RESUMEN
Designing highly active and cost-effective electrocatalysts for seawater-splitting with large current densities is compelling for developing hydrogen energy. Great advancements in hydrogen evolution reaction (HER) have been achieved, but the progress on driving HER in seawater is still limited. Herein, Fe-doped MoS2nanoshseets array supported by 3D carbon fibers was explored to be an efficient HER electrocatalyst operating in seawater. Strikingly, it exhibited small overpotentials of 119 and 300 mV to reach the current densities of 10 and 250 mA cm-2in buffered seawater, respectively, both of them are comparable to the best-reported values under similar conditions. Meantime, the catalyst could keep the stable HER activity for 30 h without notable loss. Theoretical calculations revealed that Fe doping increases the S-edge activity. Our work provides a new avenue for designing MoS2-based HER electrocatalysts for industry application.
RESUMEN
Visible light-driven photoreduction of CO2and H2O to tunable syngas is an appealing strategy for both artificial carbon neutral and Fischer-Tropsch processes. However, the development of photocatalysts with high activity and selectivity remains challenging. For this case, we here design a hybrid catalyst, synthesized byin situdeposition of Ag crystals on GaN nanobelts, that delivers a tunable H2/CO ratio between 0.5 and 3 under visible light irradiation (λ > 400 nm). The obtained photocatalyst delivers a maximal turnover frequency value of 3.85 h-1and a corresponding yield rate of 2.12 mmol h-1g-1for CO production, while the photocatalytic activity keeps stable during five cycling tests. Additionally, syngas can be detected even atλ > 600 nm. Experiments and mechanistic studies reveal that the existence of Ag crystals not only extends the light absorption region but also promotes the charge transfer efficiency, and thereby leading to a photocatalytic improvement. Accordingly, the present work affords an opportunity for developing an efficient photo-driven system by using solar energy to alleviate CO2emissions.
RESUMEN
In recent years, with the increasing demand for high-quality Dendrobii caulis decoction piece, the identification of D. caulis decoction piece species has become an urgent issue. However, the current methods are primarily designed for professional quality control and supervision. Therefore, ordinary consumers should not rely on these methods to assess the quality of products when making purchases. This research proposes a deep learning network called improved YOLOv5 for detecting different types of D. caulis decoction piece from images. In the main architecture of improved YOLOv5, we have designed the C2S module to replace the C3 module in YOLOv5, thereby enhancing the network's feature extraction capability for dense and small targets. Additionally, we have introduced the Reparameterized Generalized Feature Pyramid Network (RepGFPN) module and Optimal Transport Assignment (OTA) operator to more effectively integrate the high-dimensional and low-dimensional features of the network. Furthermore, a new large-scale dataset of Dendrobium images has been established. Compared to other models with similar computational complexity, improved YOLOv5 achieves the highest detection accuracy, with an average mAP@.05 of 96.5%. It is computationally equivalent to YOLOv5 but surpasses YOLOv5 by 2 percentage points in terms of accuracy.