Light emitting diode structures with InGaN quantum wells have been studied as a function of hydrostatic and uniaxial pressure (along the c-axis) under different values of reverse voltage. Photocurrent measurements (with light parallel to the epitaxial layers) allow determining energies in transverse electric (TE) and transverse magnetic (TM) polarizations, which we attribute to transitions from the heavy-hole (HH) and crystal-field split (CH) band to the conduction band. The comparison of theory and experiment suggests that the electric fields are about 25% lower than the calculated values. The transitions in TE polarization agree with the theoretical predictions, while those in TM polarization do not. In particular, the splittings between HH and CH bands are lower than the theoretical predictions.

We calculate strains, polarizations, and electric fields in InGaN/GaN quantum wells (lattice matched to GaN) under the influence of hydrostatic and uniaxial (along the c-axis) pressure. We calculate the confinement energies for electrons and holes, and we derive simple expressions for the transition energies and their pressure derivatives. We include the changes of the dielectric constant with pressure. The results seem compatible with the experimental data.

The widespread problem of resistance development in bacteria has become a critical issue for modern medicine. To limit that phenomenon, many compounds have been extensively studied. Among them were derivatives of available drugs, but also alternative novel detergents such as Gemini surfactants. Over the last decade, they have been massively synthesized and studied to obtain the most effective antimicrobial agents, as well as the most selective aids for nanoparticles drug delivery. Various protocols and distinct bacterial strains used in Minimal Inhibitory Concentration experimental studies prevented performance benchmarking of different surfactant classes over these last years. Motivated by this limitation, we designed a theoretical methodology implemented in custom fast screening software to assess the surfactant activity on model lipid membranes. Experimentally based QSAR (quantitative structure-activity relationship) prediction delivered a set of parameters underlying the Diptool software engine for high-throughput agent-membrane interactions analysis. We validated our software by comparing score energy profiles with Gibbs free energy from the Adaptive Biasing Force approach on octenidine and chlorhexidine, popular antimicrobials. Results from Diptool can reflect the molecule behavior in the lipid membrane and correctly predict free energy of translocation much faster than classic molecular dynamics. This opens a new venue for searching novel classes of detergents with sharp biologic activity.