The Evolution of Active Droplets in Chemorobotic Platforms

There is great interest in oil-in-water droplets as simple systems that display astonishingly complex behaviours. Recently, we reported a chemorobotic platform capable of autonomously exploring and evolving the behaviours these droplets can exhibit. The platform enabled us to undertake a large number of reproducible experiments, allowing us to probe the non-linear relationship between droplet composition and behaviour. Herein we introduce this work, and also report on the recent developments we have made to this system. These include new platforms to simultaneously evolve the droplets’ physical and chemical environments and the inclusion of selfreplicating molecules in the droplets

A curious formulation robot enables the discovery of a novel proto-cell behaviour

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry

Origin of life from a maker's perspective -- focus on protocellular compartments in bottom-up synthetic biology

The origin of life is shrouded in mystery, with few surviving clues, obscured by evolutionary competition. Previous reviews have touched on the complementary approaches of top-down and bottom-up synthetic biology to augment our understanding of living systems. Here we point out the synergies between these fields, especially between bottom-up synthetic biology and origin of life research. We explore recent progress made in artificial cell compartmentation in line with the crowded cell, its metabolism, as well as cycles of growth and division, and how those efforts are starting to be combined. Though the complexity of current life is among its most striking characteristics, none of life's essential features require it, and they are unlikely to have emerged thus complex from the beginning. Rather than recovering the one true origin lost in time, current research converges towards reproducing the emergence of minimal life, by teasing out how complexity and evolution may arise from a set of essential components.Comment: 29 pages, 2 figures, 1 tabl

Predicting real-time scientific experiments using transformer models and reinforcement learning

Life and physical sciences have always been quick to adopt the latest advances in machine learning to accelerate scientific discovery. Examples of this are cell segmentation or cancer detection. Nevertheless, these exceptional results are based on mining previously created datasets to discover patterns or trends. Recent advances in AI have been demonstrated in real-time scenarios like self-driving cars or playing video games. However, these new techniques have not seen widespread adoption in life or physical sciences because experimentation can be slow. To tackle this limitation, this work aims to adapt generative learning algorithms to model scientific experiments and accelerate their discovery using in-silico simulations. We particularly focused on real-time experiments, aiming to model how they react to user inputs. To achieve this, here we present an encoder-decoder architecture based on the Transformer model to simulate real-time scientific experimentation, predict its future behaviour and manipulate it on a step-by-step basis. As a proof of concept, this architecture was trained to map a set of mechanical inputs to the oscillations generated by a chemical reaction. The model was paired with a Reinforcement Learning controller to show how the simulated chemistry can be manipulated in real-time towards user-defined behaviours. Our results demonstrate how generative learning can model real-time scientific experimentation to track how it changes through time as the user manipulates it, and how the trained models can be paired with optimisation algorithms to discover new phenomena beyond the physical limitations of lab experimentation. This work paves the way towards building surrogate systems where physical experimentation interacts with machine learning on a step-by-step basis.Comment: 8 pages, 5 figures, conferenc

Designing parametric matter:Exploring adaptive material scale self-assembly through tuneable environments

