Aaron M. Drews

Parallel Optimization of Synthetic Pathways within the Network of Organic Chemistry

The entire chemical-synthetic knowledge created since the days of Lavoisier to the present can be represented as a complex network (Figure 1a) comprising millions of compounds and reactions. While it is simply beyond cognition of any individual human to understand and analyze all this collective chemical knowledge, modern computers have become powerful enough to perform suitable network analyses within reasonable timescales. In this context, a problem that is both fundamentally interesting and practically important is the identification of optimal synthetic pathways leading to desired, known molecules from commercially available substrates. In either manual searches or semiautomated search tools, such as Reaxys, this procedure is done by back-tracking the possible syntheses step-by-step. Such “manual” methods, however, give virtually no chance of finding an optimal pathway, as the number of possible syntheses to consider is very large (for example, ca. 10 within five steps). Moreover, the problem becomes dramatically more complex when one aims to optimize the syntheses of multiple substances simultaneously when, for example, a company producing N products would strive to design synthetic pathways sharing many common substrates/intermediates and minimizing the overall synthetic cost (Figure 1a). As we show herein, however, judicious combination of combinatorial optimization with network search algorithms allows the parallel optimization of tens to thousands of syntheses. The algorithms we describe traverse the network of organic chemistry (henceforth, NOC or simply the network) probing different synthetic paths according to the cost criterion as defined by a combination of labor cost and the cost of staring materials. In a specific case study, we show that our optimization can reduce the cost of an existing synthetic company (here, ProChimia Surfaces) by almost 50%. Overall, this communication is the first instance in which synthetic optimizations are based on the entire body of synthetic knowledge as stored in the NOC and combined with economical descriptors (that is, prices). While each of the individual reactions in the NOC is known, the network search algorithms create new chemical knowledge in the form of near optimal reaction sequences; notably, the syntheses that are optimal for making any molecule individually can be different from those optimizing the synthesis of this and other molecules simultaneously. Our analyses are based on a network of about 7 million reactions and about 7 million substances derived as described in the first communication in this series (also see Refs. [1, 2]). While in our earlier analyses of NOC, the simple dot–arrow representation was typically sufficient, the analysis of specific syntheses involving multiple substrates and/or products requires the so-called bipartite-graph representation with two types of nodes: those corresponding to specific substances (blue dots in Figure 1b), and those representing the reactions (black dots in Figure 1b). This representation of the NOC captures the causal synthetic dependencies and accounts for the fact that a viable synthesis (see the Supporting Information, Section 2) cannot proceed without all of the necessary reactants, which must either be synthesized by another suitable reaction or purchased. Also, as our network searches are intended to compare the actual costs of syntheses, we have linked the NOC to a test Figure 1. The network of organic chemistry and its bipartite wiring plan. a) Small fraction of the network (ca. 0.025%) centered on six target compounds (red). Computational methods described herein allow for the identification of near optimal synthesis plans (inset) despite the size and complexity of the network. b) Illustration of the mapping from a list of chemical reactions to a directed, bipartite network.

Electric winds driven by time oscillating corona discharges

We investigate the formation of steady gas flows—so-called electric winds—created by point-plane corona discharges driven by time oscillating (ac) electric fields. By varying the magnitude and frequency of the applied field, we identify two distinct scaling regimes: (i) a low frequency (dc) regime and (ii) a high frequency (ac) regime. These experimental observations are reproduced and explained by a theoretical model describing the transport and recombination of ions surrounding the discharge and their contribution to the measured wind velocity. The two regimes differ in the spatial distribution of ions and in the process by which ions are consumed. Interestingly, we find that ac corona discharges generate strong electric forces localized near the tip of the point electrode, while dc corona discharges generate weaker forces distributed throughout the interelectrode region. Consequently, the velocity of the electric winds (>1 m/s) generated by ac discharges is largely independent of the position of the co...

