Quantifying the Origins of Life on a Planetary Scale

A simple, heuristic formula with parallels to the Drake Equation is introduced to help focus discussion on open questions for the origins of life in a planetary context. This approach indicates a number of areas where quantitative progress can be made on parameter estimation for determining origins of life probabilities. We also suggest that the probability of origin of life events can be dramatically increased on planets with parallel chemistries that can undergo the development of complexity, and in solar systems where more than one planet is available for chemical evolution, and where efficient impact ejecta exchange occurs, increasing the effective chemical search space and available time.Comment: 13 pages, submitted to Scienc

Beyond prebiotic chemistry

Summary: How can matter transition from the nonliving to the living state? The answer is essential for understanding the origin of life on Earth and for identifying promising targets in the search for life on other planets. Most studies have focused on the likely chemistry of RNA (1), protein (2), lipid, or metabolic “worlds” (3) and autocatalytic sets (4), including attempts to make life in the lab. But these efforts may be too narrowly focused on the biochemistry of life as we know it today. A radical rethink is necessary, one that explores not just plausible chemical scenarios but also new physical processes and driving forces. Such investigations could lead to a physical understanding not only of the origin of life but also of life itself, as well as to new tools for designing artificial biology

The digital code driven autonomous synthesis of ibuprofen automated in a 3D-printer-based robot

An automated synthesis robot was constructed by modifying an open source 3D printing platform. The resulting automated system was used to 3D print reaction vessels (reactionware) of differing internal volumes using polypropylene feedstock via a fused deposition modeling 3D printing approach and subsequently make use of these fabricated vessels to synthesize the nonsteroidal anti-inflammatory drug ibuprofen via a consecutive one-pot three-step approach. The synthesis of ibuprofen could be achieved on different scales simply by adjusting the parameters in the robot control software. The software for controlling the synthesis robot was written in the python programming language and hard-coded for the synthesis of ibuprofen by the method described, opening possibilities for the sharing of validated synthetic ‘programs’ which can run on similar low cost, user-constructed robotic platforms towards an ‘open-source’ regime in the area of chemical synthesis

Designing algorithms to aid discovery by chemical robots

Recently, automated robotic systems have become very efficient, thanks to improved coupling between sensor systems and algorithms, of which the latter have been gaining significance thanks to the increase in computing power over the past few decades. However, intelligent automated chemistry platforms for discovery orientated tasks need to be able to cope with the unknown, which is a profoundly hard problem. In this Outlook, we describe how recent advances in the design and application of algorithms, coupled with the increased amount of chemical data available, and automation and control systems may allow more productive chemical research and the development of chemical robots able to target discovery. This is shown through examples of workflow and data processing with automation and control, and through the use of both well-used and cutting-edge algorithms illustrated using recent studies in chemistry. Finally, several algorithms are presented in relation to chemical robots and chemical intelligence for knowledge discovery

A self optimizing synthetic organic reactor system using real-time in-line NMR spectroscopy

A configurable platform for synthetic chemistry incorporating an in-line benchtop NMR that is capable of monitoring and controlling organic reactions in real-time is presented. The platform is controlled via a modular LabView software control system for the hardware, NMR, data analysis and feedback optimization. Using this platform we report the real-time advanced structural characterization of reaction mixtures, including 19F, 13C, DEPT, 2D NMR spectroscopy (COSY, HSQC and 19F-COSY) for the first time. Finally, the potential of this technique is demonstrated through the optimization of a catalytic organic reaction in real-time, showing its applicability to self-optimizing systems using criteria such as stereoselectivity, multi-nuclear measurements or 2D correlations

Combining 3D printing and liquid handling to produce user-friendly reactionware for chemical synthesis and purification

We use two 3D-printing platforms as solid- and liquid-handling fabricators, producing sealed reactionware for chemical synthesis with the reagents, catalysts and purification apparatus integrated into monolithic devices. Using this reactionware, a multi-step reaction sequence was performed by simply rotating the device so that the reaction mixture flowed through successive environments under gravity, without the need for any pumps or liquid-handling prior to product retrieval from the reactionware in a pure form

On the fly multi-modal observation of ligand synthesis and complexation of Cu complexes in flow with ‘benchtop’ NMR and mass spectrometry

Exploring complex chemical systems requires reproducible and controllable ways to access non-equilibrium conditions. Herein we present a programmable flow system that can do both ligand synthesis and complexation on the fly, and the conditions of the reaction can be monitored using two simultaneous techniques, namely NMR and mass spectrometry. By using this approach we monitored the formation of unknown complexes, followed by crystallization that resulted in the characterisation of their structures giving 5 new compounds (4 isolated and fully characterised) which can be formulated as: Cu2(L1)4(μ-CO3)](BF4)2 (2); [Cu3(L1)6(μ-CO3)](PF6)2(OH)2 (3) [Cu2(L2)2](BF4)2 (4) and [Cu(L2)2](BF4)2·CH3CN (5)

Towards heterotic computing with droplets in a fully automated droplet-maker platform

The control and prediction of complex chemical systems is a difficult problem due to the nature of the interactions, transformations and processes occurring. From self-assembly to catalysis and self-organization, complex chemical systems are often heterogeneous mixtures that at the most extreme exhibit system-level functions, such as those that could be observed in a living cell. In this paper, we outline an approach to understand and explore complex chemical systems using an automated droplet maker to control the composition, size and position of the droplets in a predefined chemical environment. By investigating the spatio-temporal dynamics of the droplets, the aim is to understand how to control system-level emergence of complex chemical behaviour and even view the system-level behaviour as a programmable entity capable of information processing. Herein, we explore how our automated droplet-maker platform could be viewed as a prototype chemical heterotic computer with some initial data and example problems that may be viewed as potential chemically embodied computations