Progress of Geodesy Related Ionosphere from Chinese Scientists in the Period of 2019—2023

The ionosphere is the ionized part of the upper atmosphere of the Earth,which plays an important role in atmospheric electricity and forms the inner edge of the magnetosphere.It influences radio propagation significantly,such as the Global Navigation Satellite System(GNSS).Meanwhile,the GNSS is also an essential technique for sensing the variation of ionosphere.During the years of 2019—2023,a large number of Chinese geodesy scientists devoted much efforts to the geodesy related ionosphere.Due to the very limited length,the achievements are carried out from the following six aspects,including:①The ionospheric correction models for BDS and BDSBAS;②Real-time global ionospheric monitoring and modeling;③The ionospheric 2D and 3D modeling based on GNSS and LEO satellites;④The ionospheric prediction based on artificial intelligence;⑤The monitoring and mitigation of ionospheric disturbances for GNSS users;⑥The ionospheric related data products and classical applications.

An Investigation of Optimal Machine Learning Methods for the Prediction of ROTI

The rate of the total electron content(TEC)change index(ROTI)can be regarded as an effective indicator of the level of ionospheric scintillation,in particular in low and high latitude regions.An accurate prediction of the ROTI is essential to reduce the impact of the ionospheric scintillation on earth observation systems,such as the global navigation satellite systems.However,it is difficult to predict the ROTI with high accuracy because of the complexity of the ionosphere.In this study,advanced machine learning methods have been investigated for ROTI prediction over a station at high-latitude in Canada.These methods are used to predict the ROTI in the next 5 minutes using the data derived from the past 15 minutes at the same location.Experimental results show that the method of the bidirectional gated recurrent unit network(BGRU)outperforms the other six approaches tested in the research.It is also confirmed that the RMSEs of the predicted ROTI using the BGRU method in all four seasons of 2017 are less than 0.05 TECU/min.It is demonstrated that the BGRU method exhibits a high level of robustness in dealing with abrupt solar activities.

All-Biobased Hydrovoltaic-Photovoltaic Electricity Generators for All-Weather Energy Harvesting

Hygroelectricity generators(HEGs)utilize the latent heat stored in environmental moisture for electricity generation,but nevertheless are showing relatively low power densities due to their weak energy harvesting capacities.Inspired by epiphytes that absorb ambient moisture and concurrently capture sunlight for dynamic photosynthesis,we propose herein a scenario of all-biobased hydrovoltaic-photovoltaic electricity generators(HPEGs)that integrate photosystem II(PSII)with Geobacter sulfurreducens(G.s)for simultaneous energy harvesting from both moisture and sunlight.This proof of concept illustrates that the all-biobased HPEG generates steady hygroelectricity induced by moisture absorption and meanwhile creates a photovoltaic electric field which further strengthens electricity generation under sunlight.Under environmental conditions,the synergic hydrovoltaic-photovoltaic effect in HPEGs has resulted in a continuous output power with a high density of 1.24 W/m^(2),surpassing ali HEGs reported hitherto.This work thus provides a feasible strategy for boosting electricity generation via simultaneous energy harvesting from ambient moisture and sunlight.

Phosphate recovery with granular acid-activated neutralized red mud:Fixed-bed column performance and breakthrough curve modelling

Granular acid-activated neutralized red mud(AaN-RM)has been successfully prepared with good chemical stability and physical strength.However,its potential for industrial application remains unknown.Therefore,the performance of granular AaN-RM for phosphate recovery in a fixed-bed column was investigated.The results demonstrated that the phosphate adsorption performance of granular AaN-RM in a fixed-bed column was affected by various operational parameters,such as the bed depth,flow rate,initial solution pH and initial phosphate concentration.With the optimal empty-bed contact time(EBCT)of 24.27 min,the number of processed bed volumes and the phosphate adsorption capacity reached 496.95 and 84.80 mg/g,respectively.Then,the saturated fixed-bed column could be effectively regenerated with a0.5 mol/L HCl solution.The desorption efficiency remained as high as 83.45%with a low weight loss of 3.57%in the fifth regeneration cycle.In addition,breakthrough curve modelling showed that a 5-9-1 feed-forward artificial neural network(ANN)could be effectively applied for the optimization of the fixed-bed adsorption system;the coefficient of determination(R^2)and the root mean square error(RMSE)evaluated on the validation-testing data were 0.9987 and 0.0183,respectively.Therefore,granular AaN-RM fixed-bed adsorption exhibits promising potential for phosphate removal and recovery from polluted water.

生物杂化体介导的半人工光合作用:机理、进展及展望

半人工光合系统通过利用人工光合系统与自然光合系统关键功能组分的协同效应以实现太阳能-化学能的转化。生物杂化体介导的半人工光合系统(biohybrid mediated semi-artificial photosynthetic system,BMSAPS)创新性地耦合了光敏剂优异的光捕获特性及生物催化剂高效的催化能力,从而利用太阳能高效驱动特定的化学转化过程。强化光敏剂与生物催化剂微界面间电子的产生、传输及利用是提高BMSAPS性能的关键。本文从BMSAPS的基本原理出发,分析了BMSAPS构建的关键科学问题及研究现状,阐述了该系统光生电子传递的相关机制及研究手段,总结了其在可再生能源转化、二氧化碳减排等方面的研究进展,并就未来的研究方向提出展望。本文有助于加深对BMSAPS的认识,从而为进一步优化其在能源生产和环境修复领域的应用提供理论基础和技术支撑。