The biogeochemical transformation of gold(Au),i.e.its dissolution and re-precipitation,is critical in supergene transport of Au and formation of Au granules.Besides biogenic reduction,the formation Au granules can als...The biogeochemical transformation of gold(Au),i.e.its dissolution and re-precipitation,is critical in supergene transport of Au and formation of Au granules.Besides biogenic reduction,the formation Au granules can also be driven by chemical processes.Previous studies have showed the fo rmation of Au nanoparticles(AuNPs)from ionic Au(Ⅲ)can be mediated by dissolved organic matter under sunlight.In this letter,we further demonstrated that these AuNPs can further slowly(in years)grow into visible Au granules.Different sized nano-flower and fractal dendrite-like branched gold structures(from tens of nanometres to over 100μm)were observed in the Au granule sample.This growth of AuNPs into visible Au granules may play a critical role in the supergene mineralization and enrichment of secondary Au and drive the biogeochemical cycle of Au.展开更多
基金the National Natural Science Foundation of China(No.21777178)Key Projects for Frontier Sciences ofthe Chinese Academy of Sciences(No.QYZDB-SSWDQC018)+2 种基金the CAS Interdisciplinary Innovation Team(No.JCTD-2018-04)supports from the National Young Top-Notch Talents(No.W03070030)Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2016037)。
文摘The biogeochemical transformation of gold(Au),i.e.its dissolution and re-precipitation,is critical in supergene transport of Au and formation of Au granules.Besides biogenic reduction,the formation Au granules can also be driven by chemical processes.Previous studies have showed the fo rmation of Au nanoparticles(AuNPs)from ionic Au(Ⅲ)can be mediated by dissolved organic matter under sunlight.In this letter,we further demonstrated that these AuNPs can further slowly(in years)grow into visible Au granules.Different sized nano-flower and fractal dendrite-like branched gold structures(from tens of nanometres to over 100μm)were observed in the Au granule sample.This growth of AuNPs into visible Au granules may play a critical role in the supergene mineralization and enrichment of secondary Au and drive the biogeochemical cycle of Au.
文摘养殖水体中溶解氧浓度一直是最重要的水质参数之一。为了精准地对水体溶解氧进行调控,提高养殖生产效率,降低养殖风险,该研究考虑外部天气条件对溶解氧的影响以及溶解氧自身的昼夜变化特征,提出一种基于正则化极限学习机(principal component analysis and clustering method optimized regularized extreme learning machine,PC-RELM)的养殖水体溶解氧数据流预测模型。首先,采用主成分分析法判断影响溶解氧浓度的强重要性因子,降低预测模型的数据维度;其次,利用熵权法计算各时刻点的天气环境指数,并利用快速动态时间规整算法(fast dynamic time warping,FastDTW)完成时间序列数据流在不同天气环境下的相似度度量;然后使用k-means算法对时间序列的相似度进行聚类分簇,并基于分簇结果完成正则化极限学习机预测模型的构建,实现溶解氧浓度的估算。最后将PC-RELM模型应用到无锡南泉试验基地养殖池塘的溶解氧预测调控过程中。试验结果表明:PC-RELM的预测均方根误差值(root mean square error,RMSE)为0.9619,与PLS-ELM(partial least squares optimized ELM)、最小二乘支持向量机(least square support vector machine,LSSVM)以及BP神经网络模型进行对比,其RMSE值分别降低了41.54%、54.58%和67.16%。该预测模型可以有效地捕捉不同天气条件下溶解氧的变化特点,具有较高的预测精度和效率。
