Polydimethylsiloxane (PDMS) has emerged as a promising candidate for the dielectric layer in implantable sensors due to its exceptional biocompatibility, stability, and flexibility. This study introduces an innovative approach to produce graphene-reinforced PDMS (Gr-PDMS), where graphite powders are exfoliated into mono- and few-layer graphene sheets within the polymer solution, concurrently forming cross-linkages with PDMS. This method yields a uniformly distributed graphene within the polymer matrix with improved interfaces between graphene and PDMS, significantly reducing the percolation threshold of graphene dispersed in PDMS from 10% to 5%. As-synthesized Gr-PDMS exhibits improved mechanical and electrical properties, tested for potential use in capacitive pressure sensors. The results demonstrate an impressive pressure sensitivity up to 0.0273 kpa⁻¹, 45 times higher than that of pristine PDMS and 2.5 times higher than the reported literature value. The Gr-PDMS showcases excellent pressure sensing ability and stability, fulfilling the requirements for implantable intracranial pressure (ICP) sensors.

For the first time, we propose the design and fabrication of 3D-stacked 2T0C-DRAM cells with Al2O3/TiO2-based 2DEG FETs as the building blocks. The robust carrier transport nature in the 2DEG channel at the Al2O3/TiO2 interface is beneficial for steep FET switching. The design and process of the gate stack in Al2O3/TiO2 FET are optimized, and the off-state leakage is effectively suppressed ( $\sim {2}\times {10}^{-{17}}$ A/ $\mu $ m) by alleviating the over-negative V $_{\textit {th}}$ issue with dual-gate manipulation. This provides fundamental basis of the excellent retention over ~400 s in 2T0C-DRAM cells. The 3D stacking process is further developed with two layers of DRAM cells showing retention time of ~30 s and ~4 s, respectively. Our results have demonstrated great potential for future low-power and high-density monolithic 3D DRAM applications.

In this article, we developed a new method to analyze the complex chemical reactions induced by electron beam radiolysis based on big data analysis. At first, we built an element transport network to show the chemical reactions. Furthermore, the linearity between the species was quantified by Pearson correlation coefficient analysis. Based on the analysis, the mechanism of the high linearity between the special species pairs was interpreted by the element transport roadmap and chemical equations. The time variation of the pH of the solution and bubble formation in the solution were analyzed by simulation and data analysis. The simulation indicates that O₂ and H₂ can easily oversaturate and form bubbles. Finally, the radiolysis of high-energy electrons in pure water was analyzed as a reference for the radiolysis of high-energy electrons in saline solution. This work provides a new method for investigating a high-energy electron radiolysis process and for simplifying a complex chemical reaction based on quantitative analysis of the species variation in the reaction.