Rheological dynamics and structural characteristics of supramolecular assemblies of ß-cyclodextrin and sulfonic surfactants.

Cyclodextrins are highly functional compounds with a hydrophobic cavity capable of forming supramolecular inclusion complexes with various classes of molecules including surfactants. The resultant rich nanostructures and their dynamics are an interesting research problem in the area of soft condensed matter and related applications. Herein, we report novel dynamical supramolecular assemblies based on the complexation of ß-cyclodextrin with 3 different sulfonic surfactants, which are sodium hexadecylsulfate, sodium dodecylbenzenesulfonate, and myristyl sulfobetaine. It was observed that a ß-cyclodextrin : surfactant/2 : 1 molar ratio was ideal for inducing axial growth and imparting large viscosities in the suspensions. Such complexation processes were accompanied by intriguing nanostructural phase behaviors and rheological properties that were very sensitive to the molecular architecture of sulfonic surfactants. The presence of an amino group in the head group of the surfactant allowed for large viscosities that reached 2.4 × 104 Pa s which exhibited gel-like behavior. In contrast, smaller viscosity values with a lower consistency index were observed when a bulky aromatic ring was present instead. DIC microscopy was used to visually probe the microstructure of the systems with respect to sulfonate molecular architecture. Additionally, surface tension measurements, and FTIR and NMR spectroscopies were used to gain insights into the nature of interactions that lead to the complexation and nanostructural characteristics. Finally, mechanics correlating the supramolecular morphologies to the rheological properties were proposed.

pH- and temperature-responsive supramolecular assemblies with highly adjustable viscoelasticity: a multi-stimuli binary system.

Stimuli-responsive materials are increasingly needed for the development of smart electronic, mechanical, and biological devices and systems relying on switchable, tunable, and adaptable properties. Herein, we report a novel pH- and temperature-responsive binary supramolecular assembly involving a long-chain hydroxyamino amide (HAA) and an inorganic hydrotrope, boric acid, with highly tunable viscous and viscoelastic properties. The system under investigation demonstrates a high degree of control over its viscosity, with the capacity to achieve over four orders of magnitude of control through the concomitant manipulation of pH and temperature. In addition, the transformation from non-Maxwellian to Maxwellian fluid behavior could also be induced by changing the pH and temperature. Switchable rheological properties were ascribed to the morphological transformation between spherical vesicles, aggregated/fused spherical vesicles, and bicontinuous gyroid structures revealed by cryo-TEM studies. The observed transitions are attributed to the modulation of the head group spacing between HAA molecules under different pH conditions. Specifically, acidic conditions induce electrostatic repulsion between the protonated amino head groups, leading to an increased spacing. Conversely, under basic conditions, the HAA head group spacing is reduced due to the intercalation of tetrahydroxyborate, facilitated by hydrogen bonding.

SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructures and dimensions.

Recent studies have shown that solvated amphiphiles can form nanostructured self-assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the nanostructures of DBCs are directly related to their viscoelastic properties, it is important to understand how the nanostructures change under different solution conditions. However, it is challenging to obtain a three-dimensional molecular description of these nanostructures by utilizing conventional experimental characterization techniques or thermodynamic models. To this end, we combined the structural data from small angle X-ray scattering (SAXS) experiments and thermodynamic knowledge from coarse-grained Monte Carlo (CGMC) simulations to identify the detailed three-dimensional nanostructure of DBCs. Specifically, unbiased CGMC simulations are performed with SAXS-guided initial conditions, which aids us to sample accurate nanostructures in a computationally efficient fashion. As a result, an elliptical bilayer nanostructure is obtained as the most probable nanostructure of DBCs whose dimensions are validated by scanning electron microscope (SEM) images. Then, utilizing the obtained molecular model of DBCs, we could also explain the pH tunability of the system. Overall, our results from SAXS-guided unbiased CGMC simulations highlight that using potential energy combined with SAXS data, we can distinguish otherwise degenerate nanostructures resulting from the inherent ambiguity of SAXS patterns.

The influence of heteromultivalency on lectin-glycan binding behavior.

