Neutral and cationic series of new trimeric b-hydroxy amino or ammonium surfactants were synthesized via a two-step process involving the Williamson etherification and regioselective oxirane ring opening with primary and tertiary amines, which afforded good to excellent yields. The synthesized compounds were obtained in high purity by a simple purification procedure on column chromatography. The critical micelle concentration (CMC), effectiveness of surface tension reduction (c CMC ), surface excess concentration (C), and area per molecule at the interface (A) were determined and values indicate that the cationic series is characterized by good surface-active and self-aggregation properties. The antimicrobial activities are reported for the first time against representative bacteria and fungi for trimeric compounds. The antimicrobial potency was found to be dependent on the target microorganism (Gram-positive bacteria [ fungi [ Gram-negative bacteria), as well as both the neutral or ionic nature (cationic [ neutral) and alkyl chain length (tri-C 12 [ tri-C 18 [ tri-C 8 ) of the compounds. The tri-C 8 and tri-C 18 compounds were found to be almost inactive and the tri-C 12 compounds, the most potent antimicrobial surface-active agents from the synthesized series. The trimeric C 12 cationic compound was found to be comparable to benzalkonium chloride against Gram-positive bacteria and fungi, in vitro. The antimicrobial effectiveness of this new compound and the facile two-step procedure for synthesizing it with an excellent overall yield (92%) provide a cost effective trimeric gemini surfactant.

Finding optimal operating conditions fast with a scarce budget of experimental runs is a key problem to speeding up the development of innovative products and processes. Modeling for optimization is proposed as a systematic approach to bias data gathering for iterative policy improvement through experimental design using first-principles models. Designing dynamic experiments that are optimally informative in order to reduce the uncertainty about the optimal operating conditions is addressed by integrating policy iteration based on the Hamilton-Jacobi-Bellman optimality equation with global sensitivity analysis. A conceptual framework for run-to-run convergence of a model-based policy iteration algorithm is proposed. Results obtained in the fed-batch fermentation of penicillin G are presented. The well-known Bajpai and Reuss bioreactor model validated with industrial data is used to increase on a run-to-run basis the amount of penicillin obtained by input policy optimization and selective (re)estimation of relevant model parameters. A remarkable improvement in productivity can be gain using a simple policy structure after only two modeling runs despite initial modeling uncertainty.