Counterintuitive solvation effect of ionic-liquid/DMSO solvents on acidic C–H dissociation and insight into respective solvation

How would acidic bond dissociation be affected by adding a small quantity of a weakly polar ionic liquid IL (the “apparent” or “measured” dielectric constant ε of the IL is around 10–15) into a strongly polar molecular solvent (e.g., ε of DMSO: 46.5), or vice versa? The answer is blurred, because no previous investigation was reported in this regard. Toward this, we, taking various IL/DMSO mixtures as representatives, have thoroughly investigated the effects of the respective solvent in ionic–molecular binary systems on self-dissociation of C–H acid phenylmalononitrile PhCH(CN)₂via pK_a determination. As disclosed, in this category of binary media, (1) no linear correspondence exists between pK_a and molar fractions of the respective solvent components; (2) only ∼1–2 mol% of weakly polar ILs in strongly polar DMSO make C–H bonds even more dissociative than in neat DMSO; (3) a small fraction of DMSO in ILs (<10 mol%) can dramatically ease acidic C–H-dissociation; and (4) while the DMSO fraction further increases, its acidifying effect becomes much attenuated. These findings, though maybe counterintuitive, have been rationalized on the basis of the precise pK_a measurement of this work in relation to the respective roles of each solvent component in solvation.

The dependence of PhCH(CN)₂ pK_a on the molar fraction of ionic liquids in ionic–molecular binary mixtures showed a nonlinear three-fragment plot, which was rationalized for the first time by the respective roles of each solvent component for solvation.     

A protecting group strategy to access stable lacunary polyoxomolybdates for introducing multinuclear metal clusters

Although metal-containing polyoxomolybdates (molybdenum oxide clusters) exhibit outstanding catalytic properties, their precise synthetic method has not yet been developed. This is mainly because the very low stability of the multivacant lacunary polyoxomolybdates limited their use as synthetic precursors. Here, we present a “protecting group strategy” in polyoxometalate synthesis and successfully develop an efficient method for synthesising multinuclear metal-containing polyoxomolybdates using pyridine as a protecting group for unstable trivacant lacunary Keggin-type polyoxomolybdate [PMo₉O₃₄]⁹⁻. Specifically, tetranuclear cubane- and planar-type manganese clusters were selectively synthesised in the polyoxomolybdates using the present method. The importance of this work is that, in addition to being the first practical way of utilizing multivacant lacunary polyoxomolybdates as precursors, this new “protecting group strategy” will make it possible to produce polyoxometalates with unexplored structures and properties.

A “protecting group strategy” for unstable lacunary polyoxomolybdates enabled successful synthesis of two types of tetranuclear manganese clusters.     

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant–solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water–cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

Solvent-mediated, acid-catalyzed reaction rates relevant to the upgrading of biomass into high-value chemicals are accurately predicted using a combination of molecular dynamics simulations and 3D convolutional neural networks.     

Discrete Helmholtz model: a single layer of correlated counter-ions. Metal oxides and silica interfaces,ion-exchange and biological membranes

The mechanism by which interfaces in solution can be polarised depends on the nature of the charge carriers. In the case of a conductor, the charge carriers are electrons and the polarisation is homogeneous in the plane of the electrode. In the case of an insulator covered by ionic moieties, the polarisation is inhomogeneous and discrete in the plane of the interface. Despite these fundamental differences, these systems are usually treated in the same theoretical framework that relies on the Poisson–Boltzmann equation for the solution side. In this perspective, we show that interfaces polarised by discrete charge distributions are rather ubiquitous and that their associated potential drop significantly differs from those of conductor–electrolyte interfaces. We show that these configurations, spanning liquid–liquid interfaces, charged silica–water interfaces, metal oxide interfaces, supercapacitors, ion-exchange membranes and even biological membranes can be uniformly treated under a common “Discrete Helmholtz” model where the discrete charges are compensated by a single layer of correlated counter-ions, thereby generating a sharp potential drop at the interface.

Electrolytes in solution are strongly correlated with discrete charges at insulating interfaces inducing a behavior significantly different from that of conducting interfaces.     