We recently discovered that the nature of lectin multivalency and glycolipid diffusion on cell membranes could lead to the heteromultivalent binding (i.e., a single lectin simultaneously binding to different types of glycolipid ligands). This heteromultivalent binding may even govern the lectin-glycan recognition process. To investigate this, we developed a kinetic Monte Carlo simulation, which only considers the fundamental physics/chemistry principles, to model the process of lectin binding to glycans on cell surfaces. We found that the high-affinity glycan ligands could facilitate lectin binding to other low-affinity glycan ligands, even though these low-affinity ligands are barely detectable in microarrays with immobilized glycan ligands. Such heteromultivalent binding processes significantly change lectin binding behaviors. We hypothesize that living organisms probably utilize this mechanism to regulate the downstream lectin functions. Our finding not only offers a mechanism to describe the concept that lectins are pattern recognition molecules, but also suggests that the two overlooked parameters, surface diffusion of glycan ligand and lectin binding kinetics, can play important roles in glycobiology processes. In this paper, we identified the critical parameters that influence the heteromultivalent binding process. We also discussed how our discovery can impact the current lectin-glycan analysis.

Stimuli-responsive viscosity modifiers.

Stimuli responsive viscosity modifiers entail an important class of materials which allow for smart material formation utilizing various stimuli for switching such as pH, temperature, light and salinity. They have seen applications in the biomedical space including tissue engineering and drug delivery, wherein stimuli responsive hydrogels and polymeric vessels have been extensively applied. Applications have also been seen in other domains like the energy sector and automobile industry, in technologies such as enhanced oil recovery. The chemistry and microstructural arrangements of the aqueous morphologies of dissolved materials are usually sensitive to the aforementioned stimuli which subsequently results in rheological sensitivity as well. Herein, we overview different structures capable of viscosity modification as well as go over the rheological theory associated with classical systems studied in literature. A detailed analysis allows us to explore correlations between commonly discussed models such as molecular packing parameter, tube reptation and stress relaxation with structural and rheological changes. We then present five primary mechanisms corresponding to stimuli responsive viscosity modification: (i) packing parameter modification via functional group conditioning and (ii) via dynamic bond formation, (iii) mesh formation by interlinking of network nodes, (iv) viscosity modification by chain conformation changes and (v) viscosity modification by particle jamming. We also overview several recent examples from literature that employ the concepts discussed to create novel classes of intriguing stimuli responsive structures and their corresponding rheological properties. Furthermore, we also explore systems that are responsive to multiple stimuli which can provide enhanced functionality and versatility by providing multi-level and precise actuation. Such systems have been used for programmed site-specific drug delivery.

Heterointerface promoted trifunctional electrocatalysts for all temperature high-performance rechargeable Zn-air batteries.

The rational design of wide-temperature operating Zn-air batteries is crucial for their practical applications. However, the fundamental challenges remain; the limitation of the sluggish oxygen redox kinetics, insufficient active sites, and poor efficiency/cycle lifespan. Here we present heterointerface-promoted sulfur-deficient cobalt-tin-sulfur (CoS1-δ/SnS2-δ) trifunctional electrocatalysts by a facile solvothermal solution-phase approach. The CoS1-δ/SnS2-δ displays superb trifunctional activities, precisely a record-level oxygen bifunctional activity of 0.57 V (E1/2 = 0.90 V and Ej=10 = 1.47 V) and a hydrogen evolution overpotential (41 mV), outperforming those of Pt/C and RuO2. Theoretical calculations reveal the modulation of the electronic structures and d-band centers that endorse fast electron/proton transport for the hetero-interface and avoid the strong adsorption of intermediate species. The alkaline Zn-air batteries with CoS1-δ/SnS2-δ manifest record-high power density of 249 mW cm-2 and long-cycle life for >1000 cycles under harsh operations of 20 mA cm-2, surpassing those of Pt/C + RuO2 and previous state-of-the-art catalysts. Furthermore, the solid-state flexible Zn-air battery also displays remarkable performance with an energy density of 1077 Wh kg-1, >690 cycles for 50 mA cm-2, and a wide operating temperature from +80 to -40 °C with 85% capacity retention, which provides insights for practical Zn-air batteries.

Communication: phase transitions, criticality, and three-phase coexistence in constrained cell models.

