www.ietdl.org Published in IET Nanobiotechnology Received on 23rd May 2014 Revised on 11th August 2014 Accepted on 22nd August 2014 doi: 10.1049/iet-nbt.2014.0020

ISSN 1751-8741

Self-assembly: a review of scope and applications Anusha Subramony Iyer1, Kolin Paul1,2 1

Amarnath and Shashi Khosla School of Information Technology, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India 2 Department of Computer Science and Engineering, Indian Institute of Technology, Hauz Khas, New Delhi 110016, India E-mail: [email protected]

Abstract: Self-assembly (SA) is the preferred growth mechanism in the natural world, on scales ranging from the molecular to the macro-scale. It involves the assembling of components, which governed by a set of local interaction rules, lead to the formation of a global minimum energy structure. In this survey, the authors explore the extensive research conducted to exploit SA in three domains; first, as a bottom-up approach to fabricate semiconductor heterostructures and nano-scale devices composed of carbon nanotubes and nanowires; second, for meso-scale assembly to build systems such as three-dimensional electrical networks and microelectromechanical systems by utilising capillary force, external magnetic field and so on as the binding force; and third, as an emerging means to achieve computing via tiling, biomolecular automata and logic gates. DNA, in particular, has been a molecule of choice because of its easy availability, biological importance and high programmability as a result of its highly specific component bases.

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Introduction

A school of fish swimming in a beautiful pattern; bees swarming towards a garden full of flowers; a single cell evolving into a full-fledged multi-cellular organism – despite being disparate in their scale and properties, all of these naturally occurring phenomena possess a common thread whereby components of a smaller unit assemble to form a bigger, more organised pattern. Popularly termed as ‘self-assembly’ (SA), it has been described as the ‘autonomous organisation of components into patterns or structures without human intervention’ by George M. Whitesides at Harvard University [1]. SA is a process in which smaller components come together spontaneously to form larger, ordered aggregates [2]. As outlined by Whitesides in his review of chemical synthesis processes, SA is one of the four possible strategies to synthesise molecules of a desired complexity [3]. While covalent synthesis and polymerisation are two common means to obtain molecules out of covalent bonding between atoms, SA has offered to the chemists competent solutions to synthesise structures out of non-covalent bonds, such as hydrogen bonds. SA uses the ability of the molecules to organise themselves into a well-defined pattern and is directed towards achieving a stable structure that represents a minimum in the free energy of the system. SA is applicable to component sizes varying from the molecular to the macroscopic. In molecular SA, the participating components involve atoms and molecules, and the binding happens via non-covalent bonds like hydrogen bonding and van der Waals forces. This form of SA is rampant in the biological world. An extremely relevant 122 & The Institution of Engineering and Technology 2015

example from the world of organisms is protein folding, which involves the SA of amino acid sequences to form thermodynamically stable protein structures [3]. Another interesting example that has also been studied extensively in SA is the Tobacco Mosaic Virus or TMV. The virus is assembled out of 2130 protein units that form a viral coat around a ribonucleic acid (RNA) thread composed of approximately 6400 nucleotides [3]. Perhaps the most popular bio-molecule known for its unique self-assembling properties is ‘deoxyribo-nucleic acid’ or DNA. The discovery of its helical structure by Watson and Crick in 1953 paved the way to a domain of exciting possibilities that seek to exploit the selective binding of DNA’s component bases. Indeed, it is because of this high selectivity that the unique base sequences ‘encode’ the signature of every living cell, and the concept of genetics emerged. Armed with the knowledge of DNA’s structure and easy modelling of its molecular dynamics, scientists have over the past few decades experimented on several ideas that could result in achieving non-biological DNA nanostructures. The potential applications for such structures include solving NP-complete problems, achieving DNA logic gates, building neural circuits, nanobots for targeted in-vivo drug delivery and scaffolds for growing quantum dots, carbon nanotubes (CNTs) and so forth. However, is SA’s potential as a process to ‘build’ or manufacture structures only limited to molecules? SA has proved to be of immense use to scientists and engineers at the mesoscopic level too. The group led by Whitesides at Harvard University has explored several self-assembling systems employing magnetism, electrostatic force and capillary force for binding the individual components. Besides the mesoscopic level, the concept of letting the IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

www.ietdl.org components build the entire system has even been extended to macroscopic objects such as mechanical robots [4]. Composed entirely of either self-propelled modules or externally propelled components that are steered via the environment, these robots embody the principles of self-configuration and even self-repair (through redundant modules, for example). These properties lend the robots a ‘biomimetic’ aspect in their mode of assembly and behaviour. The elegance of the SA process lies in its ability to create a global order out of locally defined rules. By exploiting the system’s inherent tendency to reach a minimum energy state, SA provides us a pathway to incorporate growth techniques as witnessed in the biological world. From a manufacturing perspective, SA gives us control at the atomic level, and is fast emerging as a cheaper and a more efficient means to grow nanostructures like nanowires, CNTs and quantum dots. Molecular SA has similarly improved upon the feasibility of nano-scale, low-power biomolecular electronics that can be used for sensing and drug delivery applications. DNA SA, in particular, has gained immense research momentum and has seen innovative approaches in building bio-compatible logic circuits and in providing scaffold structures for nano-circuits that are, otherwise, difficult to grow using traditional lithography process. The programmable nature of DNA molecules has been used to successfully compute NP-complete problems and to create biomolecular automata. In this survey, we seek to explore SA as a process in detail, and discuss on the relevance of SA from three major perspectives: first, as an emerging contender to lithography techniques; second, as an alternative to build systems at the meso-scale and macro-scale; and third, as a means of computing in the form of automata and logic gates.

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scales. A balance among these characteristics determines the success of any SA process. In 1959, Richard Feynman, the visionary physicist, shared his revolutionary ideas on miniaturisation in a lecture aptly titled: ‘There is plenty of room at the bottom’. He envisioned a world where we could control and manipulate at the level of the atoms, and be able to employ physics to ‘give orders’ and generate the required chemical substance [5]. Molecular SA shows tremendous potential in fulfilling on that vision. The guiding principle behind molecular SA is to design molecular building blocks that are able to undergo spontaneous stepwise interactions in a way that they self-assemble via weak, non-covalent bonding. These assemblies include one-dimensional (1D) polymolecular chains and fibres, or 2D layers and membranes, or even 3D solids. By being able to choose the initial ‘seed’ of the SA, the designer has a direct control over the entire SA process. This is akin to a type of ‘chemical programming’ where the instructions are coded into the structural framework of the molecular components. Based on the interactions among the molecules and the environment, specific interaction patterns surface over a temporal frame and this behaviour shows up as a form of algorithmic computing taking place via molecules [6]. In molecular SA, complementarity and self-stability play important roles. This means that the size and the orientation of the molecules should be such that a compatible fitting is achieved [6]. The process of SA at the molecular scale follows three basic steps: 1. Molecular recognition: Selective binding of elementary molecules. 2. Growth: The component molecules assemble sequentially or hierarchically. This is extremely helpful as the assembling can be interrupted/guided as per the requirement. 3. Termination: A built-in feature that signals the end of the assembly (usually, it is physical and/or environmental).

