Detection and quantification of a toxic salt substitute (LiCl) by using laser induced breakdown spectroscopy (LIBS)

Advances in regenerated cellulosic aerogel from waste cotton textile for emerging multidimensional applications

Rapid development of society and the improvement of people's living standards have stimulated people's keen interest in fashion clothing. This trend has led to the acceleration of new product innovation and the shortening of the lifespan for cotton fabrics, which has resulting in the accumulation of waste cotton textiles. Although cotton fibers can be degraded naturally, direct disposal not only causes a serious resource waste, but also brings serious environmental problems. Hence, it is significant to explore a cleaner and greener waste textile treatment method in the context of green and sustainable development. To realize the high-value utilization of cellulose II aerogel derived from waste cotton products, great efforts have been made and considerable progress has been achieved in the past few decades. However, few reviews systematically summarize the research progress and future challenges of preparing high-value-added regenerated cellulose aerogels via dissolving cotton and other cellulose wastes. Therefore, this article reviews the regenerated cellulose aerogels obtained through solvent methods, summarizes their structure, preparation strategies and application, aimed to promote the development of the waste textile industry and contributed to the realization of carbon neutrality.

Development of Strategies to Manufacture Low-Salt Meat Products – A review

Urbanization is usually followed by changes in eating habits, with a specific trend toward the consumption of ready-to-eat products, such as processed foods. Among the latter, meat products are known contributors to high dietary sodium owing to salt addition. Salt plays an essential role in maintaining the quality of meat products in terms of acceptability and safety. However, an excessive salt intake is linked to high blood pressure and cardiovascular diseases. Hence, several studies have been competing for the discovery of salt alternatives performing in a similar way as common salt. A number of replacements have been proposed to reduce salt consumption in meat products while taking into account consumer preferences. Unfortunately, these have resulted in poorer product quality, followed by new adverse effects on health. This review addresses these recent issues by illustrating some established approaches and providing insight into further challenges in developing low-salt meat products.

Quantitative analysis of lithium in brine by laser-induced breakdown spectroscopy based on convolutional neural network

In this study, a simple and effective method for accurate determination of lithium in brine samples was developed by the combination of laser induced breakdown spectroscopy (LIBS) and convolutional neural network (CNN). Our results clearly demonstrate that the use of CNN could efficiently overcome the complex matrix effects, and thus allows for on-site Li quantitative determination in brine samples by LIBS. Specifically, two CNN models with different input data (M-CNN with matrix emission lines, and DP-CNN with double Li lines) were constructed based on the primary matrix features on spectrum and Boltzmann equation, respectively. It was observed that DP-CNN model could greatly improve the accuracy of Li analysis. We also compared the quantitative analysis capabilities of DP-CNN model with partial least squares regression (PLSR) and principal component analysis-support vector regression (PCA-SVR) model, and the results clearly showed DP-CNN offers the best quantification results (higher accuracy and less matrix interference). Finally, five real brine samples were successfully analyzed by the proposed DP-CNN model, confirming by the average absolute error of the prediction of 0.28 mg L^-1 and the average relative error of 3.48%. These results clearly demonstrate that input data plays an important role in the training of CNN model in LIBS analysis, and the proposed DP-CNN provides an effective approach to solve the matrix effects encountered in LIBS for Li measurement in brine samples.