Wastewater recovery is one of the most pressing contaminant-related subjects in the textile industry. Many cleaning and recovery techniques have been applied in recent decades, from physical separation to chemical separation. This work reviews textile wastewater recovery by focusing on natural or synthetic nanoclays in order to compare their capabilities. Presently, a wide variety of nanoclays are available that can adsorb substances dissolved in water. This review summarizes and describes nanoclay modifications for different structures (laminar, tubular, etc.) to compare adsorption performance under the best conditions. This adsorbent capacity can be used in contaminant industries to recover water that can be used and be recontaminated during a second use to close the production circle. It explores and proposes future perspectives for the nanoclay hybrid compounds generated after certain cleaning steps. This is a critical review of works that have studied adsorption or desorption procedures for different nanoclay structures. Finally, it makes a future application proposal by taking into account the summarized pros and cons of each nanoclay. This work addresses contaminant reuse, where part of the employed dyes can be reused in printing or even dyeing processes, depending on the fixing capacity of the dye in the nanoclay, which is herein discussed.

Interest in applications of natural dye applications has increased because of their antibacterial properties and the possibility of extracting them from nature and residues. Using nanoclays as hosts to reinforce natural dye properties has been successfully demonstrated. However, no one has attempted to optimize the polymer matrix and hybrid pigment properties at the same time to ensure the best final properties for bio-composite applications. Using a statistical design for experiments, we propose the best combination of modifiers with the best nanoclay as the host of three natural dyes: chlorophyll, β-carotene, and betanine. Using the L9 Taguchi designs, we learned both the influence of the nanoclay structure, and the addition moment of surfactant, mordant salt, and silane modifiers. FTIR, XRD, DTG, integration sphere spectrophotometer, and UV-aging tests were used to characterize the hybrid pigments and epoxy bioresin composites. The degradation temperatures of the three natural dyes rose and the reinforcement of the stability of three natural dyes to UV–Vis radiation exposure was demonstrated, which avoided the migration of these dyes from bioresin to wet ribbing. Optimal results were obtained with hydrotalcite clay (calcined or not) by using surfactant and mordant before the natural dye, and before or after silane.

Sustainable and green materials have been studied in dye and pigment productions to reduce their environment impacts from being produced and applied. Although natural dyes are an excellent choice to move from agrowaste, some improvements must be made before they are applied given their poor fastness. One way of improving natural dye properties is their adsorption into nanoclay structures to give hybrid pigments. This work used tubular halloysite and laminar laponite to adsorb and stabilize natural copper chlorophyll. With a statistical design of experiments, we observed interactions between synthesis factors, such as pH, ionic strength, and surfactant or silane modification. Cool hybrid pigments with high TSR (%) values and a wide color range were obtained by using dispersions with only distilled water at room temperature. Successful chlorophyll adsorption on both nanoclay surfaces took place by XRD and DTA analyses. The maximum natural dye absorption for both nanoclay types took place under acid conditions, pH 3–4, and in the presence of mordant. The TSR (%) improved by the silane pH interaction, and halloysite hybrid pigments obtained higher TSR values than the laponite ones. Finally, a wide chromatic green color range was obtained with the surfactant modification in both nanoclays, and the color fastening was also improved in the hybrid pigments application. The samples generated with 10% of hybrid pigments from both nanoclays and an Epoxy bioresin, show higher colorfastness than the sample with the natural chlorophyll, due to the nanoclays–dye interaction and protection.

The aim of this study is to inventory and study ethnobotanical knowledge of edible plants in the Valencian Community (Spain). In respect to culinary uses, 92 species of plant were reported to be edible, finding the following uses: 58 raw, 52 cooked, 16 fried, 7 dried, 21 in liquors and beverages, 25 in dessert and sweets, 11 as seasoning, 17 in pickles, and 10 to curdle milk. We prepared a database that includes genus, family, scientific, and vernacular names in Spanish and Catalan for each plant. We also created a classification of nine edible uses and plant parts used, being Asteraceae (n = 18), Brassicaceae (n = 7), Chenopodiaceae (n = 6), and Rosaceae (n = 6) the families most characterized for gastronomic purposes. The species with the most elevated cultural importance (CI) values were Foeniculum vulgare (CI = 1.389), Cynara scolymus (CI = 1.374), Papaver rhoeas (CI = 1.211), Beta vulgaris (CI = 1.167), and Juglans regia (CI = 1.155). The most used parts were the leaves (71), flowers (25), and branches (19), while the least used were roots (9) and seeds (8). Traditional knowledge of these plants helps to preserve traditional cuisine, promote the local economy and, in several species, encourage their cultivation.

Background and objectives: The adoption in clinical practice of electronic portable blood or interstitial glucose monitors has enabled the collection, storage, and sharing of massive amounts of glucose level readings. This availability of data opened the door to the application of a multitude of mathematical methods to extract clinical information not discernible with conventional visual inspection. The objective of this study is to assess the capability of Permutation Entropy (PE) to find differences between glucose records of healthy and potentially diabetic subjects. Methods: PE is a mathematical method based on the relative frequency analysis of ordinal patterns in time series that has gained a lot of attention in the last years due to its simplicity, robustness, and performance. We study in this paper the applicability of this method to glucose records of subjects at risk of diabetes in order to assess the predictability value of this metric in this context. Results: PE, along with some of its derivatives, was able to find significant differences between diabetic and non–diabetic patients from records acquired up to 3 years before the diagnosis. The quantitative results for PE were 3.5878 ± 0.3916 for the nondiabetic class, and 3.1564 ± 0.4166 for the diabetic class. With a classification accuracy higher than 70%, and by means of a Cox regression model, PE demonstrated that it is a very promising candidate as a risk stratification tool for continuous glucose monitoring. Conclusion: PE can be considered as a prospective tool for the early diagnosis of the glucoregulatory system.

Many entropy-related methods for signal classification have been proposed and exploited successfully in the last several decades. However, it is sometimes difficult to find the optimal measure and the optimal parameter configuration for a specific purpose or context. Suboptimal settings may therefore produce subpar results and not even reach the desired level of significance. In order to increase the signal classification accuracy in these suboptimal situations, this paper proposes statistical models created with uncorrelated measures that exploit the possible synergies between them. The methods employed are permutation entropy (PE), approximate entropy (ApEn), and sample entropy (SampEn). Since PE is based on subpattern ordinal differences, whereas ApEn and SampEn are based on subpattern amplitude differences, we hypothesized that a combination of PE with another method would enhance the individual performance of any of them. The dataset was composed of body temperature records, for which we did not obtain a classification accuracy above 80% with a single measure, in this study or even in previous studies. The results confirmed that the classification accuracy rose up to 90% when combining PE and ApEn with a logistic model.