Top-down synthesis of polyoxometalate-like sub-nanometer molybdenum-oxo clusters as high-performance electrocatalysts

The top-down fabrication of catalytically active molecular metal oxide anions, or polyoxometalates, is virtually unexplored, although these materials offer unique possibilities, for catalysis, energy conversion and storage. Here, we report a novel top-down route, which enables the scalable synthesis and deposition of sub-nanometer molybdenum-oxo clusters on electrically conductive mesoporous carbon. The new approach uses a unique redox-cycling process to convert crystalline Mo^IVO₂ particles into sub-nanometer molecular molybdenum-oxo clusters with a nuclearity of ∼1–20. The resulting molybdenum-oxo cluster/carbon composite shows outstanding, stable electrocatalytic performance for the oxygen reduction reaction with catalyst characteristics comparable to those of commercial Pt/C. This new material design could give access to a new class of highly reactive polyoxometalate-like metal oxo clusters as high-performance, earth abundant (electro-)catalysts.

The top-down synthesis and deposition of polyoxometalate-like clusters on porous carbon is reported together with the high electrocatalytic oxygen reduction reactivity of the composite.     

Solvate sponge crystals of (DMF)3NaClO4: reversible pressure/temperature controlled juicing in a melt/press-castable sodium-ion conductor

A new type of crystalline solid, termed “solvate sponge crystal”, is presented, and the chemical basis of its properties are explained for a melt- and press-castable solid sodium ion conductor. X-ray crystallography and atomistic simulations reveal details of atomic interactions and clustering in (DMF)₃NaClO₄ and (DMF)₂NaClO₄ (DMF = N-N′-dimethylformamide). External pressure or heating results in reversible expulsion of liquid DMF from (DMF)₃NaClO₄ to generate (DMF)₂NaClO₄. The process reverses upon the release of pressure or cooling. Simulations reveal the mechanism of crystal “juicing,” as well as melting. In particular, cation–solvent clusters form a chain of octahedrally coordinated Na⁺–DMF networks, which have perchlorate ions present in a separate sublattice space in 3 : 1 stoichiometry. Upon heating and/or pressing, the Na⁺⋯DMF chains break and the replacement of a DMF molecule with a ClO₄⁻ anion per Na⁺ ion leads to the conversion of the 3 : 1 stoichiometry to a 2 : 1 stoichiometry. The simulations reveal the anisotropic nature of pressure induced stoichiometric conversion. The results provide molecular level understanding of a solvate sponge crystal with novel and desirable physical castability properties for device fabrication.

Stimuli-responsive “solvate-sponge”-(DMF)₃NaClO₄ exhibits linear chains of DMF–Na⁺ ions with ClO₄⁻ anions in the interstitial space. At increased pressure or temperature, DMF is expelled (reversibly), resulting in a new stoichiometry-(DMF)₂NaClO₄.     

Elucidating dissociation activation energies in host–guest assemblies featuring fast exchange dynamics

The ability to mediate the kinetic properties and dissociation activation energies (E_a) of bound guests by controlling the characteristics of “supramolecular lids” in host–guest molecular systems is essential for both their design and performance. While the synthesis of such systems is well advanced, the experimental quantification of their kinetic parameters, particularly in systems experiencing fast association and dissociation dynamics, has been very difficult or impossible with the established methods at hand. Here, we demonstrate the utility of the NMR-based guest exchange saturation transfer (GEST) approach for quantifying the dissociation exchange rates (k_out) and activation energy (E_a,out) in host–guest systems featuring fast dissociation dynamics. Our assessment of the effect of different monovalent cations on the extracted E_a,out in cucurbit[7]uril:guest systems with very fast k_out highlights their role as “supramolecular lids” in mediating a guest''s dissociation E_a. We envision that GEST could be further extended to study kinetic parameters in other supramolecular systems characterized by fast kinetic properties and to design novel switchable host–guest assemblies.

GEST-NMR is utilized for quantifying the dissociation activation energy (E_a,out) in host-guest systems featuring fast dissociation dynamics.     

