{"title":"自动化膜表征:使用 3D 打印渗透探针装置原位监测渗透液和回流液","authors":"Jonathan, Ouimet, Faraj, Al-Badani, Xinhong, Liu, Laurianne, Lair, Zachary, Muetzel, Alexander, Dowling, William, Phillip","doi":"10.26434/chemrxiv-2024-31fl1-v2","DOIUrl":null,"url":null,"abstract":"Self-driving laboratories and automated experiments can accelerate the design workflow and decrease errors associated with experiments that characterize membrane transport properties. Within this study, we use 3D printing to design a custom stirred cell that incorporates inline conductivity probes in the retentate and permeate streams. The probes provide a complete trajectory of the salt concentrations as they evolve over the course of an experiment. Here, automated diafiltration experiments are used to characterize the performance of commercial NF90 and NF270 polyamide membranes over a predetermined range of KCl concentrations from 1-100 mM. The measurements obtained by the inline conductivity probes are validated using offline post-experiment analyses. Compared to traditional filtration experiments, the probes decrease the amount of time required for an experimentalist to characterize membrane materials by more than 50× and increase the amount of information generated by 100×. Device design principles to address the physical constraints associated with making conductivity measurements in confined volumes are proposed. Overall, the device developed within this study provides a foundation to establish high-throughput, automated membrane characterization techniques.","PeriodicalId":9813,"journal":{"name":"ChemRxiv","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Membrane Characterization: In-situ Monitoring of the Permeate and Retentate Solutions using a 3D Printed Permeate Probe Device\",\"authors\":\"Jonathan, Ouimet, Faraj, Al-Badani, Xinhong, Liu, Laurianne, Lair, Zachary, Muetzel, Alexander, Dowling, William, Phillip\",\"doi\":\"10.26434/chemrxiv-2024-31fl1-v2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Self-driving laboratories and automated experiments can accelerate the design workflow and decrease errors associated with experiments that characterize membrane transport properties. Within this study, we use 3D printing to design a custom stirred cell that incorporates inline conductivity probes in the retentate and permeate streams. The probes provide a complete trajectory of the salt concentrations as they evolve over the course of an experiment. Here, automated diafiltration experiments are used to characterize the performance of commercial NF90 and NF270 polyamide membranes over a predetermined range of KCl concentrations from 1-100 mM. The measurements obtained by the inline conductivity probes are validated using offline post-experiment analyses. Compared to traditional filtration experiments, the probes decrease the amount of time required for an experimentalist to characterize membrane materials by more than 50× and increase the amount of information generated by 100×. Device design principles to address the physical constraints associated with making conductivity measurements in confined volumes are proposed. Overall, the device developed within this study provides a foundation to establish high-throughput, automated membrane characterization techniques.\",\"PeriodicalId\":9813,\"journal\":{\"name\":\"ChemRxiv\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ChemRxiv\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26434/chemrxiv-2024-31fl1-v2\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ChemRxiv","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26434/chemrxiv-2024-31fl1-v2","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
自动驾驶实验室和自动化实验可以加快设计工作流程,减少与表征膜传输特性的实验相关的误差。在这项研究中,我们使用 3D 打印技术设计了一个定制的搅拌池,在回流液和渗透液中加入了在线电导探针。探针可提供盐浓度在实验过程中演变的完整轨迹。在这里,自动重滤实验用于鉴定商用 NF90 和 NF270 聚酰胺膜在 1-100 mM 氯化钾浓度预定范围内的性能。在线电导探头获得的测量结果通过离线实验后分析进行了验证。与传统的过滤实验相比,该探头使实验人员表征膜材料所需的时间减少了 50 倍以上,所产生的信息量增加了 100 倍。针对在密闭体积内进行电导率测量的相关物理限制,提出了设备设计原则。总之,本研究开发的设备为建立高通量、自动化的膜表征技术奠定了基础。
Automated Membrane Characterization: In-situ Monitoring of the Permeate and Retentate Solutions using a 3D Printed Permeate Probe Device
Self-driving laboratories and automated experiments can accelerate the design workflow and decrease errors associated with experiments that characterize membrane transport properties. Within this study, we use 3D printing to design a custom stirred cell that incorporates inline conductivity probes in the retentate and permeate streams. The probes provide a complete trajectory of the salt concentrations as they evolve over the course of an experiment. Here, automated diafiltration experiments are used to characterize the performance of commercial NF90 and NF270 polyamide membranes over a predetermined range of KCl concentrations from 1-100 mM. The measurements obtained by the inline conductivity probes are validated using offline post-experiment analyses. Compared to traditional filtration experiments, the probes decrease the amount of time required for an experimentalist to characterize membrane materials by more than 50× and increase the amount of information generated by 100×. Device design principles to address the physical constraints associated with making conductivity measurements in confined volumes are proposed. Overall, the device developed within this study provides a foundation to establish high-throughput, automated membrane characterization techniques.