Memristors are electric components that emulate the memory and computational properties of biological synapses by remembering the current that flows through them. Here, for the first time, the memristive properties of geopolymers, inexpensive ceramic materials manufactured at room temperature from alkaline activation of amorphous aluminosilicate precursors, are presented. It is demonstrated that geopolymers present all the fingerprints of memristors, and a physics‐based model is proposed, which demonstrates that electroosmosis in the bulk geopolymer pores induces ion channels that foster change in the overall conductance of the bulk material, contributing to the observed memristive behavior. This model opens the door to a new category of porous electroosmosis‐based bulk memristors. Synaptic functions such as short‐term plasticity and long‐term plasticity, as well as endurance and retention capabilities are also demonstrated. The reported findings pave the way to the use of geopolymers for low‐cost applications in neuromorphic computing.

Whereas most memristors are fabricated using sophisticated and expensive manufacturing methods, we recently introduced low-cost memristors constructed from sustainable, porous geopolymers (GP) at room temperature via simple casting processes. These devices exhibit resistive switching via electroosmosis and voltage-driven ion mobility inside water-filled channels within the porous material, enabling promising synaptic properties. However, GP memristors were previously fabricated at the centimeter scale, too large for space-efficient neuromorphic computing applications, and displayed limited memory retention durations due to water evaporation from the pores of the GP material. In this work, we overcome these limitations by implementing (i) an inexpensive manufacturing method that allows fabrication at micron-scale (99.998% smaller in volume than their centimeter-scale counterparts) and (ii) functionalization of GPs with EMIM⁺ Otf⁻ ionic liquid (IL), which prolonged retention of the memristive switching properties by 50%. This improved class of GP-based memristors also demonstrated ideal synaptic properties in terms of paired-pulse facilitation (PPF), paired-pulse depression (PPD), and spike time dependent plasticity (STDP). These improvements pave the way for using IL-functionalized GP memristors in neuromorphic computing applications, including reservoir computing, in-memory computing, memristors crossbar arrays, and more.

Memristors are solid-state devices that share many information processing capabilities of biological synapses, allowing brain-inspired memory and computing. Among memristor-based neuromorphic computing systems, physical reservoir computing has gained significant attention in recent years due to its low computational cost and suitability in handling multiple tasks by leveraging the short-term memory property of artificial synapses. In our previous studies, we developed geopolymer (GP)-based low-cost memristors and demonstrated their activity-dependent behaviors, such as short-term plasticity behaviors [including Paired-Pulse Facilitation (PPF) and Paired-Pulse Depression (PPD)] and long-term plasticity behaviors [including spike-timing-dependent plasticity and Spike-Rate-Dependent Plasticity (SRDP)]. GPs are a class of inorganic polymers formed by the alkali activation of aluminosilicate precursors. In this study, we present an efficient and low-cost geopolymer-based reservoir computing system capable of recognizing a computer-generated 5 × 5 binary digit set and a handwritten Modified National Institute of Standards and Technology digit dataset with up to an accuracy of 89%, combining both experimental- and simulation-based approaches. The pattern recognition capability of GP memristors suggests their potential application in integrated energy-efficient and real-time structural health monitoring.