SA aspects

Although SA is a term used to define processes ranging from the non-covalent assembly of organic molecules in a solution to the growth of semiconductor quantum dots islands on solid substrates, a more formal definition describes it as a process that involves pre-existing components, is reversible, and can be controlled by a proper component design [2]. As outlined by Whitesides [2], a self-assembling system has typically five important characteristics that determine its structure: first, the sub-units that aggregate together to form a more well-defined and stable configuration. These are usually termed as components. Second, the interactions or bonds that hold the components together. On the molecular level, the interactions between the components are mostly weak (i.e. comparable to thermal energies) and non-covalent. On the meso-scale, a self-assembling system is built by exploiting magnetic, capillary, electrostatic and gravitational forces. Third, in order for the final self-assembled structure to be as stable and ordered as possible, the connections between components must be weak enough to be broken and reconfigured in order to adjust their positions. This property is called reversibility. Fourth, the environment in which the SA process is implemented plays an instrumental role in the extent to which the components are able to move and interact. Fifth, the mobility of the components is crucial in the formation of an aggregate, and this is provided by Brownian motion at the molecular scale, and by gravity and friction at larger IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

To achieve a SA process that is programmable, the atomic structure of the molecular components has to allow for several permutations of molecular association. Chemical assemblies like organic or inorganic crystals are usually composed of extremely simple components, hence rendering them as weak contenders for programmable assemblies. Several bio-molecules have been considered for their usage as SA components, mainly because of their inherent ability to self-assemble in nature. Of the repertoire of synthetically available bio-molecules, DNA (deoxy-ribonucleic acid) has emerged as a clear winner for molecular SA. The advantages of using DNA are several; programmability made possible because of the high specificity of intermolecular Watson–Crick base pairing, comprehensive understanding of the thermodynamic properties of DNA (unlike, proteins), inexpensive synthesis procedures, simple process of hybridisation and easy characterisation using available tools such as electrophoresis, atomic force microscopy (AFM) and so on are just a few of the factors behind the popularity of DNA [7]. The rapid technological advancement seen in Next Generation Sequencing (NGS) techniques for applications such as personalised medicine, study of genetic mutation and so on is a fitting demonstration of the growing significance of DNA. A graphical representation (Fig. 1) of the data released by the National Human Genome Research Institute (NHGRI) shows that DNA sequencing technologies have outpaced even Moore’s law in terms of cost reduction [8]. 123

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www.ietdl.org functional materials, usually by etching. Optical lithography has been the method of choice by the semiconductor IC manufacturers till now. This kind of lithography involves preparing a mask containing the required pattern and then transferring the mask pattern onto the resist-coated wafer. With the continued pressure to produce smaller feature sizes, optical lithography seems to be heading for a dead end. The following challenges, as pointed by Pease and Chou [12], severely limit the viability of optical lithography for the future of electronics industry, both from a technological and economical point of view (the latter being more dominating):

Fig. 1 Cost to sequence per human-sized genome has witnessed a rapid decline Introduction of NGS technologies around 2008 fuelled the drop further and has added to the popularity of DNA in genetics and healthcare [8] Figure is reprinted with permission from [8]. Please see online version for colour figures

To design a DNA nanostructure, several ssDNA (single-strand DNA) molecules with specific base sequences are chosen and these strands then further hybridise to only specific complementary segments of other ssDNA. In fact, the landmark experiment by Adleman (1994), which firmly established molecular computing as a wide new arena of scientific possibilities was conducted on DNA. Adleman [9] successfully demonstrated how the Hamiltonian path can be determined in a graph by encoding a path in the DNA double helix.

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Why SA?

Following the famous Moore’s Law, the minimum dimension of ICs has been shrinking at the rate of 30% every three years. As per the International Technology Roadmap for Semiconductors, the semiconductor industry hit the 14 nm process node in the year 2014. In September 2013, Intel introduced a new line processors named the ‘Broadwell’ based on the 14 nm node [10], which are expected to be shipped by Q4 2014 [11]. With the enormous amount of investment, both in terms of manpower and money, one is tempted to question the motivation behind this trend. The reason is pretty straightforward. Smaller dimensions mean higher operating speeds and, more importantly, much less power consumed per computing function [12]. In fact, for the recently unveiled Broadwell processors, a 30% power consumption improvement has been claimed over the 22 nm Haswell processors [10]. With electronics being increasingly hand-held, and with emerging application areas like medical technologies calling for improved devices, a smaller device scale with minimal power consumption is becoming imperative. The incredible gain in terms of device performance is, in fact, the major driving force in the research community to come up with novel patterning techniques. 3.1

Limitations of top-down techniques

As noted by Pease and Chou [12], the existing patterning techniques are mostly top-down; that is, the fabrication starts with a top-level pattern obtained on a resist film via lithography and this pattern is then built step by step on the 124 & The Institution of Engineering and Technology 2015

† Limit to resolution: Achieving submicron resolution is tough with visible and ultraviolet (UV) wavelengths. During the last 20 years, the IC industry has moved from 436–365 nm to 248–193 nm. In addition to this, residual birefringence has been observed in the material composing the mask and the refracting lenses for wavelengths beyond UV. Hence, formation of masks itself is proving to be a major bottleneck. † Limit to speed and resolution set by resists: An important characteristic of resists is the contrast or gamma that denotes the mean slope of the curve between the full thickness of the resist film and zero thickness. As the wavelengths reduce to attain smaller feature size, the image loses its contrast and the value of gamma has to be necessarily higher. The limitations of optical lithography have led to innovative ideas like immersion optics, in which the numerical aperture is made greater than 1 by immersing the space between lens and wafer with fluid, and Absorbance Modulation Optical Lithography that uses simultaneous exposure using two different wavelengths. In fact, features below 22 nm have been achieved using UV lithography and so, fundamental physics is not the real limitation. Despite new techniques, the constraints of lithography are mostly because of the exorbitant increase in the overall cost of production associated with the complex optics. The massive amount of pattern data required contributes to the cost as well. The continuous shrinking also impacts heavily on the precision rate of the entire process, hence affecting the throughput [12]. 3.2

Advantages of SA

The biggest advantage SA is the ‘autonomous’ growth of the structural components to form an ordered aggregate. The growing interest in SA can be attributed to several reasons. First and foremost, SA is central to biology and perhaps holds the key to understanding life. Living cells self-assemble to form a living organism by making use of the engrained programming in the DNA sequences. SA lets us make use of biochemical circuits (by programming molecules themselves) to control nano-scale and chemical systems [13]. Second, it is a novel way to fabricate new device structures at a scale that is not easily attainable using conventional lithography. Third, with its inherent parallel nature and application of bio-compatible molecules, SA opens the doors to new realms of computing in disciplines as diverse as healthcare and nano-robotics. Fourth, SA is not restricted to just the molecular scale and it is possible to apply the principles of autonomous growth at the meso-scale and macro-scale as well [e.g. microelectromechanical systems (MEMS) etc.]. This also gives an opening to experiment with a range of materials [2]. IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

www.ietdl.org In the following sections, we focus on the various domains being revolutionised by SA and the exciting new possibilities that are creating waves in the SA research community.

