The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-d...The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-driven approach could be of great help for EBEW and PEW prediction.We collected 91 Eu^(2+)-activated phosphors,the host structures of which exhibit a single activator site and the EBEW and PEW of which are available at the critical activator concentration.We extracted 29 descriptors(input features)that implicate the elemental and structural traits of phosphor hosts,and set up an integrated machine-learning(ML)platform consisting of 18 ML algorithms that allowed prediction of the EBEW and PEW as well as the DFT-calculated band gap(Eg).The acquired dataset involving 91 phosphors was insufficient for the 29-input-feature problem and the real-world data collected from the literature have a so-called dirty nature due to inaccurate,unstandardized experiments.Despite an unavoidable paucity of data and the dirty-data problems of real-world data-based ML implementation,we obtained acceptable holdout dataset test results for PEW predications such as R^(2)>0.6,MSE<0.02,and test_R^(2)/training_R^(2)>0.77 for four ML algorithms.The EBEW and E_(g)predictions returned slightly better test results than these PEW examples.展开更多
A recurrent pandemic with unpredictable viral nature has implied the need for a rapid diagnostic technology to facilitate timely and appropriate countermeasures against viral infections.In this study,conductive polyme...A recurrent pandemic with unpredictable viral nature has implied the need for a rapid diagnostic technology to facilitate timely and appropriate countermeasures against viral infections.In this study,conductive polymer-based nanoparticles have been developed as a tool for rapid diagnosis of influenza A(H1N1)virus.The distinctive property of a conductive polymer that transduces stimulus to respond,enabled immediate optical signal processing for the specific recognition of H1N1 virus.Conductive poly(aniline-co-pyrrole)-encapsulated polymeric vesicles,functionalized with peptides,were fabricated for the specific recognition of H1N1 virus.The low solubility of conductive polymers was successfully improved by employing vesicles consisting of amphiphilic copolymers,facilitating the viral titer-dependent production of the optical response.The optical response of the detection system to the binding event with H1N1,a mechanical stimulation,was extensively analyzed and provided concordant information on viral titers of H1N1 virus in 15 min.The specificity toward the H1N1 virus was experimentally demonstrated via a negative optical response against the control group,H3N2.Therefore,the designed system that transduces the optical response to the target-specific binding can be a rapid tool for the diagnosis of H1N1.展开更多
基金supported by the Creative Materials Discovery Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT,and Future Planning(2015M3D1A1069705),(2021R1A2C1011642)and(2021R1A2C1009144)partly by the Alchemist Project(20012196)Digital manufacturing platform(N0002598)funded by MOTIE,Korea.
文摘The prediction of excitation band edge wavelength(EBEW)and peak emission wavelength(PEW)for Eu^(2+)-activated phosphors is intricate in practice,although a theoretical interpretation has been well established.A data-driven approach could be of great help for EBEW and PEW prediction.We collected 91 Eu^(2+)-activated phosphors,the host structures of which exhibit a single activator site and the EBEW and PEW of which are available at the critical activator concentration.We extracted 29 descriptors(input features)that implicate the elemental and structural traits of phosphor hosts,and set up an integrated machine-learning(ML)platform consisting of 18 ML algorithms that allowed prediction of the EBEW and PEW as well as the DFT-calculated band gap(Eg).The acquired dataset involving 91 phosphors was insufficient for the 29-input-feature problem and the real-world data collected from the literature have a so-called dirty nature due to inaccurate,unstandardized experiments.Despite an unavoidable paucity of data and the dirty-data problems of real-world data-based ML implementation,we obtained acceptable holdout dataset test results for PEW predications such as R^(2)>0.6,MSE<0.02,and test_R^(2)/training_R^(2)>0.77 for four ML algorithms.The EBEW and E_(g)predictions returned slightly better test results than these PEW examples.
基金H.-O.Kim acknowledges support from the National Research Foundation of Korea grant funded by the Korean government(No.NRF-2019R1I1A1A01057005)Evaluation for Technology in Food,Agriculture and Forestry(IPET)through the Animal Disease Management Technology Development Program funded by Ministry of Agriculture,Food and Rural Affairs(MAFRA)(No.320056-2)+6 种基金D.Song acknowledges support from Korea Mouse Phenotyping Project(No.NRF-2019M3A9D5A01102797)Development of African Swine Fever Virus Vaccine and Assessment of Rapid Test Kit(No.NRF-2019K1A3A1A61091813)of the Ministry of Science and ICT through the National Research FoundationS.Haam acknowledges support from Technology Development Project for Biological Hazards Management in Indoor Air Program of Korea Environment Industry&Technology Institute(KEITI)funded by Korea Ministry of Environment(MOE)(No.RE202101004)Nano Material Technology Development Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(No.2017M3A7B4041798)This research was also supported by the Bio&Medical Technology Development Program(No.NRF-2018M3A9E2022819)the Bio&Medical Technology Development Program(No.NRF-2018M3A9H4056340)the National Research Foundation(NRF)funded by the Ministry of Science&ICT.
文摘A recurrent pandemic with unpredictable viral nature has implied the need for a rapid diagnostic technology to facilitate timely and appropriate countermeasures against viral infections.In this study,conductive polymer-based nanoparticles have been developed as a tool for rapid diagnosis of influenza A(H1N1)virus.The distinctive property of a conductive polymer that transduces stimulus to respond,enabled immediate optical signal processing for the specific recognition of H1N1 virus.Conductive poly(aniline-co-pyrrole)-encapsulated polymeric vesicles,functionalized with peptides,were fabricated for the specific recognition of H1N1 virus.The low solubility of conductive polymers was successfully improved by employing vesicles consisting of amphiphilic copolymers,facilitating the viral titer-dependent production of the optical response.The optical response of the detection system to the binding event with H1N1,a mechanical stimulation,was extensively analyzed and provided concordant information on viral titers of H1N1 virus in 15 min.The specificity toward the H1N1 virus was experimentally demonstrated via a negative optical response against the control group,H3N2.Therefore,the designed system that transduces the optical response to the target-specific binding can be a rapid tool for the diagnosis of H1N1.
