Oleg Borodin

Pumping iron: Mimicking biological out‐of‐equilibrium systems, here a chemically fueled out‐of‐equilibrium material system was demonstrated to generate macroscopic motion such as actuation and lifting objects. This was achieved by driving a lower critical solution temperature (LCST) transition of poly(N‐isopropylacrylamide) (pNIPAAm) hydrogels with heat generated by a copper‐catalyzed azide‐alkyne cycloaddition (CuAAc) reaction.

Abstract

In nature, living systems operate far from equilibrium by consuming and dissipating energy to perform vital processes. Biological systems use chemically derived energy to power out‐of‐equilibrium processes to generate complex macroscopic motion by dissipating energy at the molecular scale. In contrast, it remains a major challenge to create synthetic out‐of‐equilibrium systems that operate on the macroscopic scale. Herein we report a chemically fueled out‐of‐equilibrium system that can perform macroscopic actuation and do work by lifting objects. We achieve this by driving a lower critical solution temperature (LCST) transition of poly(N‐isopropylacrylamide) (pNIPAAm) hydrogels with heat generated by a copper‐catalyzed azide‐alkyne cycloaddition (CuAAc) reaction. Upon completion of the reaction, heat dissipates to the environment, and the system returns to equilibrium, completing one cycle of out‐of‐equilibrium behavior, which can be repeated for multiple cycles by adding new chemical fuels.

Adding complexity : The use of aromatic and nonpolar solvent mixtures for supramolecular polymerization of hydrogen‐bonded supermacrocycles results in complex paths attributed to the formation of competing extended hydrogen‐bonded motifs.

Abstract

Beyond phenomenon, self‐assembly of synthetic molecules, is now becoming an essential tool to design supramolecular materials not only in the thermodynamically stable state but also in kinetically trapped states. However, an approach to design complex self‐assembly processes comprising different types of self‐assembled states remains elusive. Herein, an example of such systems is demonstrated based on a unique supramolecular polymer mediated by supermacrocyclization of hydrogen‐bonding π‐conjugated molecules. By adding an aromatic solvent into nonpolar solutions of the monomer, spontaneous nucleation triggered by supermacrocyclization was suppressed so that isothermal supramolecular polymerization could be achieved from kinetically formed topological variants and amorphous agglomerates to afford helicoidal structures hitherto obtainable only with very slow cooling of a hot solution. By increasing the proportion of aromatic solvent further, another self‐assembly path was found, based on competing extended hydrogen‐bonded motifs affording crystalline nanowires.

Chain cappers are additives that reduce the length of supramolecular polymers. Designing an ideal chain capper for cooperative supramolecular polymers of discotics requires a fine control of the reactivity of the noncovalent bonds. Both conformational organization of the reactive units and steric hindrance of the motifs are important elements.

Abstract

The design and the characterization of supramolecular additives to control the chain length of benzene‐1,3,5‐tricarboxamide (BTA) cooperative supramolecular polymers under thermodynamic equilibrium is unraveled. These additives act as chain cappers of supramolecular polymers and feature one face as reactive as the BTA discotic to interact strongly with the polymer end, whereas the other face is nonreactive and therefore impedes further polymerization. Such a design requires fine tuning of the conformational preorganization of the amides and the steric hindrance of the motif. The chain cappers studied are monotopic derivatives of BTA, modified by partial N‐methylation of the amides or by positioning of a bulky cyclotriveratrylene cage on one face of the BTA unit. This study not only clarifies the interplay between structural variations and supramolecular interactions, but it also highlights the necessity to combine orthogonal characterization methods, spectroscopy and light scattering, to elucidate the structures and compositions of supramolecular systems.

Communications Chemistry, Published online: 01 May 2020; doi:10.1038/s42004-020-0296-z

Communications Chemistry, Published online: 30 April 2020; doi:10.1038/s42004-020-0300-7

Pick & mix: Dynamic combinatorial chemistry has become an emerging field studying complexities in synthetic systems. However, manmade systems are much less functional than living systems. In this Minireview, strategies that are helpful for exploring functions from dynamic combinatorial libraries are highlighted and future development of the functionalization is also discussed.

Abstract

Dynamic combinatorial chemistry (DCC) is a powerful approach for creating complex chemical systems, giving access to the studies of complexity and exploration of functionality in synthetic systems. However, compared with more advanced living systems, the man‐made chemical systems are still less functional, due to their limited complexity and insufficient kinetic control. Here we start by introducing strategies to enrich the complexity of dynamic combinatorial libraries (DCLs) for exploiting unexpected functions by increasing the species of building blocks and/or templates used. Then, we discuss how dynamic isomerization of photo‐switchable molecules help DCLs increase and alter the systemic complexity in‐situ. Multi‐phase DCLs will also be reviewed to thrive complexity and functionality across the interfaces. Finally, there will be a summary and outlook about remote kinetic control in DCLs that are realized by applying exogenous physical transduction signals of stress, light, heat and ultrasound.

Halogen bond and chalcogen bond ion‐pair heteroditopic receptors exhibit remarkable cooperative recognition of halide anions via sodium cation–benzo‐crown ether binding. The chalcogen bonding receptor displays the largest enhancement of halide binding strength of over two hundred‐fold. DFT calculations suggest sodium cation crown ether complexation induces a polarisation of the ChB and XB donor atoms as the source of cooperativity.