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SA for micro-/nano-fabrication

SA has long been a preferred technique for molecular synthesis and holds a special place in the area of supramolecular chemistry. Assembling of organic molecules through hydrogen bonding and Van der waals force has been extensively used to synthesise polymer molecules such as Zimmerman’s dendrimer [14]. Self-assembled monolayers find wide application in coating of surfaces with a hydrophilic or hydrophobic layer. The SA technique for chemical synthesis is equally useful for inorganic materials, for example, growth of germanium (Ge) quantum dots (QDs) on silicon (Si) matrix [15]. To cater to the needs of decreasing device dimensions, the semiconductor manufacturing industry is now resorting to achieving certain process steps using SA. A widespread usage can be seen in the form of strain-technology. This technique takes advantage of lattice mismatch because of the wide variation of lattice constants of two different materials. The SA of Ge islands or QDs on Si substrates is an example. Ge has a higher lattice constant as compared to Si, and hence a layer of Ge (deposited via epitaxy) on an Si layer experiences a compressive strain. The strain is just sufficient to hold the Ge atoms in place without leading to defects. Such a strained epitaxial layer is technically named as pseudomorphic. The pseudomorphic layer tends to relax by forcing sections of Ge atoms to self-assemble into island-like structures that function as QDs [15]. An application of such a Ge–Si strained epitaxial layer is in the growth of gallium arsenide (GaAs) on Si for obtaining heterostructures for high-efficiency solar cells, lasers, photodetectors and microwave devices. Typically, the lattice constant variation between GaAs and Si is too large to be

Fig. 2 TEM cross-section micrograph of AlGaAs/GaAs quantum well structure grown on top of Ge/GeSi/Si substrate Figure is reprinted with permission from [16] IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

able to obtain a pseudomorphic layer and a Ge–Si layer serves as a buffer by obtaining a graded lattice constant change across the layers, as shown in Fig. 2 [16]. Currently, QDs have been steadily gaining the place of primary constituents for applications like imaging, sensing, bioelectronics, energy harvesting and nano-photonics. Although synthesis using SA is helpful in achieving the nano-scale dimensions required for growing the QDs, it still remains an extremely slow process with little control over the pattern of the QD growth and a long-range order. To facilitate this, templated or directed SA has been found to be of immense use. Junkin et al. [17] propose a way to arrange the QDs in a desired pattern by using templates prepared from plasma surface modification. Templates were essentially 3D moulds prepared from polydimethylsiloxane (PDMS) using a photoresist mould or an optical grating as the master template. The PDMS mould had the required pattern imprinted on its surface, and was kept over the substrate over which QD growth was being targeted (in this case, polystyrene). After the setup was treated in a plasma chamber, the exposed areas on the substrate became functionalised. The substrate was then immersed into a solution of QDs. The plasma modified substrate areas latched onto the QD molecules and the result was an assembly of QDs grown in a pre-determined pattern. Besides using plasma modification to assist in the templating, DNA origami [18] too has been proposed as a solution to guide SA of nanostructures. DNA origami is a unique technique by means of which a long, single-strand of DNA gets folded to yield a 2D shape. Just as a normal paper-based origami involves the folding of a single sheet to yield complex shapes, DNA origami, as proposed by Rothemund [18], achieves the folding of a ‘scaffold’ strand by means of hybridisation and crossover formation with smaller ‘staple’ strands. A crossover is the binding of a DNA strand across two different DNA helices. Therefore, a crossover formation directs the 2D folding of the scaffold strand, and also holds the shape in place. Depending upon the sequence of the staple strands, it becomes possible to fold the scaffold strand in different ways. Several 2D shapes such as rectangles, triangles, stars and so on have been achieved by Rothemund via DNA origami. DNA origami has been shown to create the template required for 2D SA of CNTs. Maune et al. [19] took a ∼7000 base long scaffold strand bound to a series of ssDNA staple strands. The staples were further attached to two classes of staple ssDNA: one projecting out of the DNA scaffold plane, and the other directed into the plane. The single-walled CNTs (SW-CNTs) were functionalised by attaching onto their surface DNA strands that are complementary to the aforementioned ssDNA staples. The ssDNA strands get attached to the SW-CNT surface

Fig. 3 Functionalised SW-CNTs on DNA scaffold plane CNTs hook onto the complementary staple strands attached to the underlying DNA scaffold plane to create a 2D cross junction [19] 125

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www.ietdl.org non-covalently via physisorption without affecting the CNT’s electronic properties. Once mixed with the DNA substrate, the CNTs align themselves by hooking onto the staple strands (refer Fig. 3) to form cross-junctions. Such CNT junctions can prove useful for making ultra-small CNT-based field-effect transistors, as shown in [20]. By crossing a metallic CNT (m-CNT) over a semiconducting CNT (s-CNT), it is possible to modulate the current through the s-CNT by using the m-CNT as a gate electrode. As pointed out by Maune et al., the DNA origami-based templated growth of CNT junctions suffers from limitations because of defects in the DNA scaffold itself. Further, random locations of the ‘hooked’ SW-CNTs ends make contacting difficult. It is also tough to ensure that SW-CNTs with the correct electrical property (metallic or semiconducting) are forming the junctions. In spite of such process-related limitations, the idea of using origami as a template is extremely interesting. By incorporating better process control and a provision for error correction, DNA origami can be made viable for nano-scale device creation. The concept of using self-assembled DNA patterns was taken forward by Yan et al. [21] when they constructed 4 × 4 tile patterns using DNA-based cross tiles. A cross tile consists of nine DNA strands or oligonucleotides that cross over each other at four Holliday junctions and result in a four-arm structure. Holliday junction [22, 23] stands for a 4-arm junction formed from the crossover of four DNA single strands resulting in each strand being hybridised to two separate strands, as shown in Fig. 4. By adjusting the distance between the centres of adjacent cross tiles, the authors obtained two kinds of morphologies: one of uniform-width nanoribbons, and the other displaying a corrugated pattern in the form a nanogrid. The nanogrid displayed a periodic array of square cavities and these can be useful for encoding information in the form of topographic markers [21]. Another interesting application can be using the nanogrid as a scaffold for directing an assembly of desired molecules, for example, for creating QD Cellular Automata arrays by binding the nanoparticles in the cavities within the nanogrid. The authors incorporated a biotin group in one of the hairpin loops at the tile centre and added streptavidin to the solution containing the nanogrids. The streptavidin combined with the biotin groups to form periodic streptavidin arrays. Further, Yan et al. applied the nanoribbons pattern for creating conductive nanowires made of silver. The

