In this study, we have conducted a systematic investigation of the chiral recognition of carboxylic anions by D-glucuronic acid/diindolylmethane receptors. We investigate the influence of the anion structure on chiral recognition in the diindolylmethane/glucuronic acid-based receptor 1 a. We found that presence of an additional hydrogen-bond donor at the α position to the carboxylic function is essential for effective chiral differentiation in these systems. Furthermore, we present a synthetic procedure that allows for the synthesis of sugar-decorated receptors that possess a modified substituent at the anomeric position. Four new receptors 1 b–e have been synthesized, and their chiral-discrimination ability toward model carboxylates is studied. The obtained results show that the chiral recognition of these receptors can be fine-tuned by incorporation of a proper substituent into the receptor structure.

We have designed anion receptor 4 based on a conformationally labile bispyrrolylbenzene framework, the conformation of which can be changed by appropriate anionic stimuli. In the absence of fluoride anion, the pyrrole moieties rotate freely at room temperature. However, when the concentration of fluoride anion exceeds 2 equivalents, the rotation of the pyrrole units slows down and the conformation of the receptor changes to anti-anti. DFT calculations have shown that this change is due to binding of a third fluoride anion through CH interaction. Anion receptor 4 can also serve as a molecular logic gate. Anionic inputs such as fluoride and dihydrogenphosphate allow the realization of INHIBIT and NAND logic gate functions with absorption and fluorescence as readouts, respectively.

A novel strategy for classification of guest chirality based on the combination of artificial neural networks and anion-receptor chemistry is reported. The receptor reported herein forms supramolecular complexes with a variety of biologically important carboxylates, in which the chemical shift changes during addition of anions result in complex guest-stereochemistry-dependent patterns as followed by ¹H NMR spectroscopy. The neural network had learnt these patterns from a training set of 12 anions, and successfully identified the “unknown” chirality of 14 guests present in the test set. Additionally, principal component analysis could discriminate most of the guests studied (26) and allowed for identification of the receptor protons, which are responsible for information transfer of guest chirality.