In simulation studies of fluid-solid transitions, the solid phase is usually modeled as a constrained system in which each particle is confined to move in a single Wigner-Seitz cell. The constrained cell model has been used in the determination of fluid-solid coexistence via thermodynamic integration and other techniques. In the present work, the phase diagram of such a constrained system of Lennard-Jones particles is determined from constant-pressure simulations. The pressure-density isotherms exhibit inflection points which are interpreted as the mechanical stability limit of the solid phase. The phase diagram of the constrained system contains a critical and a triple point. The temperature and pressure at the critical and the triple point are both higher than those of the unconstrained system due to the reduction in the entropy caused by the single occupancy constraint.

Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification.

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecule identification. However, profiling complex samples remains a challenge because SERS peaks are likely to overlap, confounding features when multiple analytes are present in a single sample. In addition, SERS often suffers from high variability in signal enhancement due to nonuniform SERS substrate. The machine learning classification techniques widely used for facial recognition are excellent tools to overcome the complexity of SERS data interpretation. Herein, we reported a sensor for classifying coffee beverages by integrating SERS, feature extractions, and machine learning classifiers. A versatile and low-cost SERS substrate, called nanopaper, was used to enhance Raman signals of dilute compounds in coffee beverages. Two classic multivariate analysis techniques, Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC), were used to extract the significant spectral features, and the performance of various machine learning classifiers was evaluated. The combination of DAPC with Support Vector Machine (SVM) or K-Nearest Neighbor (KNN) shows the best performance for classifying coffee beverages. This user-friendly and versatile sensor has the potential to be a practical quality-control tool for the food industry.

A slip-spring framework to study relaxation dynamics of entangled wormlike micelles with kinetic Monte Carlo algorithm.

HYPOTHESIS: Wormlike micelles (WLMs) formed due to the self-assembly of amphiphiles in aqueous solution have similar viscoelastic properties as polymers. Owing to this similarity, in this work, it is postulated that kinetic Monte Carlo (kMC) sampling of slip-springs dynamics, which is able to model the rheology of polymers, can also be extended to capture the relaxation dynamics of WLMs. THEORY: The proposed modeling framework considers the following relaxation mechanisms: reptation, union-scission, and constraint release. Specifically, each of these relaxation mechanisms is simulated as separate kMC events that capture the relaxation dynamics while considering the living nature of WLMs within the slip-spring framework. As a case study, the model is implemented to a system of sodium oleate and sodium chloride to predict the linear rheology and the characteristic relaxation times associated with the individual relaxation mechanisms at different pH and salt concentrations. FINDINGS: Linear rheology predictions were found to be in good agreement with experimental data. Furthermore, the calculated relaxation times highlighted that reptation contributed to a continuous increase in viscosity while union-scission contributed to the decrease in viscosity of WLM solutions at a higher salinity and pH. This manifests the proposed model's capability to provide insights into the key processes governing WLM's rheology.

Hetero-Multivalency of Pseudomonas aeruginosa Lectin LecA Binding to Model Membranes.

A single glycan-lectin interaction is often weak and semi-specific. Multiple binding domains in a single lectin can bind with multiple glycan molecules simultaneously, making it difficult for the classic "lock-and-key" model to explain these interactions. We demonstrated that hetero-multivalency, a homo-oligomeric protein simultaneously binding to at least two types of ligands, influences LecA (a Pseudomonas aeruginosa adhesin)-glycolipid recognition. We also observed enhanced binding between P. aeruginosa and mixed glycolipid liposomes. Interestingly, strong ligands could activate weaker binding ligands leading to higher LecA binding capacity. This hetero-multivalency is probably mediated via a simple mechanism, Reduction of Dimensionality (RD). To understand the influence of RD, we also modeled LecA's two-step binding process with membranes using a kinetic Monte Carlo simulation. The simulation identified the frequency of low-affinity ligand encounters with bound LecA and the bound LecA's retention of the low-affinity ligand as essential parameters for triggering hetero-multivalent binding, agreeing with experimental observations. The hetero-multivalency can alter lectin binding properties, including avidities, capacities, and kinetics, and therefore, it likely occurs in various multivalent binding systems. Using hetero-multivalency concept, we also offered a new strategy to design high-affinity drug carriers for targeted drug delivery.