Assessing long-range contributions to the charge asymmetry of ion adsorption at the air–water interface

Anions generally associate more favorably with the air–water interface than cations. In addition to solute size and polarizability, the intrinsic structure of the unperturbed interface has been discussed as an important contributor to this bias. Here we assess quantitatively the role that intrinsic charge asymmetry of water''s surface plays in ion adsorption, using computer simulations to compare model solutes of various size and charge. In doing so, we also evaluate the degree to which linear response theory for solvent polarization is a reasonable approach for comparing the thermodynamics of bulk and interfacial ion solvation. Consistent with previous works on bulk ion solvation, we find that the average electrostatic potential at the center of a neutral, sub-nanometer solute at the air–water interface depends sensitively on its radius, and that this potential changes quite nonlinearly as the solute''s charge is introduced. The nonlinear response closely resembles that of the bulk. As a result, the net nonlinearity of ion adsorption is weaker than in bulk, but still substantial, comparable to the apparent magnitude of macroscopically nonlocal contributions from the undisturbed interface. For the simple-point-charge model of water we study, these results argue distinctly against rationalizing ion adsorption in terms of surface potentials inherent to molecular structure of the liquid''s boundary.

Cations and anions have different affinities for the air-water interface. The intrinsic orientation of surface molecules suggests such an asymmetry, but the bias is dominated by solvent response that is spatially local and significantly nonlinear.

Counter to expectations from conventional theories of solvation, there is a large body of both computational and experimental evidence indicating that small ions can adsorb to the air–water interface.^1–9 Implications across the biological, atmospheric and physical sciences have inspired efforts to understand the microscopic driving forces for ions associating with hydrophobic interfaces in general.^10–21 A particular emphasis has been placed on understanding ion specificity, i.e., why some ions exhibit strong interfacial affinity while others do not. Empirical trends indicate that ion size and polarizability are important factors, as could be anticipated from conventional theory. More surprisingly, the sign of a solute''s charge can effect a significant bias, with anions tending to adsorb more favorably than cations.Here we examine the microscopic origin of this charge asymmetry in interfacial ion adsorption. We specifically assess whether the thermodynamic preference can be simply and generally understood in terms of long-range biases that are intrinsic to an aqueous system surrounded by vapor. By “long-range” and “nonlocal” we refer to macroscopically large scales, i.e., collective forces that are felt at arbitrarily long distance. Such a macroscopically long-range bias is expected from the air–water interface due to its average polarization, and by some measures the bias is quite strong. By contrast, “local” contributions comprise the entire influence of a solute''s microscopic environment, including electrostatic forces from molecules that are many solvation shells away – any influence that decays over a sub-macroscopic length scale.The importance of macroscopically nonlocal contributions has been discussed extensively in the context of ion solvation in bulk liquid water, which we review in Section 1 as a backdrop for interfacial solvation. The notion that such contributions strongly influence charge asymmetry of solvation at the air–water interface has informed theoretical approaches and inspired criticism of widely used force fields for molecular simulation.^22,23 A full understanding of their role in interfacial adsorption, however, is lacking.In the course of this study, we will also evaluate the suitability of dielectric continuum theory (DCT) to describe the adsorption process. DCT has provided an essential conceptual framework for rationalizing water''s response to electrostatic perturbations. But a more precise understanding of its applicability is needed, particularly for the construction of more elaborate models (e.g., with heterogeneous polarizability near interfaces^24–26) and for the application of DCT to evermore complex (e.g., nanoconfined^27,28) environments.     

Impact of the macrocyclic structure and dynamic solvent effect on the reactivity of a localised singlet diradicaloid with π-single bonding character

Localised singlet diradicals are key intermediates in bond homolysis processes. Generally, these highly reactive species undergo radical–radical coupling reaction immediately after their generation. Therefore, their short-lived character hampers experimental investigations of their nature. In this study, we implemented the new concept of “stretch effect” to access a kinetically stabilised singlet diradicaloid. To this end, a macrocyclic structure was computationally designed to enable the experimental examination of a singlet diradicaloid with π-single bonding character. The kinetically stabilised diradicaloid exhibited a low carbon–carbon coupling reaction rate of 6.4 × 10³ s⁻¹ (155.9 μs), approximately 11 and 1000 times slower than those of the first generation of macrocyclic system (7.0 × 10⁴ s⁻¹, 14.2 μs) and the parent system lacking the macrocycle (5 × 10⁶ s⁻¹, 200 ns) at 293 K in benzene, respectively. In addition, a significant dynamic solvent effect was observed for the first time in intramolecular radical–radical coupling reactions in viscous solvents such as glycerin triacetate. This theoretical and experimental study demonstrates that the stretch effect and solvent viscosity play important roles in retarding the σ-bond formation process, thus enabling a thorough examination of the nature of the singlet diradicaloid and paving the way toward a deeper understanding of reactive intermediates.

