{"title":"3d打印微流控系统用于纳米颗粒合成参数的原位诊断和筛选","authors":"V.V. Shapovalov , S.V. Chapek , A.A. Tereshchenko , A.N. Bulgakov , A.P. Bagliy , V.V. Volkov , P.V. Konarev , M.A. Soldatov , S.A. Soldatov , A.A. Guda , A.V. Soldatov","doi":"10.1016/j.mne.2023.100224","DOIUrl":null,"url":null,"abstract":"<div><p>Fine tuning of the material properties requires many trials and errors during the synthesis. The metal nanoparticles undergo several stages of reduction, clustering, coalescence and growth upon their formation. Resulting properties of the colloidal solution thus depend on the concentrations of the reagents, external temperature, synthesis protocol and qualification of the researcher determines the reproducibility and quality. Automatized flow systems overcome the difficulties inherent for the conventional batch approaches. Microfluidic systems represent a good alternative for the high throughput data collection. The recent advances in 3D-printing made complex topologies in microfluidic devices cheaper and easily customizable. However, channels of the cured photopolymer resin attract metal ions upon synthesis and create crystallization centers. In our work we present 3D-printed system for the noble metal nanoparticle synthesis in slugs. Alternating flows of oil and aqueous reaction mixtures prevent metal deposition on the channel walls. Elongated droplets are convenient for optical and X-ray diagnostics using conventional methods. We demonstrate the work of the system using Ag nanoparticles synthesis for machine-learning assisted tuning of the plasmon resonance frequency.</p></div>","PeriodicalId":37111,"journal":{"name":"Micro and Nano Engineering","volume":"20 ","pages":"Article 100224"},"PeriodicalIF":2.8000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"3D-printed microfluidic system for the in situ diagnostics and screening of nanoparticles synthesis parameters\",\"authors\":\"V.V. Shapovalov , S.V. Chapek , A.A. Tereshchenko , A.N. Bulgakov , A.P. Bagliy , V.V. Volkov , P.V. Konarev , M.A. Soldatov , S.A. Soldatov , A.A. Guda , A.V. Soldatov\",\"doi\":\"10.1016/j.mne.2023.100224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Fine tuning of the material properties requires many trials and errors during the synthesis. The metal nanoparticles undergo several stages of reduction, clustering, coalescence and growth upon their formation. Resulting properties of the colloidal solution thus depend on the concentrations of the reagents, external temperature, synthesis protocol and qualification of the researcher determines the reproducibility and quality. Automatized flow systems overcome the difficulties inherent for the conventional batch approaches. Microfluidic systems represent a good alternative for the high throughput data collection. The recent advances in 3D-printing made complex topologies in microfluidic devices cheaper and easily customizable. However, channels of the cured photopolymer resin attract metal ions upon synthesis and create crystallization centers. In our work we present 3D-printed system for the noble metal nanoparticle synthesis in slugs. Alternating flows of oil and aqueous reaction mixtures prevent metal deposition on the channel walls. Elongated droplets are convenient for optical and X-ray diagnostics using conventional methods. We demonstrate the work of the system using Ag nanoparticles synthesis for machine-learning assisted tuning of the plasmon resonance frequency.</p></div>\",\"PeriodicalId\":37111,\"journal\":{\"name\":\"Micro and Nano Engineering\",\"volume\":\"20 \",\"pages\":\"Article 100224\"},\"PeriodicalIF\":2.8000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Micro and Nano Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590007223000540\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Micro and Nano Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590007223000540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
3D-printed microfluidic system for the in situ diagnostics and screening of nanoparticles synthesis parameters
Fine tuning of the material properties requires many trials and errors during the synthesis. The metal nanoparticles undergo several stages of reduction, clustering, coalescence and growth upon their formation. Resulting properties of the colloidal solution thus depend on the concentrations of the reagents, external temperature, synthesis protocol and qualification of the researcher determines the reproducibility and quality. Automatized flow systems overcome the difficulties inherent for the conventional batch approaches. Microfluidic systems represent a good alternative for the high throughput data collection. The recent advances in 3D-printing made complex topologies in microfluidic devices cheaper and easily customizable. However, channels of the cured photopolymer resin attract metal ions upon synthesis and create crystallization centers. In our work we present 3D-printed system for the noble metal nanoparticle synthesis in slugs. Alternating flows of oil and aqueous reaction mixtures prevent metal deposition on the channel walls. Elongated droplets are convenient for optical and X-ray diagnostics using conventional methods. We demonstrate the work of the system using Ag nanoparticles synthesis for machine-learning assisted tuning of the plasmon resonance frequency.