Efficient solar-driven CO2-to-fuel conversion via Ni/MgAlO_x@SiO₂ nanocomposites at low temperature

Solar-driven CO2-to-fuel conversion assisted by another major greenhouse gas CH4 is promising to concurrently tackle energy shortage and global warming problems. However, current techniques still suffer from drawbacks of low efficiency, poor stability, and low selectivity. Here, a novel nanocomposite composed of interconnected Ni/MgAlOx nanoflakes grown on SiO2 particles with excellent spatial confinement of active sites is proposed for direct solar-driven CO2-to-fuel conversion. An ultrahigh light-to-fuel efficiency up to 35.7%, high production rates of H2 (136.6 mmol min−1g− 1) and CO (148.2 mmol min−1g−1), excellent selectivity (H2/CO ratio of 0.92), and good stability are reported simultaneously. These outstanding performances are attributed to strong metal-support interactions, improved CO2 absorption and activation, and decreased apparent activation energy under direct light illumination. MgAlOx @SiO2 support helps to lower the activation energy of CH* oxidation to CHO* and improve the dissociation of CH4 to CH3* as confirmed by DFT calculations. Moreover, the lattice oxygen of MgAlO x participates in the reaction and contributes to the removal of carbon deposition. This work provides promising routes for the conversion of greenhouse gasses into industrially valuable syngas with high efficiency, high selectivity, and benign sustainability

Solar Thermochemical CO₂ Splitting Integrated with Supercritical CO₂ Cycle for Efficient Fuel and Power Generation

Converting CO2 into fuels via solar-driven thermochemical cycles of metal oxides is promising to address global climate change and energy crisis challenges simultaneously. However, it suffers from low energy conversion efficiency (ηen) due to high sensible heat losses when swinging between reduction and oxidation cycles, and a single product of fuels can hardly meet multiple kinds of energy demands. Here, we propose an alternative way to upsurge energy conversion efficiency by integrating solar thermochemical CO2 splitting with a supercritical CO2 thermodynamic cycle. When gas phase heat recovery (εgg) is equal to 0.9, the highest energy conversion efficiency of 20.4% is obtained at the optimal cycle high pressure of 260 bar. In stark contrast, the highest energy conversion efficiency is only 9.8% for conventional solar thermochemical CO2 splitting without including a supercritical CO2 cycle. The superior performance is attributed to efficient harvesting of waste heat and synergy of CO2 splitting cycles with supercritical CO2 cycles. This work provides alternative routes for promoting the development and deployment of solar thermochemical CO2 splitting techniques

Data_Sheet_1_Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm.xlsx

The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.</p