Unveiling the Stability Mechanism of Oriented Ni-Rich Layered Oxides.

Single-crystal and polycrystalline structures are the two main structural forms of the Ni-rich layered cathode for lithium-ion batteries. The structural difference is closely related to the electrochemical performance and thermal stability, but its internal mechanism is unclear and is worthy of further exploration. In this study, both polycrystalline and single-crystal LiNi0.83Co0.12Mn0.05O2 cathodes were prepared by adjusting the calcination temperature and mechanical post-treatment, respectively. Systematic comparisons were made to assess the effects of different grain structures on the electrochemical performance and thermal stability. The study revealed the superior thermal stability of monocrystalline cathodes, attributing it to oxygen vacancies and phase transitions. From the perspective of grain boundaries, it was demonstrated that the diffusion of oxygen vacancies and the reduction of Ni in polycrystalline cathodes exhibit anisotropy. This research elucidates the origins of the superior thermal stability of monocrystalline cathodes in lithium-ion batteries, providing valuable insights into battery material design.

Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation.

Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell lines, CycleDNN comprises n auto-encoders. Each auto-encoder includes an encoder to convert CETSA features from one cell line into latent features in a latent space [Formula: see text]. It also features a decoder that transforms the latent features back into CETSA features for another cell line. In such a way, the proposed CycleDNN creates a cyclic prediction of CETSA features across different cell lines. The prediction loss, cycle-consistency loss, and latent space regularization loss are used to guide the model training. Experimental results on a public CETSA dataset demonstrate the effectiveness of our proposed approach. Furthermore, we confirm the validity of the predicted MS-CETSA data from our proposed CycleDNN through validation in protein-protein interaction prediction.

[Effects of Different Forms of Nitrogen Addition on Soil Physical and Chemical Properties and Microbial Community Structure of Perennial Alpine Cultivated Grassland].

This study aimed to investigate the impact of different nitrogen forms on soil physicochemical properties and microbial community structure in perennial alpine cultivated grasslands, in order to provide scientific basis for developing nitrogen addition strategies for perennial alpine cultivated grasslands. In June 2022, a 4-year-old Qinghai grassland mixed with Poa pratensis Qinghai and Festuca sinensis Qinghai was established at the Bakatai Farm in Gonghe County, Hainan Tibetan Autonomous Prefecture, Qinghai Province. The study was conducted without fertilization as a control (CK), and three different forms of nitrogen treatments were set up, namely, U:urea (amide nitrogen), A:ammonium sulfate (ammonium nitrogen), and N:calcium nitrate (nitrate nitrogen); the nitrogen application rate for each treatment was 67.5 kg·(hm2·a)-1, and the composition and diversity of soil nutrients and microbial communities under different treatments were analyzed. The results showed that the input of exogenous ammonium nitrogen significantly increased NH4+-N content, AP content, and EC; amide nitrogen input significantly increased SOC content and TN content; and nitrate nitrogen input significantly increased NO3--N content, AN content, and TC content. Exogenous nitrogen input changed the structure of soil bacterial and fungal communities, as well as the relative abundance of dominant phyla and genera, but it did not significantly affect the alpha diversity of bacterial and fungal communities. Principal coordinate analysis (PCoA) showed that different forms of nitrogen addition had a significant impact on the Beta diversity of bacterial communities, whereas the impact on fungal communities was not significant. Redundancy analysis (RDA) indicated that nitrogen addition mainly changed the composition and structure of microbial communities through soil ammonium nitrogen. Overall, ammonium nitrogen fertilizer should be given priority in the soil remediation process of perennial cultivated grasslands in the Qinghai Tibet Plateau.