Organic fertilization may influence soil carbon−iron(C-Fe)cycling and enhance phosphorus(P)availability,yet the direct connection between soil organic matter molecules and iron-reducing processes in long-term fertiliz...Organic fertilization may influence soil carbon−iron(C-Fe)cycling and enhance phosphorus(P)availability,yet the direct connection between soil organic matter molecules and iron-reducing processes in long-term fertilized paddy soils remains underexplored.In this study,we conducted a microcosm experiment using paddy soils treated with six distinct fertilization regimes involving varying P and organic matter inputs up to five years.We assessed P activation under reflooding conditions,evaluated Fe reduction,and characterized dissolved organic matter(DOM)at the molecular level using Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR MS),alongside profiling soil microbial community composition via high-throughput sequencing.Our findings revealed that after 25 days of reflooding,soil Olsen-P content increased by an average of 73%compared to its initial state,showing a strong correlation with the Fe reduction process.Specifically,treatments involving pig manure application exhibited higher Fe reduction rates and enhanced P activation,highlighting the role of organic matter in facilitating Fe reduction.Examination of Fe-reducing microorganisms revealed that their relative abundance was decoupled from Fe reduction and P release rates,potentially due to limitations of lower soil organic matter content.Further analysis of DOM composition and network structures suggested that high-molecular-weight DOM,particularly lignin,acted as key resources for Fe-reducing microbes,thereby driving Fe reduction and promoting P release.Overall,our study highlights the crucial role of soil DOM in enabling microbial-driven Fe reduction and enhancing P availability,providing insights valuable for sustainable agricultural practices.展开更多
Artificial intelligence for science(AI4S)has emerged as a new horizon in state-of-the-art scientific research,and single-molecule electronics could be considered an ideal prototype in AI4S due to the opportunities in ...Artificial intelligence for science(AI4S)has emerged as a new horizon in state-of-the-art scientific research,and single-molecule electronics could be considered an ideal prototype in AI4S due to the opportunities in correlating highthroughput and high-quality data with clear physical mechanisms.Towards using artificial intelligence for single-molecule electronics(AI4SME),the unsupervised extraction of lowprobability events from the massive experimental data becomes the key step,which has emerged for accurate detection of different configurations and even structural changes in singlemolecule junctions.However,the present algorithms suffer from the“uniform effect”,in which the majority events are erroneously allocated to minority ones,resulting in a relatively equal spread of cluster sizes and hindering the investigations for charge transport mechanisms with subtle and complex behaviors in single-molecule electronics.In this work,we propose a new multi-prototype clustering technique for precisely discriminating molecular events during the break junction process,especially those occurring with a probability below 10%,and further precisely extract the product species at the onset of the electric field-driven single-molecule keto-enol reaction with a probability as low as 1.5%.Our work tackles the long-term bottleneck of uniform effect for the precise detection of low-probability single-molecule events.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42477335,42077088)the Zhejiang Province“Agriculture,Rural Areas,Rural People and Nine Institutions”Science and Technology Collaboration Program(Grant No.2023SNJF039)X.P.L.was supported by a scholarship from the China Scholarship Council.
文摘Organic fertilization may influence soil carbon−iron(C-Fe)cycling and enhance phosphorus(P)availability,yet the direct connection between soil organic matter molecules and iron-reducing processes in long-term fertilized paddy soils remains underexplored.In this study,we conducted a microcosm experiment using paddy soils treated with six distinct fertilization regimes involving varying P and organic matter inputs up to five years.We assessed P activation under reflooding conditions,evaluated Fe reduction,and characterized dissolved organic matter(DOM)at the molecular level using Fourier transform ion cyclotron resonance mass spectrometry(FT-ICR MS),alongside profiling soil microbial community composition via high-throughput sequencing.Our findings revealed that after 25 days of reflooding,soil Olsen-P content increased by an average of 73%compared to its initial state,showing a strong correlation with the Fe reduction process.Specifically,treatments involving pig manure application exhibited higher Fe reduction rates and enhanced P activation,highlighting the role of organic matter in facilitating Fe reduction.Examination of Fe-reducing microorganisms revealed that their relative abundance was decoupled from Fe reduction and P release rates,potentially due to limitations of lower soil organic matter content.Further analysis of DOM composition and network structures suggested that high-molecular-weight DOM,particularly lignin,acted as key resources for Fe-reducing microbes,thereby driving Fe reduction and promoting P release.Overall,our study highlights the crucial role of soil DOM in enabling microbial-driven Fe reduction and enhancing P availability,providing insights valuable for sustainable agricultural practices.
基金supported by the National Key Research and Development Program of China(No.2024YFA1208103)the National Natural Science Foundation of China(Nos.22403079,22173075,22325303,21933012,and 22250003)+1 种基金the Fujian Provincial Department of Science and Technology(Nos.2022H6014 and 2023H6002)the Fundamental Research Funds for the Central Universities(Nos.20720220020 and 20720200068).
文摘Artificial intelligence for science(AI4S)has emerged as a new horizon in state-of-the-art scientific research,and single-molecule electronics could be considered an ideal prototype in AI4S due to the opportunities in correlating highthroughput and high-quality data with clear physical mechanisms.Towards using artificial intelligence for single-molecule electronics(AI4SME),the unsupervised extraction of lowprobability events from the massive experimental data becomes the key step,which has emerged for accurate detection of different configurations and even structural changes in singlemolecule junctions.However,the present algorithms suffer from the“uniform effect”,in which the majority events are erroneously allocated to minority ones,resulting in a relatively equal spread of cluster sizes and hindering the investigations for charge transport mechanisms with subtle and complex behaviors in single-molecule electronics.In this work,we propose a new multi-prototype clustering technique for precisely discriminating molecular events during the break junction process,especially those occurring with a probability below 10%,and further precisely extract the product species at the onset of the electric field-driven single-molecule keto-enol reaction with a probability as low as 1.5%.Our work tackles the long-term bottleneck of uniform effect for the precise detection of low-probability single-molecule events.
