9
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Advancing chirality analysis through enhanced enantiomer characterization and quantification via fast Fourier transform capacitance voltammetry

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The exploration of the chiral configurations of enantiomers represents a highly intriguing realm of scientific inquiry due to the distinct roles played by each enantiomer (D and L) in chemical reactions and their practical utilities. This study introduces a pioneering analytical methodology, termed fast Fourier transform capacitance voltammetry (FFT-CPV), in conjunction with principal component analysis (PCA), for the identification and quantification of the chiral forms of tartaric acid (TA), serving as a representative model system for materials exhibiting pronounced chiral characteristics. The proposed methodology relies on the principle of chirality, wherein the capacitance signal generated by the adsorption of D-TA and L-TA onto the surface of a platinum electrode (Pt-electrode) in an acidic solution is harnessed. The capacitance voltammograms were meticulously recorded under optimized experimental conditions. To compile the final dataset for the analyte, the average of the FFT capacitance voltammograms of the acidic solution (without the presence of the analyte) was subtracted from those containing the analyte. A distinct arrangement was obtained by employing PCA as a linear data transformation method, representing D-TA and L-TA in a two/three-dimensional space. The outcomes of the study reveal the successful detection of the two chiral forms of TA with a considerable degree of precision and reproducibility. Moreover, the proposed method facilitated the establishment of two linear response ranges for the concentration values of each enantiomer, spanning from 1 to 20 µM, and 50 to 500 µM. The respective detection limits were also determined to be 0.4 µM for L-TA and 1.3 µM for D-TA. These findings underscore the satisfactory sensitivity and efficiency of the proposed method in both qualitative and quantitative assessments of the chiral forms of TA.

          Related collections

          Most cited references43

          • Record: found
          • Abstract: not found
          • Article: not found

          Smoothing and Differentiation of Data by Simplified Least Squares Procedures.

            • Record: found
            • Abstract: found
            • Article: not found

            Machine Learning: Algorithms, Real-World Applications and Research Directions

            In the current age of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Various types of machine learning algorithms such as supervised, unsupervised, semi-supervised, and reinforcement learning exist in the area. Besides, the deep learning, which is part of a broader family of machine learning methods, can intelligently analyze the data on a large scale. In this paper, we present a comprehensive view on these machine learning algorithms that can be applied to enhance the intelligence and the capabilities of an application. Thus, this study’s key contribution is explaining the principles of different machine learning techniques and their applicability in various real-world application domains, such as cybersecurity systems, smart cities, healthcare, e-commerce, agriculture, and many more. We also highlight the challenges and potential research directions based on our study. Overall, this paper aims to serve as a reference point for both academia and industry professionals as well as for decision-makers in various real-world situations and application areas, particularly from the technical point of view.
              • Record: found
              • Abstract: not found
              • Article: not found

              Machine learning and the physical sciences

                Author and article information

                Contributors
                pnorouzi@yorku.ca
                raziehs@yorku.ca
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 October 2023
                5 October 2023
                2023
                : 13
                : 16739
                Affiliations
                [1 ]Chemistry Faculty, School of Sciences, University of Tehran, ( https://ror.org/05vf56z40) POB 14155-6455, Tehran, Iran
                [2 ]Laboratory of Advanced Biotechnologies for Health Assessments (Lab-HA), Lassonde School of Engineering, York University, ( https://ror.org/05fq50484) Toronto, M3J 1P3 Canada
                [3 ]Department of Electrical Engineering and Computer Science, York University, ( https://ror.org/05fq50484) 4700 Keele Street, Toronto, ON M3J 1P3 Canada
                [4 ]Institute of Biochemistry and Biophysics, University of Tehran, ( https://ror.org/05vf56z40) Tehran, Iran
                [5 ]Department of Physical and Environmental Sciences, University of Toronto Scarborough, ( https://ror.org/03dbr7087) 1265 Military Trail, Toronto, ON M1C 1A4 Canada
                Article
                43945
                10.1038/s41598-023-43945-7
                10556018
                37798351
                a6fdec5d-5da0-4e6b-ba56-76a2dcf9ac95
                © Springer Nature Limited 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 4 August 2023
                : 30 September 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004481, University of Tehran;
                Award ID: 234/18737
                Funded by: NSERC Discovery
                Award ID: RGPIN-2023-0509
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                analytical chemistry,electrochemistry,biomedical engineering,machine learning
                Uncategorized
                analytical chemistry, electrochemistry, biomedical engineering, machine learning

                Comments

                Comment on this article

                Related Documents Log