Search

Highlights Nowadays, non‐timber sources are of greater interest as they avoid deforestation and the pollution which occurs with cellulose obtention. An emerging alternative is using weeds such as windmill grass (Chloris spp.) to isolate cellulose since the plant grows in large quantities near crops or landfills used for farming. The present study aims to explore windmill grass as an alternative source of cellulose for applications as reinforcement in biopolymeric matrices. Treatment was conducted using the peroxyformic pulping method, varying the formic acid (FA) and hydrogen peroxide (HP) concentration to obtain an optimum cellulose extraction condition. The isolated celluloses were analyzed for chemical composition, yield, chemical functionality, crystallinity, and morphology. The optimum pulping condition (FA = 87.5%, HP = 3%) shows the gradual removal of non‐cellulosic components by microscopy. X‐ray diffraction analysis revealed a high crystallinity index (89.63%) with a characteristic cellulose type I pattern. To evaluate the reinforced effect of Chloris spp. celluloses, film formulations were made using sweet potato starch, glycerol, and cellulose at different concentrations (2.5, 5.0, and 7.5% w/w). The films' mechanical, physicochemical, and thermal properties were measured, and results indicate that adding cellulose fibers significantly improves these properties of sweet potato starch films, revealing further applications and non‐wood windmill grass added value with potential applications in the packaging sector. Windmill grass (Chloris spp.) is presented as a non‐wood source of cellulose. The peroxyformic method is reported as a less polluting cellulose extraction process. Optimal cellulose pulp presents enhanced physicochemical properties after bleaching. Cellulose‐reinforced sweet potato starch films exhibit a stable biopolymeric matrix. The films produced show enhanced water barrier and mechanical/thermal properties. [ABSTRACT FROM AUTHOR]

We present a rotational-computational investigation of the aromatic mercaptan 2-phenylethanethiol, addressing its potential energy surface, conformational equilibrium, internal dynamics and intramolecular interactions. The experiment used broadband chirped-pulse Fourier transform microwave spectroscopy in a supersonic jet expansion, recording the rotational spectrum in the 2-8 GHz frequency region. Two different conformers were detected in the spectrum. The most intense transitions correspond to a skew (gauche-gauche) conformation, identified as the global minimum. The spectra of ten different isotopologues were assigned for this species, leading to accurate effective and substitution structures. The weaker spectrum presents small tunnelling doublings caused by the torsional motion of the thiol group, which are only compatible with an antiperiplanar skeleton and a gauche thiol. The larger stability of the global minimum is attributed to an intramolecular S-H⋅⋅⋅π weak hydrogen bond. A comparison of the intramolecular interactions in the title molecule and 2-phenylethanol, similarly stabilized by a O-H⋅⋅⋅π hydrogen bond, shows the different strength of these interactions. Density functional (B3LYP-D3, B2PLYP-D3) and ab initio (MP2) calculations were conducted for the molecule., (© 2024 The Authors. ChemPhysChem published by Wiley-VCH GmbH.)

Drinking water infrastructures are systems of pipes which are generally networked. They play a crucial role in transporting and delivering clean water to people. The water quality analysis refers to the evaluation of the advective diffusion of any substance in drinking water infrastructures from source nodes. Such substances could be a contamination for the system or planned for the disinfection, e.g., chlorine. The water quality analysis is performed by integrating the differential equation in the pipes network domain using the kinetics of the substance decay and the Lagrangian scheme. The kinetics can be formulated using a specific reaction order depending on the substance characteristics. The basis for the integration is the pipes velocity field calculated by means of hydraulic analysis. The aim of the present work is to discover the intrinsic mechanism of the substance transport in drinking water infrastructures, i.e., their pipes network domain, using the symbolic machine learning, named Evolutionary Polynomial Regression, which provides "synthetic" models (symbolic formulas) from data. We demonstrated, using one real network and two test networks, that the concentration at each node of the network can be predicted using the travel time along the shortest path(s) between the source and each node. Additionally, the formula models provided by symbolic machine learning allowed discovering that a unique formula based on kinetic reaction model structure allows predicting the residual substance concentration at each node, given the source node concentration, surrogating with a good accuracy the integration of the differential equations., (© 2024. The Author(s).)