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Design of Optical Sensor Arrays Using Plasmonic and Fluorescent Nanoprobes for the Detection of Antidepressants, Biothiols, and Discrimination of the Enantiomers of Tryptophan, Histidine, and Arginine

Jafarnezhad Ivrigh, Zahra | 2024

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 57420 (03)
  4. University: Sharif University of Technology
  5. Department: Chemistry
  6. Advisor(s): Hormozinezhad, Mohammad Reza
  7. Abstract:
  8. In the first part of this research, a single-component colorimetric sensor array based on the aggregation of AuNPs has been designed for the quantification and classification of antidepressant drugs (fluvoxamine (Flu), nortriptyline (Nor), citalopram (Cit), alprazolam (Alp), diazpam (Dia), sertraline (Ser), zolpidem (Zol), and imipramine (Imi)). Tuning the pH and ionic strength enabled the single-component probe to interact with the target analytes through different binding modes, providing the required cross-reactivity. To generate the array, a single AuNP was embedded into six experimental conditions: three different buffers (acidic, neutral and basic) each at two concentration levels (1mM and 10 mM). The antidepressant drugs led to different extents of AuNPs aggregation at each of the above conditions which was accompanied by spectral changes in the plasmonic peak and color variations in the solution of nanoparticles. The collected data were statistically analyzed by various pattern recognition methods. Namely, linear discriminant analysis (LDA) and partial least squares (PLS) regression were employed for the qualitative and quantitative determination of antidepressant drugs. The responses were linearly correlated to the concentrations of the antidepressant drugs in a wide range of 1.20−10.00, 0.44−4.00, 0.83−10.00, 0.03−0.30, 0.82−10.00, 1.89−10.00, 0.08−0.90, and 0.38−4.00 μg.mL−1 with the limit of detections of 0.40, 0.15, 0.28, 0.01, 0.27, 0.63, 0.03, and 0.13 μg.mL−1 for Flu, Nor, Cit, Alp, Dia, Ser, Zol, and Imi, respectively. The prediction accuracy (R2pred) for the identification and determination of the antidepressants over their wide concentration ranges were all above 0.99. The results of multivariate calibration (R2cal> 99% and R2cv> 99%) and discrimination of antidepressants in human urine ensured the practicability of the array in complex biological fluids. In the second part of this research, by using the induction of chirality in nanoparticles through the formation of chiral self-assemblies, a chiral ratiometric probe was designed for visual recognition of tryptophan enantiomers. A combination of orange-emissive CTAB-based supramolecular assemblies of TGA-capped CdTe QDs (CTAB-QDs) and blue-emissive carbon dots (BCDs) were employed to construct this ratiometric platform; The ratiometric design was obtained due to the unchanged fluorescence signal of BCDs, which acted as an internal reference. Entirely distinct fluorescence response patterns were observed in the presence of L- and D-Trp; In the presence of D-Trp, the fluorescence of CTAB-QDs was significantly quenched, while it had almost no change in the presence of the L-Trp enantiomer. Evaluation of the enantiomeric excess exhibited a wide change in the emission color tonalities from blue to orange-peach upon increasing the %L-Trp enantiomer in the mixture and the intensity ratio of I590/I440 was positively correlated to the ee of L-Trp in the range 0−100%. The distinct response patterns of the ratiometric assay were further manipulated for the construction of a logic gate system mimicking NOT and OR functions. Moreover, the applicability of the supramolecular chiral assembly toward enantiomeric excess determination of L-Trp in food supplements was also investigated. The acquired wide range color tonalities enable simple visual tracking of D- and L-Trp with a handheld device such as a smartphone. In the third part of this research, a visual chiral recognition platform has been developed in which a combination of blueemitting carbon dots (BCDs) and mercaptopropionic acid-capped CdTe quantum dots (MPA-QDs) with inherent chiroptical activity were employed for enantiomeric detection. The ratiometric probe displayed unique fluorescence response patterns in the presence of arginine (Arg) and histidine (His) enantiomers. Upon addition of L-amino acids, successive enhancement and quenching of emission intensity as well as a red-shift in emission wavelength of MPA-QDs were observed. The emission color of the nanoprobe changed from green to pink-red and green to brick-red red by increasing the concentration of L-Arg and L-His, respectively. In contrast, their D-amino acid equivalents have a negligible influence on the emission color and fluorescence signal of the developed nanoprobe. Due to the enantioselective vibrant color changes of the nanoprobe, RGB analysis was applied for the determination of enantiomeric excess (ee) in racemic mixture with satisfactory results, allowing smartphone-based onsite visual evaluation of ee (%).Circular dichroism, lifetime, size distribution and ζ-potential measurements were employed to study the chiroselective responses. First-principle calculations were also carried out with density functional theory (DFT) to confrm the experimental observation. Furthermore, chiroselective response patterns of the ratiometric nanoprobe were manipulated to construct a logic gate system mimicking AND, OR, and INHIBIT functions. Silver shell overgrowth of gold nanorods (AuNRs) has attracted remarkable attention in the development of multi-colorimetric sensing thanks to the exceptional spectral dimensionality and the high-contrast color tonality it provides for the accurate discrimination of diverse analytes. The integration of this strategy with the outstanding discriminatory power of machine learning techniques makes this approach promising for identification of analytically-valuable species with high structural similarity. Hence, in the fourth part of this research the combination of machine learning techniques with the silver shell overgrowth of AuNRs have been used to identify and quantify of biothiols including cysteine (Cys), homocysteine (Hcy), glutathione (GSH), and glutathione disulfide (GSSG). The differential adsorption of biothiols on the surface of AuNRs inhibits the growth of the silver shell to varying degrees leading to unique colorimetric signatures that allow the effective identification and sensitive quantification of each analyte. To this end, principal component analysis was coupled with linear discriminant analysis (PCA-LDA) to process the acquired dataset for recognizing the single biothiols, as well as their mixtures. Furthermore, the performance of the proposed method in the quantitative analysis of biothiols was assessed by partial least squares regression (PLSR) over a wide concentration range. The responses were linearly correlated to the concentrations of the biothiols in a range of 0.70−25.00, 0.56−25.00, 0.10−7.00, and 1.00−15.00 μM with the limit of detections of 0.230, 0.186, 0.035, and 0.338 μM for Cys, Hcy, GSH, and GSSG, respectively. It is worth mentioning that the designed multi-colorimetric method exhibit great potentiality in the quantitative detection and discrimination of biothiols in human serum samples
  9. Keywords:
  10. Colorimetric Sensor Array ; Pattern Recognition ; Machine Learning ; Antidepressants ; Gold Nanoparticle ; Gold Nanorods ; Biothiols ; Ratiometric Fluorescence ; Supramolecular Assemblies ; Logic Gate ; Enantiomeric Oxidation ; Density Functional Theory (DFT) ; Histidine ; Arginine ; Tryptophan ; Silver Shell Overgrowth

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