In-Situ Formation of Modified Nickel–Zinc-Layered Double Hydroxide Followed by HPLC Determination of Neonicotinoid Insecticide Residues

A single-step preconcentration procedure using the in-situ formation of modified nickel–zinc-layered double hydroxides (LDHs) prior to high-performance liquid chromatography (HPLC) is investigated for the determination of neonicotinoid insecticide residues in honey samples. The LDHs could be prepared by the sequential addition of sodium hydroxide, sodium dodecyl sulfate, nickel nitrate 6-hydrate and zinc nitrate 6-hydrate, which were added to the sample solution. The co-precipitate phase and phase separation were obtained by centrifugation, and then the precipitate phase was dissolved in formic acid (concentrate) prior to HPLC analysis. Various analytical parameters affecting extraction efficiency were studied, and the characterization of the LDHs phase was performed using Fourier-transformed infrared spectroscopy and scanning electron microscopy. Under optimum conditions, the limit of detection of the studied neonicotinoids, in real samples, were 30 μg L−1, for all analytes, lower than the maximum residue limits established by the European Union (EU). The developed method provided high enrichment, by a factor of 35. The proposed method was utilized to determine the target insecticides in honey samples, and acceptable recoveries were obtained.

Investigation of the microstructure evolution in TP347HFG austenitic steel at 700°C and its characterization method

This article reports the microstructure evolution in TP347HFG austenitic steel during the aging process. The experiments were carried out at 700°C with different aging time from 500 to 3,650 h. The metallographic results show that the coherent twin and incoherent twin are existed in the original TP347HFG grains, while they gradually vanished with the increase of the aging time. After aging for 500 h, a lot of fine, dispersed particles precipitated from the matrix, but they disappeared after aging for 1,500 h. When the aging time extend to 3,650 h, the precipitates appeared apparently coarse in TP347HFG steel, which include the M23C6 and σ phase; besides, the micro-hardness of TP347HFG also changes during the aging, which was closely related to the effect of dispersion strengthening and solution strengthening. The results of the nonlinear ultrasonic measurement reveal that the β′ of TP347HFG steel was also changed with the aging time. It first increased at 0–500 h, then reduced later, and increased finally at 1,500–3,650 h. The variation of β′ in TP347HFG was influenced by a combined effect of the twin microstructure and the precipitate phase, which indicate that the nonlinear ultrasonic technique can be utilized to characterize the microstructure evolution in TP347HFG.

Evaluation of AA6061-T6 behavior subjected to Cyclic Solution Treatment

The effects of the repeated solution heat treatment on hardness, tensile strength and microstructure of aluminum were studied. For this purpose, an alloy of AA6061-T6 was undergo to cyclic solution heat treatment process which is composed of repeated period (10 min) held at 520 °C for 1, 4, 8 and 12 cycles. The hardness was tested for five aging times (as quenching, one week, three weeks, one month and five months) to all cycles (1, 4, 8 and 12) firstly and it is found that the hardness of five months as aging time for all cycles has the best results (90Hv) as compared with others (as quenching, one week, three weeks, and one month), so it was adopted for all cycles to implement the tensile test and the microstructure. Hardness results were improved to Vickers hardness of (90Hv) with increasing of cycles up to 8 cycles then decreasing after that to (45Hv). Tensile results were showed an increment (34%) also for the same group of 8 cycles compared with (17%) and (9%) for 4 and 12 cycles, respectively. Microstructure is revealed that whenever cycles are increased, the precipitate phase in alloy is increased also, thus, it is improved the hardness and tensile strength.

Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based Edge Detection

Atom-probe tomography (APT) facilitates nano- and atomic-scale characterization and analysis of microstructural features. Specifically, APT is well suited to study the interfacial properties of granular or heterophase systems. Traditionally, the identification of the interface between, for precipitate and matrix phases, in APT data has been obtained either by extracting iso-concentration surfaces based on a user-supplied concentration value or by manually perturbing the concentration value until the iso-concentration surface qualitatively matches the interface. These approaches are subjective, not scalable, and may lead to inconsistencies due to local composition inhomogeneities. We introduce a digital image segmentation approach based on deep neural networks that transfer learned knowledge from natural images to automatically segment the data obtained from APT into different phases. This approach not only provides an efficient way to segment the data and extract interfacial properties but does so without the need for expensive interface labeling for training the segmentation model. We consider here a system with a precipitate phase in a matrix and with three different interface modalities—layered, isolated, and interconnected—that are obtained for different relative geometries of the precipitate phase. We demonstrate the accuracy of our segmentation approach through qualitative visualization of the interfaces, as well as through quantitative comparisons with proximity histograms obtained by using more traditional approaches.