nanoribbons were metalised with silver and the I–V measurements showed ohmic behaviour in the range of −0.2 to 0.2 V. In addition to 2D self-assembled structures from DNA, it is also possible to grow 3D structures as shown by Seeman [24]. The authors successfully demonstrated the growth of DNA cubes from oligonucleotides forming 3-arm junctions. The implications of such a design are huge from the perspective of targeted drug delivery. Such DNA cages have been successfully delivered to mammalian cells [25], and have also been recently proposed as vehicles to carry drug molecules’ diseased cells. Edwardson et al. [26] proposed a hybrid cage comprising of a 3D DNA cube with lipid-like moieties (termed as dendritic alkyl chain-based DNA amphiphiles or D-DNA) hybridised to the cube’s edges. The mode of attachment is decisive as two scenarios emerge; first, four amphiphiles are attached to a single face of the DNA cube. This leads to the binding of two cubes to form a dimer. Second, four amphiphiles are attached to two opposite faces causing them to ‘collapse’ within the DNA cube. The second scenario helps hold a drug molecule within the cage and have it released in the presence of a specific DNA sequence. Cancer cells, especially, are characterised by an overexpression of genes. DNA cages stand to offer a biodegradable solution for specific delivery of drugs to such diseased cells. DNA origami has also been applied to create DNA ‘tripod’ motifs, which can be then used for a hierarchical assembly of DNA polyhedra [27].

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Meso-scale SA (MESA) refers to components with sizes falling in the range of nanometres to millimetres. Boncheva and Whitesides [28] distinguished four broad categories of ‘building’ or ‘making’ things: chemical synthesis, for making molecules; photolithography, for using light to make the desired circuit patterns; mechanical manufacturing, for robots and machines; construction, which is extensively used for building large structures like houses, roads and so on. MESA expands the scope of SA to build structures that are too large to be prepared by chemical synthesis and too small to be prepared by mechanical methods [28].

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Fig. 4 Schematic of Holliday junction 126 & The Institution of Engineering and Technology 2015

SA at meso-scale

Why non-molecular SA?

As outlined by Whitesides and Boncheva [2] in their survey of non-molecular SA, thinking beyond molecules proves to be useful in four major ways. First, it allows us to play with the concept of SA using components not limited to atoms. If we consider only the molecular level, we have very little control over the interactions possible between atoms. However, going beyond molecules opens up a spectrum of interactive forces we can choose from (e.g. van der Waals, ionic, electrostatic, capillary, magnetic, gravitational and so on). This fundamentally makes non-molecular SA far more flexible than molecular SA. Second, it is easier to fabricate and observe components at the meso-scale. Third, as opposed to photolithography, SA is a viable process option to assemble 3D structures. Fourth, such SA has the potential to address manufacturing problems in robotics. IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

www.ietdl.org 5.2

Progress in MESA

In 1997, Bowden et al. [29] demonstrated a non-molecular self-assembled structure from millimetre-scale components by making use of the wettability of their surfaces. Regular 2D arrays were created from solid objects interacting by lateral capillary forces. The experiment decisively shows that SA can be achieved at the meso-scale and that order can be achieved in a system by manipulating the forces experienced by the constituent objects and the enclosing environment. To create the array, Bowden et al. first moulded the individual components into the desired shapes (between 1 and 10 mm in length) from PDMS, a hydrophobic polymer. The PDMS objects were left floating in a mobile environment created out of a fluid interface between water and perfluorodecalin (C10F18). The lower face of the objects was hydrophobic and hence, was in contact with the C10F18. The upper face was made hydrophilic by oxidation using O2 plasma and the sides were left hydrophobic or made hydrophilic depending upon the final structure to be created. The system of C10F18–H2O interface and the PDMS objects was shaken gently along the plane of the interface in order to let the objects interact with each other. The frequency used was 1–2 Hz. The interaction between the objects was determined by the minimisation of the interfacial free energy caused by elimination of the curved menisci at the hydrophobic sides. Usually, when the hydrophobic sides moved within a critical distance of ∼5 mm of each other, they established a contact. Fig. 5 shows the variety of aggregates obtained by adjusting the shape of the PDMA objects. The concept of using lateral capillary force to create ordered arrays was extended for computing purposes by Rothemund [30]. Rothemund tested the ability of the MESA structures made from plastic tiles to solve complex problems like periodic checkerboard tiling, an aperiodic Penrose tiling and a 1D cellular automaton using XOR update rules. For the XOR operation, four tiles (two tiles for binary ‘0’ and two for ‘1’) were taken with edges subjected to a ‘wetting code’. A ‘wetting code’ consists of a sequence of hydrophilic/hydrophobic surfaces and has been utilised to ensure that only the complementary edges bind via capillary force. For any given input binary sequence, the tiles attach to each other on the complementary edges such that a 1D XOR operation is performed. A theoretical

approach on the use of tile SA for a similar XOR operation has been proposed by Winfree and co-workers [31] and is discussed in detail in Section 6.2. In an attempt to demonstrate the utility of SA for fabricating 3D microelectronic structures, Whitesides and co-workers [32] performed an experiment in which they formed spherical structures out of self-assembled hexagonal plates. The traditional microelectronic fabrication processes involving photolithographic techniques are mostly planar in nature. Emphasising the need for a process that allows 3D structure creation for applications like photonic crystals and biomimetic systems, the authors applied the concept of using surface tension for MESA. Two types of hexagonal plates were prepared from electroplated gold: hexagon A, which has the upper face made hydrophilic by a thin layer of SiO2; and hexagon B, which has only the lower face hydrophobic, and all other sides and the upper face made hydrophilic. The hexagonal plates, thus obtained, were 100 µm in length and 6 µm in depth. In a form of directed SA, a liquid drop was used as a template to aggregate the plates. Hexagon A was suspended in heptane, and a water drop was added. The hydrophilic upper faces of the plates latched onto the water drop and self-assembled to form a sphere. Similarly, hexagon B plates assembled into a sphere around a drop of perfluorodecalin in water. Gracias et al. [33] further used the idea of SA for creation of 3D structures to form 3D electrical networks. The authors used polyhedron (a truncated octahedron or TO) units for the assembly. The authors programmed the assembly by using patterns of solder dots and wires. Each TO unit consists of light-emitting diode (LED) circuits imprinted on its faces and when assembled, the TO units led to series/parallel connections of the LEDs depending on their configuration. Fig. 6 shows the assembled TO units forming a functional electrical network. 3D SA can be especially useful as an alternative approach to the fabrication of MEMS. In the review paper on SA techniques, Mastrangeli et al. [34] discussed the use of an external magnetic field to lift up the microstructures off the plane. A separate lock-in part is finally lifted to hold the microstructure in place. In addition to magnetic forces, capillary force too can be applied to grow the mechanical structures via self-folding of 2D patterns. Another interesting example is the use of low-temperature melting solder for assembling LED arrays on a flexible substrate. The interaction force at play here is capillary force because

