover a 2lπ phase change, thus efficiently converting incident light into high-purity optical vortices with different topological charges l. Benefiting from the nanoscale footprint of the meta-quadrumers, we demonstrate high-density vortex beam arrays and high-dimensional information encryption, bringing a new degree of freedom to many designs of meta-devices.
RESUMEN
The study aims to execute machine learning (ML) method for building an intelligent prediction system for catalytic activities of a relatively big dataset of 1056 transition metal complex precatalysts in ethylene polymerization. Among 14 different algorithms, the CatBoost ensemble model provides the best prediction with the correlation coefficient (R2 ) values of 0.999 for training set and 0.834 for external test set. The interpretation of the obtained model indicates that the catalytic activity is highly correlated with number of atom, conjugated degree in the ligand framework, and charge distributions. Correspondingly, 10 novel complexes are designed and predicted with higher catalytic activities. This work shows the potential application of the ML method as a high-precision tool for designing advanced catalysts for ethylene polymerization.