Plant gums in Pickering emulsions: A review of sources, properties, applications, and future perspectives.

Recently, there has been an increasing research interest in the development of Pickering emulsions stabilized with naturally derived biopolymeric particles. In this regard, plant gums, obtained as plant exudates or from plant seeds, are considered promising candidates for the development of non-toxic, biocompatible, biodegradable and eco-friendly Pickering stabilizers. The main objective of this review article is to provide a detailed overview and assess the latest advances in the formulation of Pickering emulsions stabilized with plant gum-based particles. The plant gum sources, types and properties are outlined. Besides, the current methodologies used in the production of plant gum particles formed solely of plant gums, or through interactions of plant gums with proteins or other polysaccharides are highlighted and discussed. Furthermore, the work compiles and assesses the innovative applications of plant gum-based Pickering emulsions in areas such as encapsulation and delivery of drugs and active agents, along with the utilization of these Pickering emulsions in the development of active packaging films, plant-based products and low-fat food formulations. The last part of the review presents potential future research trends that are expected to motivate and direct research to areas related to other novel food applications, as well as tissue engineering and environmental applications.

A Microporous Multi-Cage Metal-Organic Framework for an Effective One-Step Separation of Branched Alkanes Feeds.

The improvement of the Total Isomerization Process (TIP) for the production of high-quality gasoline with the ultimate goal of reaching a Research Octane Number (RON) higher than 92 requires the use of specific sorbents to separate pentane and hexane isomers into classes of linear, mono- and di-branched isomers. Herein we report the design of a new multi-cage microporous Fe(III)-MOF (referred to as MIP-214, MIP stands for materials of the Institute of Porous Materials of Paris) with a flu-e topology, incorporating an asymmetric heterofunctional ditopic ligand, 4-pyrazolecarboxylic acid, that exhibits an appropriate microporous structure for a thermodynamic-controlled separation of hydrocarbon isomers. This MOF produced via a direct, scalable, and mild synthesis route was proven to encompass a unique separation of C5/C6 isomers by classes of low RON over high RON alkanes with a sorption hierarchy: (n-hexane≫n-pentane≈2-methylpentane>3-methylpentane)low RON≫(2,3-dimethylbutane≈i-pentane≈2,2-dimethylbutane)high RON following the adsorption enthalpy sequence. We reveal for the first time that a single sorbent can efficiently separate such a complex mixture of high RON di-branched hexane and mono-branched pentane isomers from their low RON counterparts, which is a major achievement reported so far.

Disclosing the potential of Cupressus leylandii A.B. Jacks & Dallim, Eucalyptus globulus Labill., Aloysia citrodora Paláu, and Melissa officinalis L. hydrosols as eco-friendly antimicrobial agents.

Antimicrobial resistance is a major global health concern, threatening the effective prevention and treatment of infections caused by microorganisms. These factors boosted the study of safe and green alternatives, with hydrosols, the by-products of essential oils extraction, emerging as promising natural antimicrobial agents. In this context, four hydrosols obtained from Cupressus leylandii A.B. Jacks & Dallim, Eucalyptus globulus Labill., Aloysia citrodora Paláu and Melissa officinalis L. were studied. Their chemical composition comprises neral, geranial, 1,8-cineole, terpinen-4-ol, and oplopanonyl acetate, compounds with recognised antimicrobial activity. Concerning antimicrobial activity, significant differences were found using different hydrosol concentrations (10-20% v/v) in comparison to a control (without hydrosol), showing the potential of the tested hydrosols to inhibit the microbial growth of Escherichia coli, Staphylococcus aureus, and Candida albicans. A. citrodora hydrosol was the most effective one, inhibiting 90% of E. coli growth and 80% of C. albicans growth, for both hydrosol concentrations (p < 0.0001). With hydrosol concentration increase, it was possible to observe an improved antimicrobial activity with significant reductions (p < 0.0001). The findings of this work indicate the viability of reusing and valuing the hydrosols, encouraging the development of green applications for different fields (e.g., food, agriculture, pharmaceuticals, and cosmetics).

Separation of the heme protein cytochrome C using a 3D structured graphene oxide bionanocomposite as an adsorbent.