Fig. 5 Crystalline aggregates obtained by MESA a Hexagons in an open network b Hexagons in closed network Darkened edges represent hydrophobic edges [29] IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

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www.ietdl.org of programmability helps to embed graph information in the molecules. Hybridisation happens spontaneously for all possible sequences and results in the formation of double-strand DNA (dsDNA) molecules whose sequence encodes a valid path in the graph. This experiment paved the way for ‘algorithmic SA’, a novel technique in which algorithms can be encoded efficiently in molecules that show programmable binding. 6.1

Fig. 6 Final assembled 3D LED network Figure is reprinted with permission from [33]

of the liquid solder. Morris et al. [35] demonstrated using long-range magnetic forces and short-range capillary interactions in order to assemble 280 µm-sized silicon blocks. Such a technique has the potential in the post-processing of MEMS or CMOS.

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SA for computing

In 1994, Adleman [9] conducted an ‘out of the box’ experiment in which he set out to demonstrate that building blocks made of DNA can be used for solving NP-complete computing problems. For this, he attempted to solve the Hamiltonian path for a given graph. The Hamiltonian path for a given graph is the path that traverses each vertex at least and at most once. To determine this, Adleman defined the vertices and the edges of the graph with single-strand DNA (ssDNA) molecules. The ssDNA sequences were uniquely defined so that they hybridise in a pre-determined manner (‘programmable rules’) and allow only certain vertices to follow other vertices to form a path. This form

Algorithmic SA using DNA tiles

Winfree [31, 36, 37] extended the idea of DNA computing to tiling theory. In the tiling theory, basic shapes called tiles fit together to form an arrangement over an infinite plane. Wang [38] proved that it is not possible to determine whether any known periodic patterns can be obtained at all for a finite set of tiles. In fact, the tiles can end up arranging aperiodically too. More importantly, Wang also showed how a set of tiles that fit together emulate the working of a Turing machine. To fully explore the computing capability of tiling, Winfree and co-workers [31] looked to achieve two things: first, designing molecular Wang tiles; and second, precise rules through which the tile growth can be programmed and implemented reliably. To address the first requirement, Winfree turned to the work done by Seeman [24] and considered the double-crossover (DX) DNA molecules as potential candidates for making the Wang tiles. Wang tiles were proposed to have four differently labelled sides. Similarly, the DX molecules have four arms corresponding to the four sides of the Wang tiles and each of these arms can be given a sequence. The experimental results obtained by using DX molecules are discussed in further detail in Section 6.2. From a theoretical point of view, Winfree proposed a formalised model for tile arrangement called the Tile Assembly Model (TAM). In TAM, it is supposed that each side of the Wang tile binds with a certain strength (typically, 0, 1 or 2). If ‘strength’ is 0 then the side does not bind at all. Tile assembly continues to grow only if the

Fig. 7 Self-assembled binary counter from the seven counter tiles a Three ‘boundary’ tiles that bind with strength-two edges (with a single-lined segment) to create a V-shaped corner site, and four ‘rule’ tiles, which attach to the corner site with strength-one edge (double-lined, single-dotted and two-dotted segments) Rest of the edges have strength ‘0’ Only edges with a similarly patterned line segment attach to each other b Total strength of edges ≧ threshold of ‘2’ leads to binding of tiles As per TAM, tile rotation is not permitted [31] 128 & The Institution of Engineering and Technology 2015

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www.ietdl.org sum of the strengths of the edges that stick is greater than the threshold t (usually, 1 or 2). Any other binding causes the tiles to fall off. This model helps set some rules for the growth of the assembly from a seed tile. An example of such a self-assembling program is a binary counter, as shown in Fig. 7. 6.2

Self-assembled programs as circuit templates

Winfree and co-workers [31] believed that SA computation might not be able to match up to the speed of conventional circuits. Also, once a computation is complete, the resultant pattern is static and it is not possible yet to enable it to make state transition. Precisely because of this reason, however, tiling can definitely prove to be a viable fabrication technique for creating patterns of electronic circuits. The idea is to use self-assembled patterns as templates for the bottom-up growth of a complex circuit, thus directly addressing the resolution problem of traditional lithography processes. Circuits having a methodical and regular pattern are suitable candidates for SA. An example circuit that is highly regular is the Random Access Memory (RAM). Building onto the binary counter example discussed in Section 6.1, Winfree et al. suggested using the binary counter to self-assemble the demultiplexer unit for a RAM circuit. The tiles in the TAM can be composed both at the molecular (e.g. DNA) level or at the micro-scale and milli-scale level. At the micro-scale, the tiles can be embedded electronics. Exploring further on the range of possible patterns, the fractal pattern of Sierpinski triangle emerges as an interesting candidate. The Sierpinski triangle is basically a cellular automaton obtained as a result of XOR operation over space and time. As shown in Fig. 8, its formation requires seven tiles with a threshold t = 2. From an application point of view, the pseudowavelet transform and the Hadamard transform make use of matrices similar to the Sierpinski triangle. These transforms are useful in signal processing and image compression functions. Although the pseudowavelet matrix pattern is achievable by using the rectangular tiles similar to the ones

used for Sierpinski matrix, the Hadamard matrix requires more thought and can be obtained by using a hexagonal tile set [31]. The theory of self-assembled patterns has even been experimentally verified. Rothemund et al. [39] demonstrated the 2D algorithmic self-assembly of DX (double crossover) DNA tiles to form the fractal pattern of the Sierpinski triangle. The SA resulted in the formation of a cellular automaton with XOR operation as the update rule. The error rates of the formation ranged from 1 to 10%. Despite this, the generation of the fractal using real molecules decisively shows that the algorithmic SA is a wonderful access to create arbitrary patterns. Similarly, Mao et al. [40] performed XOR computation using tiles made of triple-crossover (TX) DNA molecules. They made four corresponding tiles for the four possible combinations of the 2-input XOR truth table. Fig. 9 shows the expected growth of the tiles starting from the seed tiles of C1 and C2, and an input sequence of ‘1110’. Mao et al. suggested that such a methodology of pattern formation can prove to be of use in applications like one-time pad cryptosystem, creation of templates to lay out circuit components and smart materials. 6.3

