Jason D. Williams , Peter Sagmeister , C. Oliver Kappe
{"title":"数据丰富的动态优化流动实验","authors":"Jason D. Williams , Peter Sagmeister , C. Oliver Kappe","doi":"10.1016/j.cogsc.2024.100921","DOIUrl":null,"url":null,"abstract":"<div><p>Flow chemistry is having an increasing influence on manufacturing in the chemical industry, but significant barriers remain in the development of these continuous processes. Dynamic flow experiments have the potential to democratize and accelerate process development in a data-rich manner, reducing time and material wastage. Models based on the data gathered can also be leveraged to decrease waste in a manufacturing environment. Here, we summarize the literature reports of dynamic flow experiments (most of which are from the past 5 years), with a focus on experiment design, process analytics, and utilization of the resulting data. Finally, an example of dynamic experiments in pharmaceutical development is discussed in detail. A higher uptake of dynamic experiments in industrial environments in the coming years will undoubtedly facilitate greener manufacturing processes.</p></div>","PeriodicalId":54228,"journal":{"name":"Current Opinion in Green and Sustainable Chemistry","volume":"47 ","pages":"Article 100921"},"PeriodicalIF":9.3000,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2452223624000427/pdfft?md5=c5a85c6dc4eeecf664fe0b5c554c3844&pid=1-s2.0-S2452223624000427-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Dynamic flow experiments for data-rich optimization\",\"authors\":\"Jason D. Williams , Peter Sagmeister , C. Oliver Kappe\",\"doi\":\"10.1016/j.cogsc.2024.100921\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Flow chemistry is having an increasing influence on manufacturing in the chemical industry, but significant barriers remain in the development of these continuous processes. Dynamic flow experiments have the potential to democratize and accelerate process development in a data-rich manner, reducing time and material wastage. Models based on the data gathered can also be leveraged to decrease waste in a manufacturing environment. Here, we summarize the literature reports of dynamic flow experiments (most of which are from the past 5 years), with a focus on experiment design, process analytics, and utilization of the resulting data. Finally, an example of dynamic experiments in pharmaceutical development is discussed in detail. A higher uptake of dynamic experiments in industrial environments in the coming years will undoubtedly facilitate greener manufacturing processes.</p></div>\",\"PeriodicalId\":54228,\"journal\":{\"name\":\"Current Opinion in Green and Sustainable Chemistry\",\"volume\":\"47 \",\"pages\":\"Article 100921\"},\"PeriodicalIF\":9.3000,\"publicationDate\":\"2024-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2452223624000427/pdfft?md5=c5a85c6dc4eeecf664fe0b5c554c3844&pid=1-s2.0-S2452223624000427-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Opinion in Green and Sustainable Chemistry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2452223624000427\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Green and Sustainable Chemistry","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2452223624000427","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Dynamic flow experiments for data-rich optimization
Flow chemistry is having an increasing influence on manufacturing in the chemical industry, but significant barriers remain in the development of these continuous processes. Dynamic flow experiments have the potential to democratize and accelerate process development in a data-rich manner, reducing time and material wastage. Models based on the data gathered can also be leveraged to decrease waste in a manufacturing environment. Here, we summarize the literature reports of dynamic flow experiments (most of which are from the past 5 years), with a focus on experiment design, process analytics, and utilization of the resulting data. Finally, an example of dynamic experiments in pharmaceutical development is discussed in detail. A higher uptake of dynamic experiments in industrial environments in the coming years will undoubtedly facilitate greener manufacturing processes.
期刊介绍:
The Current Opinion journals address the challenge specialists face in keeping up with the expanding information in their fields. In Current Opinion in Green and Sustainable Chemistry, experts present views on recent advances in a clear and readable form. The journal also provides evaluations of the most noteworthy papers, annotated by experts, from the extensive pool of original publications in Green and Sustainable Chemistry.