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Review
. 2022 Dec 16;7(51):47471-47489.
doi: 10.1021/acsomega.2c03629. eCollection 2022 Dec 27.

A Review on Interactions between Amino Acids and Surfactants as Well as Their Impact on Corrosion Inhibition

Anirudh Pratap Singh Raman  1 , Amina Abdullahi Muhammad  2 , Harpreet Singh  2 , Thishana Singh  3 , Zimbili Mkhize  4 , Pallavi Jain  5 , Shailendra Kumar Singh  6 , Indra Bahadur  4 , Prashant Singh  1
Affiliations
Review

A Review on Interactions between Amino Acids and Surfactants as Well as Their Impact on Corrosion Inhibition

Anirudh Pratap Singh Raman et al. ACS Omega. .

Abstract

Amino acid-surfactant interactions are central to numerous studies because of their increased effectiveness in chemical, biological, household and industrial use. This review will focus on the impact and effect of the physicochemical properties, temperature, pH, and surfactant chain length of the amino acid for detailed exploration of amino acids and surfactants in aqueous medium. The impact of cosolvent on self-aggregation, critical micelle concentration (CMC), and binding affinity with other biomolecules, as well as amino acid-surfactant interactions, are the epicenters. The results show that increasing the temperature causes negative enthalpy for ionic surfactants and micellization, implying that micellization and amino acids are thermodynamically spontaneous and exothermic, accompanied by positive entropy. As these physicochemical studies are additive, the amino acid and ionic surfactant interactions provide clues on protein unfolding and denaturation under different media, which further changes with a change in physiological conditions like pH, cosolvent, chain length, and temperature. On varying the pH, the net charge of the amino acid also changes and, subsequently, the binding efficiency of the amino acids to the surfactants. The presence of cosolvent causes a lowering in the hydrophobic chain, which changes the surfactant's CMC. At a reduced CMC, the hydrophobic characteristic of amino acid-surfactant associations is amplified, leading to rapid denaturation of proteins that act as propulsion under the influence of extended chain surfactants. Amino acids are one of the most intriguing classes of chemicals that produce high inhibitory efficacy. Amino acids are also a component of proteins and therefore, found in a significant part of the human body, further making them a promising candidate as corrosion inhibitors. In this review article, authors have also focused on the collection and investigation for application of amino acid-surfactant interactions in corrosion inhibition. Various predictive studies/in silico studies are also reported by many research groups, such as density functional theory (DFT) calculations and molecular dynamics simulations to obtain tentative electronic, structural, and physiochemical characteristics like energies of the highest occupied molecular orbitals and lowest unoccupied molecular orbitals, binding energy, Gibb's free energy, electronegativity, polarizability, and entropy. In silico studies are helpful for the mechanism predictions of the process occurring on metal surfaces.

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Conflict of interest statement

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Variation of specific conductivity on SDS in 0–0.10 m aqueous (a) glycine, (b) alanine, and (c) glycylglycine at different temperatures. Reprinted with permission from ref (11). Copyright 2010 John Wiley and Sons, Ltd.
Figure 2
Figure 2
Enthalpic (●) and entropic (■) contributions to Gibb’s free energy of micellization at different temperatures in 0.10 m aqueous (a) glycine, (b) alanine, and (c) glycylglycine. Reprinted with permission from ref (11). Copyright 2010 John Wiley and Sons, Ltd.
Figure 3
Figure 3
Surface tension of CTAB in the presence of different glycine concentrations in Tris-HCl buffer solution at 27 °C. Reprinted with permission from ref (14). Copyright 2005 Elsevier, Ltd.
Figure 4
Figure 4
Plots of γ versus log C and log cac and pC20 versus the number of carbon atoms in the SAIL alkyl chain for Cn MeIm-LS (panels a, c, and d, respectively) and Cn Pyr-LS (panels b, c, and d, respectively) cationic mixtures. Reprinted with permission from ref (35). Copyright 2020 Elsevier, Ltd..
Figure 5
Figure 5
Variation in physical properties of surfactant solutions below and above the CMC value. Reprinted with permission from ref (41). Copyright 2003 Royal Society of Chemistry. Reproduced from ref (45). Copyright 1948 American Chemical Society.
Figure 6
Figure 6
Chemical structure of (a) l-threonine, (b) phenylalanine, (c) dl-methionine, and (d) sodium dodecyl sulfate. Reprinted with permission from ref (49). Copyright 2022 Elsevier, Ltd.
Figure 7
Figure 7
Titration curves of 0.1 M SLSar/CAHS systems with 0.1 M HCl. Reprinted with permission from ref (53). Copyright 2020 Elsevier, Ltd.
Figure 8
Figure 8
Series of Nyquist plot, Bode plot, and phase angle plot for AA2024-T3 in the absence or the presence of RNC-14 at various concentrations for AA2024-T3 without and with surfactants at different concentrations in 3.5% sodium chloride solution: (a) RNC-8, (b) RNC-12, and (c) RNC-14. (d) Equivalent circuit. Reprinted with permission from ref (57). Copyright 2022 Elsevier, Ltd.
Figure 9
Figure 9
Interaction of SCA molecules on (a) carbonate and (b) quartz rock surface. Reprinted with permission from ref (61). Copyright 2020 Elsevier, Ltd.
Figure 10
Figure 10
Variation of free energy change of micellization with the composition in bulk solution at T = 298.15 K. Reprinted with permission from ref (62). Copyright 2021 Elsevier, Ltd.
Figure 11
Figure 11
Viscosity versus shear rate curves for the LAD-TEA-1/water/CTAB system as a function of CTAB at fixed LAD-TEA-1/water = 15/85 at 25 °C. Reprinted with permission from ref (63). Copyright 2007 Elsevier, Ltd.
Figure 12
Figure 12
Cell viability (%) of Caco-2 (A) and Calu-3 (B) cell lines after exposure to different concentrations of quaternary ammonium surfactants derived from leucine and methionine and the reference compound BAC. Dots represent the mean, and bars are the standard error (n = 4). Reprinted with permission from ref (65). Copyright 2019 Elsevier, Ltd.
Figure 13
Figure 13
Langmuir isotherm adsorption model of different concentrations of (a) DHNMMB and (b) DHNBMBDMB on the CS surface in 5% HCl at different temperatures. Reprinted with permission from ref (68). Copyright 2022 Elsevier, Ltd.
Figure 14
Figure 14
Plots of CMC and the stoichiometric mole fractions of (a) G5-CPC, (b) G5-AOT, and (c) G5-Brij56. Reprinted with permission from ref (72). Copyright 2011, Elsevier, Ltd.
Figure 15
Figure 15
RDF profiles between surfactants with different EO group numbers. Reprinted with permission from ref (73). Copyright 2022 Elsevier, Ltd.
Figure 16
Figure 16
RDF of TGETBAE epoxy polymer on the Fe(110) surface. Reprinted with permission from ref (77). Copyright 2020 Elsevier, Ltd.
Figure 17
Figure 17
Molecular simulations for the most favorable modes of adsorption obtained for the investigated inhibitors on the Fe(110) surface, side and top view. Reprinted with permission from ref (94). Copyright 2022 Elsevier, Ltd.
Figure 18
Figure 18
(a) Equilibrium configuration of AA2 adsorbed on the Fe(110) surface. (b) Dependence of adsorption energy on the temperature for the adsorbed AA2 molecule on Fe(110). Reprinted with permission from ref (95). Copyright 2020 Elsevier, Ltd.
Figure 19
Figure 19
Top and side view of adsorption of His and Tryp on Fe(110). Reprinted with permission from ref (96). Copyright 2022 Elsevier, Ltd.
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