The rapid rise of IoT and AI is exposing the energy and data movement limits of von Neumann computing. Neuromorphic computing, especially spiking neural networks, offers a compelling alternative but demands compact hardware with neuron like nonlinear dynamics. In the new Nano Research review, Prof. Chuanyu Han’s team highlights recent progress in artificial spiking neurons built from emerging electronic devices, spanning memristor-based and transistor-based implementations. The article distills key spiking mechanisms, practical circuit design templates, and system demonstrations for neuromorphic perception and brain inspired computing, and introduces a rigorous unified benchmarking framework to help compare technologies and accelerate interdisciplinary work across materials science and microelectronics.

Infrared focal plane arrays (FPAs) are image sensors, widely used in thermal imaging systems across various fields. Future advancements in FPAs are expected to require high data rates, which can cause heat leakage from electrical interconnects into FPAs, increasing noise and raising cooling requirements. In a new study, researchers demonstrated cryogenic vertical-cavity surface-emitting laser (VCSEL)-based optical interconnects that can achieve high-speed communication while reducing thermal leakage and power consumption.

Waveguide-based electro-optic (E-O) frequency combs are prized for their flexible pump wavelength, tunable repetition rate and near-100% conversion efficiency, but their spectral span is usually limited to a few nanometers. Researchers now report a broadband waveguide E-O comb based on "mode circulation": a single beam is recirculated through the same phase-modulation region eight times as four distinct waveguide modes (TE0–TE3). Enabled by a Z-propagation mode multiplexer that suppresses mode hybridization in anisotropic lithium-niobate-on-insulator waveguides, the device boosts the modulation index by ~7.5×, generating 128 comb lines across 25.6 nm at 25 GHz with an effective half-wave voltage of just 1.24 V and only 7 dB on-chip loss.

A new study published in the Chinese Journal of Aeronautics （https://doi.org/10.1016/j.cja.2025.103943） tries to break this deadlock. Researchers from Northwestern Polytechnical University and Tsinghua University have developed a novel modeling framework called differential equation model long-short term memory neural network (DEM-LSTM). This hybrid approach integrates the theoretical rigor of differential equations with the nonlinear mapping capabilities of LSTM neural network, offering a solution that is both accurate and physically interpretable.

Scientists have made major progress in explaining friction, but predicting and controlling it still depends heavily on experiments. A comprehensive review examines the physics-based models behind friction, highlights why friction remains difficult to predict, and identifies key challenges that could make friction control more precise and reliable.

PFAS, often called “forever chemicals,” are a growing global concern because they persist in water, are difficult to remove, and have been linked to health risks, including certain cancers. A new review by researchers from Hanyang University shows that the length of a PFAS molecule’s fluorinated carbon chain is a key predictor of its environmental behavior and treatment response. The study found that short-chain PFAS are generally more mobile and harder to remove, providing a practical framework for improving water treatment and contamination cleanup.

In the modern era marked by rapid technological advancements, ferroelectric materials have gradually emerged as highly promising candidates for a wide range of applications, due to their distinctive polarizationcharacteristics. A common strategy to address the low ferroelectric polarization caused by lattice mismatch between the substrate and ferroelectric film is the insertion of a buffer layer. By precisely controlling the thickness of the buffer layer, the maximum polarization was increased from 126.3 μC/cm² to 142.6 μC/cm², providing robust support for accurate recognition in an intelligent traffic-intersection recognition system.