Proteins are of great importance for medicine and the pharmaceutical and food industries. However, proteins need to be purified prior to their application. This work investigated the application of a hydrogel bionanocomposite based on agar and graphene oxide (GO) for capturing cytochrome C (Cyto C) heme protein by adsorption from aqueous solutions with other proteins. Although applications of GO-based materials in adsorption are widely studied, the focus on semi-continuous processes remains limited. Adsorption experiments were carried out in batch and fixed bed columns. The effect of pH and ionic strength on adsorption was investigated, and there is evidence that electrostatic interactions between Cyto C and the nanocomposite were favoured at pH = 7; the adsorption capacity decreased as NaCl and KCl concentrations increased, ascribed to the weak electrostatic interaction between the protein and GO active sites in the bionanocomposite. All adsorption isotherm models (Langmuir, Freundlich, Sips) used gave suitable adjustments to the equilibrium experimental data and the kinetic models applied. The maximum adsorption capacity predicted by the Langmuir isotherm was ∼400 mgCytoC gadsorbent,dry-1, and the adsorption thermodynamics indicated a physisorption process. Tests were performed to evaluate the co-adsorption in batch, and the composite was effective in adsorbing Cyto C in solution with bovine serum albumin (BSA) and L-phenylalanine. Fixed bed tests were performed, and although protein adsorption onto nanoparticles can be challenging, the Cyto C adsorbed could be successfully recovered after desorption. Overall, the GO-based hydrogel was an effective method for cytochrome C adsorption, exhibiting a notorious potential for applications in protein separation processes.

Agar/graphene oxide hydrogels as nano-bioadsorbents: a comparative analysis for dye removal.

Nano-biocomposite hydrogel samples were produced using graphene oxide (GO) and agar and applied as adsorbents of organic components in water. The hydrogels were prepared by varying the wt% of Agar and GO. The samples were characterized, and batch adsorption experiments evaluated the effect of initial pH, equilibrium isotherms, and kinetics for the adsorption of the anionic dye Acid Orange 7 (AO) and the cationic dyes Nile Blue A (NB) and methylene blue (MB) in an aqueous medium. Overall, both hydrogel samples exhibited satisfactory results for removing NB and MB; however, there was no effective removal for the anionic dye AO. Adsorption equilibrium isotherms were obtained, and Freundlich, Langmuir, and Sips models were fitted to the experimental equilibrium data; moreover, kinetic data were adjusted to driving force models and particle mass balance. The maximum experimental adsorption capacities, 141.48 mg·g-1 (MB) and 284.69 mg·g-1 (NB), were obtained, on a dry basis, for the sample produced with 70 wt% of agar and 30 wt% of GO. Both hydrogels exhibited remarkable regenerative potential for NB and MB, with the adsorption capacity remaining constant, even after five adsorption/desorption cycles.

Sugarcane Light-Colored Lignin: A Renewable Resource for Sustainable Beauty.

Lignin has emerged as a promising eco-friendly multifunctional ingredient for cosmetic applications, due to its ability to protect against ultraviolet radiation and its antioxidant and antimicrobial properties. However, its typical dark color and low water solubility limit its application in cosmetics. This study presents a simple process for obtaining light-colored lignin (LCLig) from sugarcane bagasse (SCB) alkaline black liquor, involving an oxidation treatment with hydrogen peroxide, followed by precipitation with sulfuric acid. The physico-chemical characterization, antioxidant and emulsifying potential of LCLig, and determination of its safety and stability in an oil-in-water emulsion were performed. A high-purity lignin (81.6%) with improved water solubility was obtained, as a result of the balance between the total aromatic phenolic units and the carboxylic acids. In addition, the antioxidant and emulsifying capacities of the obtained LCLig were demonstrated. The color reduction treatment did not compromise the safety of lignin for topical cosmetic applications. The emulsion was stable in terms of organoleptic properties (color, pH, and viscosity) and antioxidant activity over 3 months at 4, 25, and 40 °C.

A framework for predicting odor threshold values of perfumes by scientific machine learning and transfer learning.

Knowledge of odor thresholds is very important for the perfume industry. Due to the difficulty associated with measuring odor thresholds, empirical models capable of estimating these values can be an invaluable contribution to the field. This work developed a framework based on scientific machine learning strategies. A transfer learning-based strategy was devised, where information from a graph convolutional network predicting semantic odor descriptors was used as input data for the feedforward neural network responsible for estimating odor thresholds for chemical substances based on their molecular structures. The predictive performance of this model was compared to a benchmark odor threshold prediction model based on molecular structures that did not utilize transfer learning. Furthermore, the prediction was compared to a correlation previously proposed in the literature and a dummy regressor. Results demonstrated that the transfer learning-based strategy displayed a better predictive performance, suggesting this technique can be useful for predicting odor thresholds.

