Semi-implantable device based on multiplexed microfilament electrode cluster for continuous monitoring of physiological ions

Modern medicine is increasingly interested in advanced sensors to detect and analyze biochemical indicators.Ion sensors based on potentiometric methods are a promising platform for monitoring physiological ions in biological subjects.Current semi-implantable devices are mainly based on single-parameter detection.Miniaturized semi-implantable electrodes for multiparameter sensing have more restrictions on the electrode size due to biocompatibility considerations,but reducing the electrode surface area could potentially limit electrode sensitivity.This study developed a semi-implantable device system comprising a multiplexed microfilament electrode cluster(MMEC)and a printed circuit board for real-time monitoring of intra-tissue K^(+),Ca^(2+),and Na^(+)concentrations.The electrode surface area was less important for the potentiometric sensing mechanism,suggesting the feasibility of using a tiny fiber-like electrode for potentiometric sensing.The MMEC device exhibited a broad linear response(K^(+):2–32 mmol/L;Ca^(2+):0.5–4 mmol/L;Na^(+):10–160 mmol/L),high sensitivity(about 20–45 mV/decade),temporal stability(>2weeks),and good selectivity(>80%)for the above ions.In vitro detection and in vivo subcutaneous and brain experiment results showed that the MMEC system exhibits good multi-ion monitoring performance in several complex environments.This work provides a platform for the continuous real-time monitoring of ion fluctuations in different situations and has implications for developing smart sensors to monitor human health.

Easy-to-perform organic-solvent-free synthesis of carbon dots with strong green photoluminescence

Carbon dots(CDs)have been extensively studied owing to their fascinating optical properties and wide potential applications.Here,we report an easy-to-perform and organic-solvent-free synthesis strategy for green-emissive CDs(G-CDs)possessing high photoluminescence(PL)quantum yield(QY).The G-CDs are synthesized by heating the homogeneous precursors of citric acid and cyanamide in an open vessel,circumventing the use of organic solvents,complex operations,high-pressure reactors,and expensive instruments in the synthesis process.The effect of various reaction variables on the formation and the optical properties of G-CDs are systematically investigated.The resultant G-CDs show bright PL emission at 521 nm with PL QY up to 73%.Then a white light-emitting diode(LED)with Commission Internationable de L'Eclairage(CIE)coordinates of(0.33,0.34)and color rendering index(CRI)of 92 is constructed based on G-CDs/thermoplastic polyurethane(TPU)composite.Moreover,a visual microfluidic detection platform is designed by using G-CDs as fluorescent probes for rapid quantitative detection of Fe^(3+),Cu^(2+),and Mn^(2+)metal ions,which can realize synchronized testing of multiple samples.This study might promote the development and preparation methods of high-performance CDs with various optical applications.

MobSafe:Cloud Computing Based Forensic Analysis for Massive Mobile Applications Using Data Mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win＋Intel alliance in PC, Android＋ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app＇s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.