3D designs can be created using generative processes, which can be transformed and adapted almost infinitely if they remain within their digital design software. For example, it is easy to alter a 3D object's colour, size, transparency, topology and geometry by adjusting values associated with those attributes. Significantly, these design processes can be seen as morphogenetic, where form is grown out of bottom-up logic’s and processes. However, when the designs created using these processes are fabricated using traditional manufacturing processes and materials they lose all of these abilities. For example, even the basic ability to change a shapes' size or colour is lost. This is partly because the relationships that govern the changes of a digital design are no longer present once fabricated. The motivating aim is: how can structures be grown and adapted throughout the fabrication processes using programmable self-assembly? In comparison the highly desirable attribute of physical adaptation and change is universally present within animals and biological processes. Various biological organisms and their systems (muscular or skeletal) can continually adapt to the world around them to meet changing demands across different ranges of time and to varying degrees. For example, a cuttlefish changes its skin colour and texture almost immediately to hide from predators. Muscles grow in response to exercise, and over longer time periods bones remodel and heal when broken, meaning biological structures can adapt to become more efficient at meeting regularly imposed demands. Emerging research is rethinking how digital designs are fabricated and the materials they are made from, leading to physically responsive and reconfigurable structures. This research establishes an interdisciplinary and novel methodology for building towards an adaptive design and fabrication system when utilising material scale computation process (e.g. self-assembly) within the fabrication process, which are guided by stimuli. In this context, adaption is the ability of a physical design (shape, pattern) to change its local material and or global properties, such as: shape, composition, texture and volume. Any changes to these properties are not predefined or constrained to set limits when subjected to environmental stimulus, (temperature, pH, magnetism, electrical current). Here, the stimulus is the fabrication mechanisms, which are governed and monitored by digital design tools. In doing so digital design tools will guide processes of material scale self-assembly and the resultant physical properties. The fabrication system is created through multiple experiments based on various material processes and platforms, from paint and additives, to ink diffusion and the mineral accretion process. A research through design methodology is used to develop the experiments, although the experiments by nature are explorative and incremental. Collectively they are a mixture of analogue and digital explorations, which establish principles and a method of how to grow physical designs, which can adapt based on digital augmentations by guiding material scale self-assembly. The results demonstrate that it is possible to grow physical 2D and 3D designs (shapes and patterns) that could have their properties tuned and adapted by creating tuneable environments to guide the mineral accretion process. Meaning, the desirable and dynamic traits of digital computational designs can be leveraged and extended the as they are made physical. Tuneable environments are developed and defined thought the series experiments within this thesis. Tuneable environments are not restricted to the mineral accretion process, as it is demonstrated how they can manipulate ink cloud patterns (liquid diffusion), which are less constrained in comparison to the mineral accretion process. This is possible due to the use of support mediums that dissipate energy and also contrast materially (they do not diffuse). Combining contrasting conditions (support mediums, resultant material effects) with the idea of tuneable environments reveals how: 1) material growth and properties can be monitored and 2) the possibilities of growing 3D designs using material scale self-assembly, which is not confined to a scaffold framework. The results and methodology highlight how tuneable environments can be applied to advance other areas of emerging research, such as altering environmental conditions during methods of additive manufacturing, such as, suspended deposition, rapid liquid printing, computed axial lithography or even some strategies of bioprinting. During the process, deposited materials and global properties could adapt because of changing conditions. Going further and combining it with the idea of contrasting mediums, this could lead to new types 3D holographic displays, which are grown and not restricted to scaffold frameworks. The results also point towards a potential future where buildings and infrastructure are part of a material ecosystem, which can share resources to meet fluctuating demands, such as, solar shading, traffic congestion, live loading

Electrokinetic phenomena of electrically induced Janus droplets and their applications

