Self-assembly of like-charged nanoparticles into microscopic crystals

Like-charged nanoparticles, NPs, can assemble in water into large, faceted crystals, each made of several million particles. These NPs are functionalized with mixed monolayers comprising ligands terminating in carboxylic acid group ligands as well as positively charged quaternary ammonium ligands. The latter groups give rise to electrostatic interparticle repulsions which partly offset the hydrogen bonding between the carboxylic acids. It is the balance between these two interactions that ultimately enables self-assembly. Depending on the pH, the particles can crystallize, form aggregates, remain unaggregated or even-in mixtures of two particle types-can choose whether to crystallize with like-charged or oppositely charged particles.open

Self-assembly of like-charged nanoparticles into microscopic crystals

Like-charged nanoparticles, NPs, can assemble in water into large, faceted crystals, each made of several million particles. These NPs are functionalized with mixed monolayers comprising ligands terminating in carboxylic acid group ligands as well as positively charged quaternary ammonium ligands. The latter groups give rise to electrostatic interparticle repulsions which partly offset the hydrogen bonding between the carboxylic acids. It is the balance between these two interactions that ultimately enables self-assembly. Depending on the pH, the particles can crystallize, form aggregates, remain unaggregated or even-in mixtures of two particle types-can choose whether to crystallize with like-charged or oppositely charged particles.open

AlphaFold2 can predict single-mutation effects on structure and phenotype

AlphaFold2 (AF) is a promising tool, but is it accurate enough to predict single mutation effects? Here, we report that a measure for localized structural deformation between protein pairs differing by only 1-3 mutations is correlated across 4,645 experimental and AF-predicted structures. Furthermore, analysis of $\sim$11,000 proteins shows that the local structural change correlates with various phenotypic changes. These findings suggest that AF can predict the magnitude of single-mutation effects in many proteins, and we propose a method to identify those proteins for which AF is most predictive

Saturation of front propagation in a reaction-diffusion process describing plasma damage in porous low-k materials

We propose a three-component reaction-diffusion system yielding an asymptotic logarithmic time-dependence for a moving interface. This is naturally related to a Stefan-problem for which both one-sided Dirichlet-type and von Neumann-type boundary conditions are considered. We integrate the dependence of the interface motion on diffusion and reaction parameters and we observe a change from transport behavior and interface motion \sim t^1/2 to logarithmic behavior \sim ln t as a function of time. We apply our theoretical findings to the propagation of carbon depletion in porous dielectrics exposed to a low temperature plasma. This diffusion saturation is reached after about 1 minute in typical experimental situations of plasma damage in microelectronic fabrication. We predict the general dependencies on porosity and reaction rates.Comment: Accepted for publication in Phys. Rev.

Switchable counterion gradients around charged metallic nanoparticles enable reception of radio waves

Mechanically flexible, easy-to-process, and environmentally benign materials capable of current rectification are interesting alternatives to "hard" silicon-based devices. Among these materials are metallic/charged-organic nanoparticles in which electronic currents though metal cores are modulated by the gradients of counterions surrounding the organic ligands. Although layers of oppositely charged particles can respond to both electronic and chemical signals and can function even under significant mechanical deformation, the rectification ratios of these "chemoelectronic" elements have been, so far, low. This work shows that significantly steeper counterion gradients and significantly higher rectification ratios can be achieved with nanoparticles of only one polarity but in contact with a porous electrode serving as a counterion "sink." These composite structures act as rectifiers even at radio frequencies, providing a new means of interfacing counterions' dynamics with high-frequency electronic currents

Machine Learning May Sometimes Simply Capture LiteraturePopularity Trends: A Case Study of Heterocyclic Suzuki-MiyauraCoupling br

Applications of machine learning (ML) to synthetic chemistry rely on the assumption that large numbers ofliterature-reported examples should enable construction of accurate and predictive models of chemical reactivity. This paperdemonstrates that abundance of carefully curated literature data may be insufficient for this purpose. Using an example of Suzuki-Miyaura coupling with heterocyclic building blocks & xe0d5;and a carefully selected database of >10,000 literature examples & xe0d5;we show thatML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to onlysolvents and bases. This result holds irrespective of the ML model applied (from simple feed-forward to state-of-the-art graph-convolution neural networks) or the representation to describe the reaction partners (variousfingerprints, chemical descriptors,latent representations, etc.). In all cases, the ML methods fail to perform significantly better than naive assignments based on thesheer frequency of certain reaction conditions reported in the literature. These unsatisfactory results likely reflect subjectivepreferences of various chemists to use certain protocols, other biasing factors as mundane as availability of certain solvents/reagents,and/or a lack of negative data. Thesefindings highlight the likely importance of systematically generating reliable and standardizeddata sets for algorithm training

Swarming in shallow waters

A swarm is a collection of separate objects that move autonomously in the same direction in a concerted fashion. This type of behavior is observed in ensembles of various organisms but has proven inherently difficult to realize in artificial chemical systems, where the components have to self-assemble dynamically and, at the same time, propel themselves. This paper describes a class of systems in which millimeter-sized components interact hydrodynamically and organize into dissipative structures that swarm in thin fluid layers. Depending on the geometry of the particles, various types of swarms can be engineered, including ensembles that rotate, follow a "leader", or are pushed in front of a larger particle

Computational planning of the synthesis of complex natural products

Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years(1-7). However, the field has progressed greatly since the development of early programs such as LHASA(1,7), for which reaction choices at each step were made by human operators. Multiple software platforms(6,8-14) are now capable of completely autonomous planning. But these programs 'think' only one step at a time and have so far been limited to relatively simple targets, the syntheses of which could arguably be designed by human chemists within minutes, without the help of a computer. Furthermore, no algorithm has yet been able to design plausible routes to complex natural products, for which much more far-sighted, multistep planning is necessary(15,16) and closely related literature precedents cannot be relied on. Here we demonstrate that such computational synthesis planning is possible, provided that the program's knowledge of organic chemistry and data-based artificial intelligence routines are augmented with causal relationships(17,18), allowing it to 'strategize' over multiple synthetic steps. Using a Turing-like test administered to synthesis experts, we show that the routes designed by such a program are largely indistinguishable from those designed by humans. We also successfully validated three computer-designed syntheses of natural products in the laboratory. Taken together, these results indicate that expert-level automated synthetic planning is feasible, pending continued improvements to the reaction knowledge base and further code optimization. A synthetic route-planning algorithm, augmented with causal relationships that allow it to strategize over multiple steps, can design complex natural-product syntheses that are indistinguishable from those designed by human experts