Bottom-Up Assembly of Hydrogels from Bacteriophage and Au Nanoparticles: The Effect of Cis- and Trans-Acting Factors

Hydrogels have become a promising research focus because of their potential for biomedical application. Here we explore the long-range, electrostatic interactions by following the effect of trans-acting (pH) and cis-acting factors (peptide mutation) on the formation of Au-phage hydrogels. These bioinorganic hydrogels can be generated from the bottom-up assembly of Au nanoparticles (Au NP) with either native or mutant bacteriophage (phage) through electrostatic interaction of the phage pVIII major capsid proteins (pVIII). The cis-acting factor consists of a peptide extension displayed on the pVIII that mutates the phage. Our results show that pH can dictate the direct-assembly and stability of Au-phage hydrogels in spite of the differences between the native and the mutant pVIII. The first step in characterizing the interactions of Au NP with phage was to generate a molecular model that identified the charge distribution and structure of the native and mutant pVIII. This model indicated that the mutant peptide extension carried a higher positive charge relative to the native pVIII at all pHs. Next, by monitoring the Au-phage interaction by means of optical microscopy, elastic light scattering, fractal dimension analysis as well as Uv-vis and surface plasmon resonance spectroscopy, we show that the positive charge of the mutant peptide extension favors the opposite charge affinity between the phage and Au NP as the pH is decreased. These results show the versatility of this assembly method, where the stability of these hydrogels can be achieved by either adjusting the pH or by changing the composition of the phage pVIII without the need of phage display libraries

Is a more physical representation of aerosol activation needed for simulations of fog?

Aerosols play a crucial role in the fog life cycle, as they determine the droplet number concentration and hence droplet size, which in turn controls both the fog's optical thickness and lifespan. Detailed aerosol-microphysics schemes which accurately represent droplet formation and growth are unsuitable for weather forecasting and climate models, as the computational power required to calculate droplet formation would dominate the treatment of the rest of the physics in the model. A simple method to account for droplet formation is the use of an aerosol activation scheme, which parameterises the droplet number concentration based on a change in supersaturation at a given time. Traditionally, aerosol activation parameterisation schemes were designed for convective clouds and assume that supersaturation is reached through adiabatic lifting, with many imposing a minimum vertical velocity (e.g. 0.1 m s−1) to account for the unresolved subgrid ascent. In radiation fog, the measured updraughts during initial formation are often insignificant, with radiative cooling being the dominant process leading to saturation. As a result, there is a risk that many aerosol activation schemes will overpredict the initial fog droplet number concentration, which in turn may result in the fog transitioning to an optically thick layer too rapidly. This paper presents a more physically based aerosol activation scheme that can account for a change in saturation due to non-adiabatic processes. Using an offline model, our results show that the equivalent cooling rate associated with the minimum updraught velocity threshold assumption can overpredict the droplet number by up to 70 % in comparison to a typical cooling rate found in fog formation. The new scheme has been implemented in the Met Office Natural Environment Research Council (NERC) Cloud (MONC) large eddy simulation (LES) model and tested using observations of a radiation fog case study based in Cardington, UK. The results in this work show that using a more physically based method of aerosol activation leads to the calculation of a more appropriate cloud droplet number. As a result, there is a slower transition to an optically thick (well-mixed) fog that is more in line with observations. The results shown in this paper demonstrate the importance of aerosol activation representation in fog modelling and the impact that the cloud droplet number has on processes linked to the formation and development of radiation fog. Unlike the previous parameterisation for aerosol activation, the revised scheme is suitable to simulate aerosol activation in both fog and convective cloud regimes