Microchemical Journal最新文献

This paper aimed at putting forward an approach integrating the improved clustering by fast search and find of density peaks (I-CFSFDP) algorithm with Ultraviolet (UV) spectroscopy for identifying the species of NaCl, NaOH, β-phenylethylamine(PEA) and their mixtures. For solving the issue that the clustering precision of the CFSFDP algorithm relies on the density forecast of the dataset and the manually selection of the truncated distance d_c. The idea of kernel density forecast was adopted to the I-CFSFDP algorithm. The I-CFSFDP algorithm can observe the clusters of arbitrary shapes and use an adaptive method to evaluate the truncated distance d_c, thereby generating more accurate clusters and identifying the core points in the clusters effectively. The dimensions of the UV spectra was reduced with principal component analysis (PCA), and the results of PCA were invoked as the input of the I-CFSFDP algorithm. Meanwhile, the effect of PCA-I-CFSFDP was evaluated by recall, accuracy, F-Score and precision. Besides, the DBSCAN and PCA-CFSFDP algorithms were used to compare with the PCA-I-CFSFDP algorithm. All of the classification outcomes displayed that the PCA-I-CFSFDP algorithm has better performance than the DBSCAN and PCA-CFSFDP algorithms. Therefore, the PCA-I-CFSFDP algorithm integrated with UV spectroscopy is a simple, quick and credible identification approach for detecting PEA, NaCl, NaOH and the mixtures.

The research octane number (RON) has guiding significance for evaluating the quality of gasoline, while near-infrared (NIR) spectroscopy analysis technology provides an important means for the detection of RON non-destructively and rapidly. When using a near-infrared spectrometer to obtain the RON of gasoline, if the analysis model can be shared among different instruments, it will greatly reduce the cost of re-modeling or model maintenance. Aiming to achieve the sharing of a NIR spectroscopy analysis model for RON between two portable near-infrared spectrometers of the same model, two ensemble learning algorithms, random forest (RF) and extreme gradient boosting (XGBoost), were employed for investigation, as well as two other machine learning algorithms, support vector regression (SVR) and decision tree (DT). Based on the RON of 120 gasoline samples and their NIR spectroscopy collected on the two spectrometers, hybrid and pure models were established to evaluate their sharing performance among SVR, DT, RF and XGBoost. In order to further simplify the model and improve its robustness and prediction accuracy, the characteristic wavelength selection strategies, including elimination of uninformative variables (UVE), successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS), were also adopted to optimize the model. The results showed that the hybrid model based on the CARS-RF method yielded the best prediction performance, with the coefficient of determination (R²) of 0.96, 0.86, and 0.94 for the single prediction sets of instrument A, instrument B, and the hybrid prediction set of the two instruments, respectively. Therefore, the hybrid modeling method based on ensemble learning algorithms combined with an appropriate wavelength selection strategy can effectively improve the robustness and universality of the model, and achieve the sharing of gasoline RON models on two near-infrared spectrometers of the same model.

A photoelectrochemical (PEC) sensor based on a BiVO₄ semiconductor and gold nanoparticles (AuNPs) was successfully prepared for the sensitive detection of Cr(Ⅵ) ions. The calcination temperature was found to be very important for the PEC properties of BiVO₄. The BiVO₄-modified indium tin oxide electrode (ITO/BiVO₄) calcined at 300 °C generated a low cathodic photocurrent response under light irradiation. BiVO₄/AuNPs materials were coupled via electrostatic adsorption to enhance the photoelectric conversion performance of BiVO₄. The use of self-assembled monolayers (SAMs) to modify electrodes reduce the background current. The developed ITO/BiVO₄/AuNPs/L-Cys PEC sensor was used to detect of Cr(VI). The Cr(Ⅵ) ion concentrations showed a good linearity between 10 pM and 1 μM with a detection limit of 9.1 pM (S/N = 3). The prepared PEC sensor exhibited high sensitivity, a wide linear range, and good selectivity. This work provides a promising platform for the analytical detection of heavy metal ions.

Trace metals, including Rare Earth Elements (REEs), have been widely used in oceanography, acting as tracers to evaluate biogeochemical cycles, water mass transport, rock-water interaction, and external input or deposition. However, a reliable and reproducible determination is still challenging due to many factors, especially matrix effects from seawater matrices (seawater and porewater). Since trace element determination in seawater matrices is still not a routine procedure, the ongoing analytical development in this field has thus eventually attracted both analytical and marine geochemistry communities. Therefore, this paper reviews analytical methods, major challenges, calibration strategies, and future outlooks for trace elements preconcentration in seawater matrices using various commercially available chelating resins. It is known that there has been a move towards sample treatment simplification, a wide range of operating pH and sample matrices, onboard preconcentration, and simultaneous multielement (trace metals including REEs) analysis, demonstrating that there are still emerging analytical and environmental chemistry issues related to this field.

The quantitative analysis of chloride ions is crucial due to the important roles in physiological functions and environmental protection. There are so many methods for chloride ion detection that it is not easy to select the most suitable method for a specific field. This paper overviews the significant applications of chloride ions in various fields, the reasons for concentration changes and the adverse effects caused by concentration changes. In order to rapidly and accurately detect chloride concentrations in various bodily fluids, foods and environmental waters, many highly sensitive and selective fluorescent probes and portable devices based on electrochemical and colorimetric methods have been developed and successfully applied. In this paper, the most suitable detection methods and the corresponding application fields as well as important detection parameters are summarized in the tables. These tables would help professionals select efficient methods to determine chloride ions in different fields quickly. According to advantages and disadvantages of the advanced methods mentioned here, the development directions of chloride ion detection methods in the fields of physiology, food and ecology are elucidated.

Label-free surface-enhanced Raman spectroscopy (SERS) holds promise for detecting pesticide residues, yet its broader application in food safety is limited by the weak affinity between pesticides and SERS substrates. This study introduces an electrochemical surface-enhanced Raman spectroscopy (EC-SERS) sensor that utilizes potential strengthened molecular interactions and Ag@SiO₂ nanospheres as SERS substrates, significantly enhancing the detection sensitivity for acetamiprid (AAP). The dense distribution of silver nanoparticles (Ag NPs) on the SiO₂ surfaces creates numerous “hot spots,” significantly improving the SERS performance for AAP detection. A potential of −0.5 V substantially boosts the SERS signal intensity for AAP compared to without applied potential, notably achieving a 4.3-fold increase at the 631 cm⁻¹ signal peak. Under optimal conditions, the EC-SERS method achieved a limit of detection (LOD) for AAP at 0.046 μM, spanning a linear range from 0.05 μM to 0.1 mM, which is 185 times more sensitive than conventional SERS approaches. When applied to vegetable samples, the method showed recoveries between 95.56 % and 109.33 %, with results corroborated by HPLC-MS analysis. Thus, this study provides an effective and facile strategy for the detection of AAP in the food safety field.