Janus droplets refer to droplets comprised of two hemispheres with different properties. Among the Janus droplets, electrically anisotropic Janus droplets with two sides carrying opposite signs of surface charges are unique. Due to their specific properties, electrokinetic phenomena of the electrically anisotropic Janus droplets are quite different from homogeneous ones and are not covered by the classical electrokinetic theory. The electrically heterogeneous Janus droplets have great potential in many fields, such as biotechnology, materials science, pharmaceutical science, food analysis as well as chemistry. Although various techniques have been developed to form the Janus droplets with different anisotropic properties, the techniques for generating the electrically anisotropic Janus droplets are seldom reported. Restricted by the fabrication methods, the studies of electrokinetic phenomena of electrically anisotropic Janus droplets and the applications are limited. This thesis systematically studies the electrokinetic phenomena of electrically induced Janus droplets (EIJDs), as well as their corresponding applications in microfluidic systems. The initial stage of the thesis is focused on developing simple and controllable methods for generating EIJDs and droplets with multiple heterogeneous surface strips with nanoparticles. Both sessile and suspended EIJDs are formed by partially covering the oil droplets with Al2O3 nanoparticles under an electric field. Because the Al2O3 nanoparticles and the oil-water interface carrying surface charges with opposite signs, the EIJDs are electrically anisotropic. The nanoparticle coverage of the EIJDs is controllable using the concentration of the nanoparticle suspension and the electric field strength. The droplets with multiple heterogeneous surface strips are prepared in a microfluidic chip under an electric field. By controlling the delivery of nanoparticles in the microfluidic chip, different nanoparticles, Al2O3, MgO and ZnO, accumulate on the surfaces of the oil droplets to form desired strips. In fundamental part, the studies of the electrokinetic phenomena of the EIJDs are conducted, including electroosmosis, electrokinetic motion and wall-induced dielectrophoresis. Electroosmotic flow fields around sessile EIJDs are visualized with the particle tracing method. Because two sides of the Janus droplets carry opposite surface charges, vortices can be generated around the dipoles under electric field. To understand the evolution of these vortices, the effects of the electric field strength and nanoparticle coverage of the EIJDs on the vortices are studied. The comparisons between the experimental results and the numerical results indicate good agreement. The Electrokinetic motions of the suspended EIJDs in a straight microchannel under both a relatively weak electric field and a relatively high electric field are investigated, respectively. In this study of the electrokinetic motion, the effects of the electric field strength, the nanoparticle coverage of the EIJDs, the droplet size and the electrolyte concentration on the electrokinetic velocity of the EIJDs are studied systematically. The results indicate that, under weak electric field, nonlinear electrokinetic motion of the EIJDs is observed due to the variation of the nanoparticle coverage with electric field. Finally, the wall-induced dielectrophoretic lateral migration of the EIJDs in a microchannel is studied theoretically and experimentally. The lateral migration of the EIJDs is compared with that of the oil droplets, and it is shown that separation of target EIJDs is accomplishable with wall-induced dielectrophoresis. Two applications of the Janus droplets are introduced in this thesis: microvalve and micromotor. The EIJD-based microvalve is controllable using electric field. By testing the performance of the microvalve systematically, the capability of such an EIJD-based microvalve in sealing, switching time and flow rate control is confirmed. The micromotor moves spontaneously in an alkaline solution through the propulsion of gas bubbles generated on the particle-coated side of the Janus droplet. The factors affecting the motion of the microvalve include: time, pH value of the buffer solution, particle coverage and surfactant. The experimental results verify that the directional motion of the micromotor can be accomplished using an externally applied electric field. This thesis develops simple methods for fabricating EIJDs and droplets with multiple heterogeneous surface strips. The fundamental research in this thesis extends the understanding in the electrokinetic phenomena. The microvalve and the micromotor fabricated from the Janus droplets offer great potential in various microfluidic devices and applications

Designing parametric matter:Exploring adaptive self-assembly through tuneable environments

3D digital models can be created using generative processes, which can be transformed and adapted almost infinitely if they remain within their digital design software. For example, it is easy to alter a 3D structure’s/object's colour, size, geometry and topology by adjusting values associated with those attributes. However, when these digital models are fabricated using traditional, highly deterministic fabrication processes, where form is imposed upon materials, the physical structure typically loses all of these adaptive abilities. These reduced physical abilities are primarily a result of how design representations are fabricated and if they can maintain relationships with the physical counterpart/materials post-fabrication. If relationships between design representations and physical materials are removed it can lead to redundancy and significant material waste as the material make-up of a physical structure can’t accommodate fluctuating design demands (e.g. aesthetics, structural, programmatic). This raises the question: how can structures be grown and adapted throughout fabrication processes using programmable self-assembly? This research explores and documents the development of an adaptive design and fabrication system through a series of ‘material probes’, which begin to address this aim. The series of material probes have been carried out using research through design as an approach, which enables an exploration and highlights challenges, developments and reflections of the design process as well as, the potentials of rethinking design and fabrication processes and their relationships with materials. Importantly, the material probes engage with material computation (e.g. self-assembly/autonomous-assembly) and demonstrate that various patterns, shapes and structures can have various material properties (e.g. volume, composition, texture, shape) tuned and adapted throughout the fabrication process by inducing stimuli (e.g. temperature, magnetism, electrical current) and altering parameters of stimuli (e.g. duration, magnitude, location). As a result, the structures created can tune and adapt their material properties across length scales and time scales. These adaptive capacities are enabled by creating what is termed ‘tuneable environments. Significantly, tuneable environments fundamentally rethink design and fabrication processes and their relationships with materials, since inducing stimuli and controlling their parameters can be used as an approach to creating programmable self-assembly. Consequently, the material platforms’ units of matter do not have to have pre-design properties (e.g. geometries, interfaces) This research points towards future potentials of structures that can physically evolve and lead to the decarbonising of urban contexts where they could behave like ‘living material eco-systems’, and resources are shared to meet fluctuating demands through passive means

