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Implementation of the 2-Bit Decoder using the liquid handler a The truth table for the 2-bit decoder. b The initial acid (yellow) and base (blue) stocks of the encoded inputs and their complements. c The final output of the acid-base circuit after applying the AND gate.
Source publication
Acid-base reactions are ubiquitous, easy to prepare, and execute without sophisticated equipment. Acids and bases are also inherently complementary and naturally map to a universal representation of “0” and “1.” Here, we propose how to leverage acids, bases, and their reactions to encode binary information and perform information processing based u...
Citations
Chemical reaction networks (CRNs) could offer chemical complexity¹ to contribute to future intelligent material design² or neuromorphic computing technologies3,4. Recent advances in systems chemistry use the complex Belousov–Zhabotinsky⁵⁻⁷ and Formose reactions8,9, or simpler chemical systems with fewer feedback loops¹⁰⁻¹², to demonstrate that (spatio)temporal patterns can be harnessed to emulate properties important for intelligent behavior (i.e., the ability to perceive information and retain it as knowledge to execute complex tasks¹³). In all examples, autocatalysis appears an essential element for facilitating a nonlinear response. How this chemical analogue of a positive feedback mechanism14,15 can be reconfigured in a programmable manner is, however, unknown. Here, we developed a strategy that uses metal ions (Ca²⁺, La³⁺, and Nd³⁺) to control the rate of a trypsin-catalysed autocatalytic reaction network. A flow setup is employed to sustain the reaction network under out-of-equilibrium conditions and demonstrate that various kinetically controllable responses can be mapped onto polynomial and Boolean functions. Remarkably, these functions cannot only be programmed but their temporal and history-dependent nature bestows them with neuromorphic properties, promising novel strategies in designing intelligent chemical systems. Beyond, the easy-to-use method to control autocatalysis will impact future development focusing on biocatalysis, nanobiotechnology, and the chemical origin of life.
The realization of high‐performance photolithographic coplanar organic thin film transistors (OTFTs) is fundamental to boost cosmically commercial applications of organic electronics. However, photolithographic coplanar OTFTs generally suffer from poor charge injection and therefore poor filed‐effect performance. Here, a simple and effective strategy is developed to fabricate photolithographic rugged electrodes, and successfully achieve high‐density low‐contact‐resistance photolithographic coplanar OTFTs. Based on this versatile electrode, the wafer‐scale photolithographic rugged electrode can be easily achieved, and the device density of the coplanar OTFTs is as high as 28000 cm⁻². The device shows excellent electrical properties with mobility up to 2.01 cm² V⁻¹ s⁻¹ and Rc as low as 7.8 kΩ cm, which is superior to all the reported Ag‐electrode coplanar OTFTs. This work shows a reliable strategy to reduce the contact resistance of photolithographic coplanar OTFTs and elucidates the effect of injection resistance (Rinj) and access resistance (Racc) on coplanar OTFTs.
Chemical and molecular-based computers may be promising alternatives to modern silicon-based computers. In particular, hybrid systems, where tasks are split between a chemical medium and traditional silicon components, may provide access and demonstration of chemical advantages such as scalability, low power dissipation, and genuine randomness. This work describes the development of a hybrid classical-molecular computer (HCMC) featuring an electrochemical reaction on top of an array of discrete electrodes with a fluorescent readout. The chemical medium, optical readout, and electrode interface combined with a classical computer generate a feedback loop to solve several canonical optimization problems in computer science such as number partitioning and prime factorization. Importantly, the HCMC makes constructive use of experimental noise in the optical readout, a milestone for molecular systems, to solve these optimization problems, as opposed to in silico random number generation. Specifically, we show calculations stranded in local minima can consistently converge on a global minimum in the presence of experimental noise. Scalability of the hybrid computer is demonstrated by expanding the number of variables from 4 to 7, increasing the number of possible solutions by 1 order of magnitude. This work provides a stepping stone to fully molecular approaches to solving complex computational problems using chemistry.
