Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning

Liao, Jiayan1; Zhou, Jiajia1; Song, Yiliao2; Liu, Baolei1; Lu, Jie2; Jin, Dayong1,3

2021-10-21

10.1021/acs.jpclett.1c02923

Journal of Physical Chemistry Letters   影响因子和分区

1948-7185

Highly controlled synthesis of upconversion nanoparticles (UCNPs) can be achieved in the heterogeneous design, so that a library of optical properties can be arbitrarily produced by depositing multiple lanthanide ions. Such a control offers the potential in creating nanoscale barcodes carrying high-capacity information. With the increasing creation of optical information, it poses more challenges in decoding them in an accurate, high-throughput, and speedy fashion. Here, we reported that the deep-learning approach can recognize the complexity of the optical fingerprints from different UCNPs. Under a wide-field microscope, the lifetime profiles of hundreds of single nanoparticles can be collected at once, which offers a sufficient amount of data to develop deep-learning algorithms. We demonstrated that high accuracies of over 90% can be achieved in classifying 14 kinds of UCNPs. This work suggests new opportunities in handling the diverse properties of nanoscale optical barcodes toward the establishment of vast luminescent information carriers.

SCI ; EI

英语

NI论文

其他

Australian Research Council Discovery Early Career Researcher Award Scheme[DE180100669] ; Australian Research Council Laureate Fellowship Program["FL210100180","FL190100149"] ; China Scholarship Council Scholarships[201508530231,201706020170] ; Australia-China Joint Research Centre for Point-of-Care Testing["ACSRF65827","2017YFE0132300"] ; Science and Technology Innovation Commission of Shenzhen[KQTD20170810110913065] ; National Natural Science Foundation of China[61729501] ; CAS/SAFEA International Partnership Program for Creative Research Teams and Major International (Regional) Joint Research Project of NSFC[51720105015]

Chemistry ; Science & Technology - Other Topics ; Materials Science ; Physics

Chemistry, Physical ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary ; Physics, Atomic, Molecular & Chemical

WOS:000711025300033

AMER CHEMICAL SOC

20214411096015

Deep learning ; Learning algorithms ; Nanoparticles ; Optical properties ; Synthesis (chemical)

Ergonomics and Human Factors Engineering:461.4 ; Data Processing and Image Processing:723.2 ; Machine Learning:723.4.2 ; Light/Optics:741.1 ; Nanotechnology:761 ; Chemical Reactions:802.2 ; Solid State Physics:933

Web of Science

1.Univ Technol Sydney, Fac Sci, Inst Biomed Mat & Devices IBMD, Sydney, NSW 2007, Australia
2.Univ Technol Sydney, Australian Artificial Intelligence Inst, Sydney, NSW 2007, Australia
3.Southern Univ Sci & Technol, UTS SUStech Joint Res Ctr Biomed Mat & Devices, Dept Biomed Engn, Shenzhen 518055, Peoples R China

Liao, Jiayan,Zhou, Jiajia,Song, Yiliao,et al. Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning[J]. Journal of Physical Chemistry Letters,2021,12(41):10242-10248.

Liao, Jiayan,Zhou, Jiajia,Song, Yiliao,Liu, Baolei,Lu, Jie,&Jin, Dayong.(2021).Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning.Journal of Physical Chemistry Letters,12(41),10242-10248.

Liao, Jiayan,et al."Optical Fingerprint Classification of Single Upconversion Nanoparticles by Deep Learning".Journal of Physical Chemistry Letters 12.41(2021):10242-10248.