У нас вы можете посмотреть бесплатно Molecularly Imprinted Polymers for Robust, Reliable and Rapid Detection of COVID-19 или скачать в максимальном доступном качестве, видео которое было загружено на ютуб. Для загрузки выберите вариант из формы ниже:
Если кнопки скачивания не
загрузились
НАЖМИТЕ ЗДЕСЬ или обновите страницу
Если возникают проблемы со скачиванием видео, пожалуйста напишите в поддержку по адресу внизу
страницы.
Спасибо за использование сервиса ClipSaver.ru
We have developed a new molecularly imprinted polymer-based test for fast, reliable, and 6000x better accuracy compared to commercial lateral flow tests. The details of our published work, which was an ACS Editor's Choice article, can be found here: https://pubs.acs.org/doi/10.1021/acss... The work was also published in various news outlets such as Newsweek: https://www.newsweek.com/new-covid-19... Rapid antigen tests are currently used for population screening of COVID-19. However, they lack sensitivity and utilize antibodies as receptors, which can only function in narrow temperature and pH ranges. Consequently, molecularly imprinted polymer nanoparticles (nanoMIPs) are synthetized with a fast (2 h) and scalable process using merely a tiny SARS-CoV-2 fragment (∼10 amino acids). The nanoMIPs rival the affinity of SARS-CoV-2 antibodies under standard testing conditions and surpass them at elevated temperatures or in acidic media. Therefore, nanoMIP sensors possess clear advantages over antibody-based assays as they can function in various challenging media. A thermal assay is developed with nanoMIPs electrografted onto screen-printed electrodes to accurately quantify SARS-CoV-2 antigens. Heat transfer-based measurements demonstrate superior detection limits compared to commercial rapid antigen tests and most antigen tests from the literature for both the alpha (∼9.9 fg mL–1) and delta (∼6.1 fg mL–1) variants of the spike protein. A prototype assay is developed, which can rapidly (∼15 min) validate clinical patient samples with excellent sensitivity and specificity.