Experimental characterization, machine learning analysis and computational modelling of the high effective inhibition of copper corrosion by 5‐(4‐pyridyl)‐1,3,4‐oxadiazole‐2‐thiol in saline environment

Corrosion inhibition activity of a natural polysaccharide from Dysosma versipellis using tailor-made deep eutectic solvents

In this work, a total of 18 types of choline chloride, betaine, and L-proline-based deep eutectic solvents (DESs) were synthesized to determine the extraction yield of a natural polysaccharide (PSA) from Dysosma versipellis using an ultrasound-assisted extraction method. Results indicate that the choline-oxalic acid-based DES has the best extraction yield for PSA due to the proper physical-chemical properties between PSA and DES. To evaluate the optimal extraction conditions, a response surface methodology was carried out. Under the optimal conditions, the extraction yield of PSA reaches 10.37 % (± 0.03 %), higher than the conventional extraction methods. Findings from FT-IR and NMR suggest that the extracted PSA belongs to a neutral polysaccharide with (1 → 6)-linked α-d-glucopyranose in the main chain. Interestingly, results from various electrochemical measurements show the extracted PSA exhibits excellent corrosion inhibition performance for mild steel (MS) in a 0.5 M HCl solution, with 90.8 % of maximum corrosion inhibition efficiency at 210 mg L-1. SEM and XPS measurements reveal the formation of a protective layer on the MS surface. The adsorption behaviour of extracted PSA well obeys the Langmuir adsorption isotherm containing the chemisorption and physisorption. Additionally, theoretical calculations validate the experimental findings.

Interfacial Adsorption and Corrosion Inhibition Behavior of Environmentally Friendly Imidazole Derivatives for Copper in the Marine Environment

Copper and copper alloys are commonly used in industry due to their excellent mechanical properties, making research on the corrosion resistance of copper of great significance. The corrosion inhibition properties of 2-imidazolidinone and allantoin for copper in 3.5 wt % NaCl were studied by weight loss and electrochemical tests. Changes in the density of the copper corrosion current and the impedance module indicated that 2-imidazolidinone and allantoin exhibited cathodic corrosion inhibitors and a valid protective effect. Meanwhile, the weight loss tests showed that the inhibition efficiency of 2-imidazolidinone and allantoin at 3 mM reached 98.94% and 97.82%, respectively. The surface physiochemical properties were qualitatively and quantitatively studied by using SEM-EDS, XPS, white light interferometry, and contact angle analysis. The interfacial adsorption behavior revealed by QCM, synchrotron radiation micro-infrared, and adsorption isotherm analysis indicated that both imidazole derivatives formed an effective and rigid physical adsorption film and obeyed the Langmuir adsorption model on copper, while both the mass and thickness of the adsorption film formed by 2-imidazolidinone were higher than those of allantoin. This study contributed to an in-depth understanding of the interfacial adsorption behavior and corrosion inhibition ability of 2-imidazolidinone and allantoin and provided guidelines for the design and development of novel heterocycles as potential corrosion inhibitors for copper in marine environments. In particular, copper was used as a corrosion inhibitor in seawater storage and transport equipment.

Classification between Normal and Cancerous Human Urothelial Cells by Using Micro-Dimensional Electrochemical Impedance Spectroscopy Combined with Machine Learning

Although the high incidence and recurrence rates of urothelial cancer of the bladder (UCB) are heavy burdens, a noninvasive tool for effectively detecting UCB as an alternative to voided urine cytology, which has low sensitivity, is yet to be reported. Herein, we propose an intelligent discrimination method between normal (SV-HUC-1) and cancerous (TCCSUP) urothelial cells by using a combination of micro-dimensional electrochemical impedance spectroscopy (µEIS) with machine learning (ML) for a noninvasive and high-accuracy UCB diagnostic tool. We developed a unique valved flow cytometry, equipped with a pneumatic valve to increase sensitivity without cell clogging. Since contact between a cell and electrodes is tight with a high volume fraction, the electric field can be effectively confined to the cell. This enables the proposed sensor to highly discriminate different cell types at frequencies of 10, 50, 100, 500 kHz, and 1 MHz. A total of 236 impedance spectra were applied to six ML models, and systematic comparisons of the ML models were carried out. The hyperparameters were estimated by conducting a grid search or Bayesian optimization. Among the ML models, random forest strongly discriminated between SV-HUC-1 and TCCSUP, with an accuracy of 91.7%, sensitivity of 92.9%, precision of 92.9%, specificity of 90%, and F1-score of 93.8%.

Combined Electrochemical, Raman Analysis and Machine Learning Assessments of the Inhibitive Properties of an 1,3,4-Oxadiazole-2-Thiol Derivative against Carbon Steel Corrosion in HCl Solution

The inhibiting properties of 5-(4-pyridyl)-1,3,4-oxadiazole-2-thiol (PyODT) on the corrosion of carbon steel in 1.0 M HCl solution were investigated by potentiodynamic polarization, electrochemical impedance spectroscopy, Raman spectroscopy, and SEM-EDX analysis. An approach based on machine learning algorithms and Raman data was also applied to follow the carbon steel degradation in different experimental conditions. The electrochemical measurements revealed that PyODT behaves as a mixed-type corrosion inhibitor, reaching an efficiency of about 93.1% at a concentration of 5 mM, after 1 h exposure to 1.0 M HCl solution. Due to the molecular adsorption and structural organization of PyODT molecules on the C-steel surface, higher inhibitive effectiveness of about 97% was obtained at 24 h immersion. The surface analysis showed a significantly reduced degradation state of the carbon steel surface in the presence of PyODT due to the inhibitor adsorption revealed by Raman spectroscopy and the presence of N and S atoms in the EDX spectra. The combination of Raman spectroscopy and machine learning algorithms was proved to be a facile and reliable tool for an incipient identification of the corrosion sites on a metallic surface exposed to corrosive environments.