An extremely long-lived localised singlet diradical with π-single bonding character is found in a macrocyclic structure that retards the radical–radical coupling reaction by the “stretch and solvent-dynamic effects”.     

An experimental approach for controlling confinement effects at catalyst interfaces

Catalysts are conventionally designed with a focus on enthalpic effects, manipulating the Arrhenius activation energy. This approach ignores the possibility of designing materials to control the entropic factors that determine the pre-exponential factor. Here we investigate a new method of designing supported Pt catalysts with varying degrees of molecular confinement at the active site. Combining these with fast and precise online measurements, we analyse the kinetics of a model reaction, the platinum-catalysed hydrolysis of ammonia borane. We control the environment around the Pt particles by erecting organophosphonic acid barriers of different heights and at different distances. This is done by first coating the particles with organothiols, then coating the surface with organophosphonic acids, and finally removing the thiols. The result is a set of catalysts with well-defined “empty areas” surrounding the active sites. Generating Arrhenius plots with >300 points each, we then compare the effects of each confinement scenario. We show experimentally that confining the reaction influences mainly the entropy part of the enthalpy/entropy trade-off, leaving the enthalpy unchanged. Furthermore, we find this entropy contribution is only relevant at very small distances (<3 Å for ammonia borane), where the “empty space” is of a similar size to the reactant molecule. This suggests that confinement effects observed over larger distances must be enthalpic in nature.

Designable materials help pinpoint the role of steric confinement in catalysis.     

Density-functional tight-binding for phosphine-stabilized nanoscale gold clusters

We report a parameterization of the second-order density-functional tight-binding (DFTB2) method for the quantum chemical simulation of phosphine-ligated nanoscale gold clusters, metalloids, and gold surfaces. Our parameterization extends the previously released DFTB2 “auorg” parameter set by connecting it to the electronic parameter of phosphorus in the “mio” parameter set. Although this connection could technically simply be accomplished by creating only the required additional Au–P repulsive potential, we found that the Au 6p and P 3d virtual atomic orbital energy levels exert a strong influence on the overall performance of the combined parameter set. Our optimized parameters are validated against density functional theory (DFT) geometries, ligand binding and cluster isomerization energies, ligand dissociation potential energy curves, and molecular orbital energies for relevant phosphine-ligated Au_n clusters (n = 2–70), as well as selected experimental X-ray structures from the Cambridge Structural Database. In addition, we validate DFTB simulated far-IR spectra for several phosphine- and thiolate-ligated gold clusters against experimental and DFT spectra. The transferability of the parameter set is evaluated using DFT and DFTB potential energy surfaces resulting from the chemisorption of a PH₃ molecule on the gold (111) surface. To demonstrate the potential of the DFTB method for quantum chemical simulations of metalloid gold clusters that are challenging for traditional DFT calculations, we report the predicted molecular geometry, electronic structure, ligand binding energy, and IR spectrum of Au₁₀₈S₂₄(PPh₃)₁₆.

We report a parameterization of the density-functional tight-binding (DFTB) method for the accurate prediction of molecular, electronic and vibrational structure of phosphine-ligated nanoscale gold clusters, metalloids, and gold surfaces.     

