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The past is the future: from natural acid-base indicators to natural reagents in sustainable analytical chemistry.

  • Published In: Pure & Applied Chemistry, 2024, v. 96, n. 9. P. 1257 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Suteerapataranon, Siripat; Kiwfo, Kanokwan; Woi, Pei Meng; Saenjum, Chalermpong; Grudpan, Kate 3 of 3

Abstract

This article reviews the use of natural resources in analytical chemistry throughout history. Plant extracts were employed as indicators in chemistry for identifying the acidity or alkalinity of liquids as early as the 1650s. Later, as the industrial revolution altered people's lives, synthetic chemicals were used instead. Modern techniques of analysis have replaced conventional ones as a result of advancements in physics and technology. The industrial revolution was an era of excitement until the toxic pollutants released from industries severely damaged people and the environment. The concepts of green chemistry and green analytical chemistry were proposed as potential solutions to the problems. The use of natural extracts as chemical analysis reagents has been reconsidered recently as a sustainable alternative. While new technologies such as artificial intelligence (AI) will influence future trends in analytical chemistry development, the primary goal is to move toward sustainable analytical chemistry, which includes using natural reagents and reducing the amount of chemicals consumed and waste produced. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Pure & Applied Chemistry. 2024/09, Vol. 96, Issue 9, p1257
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2024
  • ISSN:0033-4545
  • DOI:10.1515/pac-2024-0204
  • Accession Number:180111171
  • Copyright Statement:Copyright of Pure & Applied Chemistry is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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