Abstract:

The dataset contains results of numerical simulations of the motion of model microorganisms within a 'floating biofilm', i.e., confinement with an air-liquid interface on one side and a liquid-liquid interface on the other. The microorganisms are modeled as singular force distributions characteristic of the stresses exerted by the microorganism on its surrounding fluid. The study was performed to investigate how microbial morphology affects their hydrodynamic interactions with interfaces. In addition, our study informs us how these hydrodynamic interactions bias the accumulation of microorganisms toward either the air-liquid interface or the liquid-liquid interface. This analysis is very relevant in the study of biofilm incipience on liquid-liquid interfaces. The data has been obtained as a result of numerical simulations of microbial motion within the 'floating film.' In the simulation, a given number 'N' (1,000 or 10,000) of non-interacting microorganisms (also referred to as 'swimmers') are initially 'placed' uniformly within the film and are assigned random initial orientations. The simulations are run until a statistically steady state is reached. The swimmer trajectories evolve according to the hydrodynamic interactions underpinning their motion. The data contain the time-averaged position and orientation of the 'N' micro-swimmers in the simulations. These quantities will, in general, depend on the morphology of the microorganisms, e.g., the location of their flagella with respect to the direction of motion, the relative lengths of the flagellum and the cell body, the elongation of the micro-swimmer, etc. All these dependencies are explored in the numerical simulations, and the data thus generated are shared here. This dataset supports the publication: Desai, N., & Ardekani, A. M. (2020). Biofilms at interfaces: microbial distribution in floating films. Soft Matter, 16(7), 1731–1750. doi:10.1039/c9sm02038a

Suggested Citation:

Nikhil Desai, Arezoo M. Ardekani. 2020. Dataset for "Biofilms at interfaces: microbial distribution in floating films". Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/2A954ZGZ

Publications:

Desai, N., & Ardekani, A. M. (2020). Biofilms at interfaces: microbial distribution in floating films. Soft Matter, 16(7), 1731–1750. doi:10.1039/c9sm02038a

Purpose:

The data were generated to investigate how microbial morphology affects their hydrodynamic interactions with interfaces. This informs us how hydrodynamic interactions bias the accumulation of microorganisms toward either the air-liquid interface or the liquid-liquid interface. Such an analysis is very relevant in the study of biofilm incipience on liquid-liquid interfaces.

Data Parameters and Units:

The individual data parameters are described in the 'ReadMe' files included in the dataset. All data are provided in dimensionless form, with the following normalizing schemes: 1. All lengths are made dimensionless with the height of the 'floating biofilm', H (typical values: 20 - 100 microns). 2. All velocities are made dimensionless with the swimming speed of the microorganism, V_s (typical values: 50 - 100 micron/s). 3. All times are made dimensionless with the characteristic time scale H/V_s. 4. The force dipole strength is made dimensionless with the quantity H^2*V_s. 5. The source dipole and force quadrupole strengths are made dimensionless with the quantity H^3*V_s. For more information about the data parameters and units, please refer to the associated publication titled "Biofilms at interfaces: microbial distribution in floating films", authored by Nikhil Desai and Arezoo M. Ardekani.

Methods:

Microbial motion within the 'floating film' is simulated with a given number 'N' (1,000 or 10,000) of non-interacting microorganisms (also referred to as 'swimmers'). The swimmers are initially 'placed' uniformly within the film of height 'H' and assigned random initial orientations. The simulations are run until a statistically steady state is reached. The swimmer trajectories evolve according to the hydrodynamic interactions underpinning their motion.

Individual Files: