Machine learning predicts the serum PFOA and PFOS levels in pregnant women: Enhancement of fatty acid status on model performance.

Human exposure to per- and polyfluoroalkyl substances (PFASs) has received considerable attention, particularly in pregnant women because of their dramatic changes in physiological status and dietary patterns. Predicting internal PFAS exposure in pregnant women, based on external and relevant parameters, has not been investigated. Here, machine learning (ML) models were developed to predict the serum concentrations of PFOA and PFOS in a large population of 588 pregnant participants.

Hydrogen peroxide sensing using ultrathin platinum-coated gold nanoparticles with core@shell structure.

Ultrathin platinum-coated gold (Pt@Au) nanoparticles with core@shell structure have been developed by under-potential deposition (UPD) redox replacement technique. A single UPD Cu replacement with Pt(2+) produced a uniform Pt monolayer on the surface of gold nanoparticles, which are immobilized on glassy carbon electrode (GCE) surface based on electrostatic interaction. The ultrathin Pt@Au nanoparticles were confirmed by cyclic voltammetry and X-ray photoelectron spectroscopy (XPS).

Electrochemical determination of dopamine in the presence of ascorbic acid and uric acid using the synergistic effect of gold nanoflowers and L-cysteamine monolayer at the surface of a gold electrode.

This paper describes a facile and effective method to synthesize gold nanoflowers (AuNFs) by a controllable electrodeposition method induced by a L-cysteamine (L-Cys) monolayer self-assembled on the surface of a gold electrode. The AuNFs/L-Cys/Au electrodes were characterized by field emission scanning electron microscopy (FE-SEM), cyclic voltammetry, and AC impedance spectroscopy methods. This obtained AuNFs/L-Cys/Au electrode exhibits excellent electrocatalytic activity towards the oxidation of dopamine (DA) due to the synergistic effect of AuNFs and a L-Cys monolayer.