{"title":"微机器人在血液中受到的阻力","authors":"Chenjun Wu, Toshihiro Omori, Takuji Ishikawa","doi":"10.1038/s42005-024-01724-4","DOIUrl":null,"url":null,"abstract":"Controlling microrobot locomotion in vessels and capillaries is crucial for precise drug delivery and minimally invasive surgeries. However, this is challenging due to the complex interactions with red blood cells (RBCs) and the difficulty navigating within the dense environment. Here, we construct a numerical framework to evaluate the relative resistance coefficient ( $${C}_{{{{{{{{\\rm{r}}}}}}}}}^{* }$$ ) of a microrobot propelled through RBC suspensions. Our experiments validate the numerical results. We find that $${C}_{{{{{{{{\\rm{r}}}}}}}}}^{* }$$ increases for smaller microrobots and higher hematocrit levels, while magnetic force strength weakly impacts $${C}_{{{{{{{{\\rm{r}}}}}}}}}^{* }$$ . $${C}_{{{{{{{{\\rm{r}}}}}}}}}^{* }$$ is smaller than the resistance coefficient of a macroscale robot estimated from the apparent viscosity of the RBC suspension. The aspect ratio of a prolate ellipsoidal microrobot influences $${C}_{{{{{{{{\\rm{r}}}}}}}}}^{* }$$ along its long-axis direction. Additionally, machine learning accurately predicts $${C}_{{{{{{{{\\rm{r}}}}}}}}}^{* }$$ . These insights could enhance the design and control of microrobots for medical applications. Controlling microrobot movement in blood vessels is vital for medical treatments but is challenging due to red blood cells. This study combines simulations, experiments, and machine learning to demonstrate how hematocrit levels and robot geometry affect its locomotion characteristics in blood","PeriodicalId":10540,"journal":{"name":"Communications Physics","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.nature.com/articles/s42005-024-01724-4.pdf","citationCount":"0","resultStr":"{\"title\":\"Drag force on a microrobot propelled through blood\",\"authors\":\"Chenjun Wu, Toshihiro Omori, Takuji Ishikawa\",\"doi\":\"10.1038/s42005-024-01724-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Controlling microrobot locomotion in vessels and capillaries is crucial for precise drug delivery and minimally invasive surgeries. However, this is challenging due to the complex interactions with red blood cells (RBCs) and the difficulty navigating within the dense environment. Here, we construct a numerical framework to evaluate the relative resistance coefficient ( $${C}_{{{{{{{{\\\\rm{r}}}}}}}}}^{* }$$ ) of a microrobot propelled through RBC suspensions. Our experiments validate the numerical results. We find that $${C}_{{{{{{{{\\\\rm{r}}}}}}}}}^{* }$$ increases for smaller microrobots and higher hematocrit levels, while magnetic force strength weakly impacts $${C}_{{{{{{{{\\\\rm{r}}}}}}}}}^{* }$$ . $${C}_{{{{{{{{\\\\rm{r}}}}}}}}}^{* }$$ is smaller than the resistance coefficient of a macroscale robot estimated from the apparent viscosity of the RBC suspension. The aspect ratio of a prolate ellipsoidal microrobot influences $${C}_{{{{{{{{\\\\rm{r}}}}}}}}}^{* }$$ along its long-axis direction. Additionally, machine learning accurately predicts $${C}_{{{{{{{{\\\\rm{r}}}}}}}}}^{* }$$ . These insights could enhance the design and control of microrobots for medical applications. Controlling microrobot movement in blood vessels is vital for medical treatments but is challenging due to red blood cells. This study combines simulations, experiments, and machine learning to demonstrate how hematocrit levels and robot geometry affect its locomotion characteristics in blood\",\"PeriodicalId\":10540,\"journal\":{\"name\":\"Communications Physics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.nature.com/articles/s42005-024-01724-4.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Communications Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.nature.com/articles/s42005-024-01724-4\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications Physics","FirstCategoryId":"101","ListUrlMain":"https://www.nature.com/articles/s42005-024-01724-4","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Drag force on a microrobot propelled through blood
Controlling microrobot locomotion in vessels and capillaries is crucial for precise drug delivery and minimally invasive surgeries. However, this is challenging due to the complex interactions with red blood cells (RBCs) and the difficulty navigating within the dense environment. Here, we construct a numerical framework to evaluate the relative resistance coefficient ( $${C}_{{{{{{{{\rm{r}}}}}}}}}^{* }$$ ) of a microrobot propelled through RBC suspensions. Our experiments validate the numerical results. We find that $${C}_{{{{{{{{\rm{r}}}}}}}}}^{* }$$ increases for smaller microrobots and higher hematocrit levels, while magnetic force strength weakly impacts $${C}_{{{{{{{{\rm{r}}}}}}}}}^{* }$$ . $${C}_{{{{{{{{\rm{r}}}}}}}}}^{* }$$ is smaller than the resistance coefficient of a macroscale robot estimated from the apparent viscosity of the RBC suspension. The aspect ratio of a prolate ellipsoidal microrobot influences $${C}_{{{{{{{{\rm{r}}}}}}}}}^{* }$$ along its long-axis direction. Additionally, machine learning accurately predicts $${C}_{{{{{{{{\rm{r}}}}}}}}}^{* }$$ . These insights could enhance the design and control of microrobots for medical applications. Controlling microrobot movement in blood vessels is vital for medical treatments but is challenging due to red blood cells. This study combines simulations, experiments, and machine learning to demonstrate how hematocrit levels and robot geometry affect its locomotion characteristics in blood
期刊介绍:
Communications Physics is an open access journal from Nature Research publishing high-quality research, reviews and commentary in all areas of the physical sciences. Research papers published by the journal represent significant advances bringing new insight to a specialized area of research in physics. We also aim to provide a community forum for issues of importance to all physicists, regardless of sub-discipline.
The scope of the journal covers all areas of experimental, applied, fundamental, and interdisciplinary physical sciences. Primary research published in Communications Physics includes novel experimental results, new techniques or computational methods that may influence the work of others in the sub-discipline. We also consider submissions from adjacent research fields where the central advance of the study is of interest to physicists, for example material sciences, physical chemistry and technologies.