Current Analytical Chemistry - Online First

Copper, a precious metal in e-waste, presents a substantial economic opportunity. The study estimates that ~322000 tons of copper are discarded annually worldwide as e-waste. Given the significant financial value of copper, its recovery from e-waste is beneficial and crucial. This process also plays a pivotal role in waste management and recycling hazardous waste. The potential reduction in e-waste in landfills is a direct result of this strategic approach to waste management, offering a more sustainable and optimistic outlook for the future. This research paves the way for a future where e-waste is no longer a burden on our environment.

This study is structured around a robust two-step process. It begins with an experiment focused on copper recovery using hydrometallurgical methods. The modeling leverages the power of artificial intelligence (AI) and machine learning techniques to predict copper recovery from e-waste. This innovative approach not only promises but also has the potential to revolutionize the field of copper recovery, inspiring further innovation and progress.

The model was developed using an Artificial Neural Network (ANN) and a Boosting Algorithm (BA). Based on four crucial variables (H2SO4, H2O2, Solid/Liquid ratio, and Reaction Time), this model provides a comprehensive understanding of Cu recovery. H2SO4 is a crucial component during the leaching process; H2O2 facilitates Cu oxidation, the Solid/Liquid ratio affects the efficiency, and Reaction Time determines the completion of the process. The ANN and BA-based models yield satisfactory results in Cu recovery, achieving over 94% yield under optimized conditions.

The model developed in this study can potentially revolutionize copper recovery. By automating the process, we can significantly reduce the stress of copper mining, which relieves the environment. We can also promote a circular economy, offering a promising future for sustainable copper recovery. This could be a game-changer in the field of waste management and recycling.

The determination of flavonoid content in propolis is very important because these substances are assigned various biological properties present in propolis and their content is regulated by Brazilian legislation. The spectrophotometric method, based on the formation of a yellow complex between Al(III) and carbonyl and hydroxyl groups of flavonoids, is the most used to determine flavonoid content but is time-consuming (only after one hour the absorbance of the solutions can be read) and reagents.

This work proposes a simple method to determine flavonoid content using UV-Vis and Partial Least Squares (PLS) regression.

A robust PLS spectrophotometric method for the quantification of flavonoids in propolis, based on spectra of ethanol-diluted samples, was developed and a complete validation was done in this model, estimating several figures of merit.

The model built proved to be very effective, showing good results for flavonoid content, with a range of 0.06 to 1.50%mass, providing root mean square error of prediction (RMSEP) of 0.05%mass.

This proposed model has the advantage of being less laborious and faster, involves a small amount of solvents, is an alternative to routine analysis, and can be used as a screening method.

Venetoclax is a selective inhibitor of the prosurvival protein BCL-2 approved by the Food Drug Administration in 2016, restoring the apoptic ability of malignant cells. In this study, a fast, highly accurate and precise HPLC method was developed for the analysis of Venetoclax in human plasma.

The optimization of the method was investigated according to Box-Bhenken Design combined with 3D surface methodology. The chromatographic separation was performed in gradient mode with an Ascentis Express C8 column (2.7 μm, 4.6 mm × 10 cm). Agomelatine was used as an internal standard to increase accuracy. The method was completely validated according to ICH guideline M10 bioanalytical method validation. Additionally, the greenness of the method was scaled with NEMI, Analytical Ecoscale, AGREE, and GAPI greenness metrics.

The method was linear in the range of 1.67-12.50 µg/mL with a calculated R² of 0.99; LOD and LOQ were 0.34 µg/mL and 1.02 µg/mL, respectively. The recovery was in the range of 102.6% to 99.08%, and with an RSD% of less than 1.00%. The analytical eco scale and AGREE score of the current method were 85 and 0.55, respectively.

The approach that was developed herein exhibits green, rapidity, high levels of accuracy and precision, cost-effectiveness, and ease of use in the context of clinical and pharmacokinetic investigations.

Due to the segregation of lightweight aggregate concrete, there is a phenomenon of uneven distribution of ceramsite (aggregate) in commercial ceramsite concrete, lightweight wallboard, which the specific performance of wallboard can reflect.

In this paper, the non-uniformity of six commercial ceramsite concrete wallboards with different curing conditions was analyzed. Six wallboards were cut into 648 samples and photographed, and ImageJ image analysis technology was combined to analyze the non-uniformity of the ceramsite concrete wallboard under different curing conditions in terms of the proportion of ceramsite particle area, density, compressive strength, and ultrasonic velocity. At the same time, the reasons for the difference in wallboard performance under different curing methods were explained from the microscopic perspective by investigating the ITZ interface.

Compared with natural curing, sealing curing can significantly optimize the performance distribution of the wallboard and effectively improve the inhomogeneity of the wallboard. The average density of the specimen is increased by about 13%, and the average compressive strength by about 20%. The maximum density of the wallboard samples is increased by 105 kg/m³, which is about 15% higher than that of the naturally cured wallboard.

This paper quantifies the magnitude of the effect of sealing curing on wallboards and provides a basis for the selection of curing methods for ceramsite concrete wallboards.