Microfluidic construction and operation of artificial cell chassis encapsulating living cells and pharmaceutical compounds towards their controlled interaction

Droplet-based microfluidic devices can generate complex, soft-matter emulsion systems towards drug screening applications and artificial cell membrane studies. This thesis investigates a methodology for the eventual ‘programmed’ release of pharmaceuticals to treat breast cancer cells that are encapsulated and cultured within small diameter (<2 mm), artificial cell chassis hydrogel capsules. A pharmaceutical analogue was compartmentalised within smaller, membrane-bound, inner cores, that are arranged inside the overall hydrogel capsule. The membrane was based upon droplet interface bilayers (DIBs), which are widely employed for the study of artificial cell membrane transport properties. The whole capsule and contents were produced using enclosed 3D-printed multi-material, microfluidic devices. Methods to control the (programmed) release of compounds from the inner cores to the hydrogel shell, were investigated. The application-specific study was used as an exemplar for a more generally applicable model system. Monolithic microfluidic devices were fabricated using 3D printing and filaments of cyclic olefin copolymer (COC) and nylon for the production of single, double and triple emulsions. With these devices, monodispersed single-emulsion microgels suitable for cell encapsulation were produced, whilst dual-junction devices generated double-emulsion capsules with a controlled number of oil cores. Multi-junction devices also produced triple emulsion, encapsulated droplet interface bilayers (eDIBs), which were subsequently monitored and characterised. Additionally, to demonstrate the ability of eDIBs to act as programmed pharmaceutical delivery systems, assays were performed to induce core release, using membrane modulation by lysolipids (LPC). Computational simulations and DIB electrophysiology experiments were performed to investigate the effect of LPC on the system. MCF-7 model breast cancer cells were encapsulated in alginate-collagen emulsion capsules and their viability was assessed. Moreover, multicellular tumour spheroids (MCTSs) in oil core microgels showed no response to tested doxorubicin concentrations, while proliferated at certain LPC concentrations. Encapsulated cells in eDIBs formed tumour spheroids, however, the DIB survival was low. The integration of living cells and artificial cell membranes within a single entity presents a hybrid model for studying their interaction, towards applications in synthetic biology and drug delivery/screening

Reservoir Computing in Materio

Reservoir Computing first emerged as an efficient mechanism for training recurrent neural networks and later evolved into a general theoretical model for dynamical systems. By applying only a simple training mechanism many physical systems have become exploitable unconventional computers. However, at present, many of these systems require careful selection and tuning by hand to produce usable or optimal reservoir computers. In this thesis we show the first steps to applying the reservoir model as a simple computational layer to extract exploitable information from complex material substrates. We argue that many physical substrates, even systems that in their natural state might not form usable or "good" reservoirs, can be configured into working reservoirs given some stimulation. To achieve this we apply techniques from evolution in materio whereby configuration is through evolved input-output signal mappings and targeted stimuli. In preliminary experiments the combined model and configuration method is applied to carbon nanotube/polymer composites. The results show substrates can be configured and trained as reservoir computers of varying quality. It is shown that applying the reservoir model adds greater functionality and programmability to physical substrates, without sacrificing performance. Next, the weaknesses of the technique are addressed, with the creation of new high input-output hardware system and an alternative multi-substrate framework. Lastly, a substantial effort is put into characterising the quality of a substrate for reservoir computing, i.e its ability to realise many reservoirs. From this, a methodological framework is devised. Using the framework, radically different computing substrates are compared and assessed, something previously not possible. As a result, a new understanding of the relationships between substrate, tasks and properties is possible, outlining the way for future exploration and optimisation of new computing substrates