Biomolecular automata

A programmable and autonomous finite automaton was developed by Benenson et al. [41] using DNA molecules. The designed finite automata consisted of two states: S0 and S1, and two input symbols: ‘a’ and ‘b’. Thus, the automaton is dictated by eight transition rules, T1–T8. The authors built the automata from eight short dsDNA molecules to represent the transition rules. The states and the symbols were encoded using specific base sequences. The input sequence is also finite and has a termination sequence at the end. The computation happens through a series of restriction (DNA cleaving) and ligation (combination) steps to finally result in the output. Benenson et al. [41] computed different automata programs on the setup and reported a 99% fidelity in the transition steps. However, most importantly, the total power

Fig. 8 Self-assembled Sierpinski triangle using TAM a Four rule tiles and three boundary tiles (in total seven tiles) required for the Sierpinski triangle formation The tile edges bind only when with a similarly patterned segment (single-lined, double-lined or single-dotted) b Assembly starts with the seed ‘S’ tile binding with left ‘L’ and right ‘R’ boundary tiles ‘0’ and ‘1’ tiles grow in space and time following an XOR rule along the diagonal ‘0’ tiles evolve to form the pattern of Sierpinski triangle [31] IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

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Fig. 9 Algorithmic growth based on XOR operation using TX DNA tiles a Tiles used for the algorithmic SA with an XOR update rule, as shown in the truth table b Each of the eight unit tiles has four sticky ends Complementing ends between any two tiles are chosen as per the XOR rule Tiles ‘C1’ and ‘C2’ denote the ‘seed’ tiles; ‘C1’ acts as ‘yi−1’ = 0 and ‘C2’ attaches to ‘C1’ and an ‘x’ tile c Pattern obtained for the input sequence of ‘1110’ [40]

dissipation of the computation was observed to be around 10−10 W. The proposal, however, has its side of disadvantages too. Consumption of the transition molecules requires a continuous recycling and additional energy is required to ‘read’ the output via gel electrophoresis. Shapiro and Benenson [42] applied the idea of the DNA automaton designed by them for disease diagnosis and ‘smart’ drugs that have the in-built intelligence to decide the releasing of a drug molecule. A two-state automaton is ideal for determining a ‘yes’ or a ‘no’ output needed to diagnose a particular condition indicative of a disease. Cancers, for instance, can be detected through abnormality in certain protein levels. The abnormality arises because of an overexpression or underexpression of genes and shows up in the form of high levels of the associated mRNA (messenger RNA) molecules. These mRNA molecules are the encryption of the amino acids responsible for protein generation inside the cell. The proposed biomolecular automaton consists of two complementary ‘software’ strands, one of which is hybridised to a ‘protector’ strand. The ‘protector’ strand has, however, higher affinity for the target mRNA molecule. Upon binding with the mRNA, the ‘protector’ strand lets go of its ‘software’ strand, which eventually hybridises to the second ‘software strand’. The newly formed ‘software’ double strand contains a sticky end that, along with an enzyme, serves to attach and cleave the diagnostic molecule (equivalent to a sequence of input symbols). An excess of mRNA interactions leads to complete cleavage of the diagnostic molecule and ends up releasing the drug. The automaton has been extended to detection of multiple disease indicators, and covers not just mRNAs but also microRNA (miRNA), proteins and ATP [43]. 130 & The Institution of Engineering and Technology 2015

A major drawback of Benenson’s automaton is that it involves restriction enzymes as primary constituents of the molecular hardware. Reif and Sahu [44] strive to achieve an automaton free of any enzyme usage in their DNAzyme Finite State Automaton (FSA). DNAzyme is a DNA molecule that can cleave an RNA with sequence specificity, and is the basic ingredient for the realisation of Reif’s FSA (DNAzyme-based FSA). Such an FSA encodes the input symbols in the form of concatenated RNA strands: ‘0’ is represented by ‘x1.a1.x2.a2’ and ‘1’ by ‘x1.b1.x2.b2’. The active symbol is left hanging at one sticky end and the rest of the symbols are made passive via protecting sequences that are actually partially hybridised to the input sequence strand. The state transitions are implemented by using a DNAzyme stator for each transition rule. These DNAzymes are spread over a 2D area and the input sequence traverses over them by means of ‘strand displacement’. The ‘strand displacement’ technique essentially involves the hybridisation of two complementary strands at the expense of displacing a pre-hybridised strand, as explained by Zhang and Seelig [45]. An input sequence over a DNAzyme at any given time indicates the FSA’s state at that time. Fig. 10 illustrates an example state transition in the DNAzyme FSA. The technique used by Reif and Sahu is completely autonomous, avoids usage of restriction enzymes and also, allows the input strand to traverse across a surface. The application of the DNAzyme stators makes it possible to test for multiple RNA expression tests for disease diagnosis and has been termed as DNAzyme Doctor. The authors have also proposed that the mobility induced in the input IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

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Fig. 10 Dx,sy denotes the DNAzyme used for input symbol ‘x’ and state ‘y’ a 2D plane surface consisting of an array of DNAzyme stators b Example input sequence ‘010’ with active symbol ‘0’ Initial state is ‘s1’ and the state transition happens from ‘s1’ to ‘s2’ Transition occurs via a series of steps: the RNA sequence hybridises to ‘D0,s1’, the ‘a1 x1’ section is cleaved and the input strand finally moves to the next DNAzyme by means of strand-displacement Rounded rectangles denote input sections hybridised to the DNAzyme [44]

structure can be exploited to implement a DNAzyme router. The authors have further claimed that the DNAzyme router shall be an enhancement over Mao’s crawler [46], which lacked programmability in routing, was 1D and failed to show as a carrier of nanoparticles for drug delivery. All these experiments, however, still need to be tested in a natural biological environment in order to validate their proper functioning.

