Supramolecular gels, which demonstrate tunable functionalities, have attracted much interest in a range of areas, including healthcare, environmental protection and energy-related technologies. Preparing these materials in a reliable manner is challenging, with an increased level of kinetic defects observed at higher self-assembly rates. Here, by combining biocatalysis and molecular self-assembly, we have shown the ability to more quickly access higher-ordered structures. By simply increasing enzyme concentration, supramolecular order expressed at molecular, nano-and micro-levels is dramatically enhanced, and, importantly, the gelator concentrations remain identical. Amphiphile molecules were prepared by attaching an aromatic moiety to a dipeptide backbone capped with a methyl ester. Their self-assembly was induced by an enzyme that hydrolysed the ester. Different enzyme concentrations altered the catalytic activity and size of the enzyme clusters, affecting their mobility. This allowed structurally diverse materials that represent local minima in the free energy landscape to be accessed based on a single gelator structure.M olecular self-assembly 1-7 can be controlled using a variety of stimuli, including chemical 8,9 and mechanical 10 triggers, as well as X-rays 11 . Although the traditional premise in selfassembly suggests that supramolecular material properties can be fully encoded into molecular building blocks, it is increasingly apparent that the chosen self-assembly pathway is central to the final structure and its material functionality. Biocatalytic control of self-assembly systems is a novel direction for laboratory-based self-assembly 12-17 , although it is omnipresent in the biological world. Indeed, enzymatically controlled self-assembly and disassembly underlies vital processes such as cell movement, intracellular transport and muscle contraction. In chemists' hands, the combination of biocatalysis and molecular self-assembly has recently emerged as a powerful new approach to make novel stimuli-responsive molecular materials [12][13][14][15][16][17] . We believe that catalytic control of self-assembly provides important new methodology beyond such triggering of material transitions. In particular, the combination of biological selectivity, localized action and operation under constant, physiological conditions provides a new methodology for bottomup nanofabrication of future soft materials and devices, allowing for unprecedented control of supramolecular order.Here, we focus on the control of supramolecular order with few defects. In principle, there are two possible approaches to defect reduction-either improving the fidelity of the self-assembly process (avoiding defects) or using fully reversible systems that operate under thermodynamic control (repairing defects). The latter approach is generally slow and only applicable to cases where the desired structure represents the global equilibrium state and where the system is fully reversible, that is, under thermodynamic control 16 . Many structure...

We have expanded the ligand knowledge base for monodentate P-donor ligands (LKB-P, Chem. Eur. J. 2006, 12, 291-302) by 287 ligands and added descriptors derived from computational results on a gold complex [AuClL]. This expansion to 348 ligands captures known ligand space for this class of monodentate two-electron donor ligands well, and we have used principal component analysis (PCA) of the descriptors to derive an improved map of ligand space. Potential applications of this map, including the visualization of ligand similarities/differences and trends in experimental data, as well as the design of ligand test sets for high-throughput screening and the identification of ligands for reaction optimization, are discussed. Descriptors of ligand properties can also be used in regression models for the interpretation and prediction of available response data, and here we explore such models for both experimental and calculated data, highlighting the advantages of large training sets that sample ligand space well. † Development of a Ligand Knowledge Base, Part 6. See refs 1-5 for Parts 1-5.

The ligand knowledge base approach has been extended to capture the properties of 108 bidentate P,P- and P,N-donor ligands. This contribution describes the design of the ligand set and a range of DFT-calculated descriptors, capturing ligand properties in a variety of chemical environments. New challenges arising from ligand conformational flexibility and donor asymmetry are discussed, and descriptors are related to other parameters, such as the ligand bite angle. A novel map of bidentate ligand space, potentially useful in catalyst design and discovery, has been derived from principal component analysis of the resulting LKB-PP descriptors. In addition, a range of multiple linear regression models have been derived for both experimental and calculated data, considering ligand bite angles in square-planar palladium complexes and ligand dissociation energies from octahedral chromium complexes, respectively. These data sets were fitted with models based on LKB descriptors to explore the transferability of descriptors to different coordination environments and to illustrate potential applications of such models in catalyst design, allowing predictions about novel or untested ligands.

Undercover agents: The biaryl coupling of an aryltrifluoroborate with an aryl bromide involves in situ hydrolysis of the boron reagent. The hydrolysis products are key components in ensuring that the reaction proceeds with high efficiency and avoids the extensive generation of undesired phenolic and homocoupling side products.

The kinetics of Pd-catalyzed Tsuji-Trost allylation employing simple phosphine ligands (L = Ar3P, etc.) are consistent with turnover-limiting nucleophilic attack of an electrophilic [L2Pd(allyl)]+ catalytic intermediate. Counter-intuitively, when L is made more electron donating, which renders [L2Pd(allyl)]+ less electrophilic (by up to an order of magnitude), higher rates of turnover are observed. In the presence of catalytic NaBAr'F, large rate differentials arise by attenuation of ion-pair return (via generation of [L2Pd(allyl)]+ [BAr'F]-) a process that also increases the asymmetric induction from 28 to 78% ee in an archetypal asymmetric allylation employing BINAP (L*) as ligand. There is substantial potential for analogous application of [M]n+([BAr'F]-)n cocatalysis in other transition metal catalyzed processes involving an ionic reactant or reagent and an ionogenic catalytic cycle.

This article outlines the benefits of using 'Design of Experiments' (DoE) optimisation during the development of new synthetic methodology. A particularly important factor in the development of new chemical reactions is the choice of solvent which can often drastically alter the efficiency and selectivity of a process. Whilst solvent optimisation is usually done in a non-systematic way based upon a chemist's intuition and previous laboratory experience, we illustrate how optimisation of the solvent for a reaction can be carried out by using a 'map of solvent space' in a DoE optimisation. A new solvent map has been developed specifically for optimisation of new chemical reactions using principle component analysis (PCA) incorporating 136 solvents with a wide range of properties. The new solvent map has been used to identify safer alternatives to toxic/hazardous solvents, and also in the optimisation of an S(N)Ar reaction.

We present here an informed estimate of the millions of parameter settings that might be required to optimise one typical transition-metal-catalysed reaction. We describe briefly how both Design of Experiments (DoE) and Principal Component Analysis (PCA) techniques may be combined to reduce the number of potential reaction settings to a practical number of experiments without losing critical information. A key feature of this approach is the ability to relate discrete or discontinuous parameters to one another. The methodology is presented so that any reaction may be assessed in a similar way. We believe this represents for the first time an informed estimate of the number of potential permutations that are possible for these types of reactions in particular, and therefore the enormity of the task in optimising them. The powerful combination of DoE and PCA applied systematically and in an experimentally directed approach is beneficial for optimising reactions, particularly challenging transition-metal-catalysed reactions. However, this approach is beneficial to all reactions, especially when dealing with discrete parameters, such as solvents for example.

We have expanded the ligand knowledge base for bidentate P,P- and P,N-donor ligands (LKB-PP, Organometallics20082713721383) by 208 ligands and introduced an additional steric descriptor (nHe8). This expanded knowledge base now captures information on 334 bidentate ligands and has been processed with principal component analysis (PCA) of the descriptors to produce a detailed map of bidentate ligand space, which better captures ligand variation and has been used for the analysis of ligand properties.