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In the pickling process and acidizing program for oil wells, the acidic corrosion of metals is a problem that needs to be addressed. Various acid corrosion inhibitors have been studied and used to mitigate the acidic corrosion of carbon steel. In this paper, the corrosion inhibition of 2‐aminobenzothiazole (2 N‐BT), 2‐amino‐4‐hydroxybenzothiazole (2 N4O‐BT), and 2‐amino‐6‐hydroxybenzothiazole (2 N6O‐BT) on carbon steel in 0.5 mol/L HCl was investigated using electrochemical and quantum chemical methods. All three molecules exhibited corrosion inhibition effects. Compared to 2 N‐BT, 2 N4O‐BT and 2 N6O‐BT showed less sensitivity to the environmental conditions and the surface state of the metal. Among them, 0.0010 mol/L 2 N6O‐BT demonstrated the highest and most persistent corrosion inhibition, exhibiting a mixed inhibition mechanism with cathodic inhibition dominance. The adsorption of 2 N6O‐BT on the carbon steel surface was found to be non‐uniform, preferentially adsorbing at certain active sites on the surface, following Freundlich‐Langmuir thermodynamic characteristics. It showed initial strongly adsorbed points and weakly adsorbed regions. With continued adsorption of 2 N6O‐BT, the differences between strongly adsorbed points and weakly adsorbed regions decreased. The introduction of hydroxyl groups, especially in 2 N6O‐BT, extended the negative potential region and enhanced the coordination activity of the molecule, generating the orbital distribution that was advantageous to flat adsorption and the formation of feedback bonds. This provided a molecular structural basis for the excellent corrosion inhibition properties of 2 N6O‐BT. [ABSTRACT FROM AUTHOR]

Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for the detection and assessment of internal corrosion in concrete bridge decks. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper analyzes some common abnormal data from actual bridges GPR data and proposes corresponding automatic algorithms for anomaly correction to enhance assessment accuracy. The automatic algorithm focuses on two main aspects: correcting anomalies in direct coupling wave amplitudes based on data statistics and mitigating the impact of abnormal data due to incorrectly picked rebar on depth correction using density clustering. The specific process of the automatic method can be divided into four steps. First, automatic rebar picking is performed based on the preprocessed GPR data. Next, data statistics analysis is implemented on the extracted rebar data to identify and correct abnormal amplitude data. Then, the true rebar data are identified for depth correction based on density clustering. Finally, the bridge deck corrosion map is generated based on the corrected rebar reflection amplitudes and rebar positions. The feasibility of this method was verified through a case study with GPR data from two in-service bridges. The results show that this method can effectively and automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by the proposed method is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. Practical Applications: Ground penetrating radar (GPR) is a widely utilized nondestructive testing technique for concrete bridge deck corrosion detection and assessment. However, abnormal data generated during the practical application of this technology can reduce the accuracy of concrete bridge deck corrosion assessment. Aiming at this problem, this paper proposes a set of automatic data processing procedures for anomaly correction to improve the corrosion assessment accuracy. The feasibility of the proposed algorithms was validated through a case study with GPR data from two in-service bridges. The results show that these algorithms can effectively automatically identify and correct abnormal data. Moreover, the bridge deck corrosion map obtained by these algorithms is also more accurate. It can be concluded that the proposed algorithms can be used in bridge deck corrosion detection and assessment with GPR. [ABSTRACT FROM AUTHOR]

In the pursuit of conducting a systematic investigation on the bioactivity of the family Alpinia, and searching for bioactive natural products to minimize the risks of synthetic pesticides, the Alpinia blepharocalyx K. Schum, Alpinia oxyphylla Miq. and Alpinia zerumbet × A. henryi 'Red Harvest' were selected as objects. The essential oils (EOs) of the three Alpinia were obtained by hydrodistillation and analyzed for chemical composition, evaluated for bioactivity toward the adults of Tribolium castaneum, Lasioderma serricorne and Liposcelis bostrychophila. A total of thirty-six components were identified in the extracted EOs, mainly composed of monoterpene hydrocarbons (0-91.75%) and oxygenated monoterpenes (0-57.82%). GC-MS profiling displayed perillal, ѱ-limonene, 1,8-cineole and β-pinene as major contributors to EOs area percentage but quantitative variations were observed with variability in the five EOs. The EO of A. zerumbet × A. henryi 'Red Harvest' fruit (EOAZHF) possessed the strongest contact toxicity against T. castaneum and L. bostrychophila, with the LD50 value of 14.02 μg/adult and the LC50 value of 48.64 μg/cm2, respectively. While the EO of A. oxyphylla rhizome (EOAOR) exhibited the highest contact toxicity to L. serricorne (LD50 = 8.06 μg/ adult). Upon repellence, all of the extracted EOs exhibited repellent activity, but the repellency gradually decreased with the decrease of concentration, and the target insects showed different sensitivities to the EOs. The results demonstrated that the EOs of the three Alpinia have high variability in composition and have the potential for the control of stored-product insects. [ABSTRACT FROM AUTHOR]