In this review, today's state of the art in the rheology of gels and transition through the yield stress of yielding liquids is discussed. Gels are understood as soft viscoelastic multicomponent solids that are in the incomplete phase separation state, which, under the action of external mechanical forces, do not transit into a fluid state but rupture like any solid material. Gels can "melt" (again, like any solids) due to a change in temperature or variation in the environment. In contrast to this type of rheology, yielding liquids (sometimes not rigorously referred to as "gels", especially in relation to colloids) can exist in a solid-like (gel-like) state and become fluid above some defined stress and time conditions (yield stress). At low stresses, their behavior is quite similar to that of permanent solid gels, including the frequency-independent storage modulus. The gel-to-sol transition considered in colloid chemistry is treated as a case of yielding. However, in many cases, the yield stress cannot be assumed to be a physical parameter since the solid-to-liquid transition happens in time and is associated with thixotropic effects. In this review, special attention is paid to various time effects. It is also stressed that plasticity is not equivalent to flow since (irreversible) plastic deformations are determined by stress but do not continue over time. We also discuss some typical errors, difficulties, and wrong interpretations of experimental data in studies of yielding liquids. 

Rheology of Gels and Yielding Liquids

The fluorescence quenching of carboxyl-rich g-C3N4 nanoparticles was found to be selective to Ag+ and Ce3+ with a limit of detection as low as 30 pM for Ag+ ions. A solid-state thermal polycondensation reaction was used to produce g-C3N4 nanoparticles with distinct green fluorescence and high water solubility. Dynamic light scattering indicated an average nanoparticle size of 95 nm. The photoluminescence absorption and emission maxima were centered at 405 nm and 540 nm respectively which resulted in a large Stokes shift. Among different metal ion species, the carboxyl-rich g-C3N4 nanoparticles were selective to Ag+ and Ce3+ ions, as indicated by strong fluorescence quenching. The PL sensing of mixtures of Ag+/Ce3+ and Ce3+ followed static Stern-Volmer kinetics with the Stern-Volmer constants determined to be 74.18 ×10^3 M-1 and 27.2 ×10^3 M-1 respectively. The PL sensing of Ag+ followed modified Stern-Volmer kinetics. Sensing was also demonstrated using commercial filter paper functionalized with g-C3N4 nanoparticles, enabling practical on-site applications. .

Ultrasensitive detection of Ag+ and Ce3+ ions using highly fluorescent carboxyl-functionalized carbon nitride nanoparticles.

In defense of Huxley

In order to meet the higher requirements of energy-absorbing structures in the lightweight automobile design, the mechanical design and impact energy absorption of porous TPMS structures are studied. Eight kinds of porous TPMS structure elements, Gyroid, Diamond, I-WP, Neovius, Primitive, Fischer-Koch S, F-RD, and PMY, are designed based on Matlab, and the porous structure samples composed of eight elements are printed and molded using SLM. The deformation mechanism, mechanical response, and energy absorption characteristics of different porous TPMS structures are investigated. Gyroid and Primitive elements are selected to fill the internal structure of the energy-absorbing automobile boxes. Traditional thin-walled energy-absorbing boxes served as a control group and were subjected to low-speed impact testing. The results show that the peak load of the energy-absorbing box filled with TPMS porous structures is almost equal to the average load under a 4.4 m/s impact, and the SEA of the energy-absorbing box filled with TPMS porous structures is higher than the traditional thin-walled energy-absorbing box. The problems of excessive peak load and inconsistent load fluctuation of traditional thin-walled energy-absorbing structures are effectively solved by porous TPMS structures with the assurance that the lightweight and energy-absorbing requirements are still met. 

Compressive Characteristics and Energy Absorption Capacity of Automobile Energy-Absorbing Box with Filled Porous TPMS Structures

A multiscale mechanics model for disordered biopolymer gels containing junction zones with variable length

As a metal-free and visible-light-responsive photocatalyst, graphitic carbon nitride (g-C3N4) has emerged as a new research hotspot and has attracted broad attention in the field of solar energy conversion and thin-film transistors. Liquid-phase exfoliation (LPE) is the best-known method for the synthesis of 2D g-C3N4 nanosheets. In LPE, bulk g-C3N4 is exfoliated in a solvent via high-shear mixing or sonication in order to produce a stable suspension of individual nanosheets. Two parameters of importance in gauging the performance of a solvent in LPE are the free energy required to exfoliate a unit area of layered materials into individual sheets in the solvent (ΔGexf) and the solvation free energy per unit area of a nanosheet (ΔGsol). While approximations for the free energies exist, they are shown in our previous work to be inaccurate and incapable of capturing the experimentally observed efficacy of LPE. Molecular dynamics (MD) simulations can provide accurate free-energy calculations, but doing so for every single solvent is time- and resource-consuming. Herein, machine learning (ML) algorithms are used to predict ΔGexf and ΔGsol for g-C3N4. First, a database for ΔGexf and ΔGsol is created based on a series of MD simulations involving 49 different solvents with distinct chemical structures and properties. The data set also includes values of critical descriptors for the solvents, including density, surface tension, dielectric constant, etc. Different ML methods are compared, accompanied by descriptor selection, to develop the most accurate model for predicting ΔGexf and ΔGsol. The extra tree regressor is shown to be the best performer among the six ML methods studied. Experimental validation of the model is conducted by performing dispersibility tests in several solvents for which the free energies are predicted. Finally, the influence of the selected descriptors on the free energies is analyzed, and strategies for solvent selection in LPE are proposed.

Predicting Free Energies of Exfoliation and Solvation for Graphitic Carbon Nitrides Using Machine Learning.

A critical examination of force–extension relationship for freely jointed chain model

An integrated molecular–thermodynamic framework for analyzing nanobubbles in supersaturated liquids

Due to the unique properties of biopolymer gels, they are extensively used in the food industry and biomedical applications such as drug delivery. Different from rubber-like materials, the constituting chains in many biopolymer gels randomly interlock with neighbouring chains by means of physical rather than covalent cross-linking. Such polymer networks are characterized by two principal regions: the disordered zone containing coiled chains and the ordered zone in the form of ion-mediated aggregation of macromolecules and/or helical structures. The ordered regions serve as junction zones between the disordered chains, and the size of these zones may alter as a result of zipping/unzipping phenomena. The entire polymer network can be envisaged as a collection of coil–rod structures serving as the building blocks, where the coil and rod are representative of the disordered and ordered zones, respectively. The present work aims to provide a rigorous formulation of coil–rod structure that can predict its mechanical behaviour including zipping/unzipping characteristics. The coil–rod structure is modelled by a rod attached to a freely jointed chain, where the length of the rod and the number of segments in the freely jointed chain are adjustable input parameters. The relationship between the force and end-to-end distance of the coil–rod structure is derived based on statistical mechanics. The formulation is extended to incorporate the zipping/unzipping mechanism with the aid of Boltzmann averaging. As an example of its applications, the model is implemented into the eight-chain model to describe the macroscopic mechanical response of the network. Another example is demonstrated where the zipping/unzipping mechanism is shown to be able to capture the unwinding of a double-stranded DNA under a tensile force. The formulation presented in this work paves the way for future studies that model biopolymer gel networks with potentially complex chain interactions. 

Statistical mechanics of coil-rod structure in biopolymer gels

Yarn-electrospun PVDF-HFP/CNC smart textiles for self-powered sensor in wearable electronics

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