Abstract

A series of heteroditopic receptors containing halogen bond (XB) and unprecedented chalcogen bond (ChB) donors integrated into a 3,5‐bis‐triazole pyridine structure covalently linked to benzo‐15‐crown‐5 ether motifs exhibit remarkable cooperative recognition of halide anions. Multi‐nuclear ¹H, ¹³C, ¹²⁵Te and ¹⁹F NMR, ion pair binding investigations reveal sodium cation–benzo‐crown ether binding dramatically enhances the recognition of bromide and iodide halide anions, with the chalcogen bonding heteroditopic receptor notably displaying the largest enhancement of halide binding strength of over two hundred‐fold, in comparison to the halogen bonding and hydrogen bonding heteroditopic receptor analogues. DFT calculations suggest crown ether sodium cation complexation induces a polarisation of the sigma hole of ChB and XB heteroditopic receptor donors as a significant contribution to the origin of the unique cooperativity exhibited by these systems.

A chemical robot was designed, assembled and utilised for the discovery of supramolecular architectures through the exploration of a chemical space exceeding 10⁹ possible reactions. By searching for reactivity differences, a range of new 1‐benzyl‐(1,2,3‐triazol‐4‐yl)‐N‐alkyl‐(2‐pyridinemethanimine) ligands were found and four new complexes of Fe and Co were discovered, autonomously discovering the rules of self‐assembly for these systems.

Abstract

We present a chemical discovery robot for the efficient and reliable discovery of supramolecular architectures through the exploration of a huge reaction space exceeding ten billion combinations. The system was designed to search for areas of reactivity found through autonomous selection of the reagent types, amounts, and reaction conditions aiming for combinations that are reactive. The process consists of two parts where reagents are mixed together, choosing from one type of aldehyde, one amine and one azide (from a possible family of two amines, two aldehydes and four azides) with different volumes, ratios, reaction times, and temperatures, whereby the reagents are passed through a copper coil reactor. Next, either cobalt or iron is added, again from a large number of possible quantities. The reactivity was determined by evaluating differences in pH, UV‐Vis, and mass spectra before and after the search was started. The algorithm was focused on the exploration of interesting regions, as defined by the outputs from the sensors, and this led to the discovery of a range of 1‐benzyl‐(1,2,3‐triazol‐4‐yl)‐N‐alkyl‐(2‐pyridinemethanimine) ligands and new complexes: [Fe(L¹)₂](ClO₄)₂ (1 ); [Fe(L²)₂](ClO₄)₂ (2 ); [Co₂(L³)₂](ClO₄)₄ (3 ); [Fe₂(L³)₂](ClO₄)₄ (4 ), which were crystallised and their structure confirmed by single‐crystal X‐ray diffraction determination, as well as a range of new supramolecular clusters discovered in solution using high‐resolution mass spectrometry.

Dimers of pyrrole‐based anion‐responsive π‐electronic molecules that are linked by appropriate π‐conjugated spacers were synthesized. The dimers showed [2+1]‐, [1+1]‐, and [1+2]‐type anion binding in solution, whereas [2+2]‐ and [1+2]‐type complexes were formed in the solid state. The dimer with an electron‐deficient spacer unit formed a [2+1]‐type double helical structure with shuttling of the bound anion between two binding sites.

Dimers of pyrrole‐based anion‐responsive π‐electronic molecules that are linked by appropriate π‐conjugated spacers were synthesized through metal‐catalyzed coupling reactions. The dimers showed [2+1]‐, [1+1]‐, and [1+2]‐type anion binding in solution, whereas [2+2]‐ and [1+2]‐type complexes were formed in the solid state, depending on the length and electronic properties of the spacer units. The dimer with an electron‐deficient spacer unit formed a [2+1]‐type double helical structure with shuttling of the bound anion between two binding sites.

Proof‐of‐concept of application of deep learning, an artificial intelligence technique, for high‐quality, reliable, and very fast NMR spectra reconstruction from limited experimental data.

Abstract

Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental times. We present a proof‐of‐concept of the application of deep learning and neural networks for high‐quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signals, which lifts the prohibiting demand for a large volume of realistic training data usually required for a deep learning approach.

Nature Reviews Chemistry, Published online: 15 April 2020; doi:10.1038/s41570-020-0185-0

Dimers of pyrrole‐based anion‐responsive π‐electronic molecules that are linked by appropriate π‐conjugated spacers were synthesized. The dimers showed [2+1]‐, [1+1]‐, and [1+2]‐type anion binding in solution, whereas [2+2]‐ and [1+2]‐type complexes were formed in the solid state. The dimer with an electron‐deficient spacer unit formed a [2+1]‐type double helical structure with shuttling of the bound anion between two binding sites.

Dimers of pyrrole‐based anion‐responsive π‐electronic molecules that are linked by appropriate π‐conjugated spacers were synthesized through metal‐catalyzed coupling reactions. The dimers showed [2+1]‐, [1+1]‐, and [1+2]‐type anion binding in solution, whereas [2+2]‐ and [1+2]‐type complexes were formed in the solid state, depending on the length and electronic properties of the spacer units. The dimer with an electron‐deficient spacer unit formed a [2+1]‐type double helical structure with shuttling of the bound anion between two binding sites.