An Efficient Strategy for Electroreduction Reactor Outlet Fractioning into Valuable Products.

In this work, two industrial dual-step pressure swing adsorption (PSA) processes were designed and simulated to obtain high-purity methane, CO2, and syngas from a gas effluent of a CO2 electroreduction reactor using different design configurations. Among the set of zeolites that was investigated using Monte Carlo and molecular dynamics simulations, NaX and MFI were the ones selected. The dual-PSA process for case study 1 is only capable of achieving a 90.5% methane purity with a 95.2% recovery. As for case study 2, methane is obtained with a 97.5% purity and 95.3% recovery. Both case studies can produce CO2 with high purity and recovery (>97 and 95%, respectively) and syngas with a H2/CO ratio above 4. Although case study 2 allows methane to be used as domestic gas, a much higher value for its energy consumption is observed compared to case study 1 (64.9 vs 29.8 W h molCH4-1).

Metaheuristic Framework for Material Screening and Operating Optimization of Adsorption-Based Heat Pumps.

The current methods applied to material screening for adsorption-based heat pumps are based on a fixed set of temperatures or their independent variation, providing a limited, insufficient, and unpractical evaluation of different adsorbents. This work proposes a novel strategy for the simultaneous optimization and material screening in the design of adsorption heat pumps by implementing a meta-heuristic approach, particle swarm optimization (PSO). The proposed framework can effectively evaluate variable and broad operation temperature intervals to search for viable zones of operation for multiple adsorbents at once. The criteria for selecting the adequate material were the maximum performance and the minimum heat supply cost, which were considered the objective functions of the PSO algorithm. First, the performance was assessed individually, followed by a single-objective approximation of the multi-objective problem. Next, a multi-objective approach was also adopted. With the results generated during the optimization, it was possible to find which adsorbents and temperature sets were the most suitable according to the main objective of the operation. The Fisher-Snedecor test was applied to expand the results obtained during PSO application and a feasible operating region built around the optima, enabling the arrangement of close-to-optima data into practical design and control tools. This approach allowed for a fast and intuitive evaluation of multiple design and operation variables.

Carbon dioxide separation and capture by adsorption: a review.

Rising adverse impact of climate change caused by anthropogenic activities is calling for advanced methods to reduce carbon dioxide emissions. Here, we review adsorption technologies for carbon dioxide capture with focus on materials, techniques, and processes, additive manufacturing, direct air capture, machine learning, life cycle assessment, commercialization and scale-up.

Generating Flavor Molecules Using Scientific Machine Learning.

Flavor is an essential component in the development of numerous products in the market. The increasing consumption of processed and fast food and healthy packaged food has upraised the investment in new flavoring agents and consequently in molecules with flavoring properties. In this context, this work brings up a scientific machine learning (SciML) approach to address this product engineering need. SciML in computational chemistry has opened paths in the compound's property prediction without requiring synthesis. This work proposes a novel framework of deep generative models within this context to design new flavor molecules. Through the analysis and study of the molecules obtained from the generative model training, it was possible to conclude that even though the generative model designs the molecules through random sampling of actions, it can find molecules that are already used in the food industry, not necessarily as a flavoring agent, or in other industrial sectors. Hence, this corroborates the potential of the proposed methodology for the prospecting of molecules to be applied in the flavor industry.

Exploring the Potential of Vine Shoots as a Source of Valuable Extracts and Stable Lignin Nanoparticles for Multiple Applications.

