The synthesis of inorganic nanoparticles for diverse applications is an active research area that involves physical and chemical methods, which typically are expensive, involve hazardous chemical reagents, use complex equipment and synthesis conditions, and consume large amounts of time and energy. Thus, green synthesis methods have emerged as eco-friendly and easy alternatives for inorganic nanoparticle synthesis, particularly the use of plant-based extracts from fruit juice, leaves, seeds, peel, stem, barks, and roots, which act as reducing, capping, and stabilizing agents, contributing to the Sustainable Development Goals and circular economy principles. Therefore, diverse inorganic nanoparticles have been synthesized using plant-based extracts, including gold, silver, titanium dioxide, zinc, copper, platinum, zirconium, iron, selenium, magnesium, nickel, sulfur, cobalt, palladium, and indium nanoparticles, which exhibit different biological activities such as antioxidant, antimicrobial, dye degradation, cytotoxic, analgesic, sedative, wound-healing, skin protection, sensor development, and plant-growth-promoting effects. Therefore, this review summarizes the advantages and limitations of plant-based extracts as reducing, capping, and stabilizing agents for inorganic nanoparticle green synthesis.

Plant-based materials are an important source of bioactive compounds (BC) with interesting industrial applications. Therefore, adequate experimental strategies for maximizing their recovery yield are required. Among all procedures for extracting BC (maceration, Soxhlet, hydro-distillation, pulsed-electric field, enzyme, microwave, high hydrostatic pressure, and supercritical fluids), the ultrasound-assisted extraction (UAE) highlighted as an advanced, cost-efficient, eco-friendly, and sustainable alternative for recovering BC (polyphenols, flavonoids, anthocyanins, and carotenoids) from plant sources with higher yields. However, the UAE efficiency is influenced by several factors, including operational variables and extraction process (frequency, amplitude, ultrasonic power, pulse cycle, type of solvent, extraction time, solvent-to-solid ratio, pH, particle size, and temperature) that exert an impact on the molecular structures of targeted molecules, leading to variations in their biological properties. In this context, a diverse design of experiments (DOEs), including full or fractional factorial, Plackett–Burman, Box-Behnken, Central composite, Taguchi, Mixture, D-optimal, and Doehlert have been investigated alone and in combination to optimize the UAE of BC from plant-based materials, using the response surface methodology and mathematical models in a simple or multi-factorial/multi-response approach. The present review summarizes the advantages and limitations of the most common DOEs investigated to optimize the UAE of bioactive compounds from plant-based materials.

The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.

Vacuum impregnation (VI) of natural extracts is often used as a pretreatment for fruit dehydration. Apple slices were subjected to VI [X_VP: vacuum pressure (−0.4 to −0.2 mbar), X_IT: impregnation time (2–10 min), and X_RT: restoration time (1–3 min)] of Hibiscus sabdariffa (HS) calyces aqueous extract and optimized using response surface methodology (RSM). Total soluble phenols (TSP) and flavonoids, antioxidant capacity, and physicochemical parameters were evaluated before and after vacuum impregnation. Also, optimized VI apple slices were heat air-dried and characterized for all the mentioned parameters. Under the experimental conditions, all vacuum-impregnated apple slices increased in TSP content, with impregnation time, restoration time, and the interaction between impregnation time and vacuum pressure being the key factors. According to RSM, the optimal VI conditions for TSP (R² = 0.99) were X_VP −0.4 bar, X_IT: 6.73 min, and X_RT 3 min. VI also improved flavonoid and antioxidant activities (DPPH and ABTS) of apple slices and promoted changes in total soluble solids, pH, titratable acidity, water activity, moisture, and color (luminosity, a*, and b*) parameters. Additionally, vacuum-impregnated apple slices (under optimized conditions) were further dehydrated, resulting in an increase in soluble phenols, flavonoids, and antioxidant activity. VI with HS extract is an effective alternative for developing dehydrated apple slices with an increase in antioxidant compounds.

El presente trabajo tuvo como propósito elaborar un chip de oca, teniendo como variables la temperatura (160-180°C) y tiempo (30-90 segundos) de fritura. Se aplicó un diseño factorial 3k, con tres niveles para cada factor teniendo como variables depediente la textura y luminosidad del chip de oca. La Oca (Oxalis Tuberosa) tuvo las siguientes características fisicoquímicas, humedad (%) de 79.14±1.21, proteínas (%) de 1.04±0.95, extracto etéreo (%) de 0.89±0.15, cenizas (%) de 1.01±0.08 y pH de 4.52±0.21. El valor óptimo de las variables aplicando la metodología de función deseada fueron temperatura de 172°C y tiempo de 45 segundos de fritura, con respuestas óptimas de textura de 2.305 mJ y 77.3815 de luminosidad. El chip de Oca (Oxalis Tuberosa) tuvo las siguientes características fisicoquímicas, humedad (%) de 6.90 ±0.37, Proteínas (%) de 5.37 ±0.26, extracto etéreo (%) de 1.73 ±0.05, cenizas (%) de 3.75 ±0.09, textura (mJ) de 2.31 ±0.01 y luminosidad 77.13 ±0.32.