Engineering and Architecture
Nanoscience, Materials and Chemical Engineering
Multiscale simulation of self-assembling compartmentalized nanosystems for drug delivery
Many of the new applications in biomedicine and nanotechnology are based on the possibility of controlling the formation of structures and dynamic processes at the mesoscale and processes at the micro- and even nanoscale. The understanding and control of the mechanisms that relate the functionality and supramolecular structure with the microscopic composition of the constituents is key to the development of new intelligent materials and processes having a strong impact in selective drug delivery, tissue repair, molecular recognition, and many others. To be able to deliver the products and processes previously portrayed requires the development of predictive tools capable of exploring the capacities of molecules in the formation of superstructures and functionality at the nanoscopic scale, and which avoids the need for slow and costly empirical research in a vast universe of compounds and different thermodynamic conditions. Molecular simulation allows this virtual exploration to be undertaken in a fast way and can serve as a guide for the indispensable experimental exploration in a final stage.
In this project, the student is asked to participate in one of our ongoing projects where we seek to understand the mechanisms that control the formation of micelles or nanoscale objects and their stability. Even though such systems are widely used in many industries, models which are able to quantitatively link the surfactant molecular structure to the final micellar properties are lacking. We propose to investigate these systems by using molecular simulation techniques, and in particular a methodology developed in our group known as the Self-Consistent Single-Chain Mean Field Theory (SCMFT). The SCMFT is based on simplifying the surrounding surfactants of a central chain by mean fields and allows us to access scales of time and space far beyond the ones available to standard techniques such as molecular dynamics. This aspect is crucial as the application of molecular dynamics to these systems has been strongly limited precisely due to their prohibitive computational requirements.
Although the details of the doctoral thesis will be decided on between the successful candidate and the thesis supervisor, the topic will be related to our current research lines. For example, one possible project could be centred on a highly promising methodology that we have recently developed for the study of the dynamics of micelles. Another possibility includes the application of our modelling techniques to the study of Membraneless Organelles of eukaryotic cells or the formation of liquid crystal phases based on highly hydrophobic lipids which can aggregate into cubosomes and hexasomes in collaboration with an experimental group in Barcelona.
Highly desirable attributes of the ideal candidate
* Demonstrated previous experience in one or more of the following topics: programming in scientific languages is a necessary condition (see below). A strong background is required in the Physics of Fluids and Statistical Mechanics.
* Hold a Master degree, or equivalent, in: Physics, Applied Mathematics, Mechanical, Chemical or Aeronautical Engineering, or Physical Chemistry.
* Language skills: The successful candidate must be fluent in English.
* Specific Software skills: the preferred scientific languages are Matlab and Fortran, followed by C, C++ or Python.
* Personality traits: the successful candidate must be capable of teamwork, show initiative and creativity, and comply with the ethical guidelines of the university.
The URV team and its collaborators have ample experience in the modelling and implementation of mesoscopic fluid simulation methods (see the references), which will help the successful candidate to complete the PhD work.
Ethics: This project doesn’t involve ethical aspects
Workplace Location: Campus Sescelades, Tarragona
37.5 hours a week
14 February 2022
|This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 945413|