Deep neural network analysis of nanoparticle ordering to identify defects in layered carbon materials

Smoothness/defectiveness of the carbon material surface is a key issue for many applications, spanning from electronics to reinforced materials, adsorbents and catalysis. Several surface defects cannot be observed with conventional analytic techniques, thus requiring the development of a new imaging approach. Here, we evaluate a convenient method for mapping such “hidden” defects on the surface of carbon materials using 1–5 nm metal nanoparticles as markers. A direct relationship between the presence of defects and the ordering of nanoparticles was studied experimentally and modeled using quantum chemistry calculations and Monte Carlo simulations. An automated pipeline for analyzing microscopic images is described: the degree of smoothness of experimental images was determined by a classification neural network, and then the images were searched for specific types of defects using a segmentation neural network. An informative set of features was generated from both networks: high-dimensional embeddings of image patches and statics of defect distribution.

Defectiveness of carbon material surface is a key issue for many applications. Pd-nanoparticle SEM imaging was used to highlight “hidden” defects and analyzed by neural networks to solve order/disorder classification and defect segmentation tasks.     

Understanding the geometric diversity of inorganic and hybrid frameworks through structural coarse-graining

Much of our understanding of complex structures is based on simplification: for example, metal–organic frameworks are often discussed in the context of “nodes” and “linkers”, allowing for a qualitative comparison with simpler inorganic structures. Here we show how such an understanding can be obtained in a systematic and quantitative framework, combining atom-density based similarity (kernel) functions and unsupervised machine learning with the long-standing idea of “coarse-graining” atomic structure. We demonstrate how the latter enables a comparison of vastly different chemical systems, and we use it to create a unified, two-dimensional structure map of experimentally known tetrahedral AB₂ networks – including clathrate hydrates, zeolitic imidazolate frameworks (ZIFs), and diverse inorganic phases. The structural relationships that emerge can then be linked to microscopic properties of interest, which we exemplify for structural heterogeneity and tetrahedral density.

A coarse-graining approach enables structural comparisons across vastly different chemical spaces, from inorganic polymorphs to hybrid framework materials.     

Exploring the self-assembly of dumbbell-shaped polyoxometalate hybrids,from molecular building units to nanostructured soft materials

The formation of hierarchical nanostructures using preformed dumbbell-like species made of covalent organic–inorganic polyoxometalate (POM)-based hybrids is herein described. In this system, the presence of charged subunits (POM, metal linkers, and counter ions) in the complex molecular architecture can drive their aggregation, which results from a competition between the solvation energy of the discrete species and intermolecular electrostatic interactions. We show that the nature of the POM and the charge of the metal linker are key parameters for the hierarchical nanoorganization. The experimental findings were corroborated with a computational investigation combining DFT and molecular dynamics simulation methods, which outlines the importance of solvation of the counter ion and POM/counter ion association in the aggregation process. The dumbbell-like species can also form gels, in the presence of a poorer solvent, displaying similar nanoorganization of the aggregates. We show that starting from the designed molecular building units whose internal charges can be controlled by redox trigger we can achieve their implementation into soft nanostructured materials through the control of their supramolecular organization.

The formation of hierarchical nanostructures using supramolecular dumbbell-like species made of organic–inorganic polyoxometalate-based hybrids is investigated by combination of SAXS and computational methods.     

Data-powered augmented volcano plots for homogeneous catalysis

Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithms we explore trends surrounding the thermodynamics of a model hydroformylation reaction catalyzed by group 9 metals bearing phosphine ligands. Specifically, we introduce “augmented volcano plots” as a means to easily visualize the similarity of each catalyst''s complete catalytic cycle energy profile to that of a hypothetical ideal reference profile without relying upon linear scaling relationships. In addition to quickly identifying catalysts that most closely match the ideal thermodynamic catalytic cycle energy profile, these maps also enable a more refined comparison of closely lying species in standard volcano plots. For the reaction studied here, they inherently uncover the presence of multiple sets of scaling relationships differentiated by metal type, where iridium catalysts follow distinct relationships from cobalt/rhodium catalysts and have profiles that more closely match the ideal thermodynamic profile. Reconstituted molecular volcano plots confirm the findings of the augmented volcanoes by showing that hydroformylation thermodynamics are governed by two distinct volcano shapes, one for iridium catalysts and a second for cobalt/rhodium species.

Augmented volcano plots, a tool for comparing and visualizing the similarity of a number of complete catalytic cycle energy profiles to that of an ideal reference profile without relying on linear scaling relationships, are introduced.     

