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Liu Y, Zhang S, Wang Y, Wang L, Cao Z, Sun W, Fan P, Zhang P, Chen HY, Huang S. Nanopore Identification of Alditol Epimers and Their Application in Rapid Analysis of Alditol-Containing Drinks and Healthcare Products. J Am Chem Soc 2022; 144:13717-13728. [PMID: 35867993 DOI: 10.1021/jacs.2c04595] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Alditols, which have a sweet taste but produce much lower calories than natural sugars, are widely used as artificial sweeteners. Alditols are the reduced forms of monosaccharide aldoses, and different alditols are diastereomers or epimers of each other and direct and rapid identification by conventional methods is difficult. Nanopores, which are emerging single-molecule sensors with exceptional resolution when engineered appropriately, are useful for the recognition of diastereomers and epimers. In this work, direct distinguishing of alditols corresponding to all 15 monosaccharide aldoses was achieved by a boronic acid-appended hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore (MspA-PBA). Thirteen alditols including glycerol, erythritol, threitol, adonitol, arabitol, xylitol, mannitol, sorbitol, allitol, dulcitol, iditol, talitol, and gulitol (l-sorbitol) could be fully distinguished, and their sensing features constitute a complete nanopore alditol database. To automate event classification, a custom machine-learning algorithm was developed and delivered a 99.9% validation accuracy. This strategy was also used to identify alditol components in commercially available "zero-sugar" drinks and healthcare products, suggesting their use in rapid and sensitive quality control for the food and medical industry.
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Affiliation(s)
- Yao Liu
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.,Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Shanyu Zhang
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.,Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Yuqin Wang
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.,Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Liying Wang
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.,Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Zhenyuan Cao
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.,Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Wen Sun
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.,Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Pingping Fan
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.,Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
| | - Panke Zhang
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Hong-Yuan Chen
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China
| | - Shuo Huang
- State Key Laboratory of Analytical Chemistry for Life Sciences, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.,Chemistry and Biomedicine Innovation Center (ChemBIC), Nanjing University, Nanjing 210023, China
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Assessment of Graphical Methods for Determination of the Limiting Current Density in Complex Electrodialysis-Feed Solutions. MEMBRANES 2022; 12:membranes12020241. [PMID: 35207162 PMCID: PMC8875246 DOI: 10.3390/membranes12020241] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 02/09/2022] [Accepted: 02/14/2022] [Indexed: 12/10/2022]
Abstract
Electrodialysis (ED) is a promising technology suitable for nutrient recovery from a wide variety of liquid waste streams. For optimal operating conditions, the limiting current density (LCD) has to be determined separately for each treated feed and ED equipment. LCD is most frequently assessed in the NaCl solutions. In this paper, five graphical methods available in literature were reviewed for LCD determination in a series of five feed solutions with different levels of complexity in ion and matrix composition. Wastewater from microbial fermentation was included among the feed solutions, containing charged and uncharged particles. The experiments, running in the batch ED with an online conductivity, temperature, and pH monitoring, were conducted to obtain data for the comparison of various LCD determination methods. The results revealed complements and divergences between the applied LCD methods with increasing feed concentrations and composition complexity. The Cowan and Brown method had the most consistent results for all of the feed solutions. Online conductivity monitoring was linearly correlated with the decreasing ion concentration in the feed solution and corresponding LCD. Therefore, the results obtained in this study can be applied as a base for the automatized dynamic control of the operating current density–voltage in the batch ED. Conductivity alone should not be used for the ED control since LCD depends on the ion exchange membranes, feed flow, temperature and concentration, ionic species, their concentration ratios, and uncharged particles of the feed solution.
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