Robust Method to Determine Critical Micelle Concentration via Spreading Oil Drops on Surfactant Solutions

Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms

The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimization approaches are poorly suited to multivariable multimetric photocatalytic systems because they aim to optimize one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multimetric challenge by defining a metric for holistic system performance that takes multiple figures of merit into account, and employ a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic optimization accessible for human experimentalists. As a test platform, we employ a five-component system that self-assembles into photocatalytic micelles for CO2-to-CO reduction, which we experimentally optimized to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the data set with machine learning algorithms allows quantification of each parameter's effect on overall system performance. The buffer concentration is unexpectedly revealed as the dominating parameter for optimal photocatalytic activity, and is nearly four times more important than the catalyst concentration. The expanded use and standardization of this methodology to define and optimize holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit.

A Novel Surfactant with Short Hydrophobic Head and Long Hydrophilic Tail Generates Vesicles with Unique Structural Feature

Surfactant molecules typically have a long hydrophobic tail and a short hydrophilic head group. It remains unexplored if surfactants can have a short hydrophobic head group and a long hydrophilic tail. Designing such surfactants is a challenge as a lengthy hydrophilic tail would completely solubilize the molecules. In this context, herein, the Fmoc-functionalized Gly-Pro-Hyp (GPO) tripeptide repeat-based molecule (Fm-GPO) with fluorenyl moiety as a short hydrophobic head and peptide as a long hydrophilic tail is demonstrated as a reverse surfactant at physiological pH, for the first time. π-π stacking of the fluorenyl moieties and intermolecular hydrogen bonding between the peptide chains with extended polyproline-II structure promoted the self-assembly into spherical vesicles with a unique feature of a large hydrophilic area in the interior and exterior of the bilayer. The current Fm-GPO system offers a new class of surfactants with unique features that can aid in the design of drug-loaded vehicles, which can be target-specific as the peptide chain can be manipulated with different functional ultra-short peptide sequences.

A Novel Use of Allopurinol as A Quorum-Sensing Inhibitor in Pseudomonas aeruginosa

Pseudomonas aeruginosa can cause a variety of healthcare-associated infections by its arsenal of virulence factors. Virulence factor production is largely controlled by the cell-to-cell communication system termed quorum sensing (QS). Targeting QS may be a good approach to inhibit the production of virulence factors and attenuate pathogenicity without exerting selective stress on bacterial growth. This will greatly reduce the emergence of resistant mutants. In this work, we investigated the anti-virulence and anti-QS activities of the FDA-approved drug allopurinol against the P. aeruginosa PAO1 strain. Allopurinol at 200 µg/mL (1/10 MIC) significantly decreased the production of the QS-controlled Chromobacterium violaceum CV026 violet pigment violacein and other P. aeruginosa QS-controlled virulence factors phenotypically. Furthermore, allopurinol reduced the infiltration of P. aeruginosa and leucocytes and diminished the congestion in the liver and kidney tissues of infected mice. In silico study showed that allopurinol could compete with the autoinducers on binding to the receptors LasR and RhlR by hydrogen bonding. On the molecular level, qRT-PCR proved that allopurinol showed a significant downregulating effect on all tested QS-encoding genes that regulate virulence factor production. In summary, allopurinol is a promising QS inhibitor that may be useful in the future treatment of P. aeruginosa infection.

Synthesis of size-controlled and highly monodispersed silica nanoparticles using a short alkyl-chain fluorinated surfactant

Highly monodispersed silica nanoparticles (SiNPs) were synthesised using a fluorinated surfactant, HOCH2CH(CF3)CO2H, and its efficiency was compared with efficiencies of five other surfactants. The size of the SiNPs (∼50-200 nm) was controlled by controlling the surfactant amount. The short alkyl-chain fluoro surfactant was found to be more efficient at producing monodispersed SiNPs than its long alkyl-chain fluoro or non-fluorinated surfactant counterparts.

Eureka Moment: An Archimedean Alternative for the Determination of cmc of Surfactants via Weight Measurements

Critical micelle concentration (cmc) is a key parameter of generally used surfactants, and many experimental techniques like tensiometry, conductivity, spectrophotometry, fluorometry, etc. for its determination have been reported. However, these contemporary methods for cmc determination are tedious, are time-consuming, are sensitive, and require sophisticated instrumentation. Herein, we demonstrate that the cmc of the surfactants can be estimated via monitoring the variation in the apparent weight of a density bottle floating in a surfactant solution as a function of surfactant concentration. The proposed method requires the use of a simple weighing balance; a cost-affordable instrument always available in scientific laboratories. The proposed method is simple to execute and does not require any complicated data analysis procedures. As an experimental proof attached to the claim, we demonstrate the estimation of the cmcs of all types of surfactants, viz., anionic, cationic, and nonionic, through the formulated method. The results obtained in terms of cmc values of the chosen surfactants closely match those reported through the use of different standardized protocols. The formulated experimental protocol is desirable in terms of the simplicity of the protocol, accuracy, and reproducibility of the results, and cost and accessibility of the required instrument. All these attributes of the presented protocol qualify it as an appropriate substitute to the modern techniques commonly used for the cmc determination.

Responsive drop method: quantitative in situ determination of surfactant effectiveness using reconfigurable Janus emulsions

Characterization of surfactant effectiveness and thus an evaluation of their performance in a wide range of emulsion technologies requires a precise determination of key parameters including their critical micelle concentrations as well as their ability to lower the surface tension at interfaces. In this study, we describe a new approach to quantify marginal variations in interfacial tension of surfactant stabilized fluid interfaces. The method is based on a unique chemical-morphological coupling inside bi-phasic oil-in-water Janus emulsions that undergo dynamic morphological transitions in response to changes in the surfactant type, concentration, ratio, and configuration. Variations in Janus droplet morphologies are readily monitored in situ using a simple side-view imaging setup, resulting in a fast, convenient, cost-effective, time-, and sample-saving technique for the characterization of classical surfactant systems. In addition, the reported method facilitates monitoring of triggered changes in surfactant effectiveness, e.g. invoked by external triggers, and thus proves particularly useful for the in situ analysis of stimuli-responsive surfactants and emulsions.