On-Demand Optimization of Colorimetric Gas Sensors Using a Knowledge-Aware Algorithm-Driven Robotic Experimental Platform.

Synthesizing the best material globally is challenging; it needs to know what and how much the best ingredient composition should be for satisfying multiple figures of merit simultaneously. Traditional one-variable-at-a-time methods are inefficient; the design-build-test-learn (DBTL) method could achieve the optimal composition from only a handful of ingredients. A vast design space needs to be explored to discover the possible global optimal composition for on-demand materials synthesis. This research developed a hypothesis-guided DBTL (H-DBTL) method combined with robots to expand the dimensions of the search space, thereby achieving a better global optimal performance. First, this study engineered the search space with knowledge-aware chemical descriptors and customized multiobjective functions to fulfill on-demand research objectives. To verify this concept, this novel method was used to optimize colorimetric ammonia sensors across a vast design space of as high as 19 variables, achieving two remarkable optimization goals within 1 week: first, a sensing array was developed for ammonia quantification of a wide dynamic range, from 0.5 to 500 ppm; second, a new state-of-the-art detection limit of 50 ppb was reached. This work demonstrates that the H-DBTL approach, combined with a robot, develops a novel paradigm for the on-demand optimization of functional materials.

A water-soluble membrane transporter for biologically relevant cations.

Synthetic ionophores are promising therapeutic targets, yet poor water solubility limits their potential for translation into the clinic. Here we report a water-soluble, supramolecular self-associating amphiphile that functions as a cation uniporter in synthetic vesicle systems, deriving mechanistic insight through planar bilayer patch clamp experiments.

Establishing the selective phospholipid membrane coordination, permeation and lysis properties for a series of 'druggable' supramolecular self-associating antimicrobial amphiphiles.

The rise of antimicrobial resistance remains one of the greatest global health threats facing humanity. Furthermore, the development of novel antibiotics has all but ground to a halt due to a collision of intersectional pressures. Herein we determine the antimicrobial efficacy for 14 structurally related supramolecular self-associating amphiphiles against clinically relevant Gram-positive methicillin resistant Staphylococcus aureus and Gram-negative Escherichia coli. We establish the ability of these agents to selectively target phospholipid membranes of differing compositions, through a combination of computational host:guest complex formation simulations, synthetic vesicle lysis, adhesion and membrane fluidity experiments, alongside our novel 1H NMR CPMG nanodisc coordination assays, to verify a potential mode of action for this class of compounds and enable the production of evermore effective next-generation antimicrobial agents. Finally, we select a 7-compound subset, showing two lead compounds to exhibit 'druggable' profiles through completion of a variety of in vivo and in vitro DMPK studies.

Visual recognition of ortho-xylene based on its host-guest crystalline self-assembly with α-cyclodextrin.

HYPOTHESIS: Distinguishing substituted aromatic isomers is a challenging task because of the great similarity of their physicochemical properties. Considering xylene isomers have drastically different geometrical shapes, we predict this would show great impact on the self-assembling behavior of various xylene isomer@cyclodextrin inclusion complex. EXPERIMENTS: Through host-guest crystalline self-assembly, among three isomers, only ortho-xylene is capable to form hydrogels with α-cyclodextrin. ROESY NMR, molecular simulations and circular dichroism spectra suggest that the ortho selectivity comes from the difference in the conformation of host-guest building block. The larger volume, and steric hinderance of the ortho isomer make it most possibly decrease their tendency to adopt more mobile orientations in cyclodextrin-based complex as meta and para isomers do, resulting in gel formation. FINDINGS: Herein, we report a novel, facile and environmentally-friendly protocol on the recognition of ortho benzene isomers using α-cyclodextrin through host-guest crystalline self-assembly. Visual recognition of ortho-xylene is achieved through amplifying the structural difference of xylene isomers at molecular scale into macroscopic scale. We believe this work unveils subtle rules to control macroscopic assemblies at the molecular level and highlights the potential of using macrocyclic compounds to improve the quality and reduce the energy bill for separation in petrochemical industry.