Electrochemical oxidation of resorcinol: mechanistic insights from experimental and computational studies.
Kamonwad NgamchueaBunrat TharatPussana HirunsitSuwit SuthirakunPublished in: RSC advances (2020)
This work investigates the mechanisms of resorcinol oxidation by density functional theory (DFT) calculation and cyclic voltammetry measurements. Complementary data from experimental and computational studies provide new insights into the reaction mechanisms. At both macro- and micro-electrodes, cyclic voltammetry of resorcinol is chemically and electrochemically irreversible over the whole pH range (1-14). Resorcinol molecules undergo a 1H + 1e - oxidation at pH < p K a1 and a 1e - oxidation at pH > p K a2 to form radicals. The radicals then readily react to form dimers/polymers deposited on the electrode surface. All of the experimental findings are consistent with the proposed mechanisms, including the apparent transfer coefficient ( β ) of 0.6 ± 0.1, the slope of the peak potential ( E p ) against pH of -54 mV pH -1 , the peak-shaped responses at micro-electrodes, and the fouling of the electrodes upon the oxidation of resorcinol. DFT calculation of the reaction energy of elementary steps and the eigenvalues of the highest occupied molecular orbital (HOMO) of the radical intermediates confirms that the (1H + ) 1e - oxidation is the energetically favorable pathway. In addition to mechanistic insights, an electrochemical sensor is developed for resorcinol detection at microelectrodes in low ionic strength samples with the sensitivity of 123 ± 4 nA μM -1 and the limit of detection (3 s B m -1 ) of 0.03 μM.
Keyphrases
- electron transfer
- density functional theory
- hydrogen peroxide
- molecular dynamics
- label free
- ionic liquid
- gold nanoparticles
- solid state
- visible light
- nitric oxide
- reduced graphene oxide
- big data
- loop mediated isothermal amplification
- molecular docking
- computed tomography
- diffusion weighted imaging
- climate change
- risk assessment
- machine learning
- crystal structure
- deep learning
- case control