Large amounts of vine shoots are generated every year during vine pruning. This residue still presents many of the compounds found in the original plant, including low molecular weight phenolic compounds and structural compounds such as cellulose, hemicellulose, and lignin. For wine-producing regions, the challenge is to develop alternatives that will increase the value of this residue. This work proposes the full valorization of vine shoots, focusing on the extraction of lignin by mild acidolysis for the preparation of nanoparticles. The effect of the pretreatment solvents (ethanol/toluene, E/T, and water/ethanol, W/E), on the chemical and structural features of lignin, was evaluated. The chemical analysis suggests similar composition and structure regardless of the pretreatment solvent, although lignin isolated after pretreatment of biomass with E/T showed a higher content of proanthocyanidins (11%) compared with W/E (5%). Lignin nanoparticles (LNPs) presented an average size ranging from 130-200 nm and showed good stability for 30 days. Lignin and LNPs showed excellent antioxidant properties (half maximal inhibitory concentration, IC50 0.016-0.031 mg/mL) when compared to commercial antioxidants. In addition, extracts resulting from biomass pretreatment showed antioxidant activity, with W/E presenting a lower IC50 (0.170 mg/mL) than E/T (0.270 mg/mL), correlated with the higher polyphenol content of W/E, with (+)-catechin and (-)-epicatechin being the main compounds detected. Overall, this work shows that the pre-treatment of vine shoots with green solvents can yield (i) the production of high-purity lignin samples with antioxidant properties and (ii) phenolic-rich extracts, promoting the integral reuse of this byproduct and contributing to sustainability.

A Reinforcement Learning Framework to Discover Natural Flavor Molecules.

Flavor is the focal point in the flavor industry, which follows social tendencies and behaviors. The research and development of new flavoring agents and molecules are essential in this field. However, the development of natural flavors plays a critical role in modern society. Considering this, the present work proposes a novel framework based on scientific machine learning to undertake an emerging problem in flavor engineering and industry. It proposes a combining system composed of generative and reinforcement learning models. Therefore, this work brings an innovative methodology to design new flavor molecules. The molecules were evaluated regarding synthetic accessibility, the number of atoms, and the likeness to a natural or pseudo-natural product. This work brings as contributions the implementation of a web scraper code to sample a flavors database and the integration of two scientific machine learning techniques in a complex system as a framework. The implementation of the complex system instead of the generative model by itself obtained 10% more molecules within the optimal results. The designed molecules obtained as an output of the reinforcement learning model's generation were assessed regarding their existence or not in the market and whether they are already used in the flavor industry or not. Thus, we corroborated the potentiality of the framework presented for the search of molecules to be used in the development of flavor-based products.

The potential of almonds, hazelnuts, and walnuts SFE-CO2 extracts as sources of bread flavouring ingredients.

Nuts have been part of the human diet since our early ancestors, and their use goes beyond nutritional purposes, for example, as aromatic sources for dairy products. This work explores the potential of almond (Prunus dulcis (Mill.) DA Webb), hazelnut (Corylus avellana L.), and walnut (Juglans regia L.) extracts as sources of food flavouring agents, suggesting a new added-value application for lower quality or excess production fruits. The extracts were obtained by supercritical fluid extraction with carbon dioxide and characterized by: quantification of the volatile fraction by HS-SPME GC-MS; sensory perception and description; and cytotoxicity against Vero cells. All extracts revealed potential as flavouring ingredients due to terpene abundance. No significant differences were observed for the minimal sensory perception, in which the odour threshold values ranged from 8.3 × 10-4 to 6.9 × 10-3 µg·mL-1 for walnuts and almonds extracts, respectively. In contrast, the cytotoxic potential differed significantly among the extracts, and P. dulcis extract presented lower cytotoxicity. Notes as woody, fresh, and green were identified in the volatile intensifiers obtained from the P. dulcis extract. Thus, almond extract was identified as the most promising ingredient to increase the sensory value of food products, namely bread. This potential was verified by an increase in the odour perception of bread after adding 4 µL of extract to each 100 g of bread dough. The quantified eucalyptol and d-limonene terpenes - found in the P. dulcis extract - have improved the release of the pleasant and natural volatile compounds from bread crust and crumb compared to the control bread chemical and sensory profiles.

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee.

Model-based optimization of simulated moving bed reactors (SMBRs) requires efficient solvers and significant computational power. Over the past years, surrogate models have been considered for such computationally demanding optimization problems. In this sense, artificial neural networks-ANNs-have found applications for modeling the simulated moving bed (SMB) unit but not yet been reported for the reactive SMB (SMBR). Despite ANNs' high accuracy, it is essential to assess its capacity to represent the optimization landscape well. However, a consistent method for optimality assessment using surrogate models is still an open issue in the literature. As such, two main contributions can be highlighted: the SMBR optimization based on deep recurrent neural networks (DRNNs) and the characterization of the feasible operation region. This is done by recycling the data points from a metaheuristic technique-optimality assessment. The results demonstrate that the DRNN-based optimization can address such complex optimization while meeting optimality.

