Programmable 2D Nanochannels: A Giant Leap Toward Brain-Like Memory

Programmable nanochannels mimicking brain memory

In a stunning leap toward neuromorphic computing, scientists at the University of Manchester's National Graphene Institute have successfully engineered programmable 2D nanochannels that mimic the memory functions of the human brain. The breakthrough, published in Nature Communications, introduces a new class of nanofluidic memristors capable of replicating the complex behaviors of biological synapses—all in a single, ultra-thin, reconfigurable device.

Read the original article on Phys.org

What Are Nanofluidic Memristors—and Why Do They Matter?

Memristors—short for memory resistors—are devices that retain a history of the electrical signals they've received. Unlike traditional digital memory, they emulate how neurons in the brain strengthen or weaken connections over time. Most memristors until now have relied on solid-state electron transport. But this new approach takes advantage of liquid electrolytes confined in 2D materials like molybdenum disulfide (MoS₂) and hexagonal boron nitride (hBN).

The result is a low-power, highly tunable system that more closely mimics how real brain cells operate. Think of it as a soft, ion-based memory that behaves less like a machine and more like a living system.

All Four Memory Modes—One Device

The team, led by Professor Radha Boya, achieved something unprecedented: by adjusting parameters like voltage, pH, and channel geometry, the same nanofluidic device could toggle between four distinct memristive loop types. These include two "crossing" and two "non-crossing" hysteresis behaviors, each associated with unique memory effects:

  • Ion-ion interaction memory
  • Ion-surface adsorption/desorption
  • Surface charge inversion
  • Ion concentration polarization

This level of configurability in a single device could revolutionize the way we design computing architectures—offering adaptability and energy efficiency far beyond conventional semiconductors.

Biological Memory in a Chip

In addition to multiple memory types, the nanochannels demonstrate both short-term and long-term memory dynamics. This mimics the plasticity of synapses in the human brain—where a memory of your home address might last a lifetime, but the location of your keys may fade within minutes.

The team even replicated phenomena like short-term synaptic depression, where repeated signals weaken a response—mirroring how our brains filter out background noise in a café. This suggests a route to hardware-based sensory adaptation in future AI systems.

A Unified Theoretical Model

To explain the observed behavior, the researchers proposed a minimal yet powerful model that combines ion–ion interactions, channel entrance effects, and surface adsorption. This model reproduces all four loop behaviors, offering a robust framework for the design of future ionic circuits and low-power, real-time learning devices.

Implications for AI, Bioelectronics, and Beyond

This discovery opens doors to neuromorphic systems that operate more like human brains—processing information in an adaptive, analog fashion. Potential applications span from bioelectronic implants and robotic sensing to AI hardware that learns natively through experience.

And because these devices operate at ultra-low energy using common electrolyte materials, they could pave the way for sustainable, scalable, and even biodegradable computing platforms in the near future.

🧠 Reference:
Abdulghani Ismail et al, "Programmable memristors with two-dimensional nanofluidic channels", Nature Communications (2025). DOI: 10.1038/s41467-025-61649-6

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#NeuromorphicComputing #Nanofluidics #Memristors #2DMaterials #BrainInspiredAI #SynapticDevices #MoS2 #hBN #IonicCircuits #QuantumServerNetworks #PWmat

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