6.4

DNA logic

The automata designed by Reif, as discussed in Section 6.3, did not need enzymes to enable displacement of DNA strands because of the implementation of the ‘strand displacement’ technique. A similar strategy has been adopted for building logic gates out of DNA molecules. A major challenge in building logic circuits from DNA is the limit in scaling the circuits. A more complex circuit requires an exponential increase in the number of molecules required and signal restoration becomes a big concern. The simplicity of strand displacement reactions has been exploited by Qian and Winfree [47] to develop a modular approach to growing DNA logic circuitry. To achieve this, the authors designed a basic motif for this purpose and termed it as ‘seesaw gate’. Fig. 11 explains the processes involved in a ‘seesaw gate’ in pictorial detail. The authors regard an ssDNA molecule as an active signal, which hybridises with the gate:output dsDNA molecule and releases the output signal, that is, the output ssDNA via strand displacement. This process is called ‘seesawing’. Another process named ‘thresholding’ occurs via a shorter toehold and is significantly faster than seesawing. Thresholding helps to ensure that only an input greater than the threshold quantity participates in obtaining the output. IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

This concept also makes the ‘seesaw-gate’-based strategy useful for building neural network gates [48]. The ‘fuel’ signal is used to enhance the output signal and occurs via a series of two seesawing steps: first, the input signal seesaws to produce the output and then, the fuel seesaws with the gate to release the input back into the environment. Hence, the input signal strand basically serves as a catalyst. Qian et al. used the proposed mechanism to create simple AND and OR functions, as illustrated in Fig. 12. The threshold for AND is clearly double of OR since it requires both ‘x1’ and ‘x2’ inputs to be present for an output to be produced, whereas OR only needs either of the two. The seesaw gate strategy offers several advantages; the modular nature makes scalability possible and this has been demonstrated by Qian et al. by building a four-bit square root circuit, output signal degradation can be avoided by performing restoration in the intermediate stages itself using the fuel signal, and the whole process is autonomous with no external mediation necessary. However, as observed by Reif [49] in his review of Qian’s proposed technique, there are still few challenges remaining. Firstly, the speed of computation is extremely slow (computation time of a seesaw gate is 30–60 min). Secondly, the strategy utilises trillions of molecules for computation. As keenly pointed out by Reif, this is in great contrast to biological circuits which are fast and use far lesser molecules. Reif suggests that the rate of computation can be vastly increased if the DNA computation strategies encode the state information locally (like in each individual cell in the biological world) and not globally, because a global state transition is slowed down because of dissipation delays across the system. The idea of devising logic gates is no more limited to just DNA in the world of molecular computing. The potential of using RNA for constructing synthetic devices has been 131

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Fig. 11 Proposed seesaw gate motif and the corresponding seesawing process Numbers in bold correspond to the respective DNA sequences. Rest represent the initial relative concentration. Negative concentration denotes ‘threshold’ as it absorbs/negates the input concentration available While seesawing leads to the output, thresholding produces a waste species [47]

demonstrated by Win and Smolke [50]. The authors have proposed a framework of using RNA devices to control cellular operation and offer a step in the direction of intelligent medicines. The basic building block for such a device is a Ribozyme-aptamer system. A RNA aptamer is a short RNA molecule capable of binding to a specific target molecule, and a Ribozyme is a single-stranded RNA that can cleave other RNA molecules [51]. The RNA logic gates proposed by Win and Smolke worked by combining these two capabilities in order to ensure a controlled cleaving of a target RNA molecule and hence, lead to a regulated expression. As per Win and Smolke [50], a typical RNA gate consists of three fundamental components: (i) A RNA aptamer-based sensor component, (ii) an actuator composed of a hammerhead ribozyme and (iii) a transmitter to couple the sensor and the actuator parts. Protein concentrations serve as the input and the output is in the form of the expression of green fluorescent protein or GTP. Several instances of a block consisting of the three components are embedded along the mRNA molecule whose translation to GTP needs to be controlled. The ribozyme or ‘actuator’ cleaves the mRNA because of conformational change caused by an input protein binding with the sensor component, and henceforth, inhibits the expression of GTP. The concept can be extended to perform two-input logic operations, like NAND, in

Fig. 12 Abstraction of AND and OR logical operations using seesaw gates Presence of an input strand releases the output in gate ‘2’ Threshold ‘th’ used is decisive in the type of operation performed Value of ‘th’ is 0.6 for OR operation and ‘1.2’ for AND operation Once the output of gate ‘2’ crosses the threshold of gate ‘5’, the final output is released The final output strand is detected via the release of a fluorophore ROX [47] 132 & The Institution of Engineering and Technology 2015

situations that demand that the final operation be decided by a combination of input conditions. For example, to achieve a NAND operation, two RNA aptamers are connected to a single ribozyme component. Hence, if any one or none of the input proteins is available, no mRNA cleavage occurs and GTP is produced. If, however, both the proteins are present, the conformational change triggers the mRNA cleavage and no GTP output is produced. Recently, Shapiro and co-workers [52] proposed a scheme comprising of a library of programmable gates built out of DNAzymes. These gates can perform Boolean operations based on the presence or absence of input mRNAs and miRNAs. Such operations may not be able to compete with the conventional fast semiconductor gates, but could be path-breaking for smart therapeutics in the treatment of diseases like cancer. In addition to DNA and RNA, there have been efforts to come up with synthetic molecules possessing the programmability of DNA molecules. An interesting example is the XNA molecule synthesised by Pinheiro et al. [53]. The deoxyribose sugar forms an essential component of a traditional DNA molecule. The XNA is a result of substituting the deoxyribose sugar with another sugar or sugar-like moeity, thus leading to artificial analogues of DNA. Pinheiro et al. created a total of six such XNAs (‘X’ being the sugar substitute used for the particular XNA). For example, arabinonucleic acids (ANAs) contain arabinose and cyclohexenyl nucleic acids (CeNAs) are composed of cyclohexene. The proposed techniques only allow XNA to be synthesised from DNA templates or XNA to be reverse transcribed to DNA [53]. The artificial nature of the XNAs throws up exciting possibilities in terms of application areas. Since XNAs are not naturally available, they can be used to inhibit the function of RNA without getting affected by the biological environment [54]. This property holds potential in molecular diagnostics and therapeutics. Care will have to be taken, however, to ensure that the biological host to which the XNAs are introduced suffers no harm. On a larger picture, XNAs are being touted as a means to understand the evolution of life itself. DNA and RNA are IET Nanobiotechnol., 2015, Vol. 9, Iss. 3, pp. 122–135 doi: 10.1049/iet-nbt.2014.0020

www.ietdl.org considered way too complex a molecule to be the early building blocks of life. XNAs can provide a glimpse to other possible forms of life, that are wholly different from the life forms we know of today. As clearly stated by Pinheiro et al., ‘the capacity of synthetic polymers for heredity and evolution shows that DNA and RNA are not unique as genetic materials’. In case of XNAs, encoding and transfer of genetic information (in other words, heredity) is demonstrated via synthesis and reverse transcription. The authors also demonstrated the evolutionary capability of XNA by creating HNA aptamers with specificity for target molecules such as the HIV trans-activating response RNA. There is still, however, a long way to go. If the dependence on DNA for XNA synthesis and amplification is removed, it might be possible to build genetic systems based only on XNAs [54]. Synthetic nucleic acids such as XNAs could prove to be pathbreaking in fields as varied as healthcare, manufacturing and evolutionary biology. The variety of basic blocks to perform ‘logic’ via SA only increases. However, let us take a step back. Does ‘logic’ not inherently drive the biological world in general? The human brain composed of billions of cells takes decisions based on the sensory inputs and provides the necessary outputs to the rest of the body. The only difference between biological logic operations and semiconductor logic is the ‘form’ of logic. The biological world operates with a medley of signal forms, like electrical, chemical and optical. De Silva and Uchiyama [55] showcased simple chemistry lab reactions in a new light by highlighting them not as the

traditional means to synthesise the required molecules, but as a form of logic that generates a ‘chemical’ output, given a set of ‘chemical’ inputs. Such a form of computing can be used for chemical sensing, for constructing logic gates employing internal charge transfer, and even for reconfigurable logic (e.g. the fluorescence of anthrylmethylpolyamines is dependent on the type of input ions). Indeed, computing is available even in basic chemistry.

