Exploring Lipoic Acid-Mediated Dynamic Bottlebrush Elastomers as a New Platform for the Design of High-Performance Thermally Conductive Materials.

The development of high-performance thermally conductive interface materials is the key to unlocking the serious bottleneck of modern microelectronic technology through enhanced heat dispersion. Existing methods that utilize silicone composites rely either on loading large doses of randomly distributed thermal conductive fillers or on filling prealigned thermal conductive scaffolds with liquid silicone precursors. Both approaches suffer from several limitations in terms of physical traits and processability. We describe an alternative approach in which malleable silicone matrices, based on the dynamic cyclic disulfide nature cross-linker (α-lipoic acid), are readily prepared using ring-opening polymerization. The mechanical properties of the resultant dynamic silicone matrix are readily tunable. Stress-dependent depolymerization of the disulfide network demonstrates the ability to reprocess the silicone elastomer matrix, which allows for the fabrication of highly efficient thermal conductive composites with a 3D interconnecting, thermally conductive network (3D-graphite/MxBy composites) via in situ methods. Applications of the composites as thermal dispersion interface materials are demonstrated by LEDs and CPUs, suggesting great potential in advanced electronics.

Novel Method for Comprehensive Annotation of Plant Glycosides Based on Untargeted LC-HRMS/MS Metabolomics.

Glycosides are a large family of secondary metabolites in plants, which play a critical role in plant growth and development. Due to the complexity and diversity in structures and the limited availability of authentic standards, comprehensive annotation of the glycosides remains a great challenge. In this study, using maize as an example, a deep annotation method of glycosides was proposed based on untargeted liquid chromatography-high-resolution tandem mass spectrometry metabolomics analysis. First, knowledge-based in silico aglycone and glycosyl/acyl-glycosyl libraries were built. A total of 1240 known and potential aglycones from databases and literature were recorded. Next, the MS parameters beneficial to aglycone ion-rich MS/MS were explored using 1782 high-resolution MS/MS spectra of glycosides from the MassBank of North America (MoNA) and confirmed by 52 authentic glycoside standards. Then, screening rules for aglycon ions in MS/MS were recommended. Glycoside candidates were further filtered by MS/MS-based chemical classification and MS/MS similarity of aglycon-glycoside pairs. Finally, the glycosylation sites of flavonoid mono-O-glycosides were recommended by characteristic fragmentation patterns. The developed method was validated using glycosides and nonglycosides from the MoNA library. The annotation accuracy rates were 96.8, 94.9, and 98.0% in negative ion mode (ESI-), positive ion mode (ESI+), and the combined ESI- & ESI+, respectively. The annotation specificity was 99.6% (ESI-), 99.6% (ESI+), and 99.2% (ESI- & ESI+). A total of 274 glycosides (including 34 acyl-glycosides) were tentatively annotated in maize by the developed method. The method enables effective and reliable annotation for plant glycosides.