Boosting Conversion of the Si-O Bond by Introducing Fe2+ in Carbon-Coated SiOx for Superior Lithium Storage.

SiOx-based anodes are of great promise for lithium-ion batteries due to their low working potential and high specific capacity. However, several issues involving large volume expansion during the lithiation process, low intrinsic conductivity, and unsatisfactory initial Coulombic efficiency (ICE) hinder their practical application. Here, an Fe-SiOx@C composite with significantly improved lithium-storage performance was successfully synthesized by combining Fe2+ modification with a carbon coating strategy. The results of both experiments and density functional theory calculations confirm that the Fe2+ modification not only effectively achieves uniform carbon coating but also weakens the bonding energy of the Si-O bond and boosts reversible lithiation/delithiation reactions, resulting in great improvement in the electrical conductivity, ICE, and reversible specific capacity of the as-obtained Fe-SiOx@C. Together with the coated carbon, the in situ-generated conductive Fe-based intermediates also ensure the electrical contact of active components, relieve the volume expansion, and maintain the structural integrity of the electrode during cycling. And the Fe-SiOx@C (x ≈ 1.5) electrode can deliver a high-rate capacity of 354 mA h g-1 at 2.0 A g-1 and long-term cycling stability (552.4 mA h g-1 at 0.5 A g-1 even after 500 cycles). The findings here provide a facile modification strategy to improve the electrochemical lithium-storage performance of SiOx-based anodes.

Identification of a 3-Gene Prognostic Index for Papillary Thyroid Carcinoma.

The accurate determination of the risk of cancer recurrence is a critical unmet need in managing thyroid cancer (TC). Although numerous studies have successfully demonstrated the use of high throughput molecular diagnostics in TC prediction, it has not been successfully applied in routine clinical use, particularly in Chinese patients. In our study, we objective to screen for characteristic genes specific to PTC and establish an accurate model for diagnosis and prognostic evaluation of PTC. We screen the differentially expressed genes by Python 3.6 in The Cancer Genome Atlas (TCGA) database. We discovered a three-gene signature Gap junction protein beta 4 (GJB4), Ripply transcriptional repressor 3 (RIPPLY3), and Adrenoceptor alpha 1B (ADRA1B) that had a statistically significant difference. Then we used Gene Expression Omnibus (GEO) database to establish a diagnostic and prognostic model to verify the three-gene signature. For experimental validation, immunohistochemistry in tissue microarrays showed that thyroid samples' proteins expressed by this three-gene are differentially expressed. Our protocol discovered a robust three-gene signature that can distinguish prognosis, which will have daily clinical application.