Logical circuits in colloids (2307.02664v1)
Abstract: Colloid-based computing devices offer remarkable fault tolerance and adaptability to varying environmental conditions due to their amorphous structure. An intriguing observation is that a colloidal suspension of ZnO nanoparticles in DMSO exhibits reconfiguration when exposed to electrical stimulation and produces spikes of electrical potential in response. This study presents a novel laboratory prototype of a ZnO colloidal computer, showcasing its capability to implement various Boolean functions featuring two, four, and eight inputs. During our experiments, we input binary strings into the colloid mixture, where a logical True" state is represented by an impulse of an electrical potential. In contrast, the absence of the electrical impulse denotes a logical
False" state. The electrical responses of the colloid mixture are recorded, allowing us to extract truth tables from the recordings. Through this methodological approach, we demonstrate the successful implementation of a wide range of logical functions using colloidal mixtures. We provide detailed distributions of the logical functions discovered and offer speculation on the potential impacts of our findings on future and emerging unconventional computing technologies. This research highlights the exciting possibilities of colloid-based computing and paves the way for further advancements.
- Andrew Adamatzky. Logical gates in actin monomer. Scientific reports, 7(1):1–14, 2017.
- Andrew Adamatzky. A brief history of liquid computers. Philosophical Transactions of the Royal Society B, 374(1774):20180372, 2019.
- On boolean gates in fungal colony. Biosystems, 193:104138, 2020.
- Role of surfactants on the stability of nano-zinc oxide dispersions. Part. Sci. Technol, 35:67–70, 2017.
- Conventional optics from unconventional electronics in zno quantum dots. The Journal of Physical Chemistry C, 114(20):9301–9307, 2010.
- Transition in the optical emission polarization of zno nanorods. The Journal of Physical Chemistry C, 115(32):15862–15867, 2011.
- A Chiolerio and Marco B Quadrelli. Smart fluid systems: The advent of autonomous liquid robotics. Advanced Science, 4(7):1700036, 2017.
- Alessandro Chiolerio. Liquid cybernetic systems: The fourth-order cybernetics. Advanced Intelligent Systems, 2(12):2000120, 2020.
- Resistive hysteresis in flexible nanocomposites and colloidal suspensions: interfacial coupling mechanism unveiled. RSC Advances, 6:56661–56667, 2016.
- Experimental demonstration of in-memory computing in a ferrofluid system. Advanced Materials, 35(23):2211406, 2023.
- Reservoir computing as a model for in-materio computing. In Advances in Unconventional Computing, pages 533–571. Springer, 2017.
- A substrate-independent framework to characterize reservoir computers. Proceedings of the Royal Society A, 475(2226):20180723, 2019.
- Arnold Emch. Two hydraulic methods to extract the n th root of any number. The American Mathematical Monthly, 8(1):10–12, 1901.
- Stability of zno nanoparticles in solution. influence of ph, dissolution, aggregation and disaggregation effects. Journal of Colloid Science and Biotechnology, 3(1):75–84, 2014.
- JS Frame. Machines for solving algebraic equations. Mathematics of Computation, 1(9):337–353, 1945.
- D. Gibb. The instrumental solution of numerical equations. In Ellice Martin Horsburgh, editor, Modern Instruments and Methods of Calculation: a Handbook of the Napier Tercentenary Exhibition, pages 259–268. The Royal Society of Edinburgh, 1914.
- Pavlovian reflex in colloids. arXiv preprint arXiv:2211.06699, 2022.
- Learning in colloids: Synapse-like zno+ dmso colloid. arXiv preprint arXiv:2211.00419, 2022.
- Reservoir computing with computational matter. In Computational Matter, pages 269–293. Springer, 2018.
- Methodology for sample preparation and size measurement of commercial zno nanoparticles. journal of food and drug analysis, 26(2):628–636, 2018.
- Reservoir computing approaches to recurrent neural network training. Computer Science Review, 3(3):127–149, 2009.
- Evolution in materio: Looking beyond the silicon box. In Proceedings 2002 NASA/DoD Conference on Evolvable Hardware, pages 167–176. IEEE, 2002.
- Evolution-in-materio: evolving computation in materials. Evolutionary Intelligence, 7(1):49–67, 2014.
- In materio computation using carbon nanotubes. In Computational Matter, pages 33–43. Springer, 2018.
- Julian Francis Miller. The alchemy of computation: designing with the unknown. Natural Computing, 18(3):515–526, 2019.
- Facile synthesis of quasi spherical zno nanoparticles with excellent photocatalytic activity. Journal of Cluster Science, 26(4):1187–1201, 2015.
- Combustion synthesis, characterization and raman studies of zno nanopowders. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 81(1):53–58, 2011.
- Susan Stepney. Co-designing the computational model and the computing substrate. In International Conference on Unconventional Computation and Natural Computation, pages 5–14. Springer, 2019.
- Enhanced sunlight photocatalytic performance of sn-doped zno for methylene blue degradation. Journal of Molecular Catalysis A: Chemical, 335(1-2):145–150, 2011.
- An experimental unification of reservoir computing methods. Neural networks, 20(3):391–403, 2007.