Estimation of the solubility parameters of model plant surfaces and agrochemicals: a valuable tool for understanding plant surface interactions

Background Most aerial plant parts are covered with a hydrophobic lipid-rich cuticle, which is the interface between the plant organs and the surrounding environment. Plant surfaces may have a high degree of hydrophobicity because of the combined effects of surface chemistry and roughness. The physical and chemical complexity of the plant cuticle limits the development of models that explain its internal structure and interactions with surface-applied agrochemicals. In this article we introduce a thermodynamic method for estimating the solubilities of model plant surface constituents and relating them to the effects of agrochemicals. Results Following the van Krevelen and Hoftyzer method, we calculated the solubility parameters of three model plant species and eight compounds that differ in hydrophobicity and polarity. In addition, intact tissues were examined by scanning electron microscopy and the surface free energy, polarity, solubility parameter and work of adhesion of each were calculated from contact angle measurements of three liquids with different polarities. By comparing the affinities between plant surface constituents and agrochemicals derived from (a) theoretical calculations and (b) contact angle measurements we were able to distinguish the physical effect of surface roughness from the effect of the chemical nature of the epicuticular waxes. A solubility parameter model for plant surfaces is proposed on the basis of an increasing gradient from the cuticular surface towards the underlying cell wall. Conclusions The procedure enabled us to predict the interactions among agrochemicals, plant surfaces, and cuticular and cell wall components, and promises to be a useful tool for improving our understanding of biological surface interactions

Minimal model of self-replicating nanocells: a physically embodied information-free scenario

The building of minimal self-reproducing systems with a physical embodiment (generically called protocells) is a great challenge, with implications for both theory and applied sciences. Although the classical view of a living protocell assumes that it includes information-carrying molecules as an essential ingredient, a dividing cell-like structure can be built from a metabolism-container coupled system, only. An example of such a system, modeled with dissipative particle dynamics, is presented here. This article demonstrates how a simple coupling between a precursor molecule and surfactant molecules forming micelles can experience a growth-division cycle in a predictable manner, and analyzes the influence of crucial parameters on this replication cycle. Implications of these results for origins of cellular life and living technology are outlined.Comment: 9 pages, 10 figure

Physical origin underlying the entropy loss upon hydrophobic hydration

The hydrophobic effect (HE) is commonly associated with the demixing of oil and water at ambient conditions and plays the leading role in determining the structure and stability of biomolecular assembly in aqueous solutions. On the molecular scale HE has an entropic origin. It is believed that hydrophobic particles induce order in the surrounding water by reducing the volume of con- figuration space available for hydrogen bonding. Here we show with computer simulation results that this traditional picture is not correct. Analyzing collective fluctuations in water clusters we are able to provide a fundamentally new picture of HE based on pronounced many-body correlations affecting the switching of hydrogen bonds between molecules. These correlations emerge as a non-local compensation of reduced fluctuations of local electrostatic fields in the presence of an apolar solute

Assessing the effect of dynamics on the closed-loop protein-folding hypothesis

The closed-loop (loop-n-lock) hypothesis of protein folding suggests that loops of about 25 residues, closed through interactions between the loop ends (locks), play an important role in protein structure. Coarse-grain elastic network simulations, and examination of loop lengths in a diverse set of proteins, each supports a bias towards loops of close to 25 residues in length between residues of high stability. Previous studies have established a correlation between total contact distance (TCD), a metric of sequence distances between contacting residues (cf. contact order), and the log-folding rate of a protein. In a set of 43 proteins, we identify an improved correlation ( r 2 = 0.76), when the metric is restricted to residues contacting the locks, compared to the equivalent result when all residues are considered ( r 2 = 0.65). This provides qualified support for the hypothesis, albeit with an increased emphasis upon the importance of a much larger set of residues surrounding the locks. Evidence of a similar-sized protein core/extended nucleus (with significant overlap) was obtained from TCD calculations in which residues were successively eliminated according to their hydrophobicity and connectivity, and from molecular dynamics simulations. Our results suggest that while folding is determined by a subset of residues that can be predicted by application of the closed-loop hypothesis, the original hypothesis is too simplistic; efficient protein folding is dependent on a considerably larger subset of residues than those involved in lock formation. </jats:p

Controlled exploration of chemical space by machine learning of coarse-grained representations

The size of chemical compound space is too large to be probed exhaustively. This leads high-throughput protocols to drastically subsample and results in sparse and non-uniform datasets. Rather than arbitrarily selecting compounds, we systematically explore chemical space according to the target property of interest. We first perform importance sampling by introducing a Markov chain Monte Carlo scheme across compounds. We then train an ML model on the sampled data to expand the region of chemical space probed. Our boosting procedure enhances the number of compounds by a factor 2 to 10, enabled by the ML model's coarse-grained representation, which both simplifies the structure-property relationship and reduces the size of chemical space. The ML model correctly recovers linear relationships between transfer free energies. These linear relationships correspond to features that are global to the dataset, marking the region of chemical space up to which predictions are reliable---a more robust alternative to the predictive variance. Bridging coarse-grained simulations with ML gives rise to an unprecedented database of drug-membrane insertion free energies for 1.3 million compounds.Comment: 9 pages, 5 figure

Key physicochemical characteristics governing organic micropollutant adsorption and transport in ion-exchange membranes during reverse electrodialysis

The co-generation of electricity and electrodialysis of seawater in a hybrid system is a promising approach to overcome water scarcity. Reverse electrodialysis harvests energy from the salinity gradient, where seawater is used as a high salinity stream while secondary treated wastewater can be used as a sustainable low salinity stream. Treated wastewater contains organic micropollutants, which can be transported to the seawater stream. The current research establishes a connection between adsorption and transport of organic micropollutants in ion exchange membranes, using a cross-flow stack in adsorption and zero-current experiments. To mimic the composition of treated wastewater, a mixture of nineteen organic micropollutants of varied physicochemical characteristics (e.g. size, charge, polarity, hydrogen donor/acceptor count, hydrophobicity) at environmentally relevant concentrations was used. Depending on the charge, micropollutants develop different types of mechanisms responsible for short-distance interactions with ion-exchange membranes, which has a direct influence in their transport behavior. This study provides a rational basis for the optimization/design of next-generation ion-exchange membranes, in which the permeability toward organic micropollutants should be also included. This investigation highly contributes to understanding the potential hazard posed by organic micropollutants in reverse electrodialysis in seawater desalination systems, where treated wastewater is used as a low salinity stream

Super-hydrophobic and super-wetting surfaces: analytical potential?

Roughening or texturing surfaces provides super-liquid repellent or film forming properties without alteration of the surface chemistry. These surfaces are easy to produce, can amplify wetting properties and can be either "sticky" or "slippy" to liquids. Their use as water-repellent coatings is established, but their potential for use in microfluidics and sensor applications remains largely unfulfilled. This article explains several key ideas and suggests why there may be potential for analytical applications