7

Challenges and scope

SA, besides being one of the principal techniques in chemical synthesis, is fast expanding its wings in domains pertaining to nanotechnology, healthcare and fabrication. The goal of this survey has been to present SA through three major view-points: as a bottom-up method for micro-fabrication/ nano-fabrication; a novel way to compute using molecules; and as a viable technique for manufacturing meso-scale/ macro-scale structures like MEMS. Table 1 summarises the important examples and the outlook presented. Despite the exciting promises of SA, a number of challenges remain to be solved. With sub-units assembling on their own guided by their mutual interactions, the process of assembly itself is prone to errors leading to undesired structures. A possible solution could be using directed or templated SA that controls the direction of the assembling. However, there is still a requirement to correct the errors that might sneak in. Use of redundant parts, in robotics, for example, can serve to repair the product.

Table 1 Summary of SA applications Application

Examples

Advantages

Disadvantages

Micro-/ nano-fabrication

† Strain technology [16] † Quantum dots using plasma surface modification [17] † DNA origami for templated SA of CNTs [19] † DNA cross tiles for nanogrid and nanowire creation [21]

† Nano-scale precision possible due to bottom-up guided SA † Strained layers allow lattice mismatched deposition [16] † Programmability of the template (ex: modify staple strand sequence in DNA origami) [19]

† Prone to defects in the SA template [19] † Random arrangements of CNTs make contacting difficult [19]

Meso-scale assembly

† PDMS objects assembly using selective hydrophobic/ hydrophilic surfaces [29] † 3D hexagonal plates aggregate using wettability and surface tension [32] † 3D electrical network by fluidic SA using solder [33] † Magnetic field assisted 3D MEMS [34] † Capillary force assisted Self-folded MEMS [34] † LED arrays by fluidic SA using low-temperature melting solder [34] † Hamiltonian path solution using ssDNA by Adleman [9] † Tile-based algorithmic SA [31, 36, 37] † Biomolecular Automata [41– 43] † DNAzyme FSA [44] † DNA logic gates using toehold mediated strand displacement reactions [47] † RNA synthetic device sensors [50] † Internal charge transfer based chemical logic gates [55]

† A range of interaction forces – capillary, magnetic, electrical, fluidicavailable for use † Useful for 3D arrangement of robotic parts without the aid of sophisticated machinery [2] † A bottom-up alternative to traditional planar technology for 3D structure formation

† Self-repair might need redundant parts, adding to cost [4] † Slow. Might need extra assembly steps to disassemble incorrect formations

† Self-assembled patterns useful as templates for growing nanodevices [31, 40] † DNA logic gates useful for neural computations [48] † Facilitates formation of novel sensors and smart drugs [41–43, 50, 55] † Inherent parallelism for parallel computing [59]

† Slow computational speed of molecular gates not suitable against existing semiconductor circuits [49] † Large number of molecules required for more intensive computation [49] † Error correction required [56] † Bio-compatibility issues for diagnostic purpose. † Stability of the biomolecules is a concern. † Signal transmission and observation at molecular level is a challenge

Molecular computing

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www.ietdl.org Winfree [56] suggested the application of ‘self-healing’ tile sets. Suppose, during the algorithmic tile assembly, a section of tiles breaks off. There is a definite probability, in such a scenario, that the tiles re-grow in the backward direction and result in an incorrect assembly. To avoid such a growth, the authors propose using a block of tiles programmed in a manner that allows only for forward growth. Stability is another important factor for consideration in the case of molecular SA. Organic structures are infamous for their limited shelf-life and it is imperative to ensure that the molecular structure obtained from SA is stable enough for the target application. Fortunately, the future of molecular computing structures mostly lies in the domain of diagnostics, considering the slow computing speeds of molecular logic. It has to be noted, although, that biocompatibility of molecules for healthcare applications is the need of the day. Most of the proposals on intelligent diagnostics and smart drugs have been usually tested only in a test-tube environment, and their functionality remains to be observed in actual cellular biosystems. Another possible challenge is reading the outputs of molecular computing. Far removed from the traditional electrical signal, the molecular outputs mostly exist in the form of certain DNA sequences, or chemical structure, or a particular output molecule. Existing techniques mostly rely on using fluorescent dye molecules to indicate the output in an optical form. The concept of interconnections is extremely blurred at the molecular level and the designers would need to resort to innovative means for signal transmission and observation. The challenges in SA, however, are far outweighed by one big advantage – the power to push nanotechnology to scales that are currently unreachable by traditional semiconductor devices. As aptly observed by De Silva and Uchiyama [55], active molecular devices is one of the original aims of nanotechnology and with the emerging self-assembling techniques, constructions of such devices is fast becoming a scientific reality. A plethora of possibilities beckon for further research in the field of SA. Self-assembling nanofluidic devices [57], ‘lab-on-molecule’ capable of testing healthcare checks without the need of traditional circuitry, depositing integrated circuits on low-cost, flexible plastic substrates using fluidic SA, synthesis of new drugs by allowing molecular combinations to self-assemble yielding the best compound are just few of the innovations possible through SA [58]. The area of photonics is also banking heavily on the SA process. Photonic crystals called ‘smart plastics’ have been developed using SA of polymer molecules and are capable of manipulating light in a desired fashion. These might prove to be extremely useful for LEDs and even lead to colour changing paints [58]! A very important characteristic of SA is its inherent parallelism which can be exploited for parallel computing. In fact, most of the computational techniques discussed in Section 6 utilise this property. An interesting example on parallel computing has been provided by Bandyopadhyay et al. [59], wherein the authors aim to achieve computation through a molecular cellular automaton. Drawing inspiration from the massive parallelism observed in the neural network of the human brain, the authors demonstrated a computing grid (composed of 2,3-dichloro-5,6-dicyano-pbenzoquinone) performing operations like digital logic and simulation of cancer growth. The uniqueness of the assembly is the configurability of automata rules via external biasing.

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SA is, undoubtedly, widespread in nature. Patterns exist everywhere and one needs to just understand the basic underlying rules responsible for the pattern growth. Is it possible to look for similar rules to build systems via SA? The answer should only take us one step closer to understanding the world as we know it.

8

References

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