Graphene quantum dots (GQDs), which is the latest addition to the nanocarbon material family, promise a wide spectrum of applications. Herein, we demonstrate two different functionalization strategies to systematically tailor the bandgap structures of GQDs whereby making them snugly suitable for particular applications. Furthermore, the functionalized GQDs with a narrow bandgap and intramolecular Z-scheme structure are employed as the efficient photocatalysts for water splitting and carbon dioxide reduction under visible light. The underlying mechanisms of our observations are studied and discussed.

The outbreak of COVID-19 has spread across the world and was characterized as a pandemic. To protect medical laboratory personnel from infection, most laboratories inactivate the clinical samples before testing. However, the effect of inactivation on the detection results remains unknown. Here, we used a digital PCR assay to determine the absolute SARS-CoV-2 RNA copy number in 63 nasopharyngeal samples and assess the effect of inactivation methods on viral RNA copy number. Viral inactivation was performed with three different methods: (1) incubation with TRIzol® LS Reagent for 10 min at room temperature, (2) heating in a waterbath at 56°C for 30 min, and (3) high-temperature treatment, including 121°C autoclaving for 20 min, 100°C boiling for 20 min, and 80°C heating for 20 min. Compared to the amount of RNA in the original sample, TRIzol treatment destroyed 47.54% of N gene and 39.85% of ORF 1ab. For samples treated at 56°C for 30 min, the copy number of N gene and ORF 1ab was reduced by 48.55% and 56.40%, respectively. Viral RNA copy number dropped by 50–66% after 80°C heating for 20 min. Nearly no viral RNA was detected after autoclaving at 121°C or boiling at 100°C for 20 min. These results indicated that inactivation reduced the quantity of detectable viral RNA and may cause false negative results especially in weakly positive cases. Thus, TRIzol is recommended for sample inactivation in comparison to heat inactivation as Trizol has the least effect on RNA copy number among the tested methods.

No abstract

Lignin-based graphene quantum dots are fabricated by a two-step bottom up method and their fluorescence mechanism is elaborated by DFT calculations.

SummaryWith the development of Internet of Things (IoT), more and more computation‐intensive tasks are generated by IoT devices. Due to the limitation of battery and computing capacity of IoT devices, these tasks can be offloaded to mobile edge computing (MEC) and cloud for processing. However, as the channel states and task generation process are dynamic, and the scales of task offloading problem and solution space size are increasing rapidly, the collaborative task offloading for MEC and cloud faces severe challenges. In this paper, we integrate the two conflicting offloading goals, which are maximizing the task finish ratio with tolerable delay and minimizing the power consumption of devices. We formulate the task offloading problem to balance the two conflicting goals. Then, we reformulate it as an MDP‐based dynamic task offloading problem. We design a deep reinforcement learning (DRL)‐based dynamic task offloading (DDTO) algorithm to solve this problem. Our DDTO algorithm can adapt to the dynamic and complex environment and adjust the task offloading strategies accordingly. Experiments are also carried out which show that our DDTO algorithm can converge quickly. The experiment results also validate the effectiveness and efficacy of our DDTO algorithm in balancing finish ratio and power.