Continuous removal of pharmaceutical drug chloroquine and Safranin-O dye from water using agar-graphene oxide hydrogel: Selective adsorption in batch and fixed-bed experiments.

In this work, Chloroquine diphosphate, and the cationic dye Safranin-O were selectively removed from water using the agar-graphene oxide (A-GO) hydrogel, produced via simple one-step jellification process. The morphology of the A-GO biocomposite was characterized and batch experiments were performed, with adsorption isotherms satisfactorily fitting (R2 > 0.98) Sips (Safranin-O) and Freundlich (Chloroquine) isotherms. Driving force models and Fick's diffusion equation were applied to the modeling of kinetic data, and a satisfactory fit was obtained. Selective adsorption carried out in batch indicated that competitive adsorption occurs when both components are mixed in water solution - the adsorptive capacities dropped ∼10 mg g-1 for each component, remaining 41 mg g-1 for safranin-O and 31 mg g-1 for chloroquine. Fixed-bed breakthrough curves obtained in an adsorption column showed adsorption capacities over 63 mg g-1 and 100 mg g-1 for chloroquine and safranin-O, respectively, also exhibiting outstanding regenerative potentials. Overall, the biocomposite produced using graphene oxide proved to be a viable and eco-friendly alternative to continuously remove both contaminants from water.

Microwave-Assisted Lignin Wet Peroxide Oxidation to C4 Dicarboxylic Acids.

Innovative methodologies, such as microwave-assisted reaction, can help to valorize lignin with higher productivity and better energy efficiency. In this work, microwave heating was tested in the wet peroxide oxidation of three lignins (Indulin AT, Lignol, and Eucalyptus globulus lignins) as a novel methodology to obtain C4 dicarboxylic acids. The effect of temperature, time, and catalyst type (TS-1 or Fe-TS1) was evaluated in the production of these acids. The TS-1 catalyst improved succinic acid yield, achieving up to 9.4 wt % for Lignol lignin. Moreover, the microwave heating specifically enhanced Lignol conversion to malic acid (34 wt %), even without catalyst, showing to be an attractive path for the future valorization of organosolv lignins. Overall, compared to conventional heating, microwave heating originated a rapid lignin conversion. Nevertheless, for prolonged times, conventional heating led to better results for some target products, e.g., malic and succinic acids.

On the Oxidative Valorization of Lignin to High-Value Chemicals: A Critical Review of Opportunities and Challenges.

The efficient valorization of lignin is crucial if we are to replace current petroleum-based feedstock and establish more sustainable and competitive lignocellulosic biorefineries. Pulp and paper mills and second-generation biorefineries produce large quantities of low-value technical lignin as a by-product, which is often combusted on-site for energy recovery. This Review focuses on the conversion of technical lignins by oxidative depolymerization employing heterogeneous catalysts. It scrutinizes the current literature describing the use of various heterogeneous catalysts in the oxidative depolymerization of lignin and includes a comparison of the methods, catalyst loadings, reaction media, and types of catalyst applied, as well as the reaction products and yields. Furthermore, current techniques for the determination of product yields and product recovery are discussed. Finally, challenges and suggestions for future approaches are outlined.

Hardwood and softwood lignins from sulfite liquors: Structural characterization and valorization through depolymerization.

This work aims to evaluate the structural characteristics and study the oxidative depolymerization of lignins obtained from hardwood and softwood sulfite liquors. Lignins were obtained after ultrafiltration and freeze-drying of the sulfite liquors and characterized based on inorganic content, nitrobenzene oxidation, 13C NMR, and molecular weight determination. The structural characteristics achieved allow evaluating the potential of each lignin through oxidative depolymerization to produce added-value phenolic monomers. Hardwood and softwood lignins were submitted to alkaline oxidation with oxygen and the reaction conditions optimized to obtain a final oxidation mixture with the maximum yield of phenolic monomers. Through oxidation with O2, hardwood lignin generates mostly syringaldehyde while lignin from softwood biomass mainly produces vanillin; moreover, a lower reaction time and the interruption of O2 admission avoid the degradation of the oxidation products in the final mixture for both lignins, more evidenced to hardwood lignin due to its higher reactivity. From the results, it is possible to conclude that a phenolic aldehyde-rich oxidation mixture could be obtained, confirming the viability of lignin as raw material to produce added-value products as vanillin and syringaldehyde.