Mass spectrometry-based chemical mapping and profiling toward molecular understanding of diseases in precision medicine

Precision medicine has been strongly promoted in recent years. It is used in clinical management for classifying diseases at the molecular level and for selecting the most appropriate drugs or treatments to maximize efficacy and minimize adverse effects. In precision medicine, an in-depth molecular understanding of diseases is of great importance. Therefore, in the last few years, much attention has been given to translating data generated at the molecular level into clinically relevant information. However, current developments in this field lack orderly implementation. For example, high-quality chemical research is not well integrated into clinical practice, especially in the early phase, leading to a lack of understanding in the clinic of the chemistry underlying diseases. In recent years, mass spectrometry (MS) has enabled significant innovations and advances in chemical research. As reported, this technique has shown promise in chemical mapping and profiling for answering “what”, “where”, “how many” and “whose” chemicals underlie the clinical phenotypes, which are assessed by biochemical profiling, MS imaging, molecular targeting and probing, biomarker grading disease classification, etc. These features can potentially enhance the precision of disease diagnosis, monitoring and treatment and thus further transform medicine. For instance, comprehensive MS-based biochemical profiling of ovarian tumors was performed, and the results revealed a number of molecular insights into the pathways and processes that drive ovarian cancer biology and the ways that these pathways are altered in correspondence with clinical phenotypes. Another study demonstrated that quantitative biomarker mapping can be predictive of responses to immunotherapy and of survival in the supposedly homogeneous group of breast cancer patients, allowing for stratification of patients. In this context, our article attempts to provide an overview of MS-based chemical mapping and profiling, and a perspective on their clinical utility to improve the molecular understanding of diseases for advancing precision medicine.

An overview of MS-based chemical mapping and profiling, indicating its contributions to the molecular understanding of diseases in precision medicine by answering "what", "where", "how many" and "whose” chemicals underlying clinical phenotypes.     

Attention-based generative models for de novo molecular design

Attention mechanisms have led to many breakthroughs in sequential data modeling but have yet to be incorporated into any generative algorithms for molecular design. Here we explore the impact of adding self-attention layers to generative β-VAE models and show that those with attention are able to learn a complex “molecular grammar” while improving performance on downstream tasks such as accurately sampling from the latent space (“model memory”) or exploring novel chemistries not present in the training data. There is a notable relationship between a model''s architecture, the structure of its latent memory and its performance during inference. We demonstrate that there is an unavoidable tradeoff between model exploration and validity that is a function of the complexity of the latent memory. However, novel sampling schemes may be used that optimize this tradeoff. We anticipate that attention will play an important role in future molecular design algorithms that can make efficient use of the detailed molecular substructures learned by the transformer.

An implementation of attention within the variational autoencoder framework for continuous representation of molecules. The addition of attention significantly increases model performance for complex tasks such as exploration of novel chemistries.     

“Eggs in egg cartons”: co-crystallization to embed molecular cages into crystalline lattices

Discrete (M₃L₂)_n cages assembled from a tripodal ligand (L) and metal ions (M: Cu(i) or Ag(i)) are embedded in networked coordination hosts formed by partial dissociation of the same discrete cages during the crystallization process. The resulting “eggs-in-an-egg-carton” structures provide unique examples of the co-crystallization of discrete and infinite coordination frameworks.

Discrete coordination cages were connected into the infinite lattices via shape-complementary co-crystallization with networked coordination hosts in the “eggs-in-an-egg-carton” styles.     

Leveraging autocatalytic reactions for chemical domain image classification

Autocatalysis is fundamental to many biological processes, and kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use an autocatalytic reaction, the copper-catalyzed azide–alkyne cycloaddition, to perform digital image recognition tasks. Images are encoded in the concentration of a catalyst across an array of liquid samples, and the classification is performed with a sequence of automated fluid transfers. The outputs of the operations are monitored using UV-vis spectroscopy. The growing interest in molecular information storage suggests that methods for computing in chemistry will become increasingly important for querying and manipulating molecular memory.

Kinetic models of autocatalytic reactions have mathematical forms similar to activation functions used in artificial neural networks. Inspired by these similarities, we use a copper-catalyzed reaction to perform digital image recognition tasks.