Shape control and compartmentalization in active colloidal cells

Significance Advances in simulation and synthesis of nanoparticles and colloids are leading to a new class of active colloidal systems where self-propelled and self-rotated particles convert energy to motion. Such systems hold promise for the possibility of colloidal machines––integrated systems of colloids able to carry out functions. An important step in this direction is appropriately confining colloids within cells whose shape can be controlled and within which activity can be compartmentalized. This paper uses theory and computer simulation to propose active colloidal cells and investigates their behavior. Our findings provide motivation and design rules for the fabrication of primitive colloidal machines. Small autonomous machines like biological cells or soft robots can convert energy input into control of function and form. It is desired that this behavior emerges spontaneously and can be easily switched over time. For this purpose we introduce an active matter system that is loosely inspired by biology and which we term an active colloidal cell. The active colloidal cell consists of a boundary and a fluid interior, both of which are built from identical rotating spinners whose activity creates convective flows. Similarly to biological cell motility, which is driven by cytoskeletal components spread throughout the entire volume of the cell, active colloidal cells are characterized by highly distributed energy conversion. We demonstrate that we can control the shape of the active colloidal cell and drive compartmentalization by varying the details of the boundary (hard vs. flexible) and the character of the spinners (passive vs. active). We report buckling of the boundary controlled by the pattern of boundary activity, as well as formation of core–shell and inverted Janus phase-separated configurations within the active cell interior. As the cell size is increased, the inverted Janus configuration spontaneously breaks its mirror symmetry. The result is a bubble–crescent configuration, which alternates between two degenerate states over time and exhibits collective migration of the fluid along the boundary. Our results are obtained using microscopic, non–momentum-conserving Langevin dynamics simulations and verified via a phase-field continuum model coupled to a Navier–Stokes equation.

Contact charge electrophoresis: experiment and theory.

Contact charge electrophoresis (CCEP) uses steady electric fields to drive the continuous, oscillatory motion of conductive particles and droplets between two or more electrodes. These rapid oscillations can be rectified to direct the motion of objects within microfluidic environments using low-power, dc voltage. Here, we compare high precision experimental measurements of CCEP within a microfluidic system to equally detailed theoretical predictions on the motion of a conductive particle between parallel electrodes. We use a simple, capillary microfluidic platform that combines high-speed imaging with precision electrical measurements to enable the synchronized acquisition of both the particle location and the electric current due to particle motion. The experimental results are compared to those of a theoretical model, which relies on a Stokesian dynamics approach to accurately describe both the electrostatic and hydrodynamic problems governing particle motion. We find remarkable agreement between theory and experiment, suggesting that particle motion can be accurately captured by a combination of classical electrostatics and low-Reynolds number hydrodynamics. Building on this agreement, we offer new insight into the charge transfer process that occurs when the particle nears contact with an electrode surface. In particular, we find that the particle does not make mechanical contact with the electrode but rather that charge transfer occurs at finite surface separations of >0.1 μm by means of an electric discharge through a thin lubricating film. We discuss the implications of these findings on the charging of the particle and its subsequent dynamics.

Ratcheted electrophoresis for rapid particle transport

Ratcheted electrophoresis of contact-charged particles allows for high speed transport through microfluidic channels over large distances and even against fluid flows. Using a set of predictive design heuristics, we demonstrate an extension of this microfluidic ratchet to separate conductive particles from a particle suspension.

Charge and force on a conductive sphere between two parallel electrodes: A Stokesian dynamics approach

We present an accurate and efficient method to compute the electrostatic charge and force on a conductive sphere between two parallel electrodes. The method relies on a Stokesian dynamics-like approach, in which the capacitance tensor is divided into two contributions: (1) a far field contribution that captures the long range, many body interactions between the sphere and the two electrodes and (2) a near field contribution that captures the pairwise interactions between nearly contacting surfaces. The accuracy of this approach is confirmed by comparison to “exact” numerical results obtained by finite element modeling. From the capacitance tensor, we derive the charge and dipole moment on the sphere, the electrostatic free energy of the system, and the electrostatic force on the sphere. These quantities are used to describe the dynamics of micron-scale particles oscillating within a viscous dielectric liquid between two parallel electrodes subject to constant voltage. Simulated particle trajectories agree quantitatively with those captured experimentally by high speed imaging.

Contact Charge Electrophoresis: Fundamentals and Microfluidic Applications

Contact charge electrophoresis (CCEP) uses steady electric fields to drive the oscillatory motion of conductive particles and droplets between two or more electrodes. In contrast to traditional forms of electrophoresis and dielectrophoresis, CCEP allows for rapid and sustained particle motions driven by low-power dc voltages. These attributes make CCEP a promising mechanism for powering active components for mobile microfluidic technologies. This Feature Article describes our current understanding of CCEP as well as recent strategies to harness it for applications in microfluidics and beyond.