soft machine
In the film Terminator, the T-1000 is a transformable robot made of fluid polycrystalline metal.
In the real world, however, machines and devices called soft machines, which are made of flexible and stretchable soft materials and are capable of soft movements and deformation, are being considered. Soft machines are being considered.
The features and advantages of these soft machines include
- Flexibility: soft machines are made of soft materials such as rubber or gel and can make gentle contact with objects compared to rigid machines. This allows them to handle fragile objects and interact with humans safely.
- Transformability: soft machines can change shape in response to the environment and movement, enabling them to enter tight spaces or encompass objects with complex shapes.
- Safety: when working with humans, soft machines have the advantage of being made of soft material, which reduces the risk of injury in the event of a collision, especially in the medical and care sectors.
- Biomimetics: the movement of living organisms in nature can be imitated. For example, the movements of organisms such as octopuses and slugs can be reproduced to enable smooth and supple movements.
- Medical applications: soft robotics may also be used as medical devices. For example, in soft endoscopes and flexible capsules for administering drugs inside the body, and soft machines are also increasingly being applied in surgical robots and rehabilitation devices.
Current soft machine mechanisms use pneumatic, fluid pressure or actuators such as shape memory alloys or artificial muscles, which allow movement and shape changes in response to electrical or mechanical stimuli, but autonomous movement such as that of the aforementioned T-1000 has not yet been achieved.
Artificial synthesis of acteon
Towards the realisation of such autonomous soft machines, a new amoeba-like material that repeats sol-gel changes by itself has been synthesised artificially and reported by the Institute for Solid State Physics at the University of Tokyo.’ An important clue to the development of new soft machines that move with autonomy like in science fiction films.’
This is the development of a material with sol (liquid) and gel (quasi-solid) states that repeatedly changes its fluidity autonomously, whereas normal materials, such as jelly and agar, change their fluidity when warmed or cooled, without the need to add any electricity, light or heat from the outside. In contrast, the development of artificially synthesised polymers has enabled materials to undergo these changes without the need for any external addition of electricity, light, heat, etc.
Such cyclic sol-gel changes are frequently observed in living organisms in cell division, wound repair, cancer cell metastasis and amoeba movement, etc. In living organisms, the biopolymer actin is known to ‘repeat assembly and dispersion by itself’.
The University of Tokyo’s report shows that synthetic polymers have mimicked the functions of acteon to artificially reproduce some of the biological behaviours observed in vivo, and is thought to provide clues for future studies on the mechanisms of amoeba locomotion and other autonomous aspects of life.
In vivo, acteons are responsible for amoeboid locomotion, cell motility in which cells stretch and contract during cell migration, muscle contraction in muscle cells, part of the muscle contraction/relaxation mechanism, intracellular trajectories for the transport of substances, and the promotion of cytoplasmic division. Artificial generation of these functions is expected to lead to the realisation of new soft machines that move with the autonomy of living organisms.
Next steps to soft machine realisation
The above-mentioned actioin is the realisation of an actuator in a robot, and to realise a soft machine, the formation of an information transmission network and the formation of a bio-computer to process the information are essential, as described in ‘Emotion, autonomic nervous system and the “regulating” effect’. The formation of a bio-computer that processes the information is essential.
With regard to the autonomic nervous system, attempts are being made to artificially design neural circuits and signalling pathways using synthetic biology. By manipulating specific neurons and chemicals and synthesising artificially controlled nervous systems, the aim is to create systems that operate autonomously. For example, by applying ‘optogenetics’ technology, which uses light-sensitive nerve cells, it is possible to control neural activity with light and artificially mimic autonomic-like responses.
Biological computers (biocomputers) are computers that use biological elements to process and calculate information, and unlike conventional silicon-based computers, they are a form of computer that uses biomolecules and cells as a foundation. These computers use biological materials such as DNA, proteins, enzymes and cells to perform computational functions and data storage.
Biological computers are based on the following technologies.
1. DNA computing: a computational technique using DNA that exploits the bonding properties of DNA base pairs (A and T, C and G in specific combinations) to perform calculations; by combining DNA sequences, it is possible to process information in a parallel fashion. An early example is the ‘Hamilton closed circuit problem’ (a type of traveller’s problem) using DNA by Leonard Adelman in 1994.
2. enzyme-based computing: enzymes are biocatalysts that facilitate specific chemical reactions, and these reactions are combined to perform calculations. The reaction rate and substrate specificity of enzymes can be used to perform information processing functions.
3. cellular computers: computers based on living cells, in which cells can trigger specific reactions in response to external stimuli and utilise their signalling and metabolic processes to process and store information. Cellular computers, in which cells are designed by synthetic biology to produce deterministic outputs in response to specific inputs, are also being studied.
4. neural computers: computers that process information by mimicking or directly utilising neurons or neural networks, with the aim of having brain-like parallel computing power and flexible learning capabilities, also facilitating the development of brain-based computing models.
Applications of bio-computers include the following
- Medical applications: biocomputers are expected to be used as ‘smart drugs’ or ‘biosensors’ that operate inside the body using cells and DNA, for example, a DNA computer could be introduced into the body to detect certain chemicals or disease markers and control the release of drugs Systems are being devised.
- Gene circuits: utilising genetic engineering and synthetic biology, gene circuits embedded in cells act like computers, enabling cells to act autonomously and respond to changes in their environment. This is expected to generate therapeutic cells and bioproducts.
- Parallel computing: DNA computing allows large amounts of DNA to be combined and broken down at the same time, making parallel computing more efficient than conventional computers and effective in solving complex optimisation and combinatorial problems.
- Artificial life and synthetic biology: research is also underway to use bio-computer technology to design artificial life and synthetic cells and develop ‘biological robots’ that work under specific environmental conditions.
Challenges with bio-computers include long-term stability, as biomolecules and cells are susceptible to external environmental influences; computational speed, as they are slower than conventional silicon-based computers; and ethical concerns, as they use living organisms, especially when cellular computers or genetic circuits are used. associated with them, among others.
Soft machines can be achieved by combining these technologies, but there are many challenges and a long road to practical use is needed.
reference book
Reference books on soft machines include the following books.
1. “Soft Machines: Nanotechnology and Life“ by Richard A.L. Jones
2. “Bioinspired and Biomimetic Polymer Systems for Soft Robotics“ edited by
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