RESUMEN
In many applications of polyelectrolyte/surfactant (P/S) mixtures, it is difficult to fine-tune them after mixing the components without changing the sample composition, e.g. pH or the ionic strength. Here we report on a new approach where we use photoswitchable surfactants to enable drastic changes in both the bulk and interfacial properties. Poly(diallyldimethylammonium chloride) (PDADMAC) mixtures with three alkyl-arylazopyrazole butyl sulfonates (CnAAP) with -H, -butyl and -octyl tails are applied and E/Z photoisomerization of the surfactants is used to cause substantially different hydrophobic interactions between the surfactants and PDADMAC. These remotely controlled changes affect significantly the P/S binding and allows for tuning both the bulk and interfacial properties of PDADMAC/CnAAP mixtures through light irradiation. For that, we have fixed the surfactant concentrations at values where they exhibit pronounced surface tension changes upon E/Z photoisomerization with 365 nm UV light (Z) and 520 nm green (E) light and have varied the PDADMAC concentration. The electrophoretic mobility can be largely tuned by photoisomerisation of CnAAP surfactants and P/S aggregates, which can even exhibit a charge reversal from negative to positive values or vice versa. In addition, low colloidal stability at equimolar concentrations of PDADMAC with CnAAP surfactants in the E configuration lead to the formation of large aggregates in the bulk which can be broken up by irradiation with UV light when the surfactant's alkyl chain is short enough (C0AAP). Vibrational sum-frequency generation (SFG) spectroscopy reveals changes at the interface similar to the bulk, where the charging state at air-water interfaces can be modified with light irradiation. Using SFG spectroscopy, we interrogated the O-H stretching modes of interfacial H2O and provide qualitative information on surface charging that is complemented by neutron reflectometry, from which we resolved the surface excesses of PDADMAC and CnAAP at the air-water interface, independently.
RESUMEN
Polyelectrolyte/surfactant (P/S) mixtures find many applications but are static in nature and cannot be reversibly reconfigured through the application of external stimuli. Using a new type of photoswitchable surfactants, we use light to trigger property changes in mixtures of an anionic polyelectrolyte with a cationic photoswitch such as electrophoretic mobilities, particle size, as well as their interfacial structure and their ability to stabilize aqueous foam. For that, we show that prevailing hydrophobic intermolecular interactions can be remotely controlled between poly(sodium styrene sulfonate) (PSS) and arylazopyrazole tetraethylammonium bromide (AAP-TB). Shifting the chemical potential for P/S binding with E/Z photoisomerization of the surfactants can reversibly disintegrate even large aggregates (>4 µm) and is accompanied by a substantial change in the net charging state of PSS/AAP-TB complexes, e.g., from negative to positive excess charges upon light irradiation. In addition to the drastic changes in the bulk solution, also at air-water interfaces, the interfacial stoichiometry and structure change drastically on the molecular level with E/Z photoisomerization, which can also drive the stability of aqueous foam on a macroscopic level.
RESUMEN
The estimation of parameters controlling the electrical properties of biological neurons is essential to determine their complement of ion channels and to understand the function of biological circuits. By synchronizing conductance models to time series observations of the membrane voltage, one may construct models capable of predicting neuronal dynamics. However, identifying the actual set of parameters of biological ion channels remains a formidable theoretical challenge. Here, we present a regularization method that improves convergence towards this optimal solution when data are noisy and the model is unknown. Our method relies on the existence of an offset in parameter space arising from the interplay between model nonlinearity and experimental error. By tuning this offset, we induce saddle-node bifurcations from sub-optimal to optimal solutions. This regularization method increases the probability of finding the optimal set of parameters from 67% to 94.3%. We also reduce parameter correlations by implementing adaptive sampling and stimulation protocols compatible with parameter identifiability requirements. Our results show that the optimal model parameters may be inferred from imperfect observations provided the conditions of observability and identifiability are fulfilled.
