A review, published on September 11 in Cyborg and Bionic Systems by researchers at Tianjin University, summarizes recent advances in intracortical microstimulation (ICMS), a technique that uses tiny electrodes implanted in the brain to activate specific groups of neurons. The researchers discuss how ICMS has evolved from a tool for mapping brain function into a potential technology for sensory feedback, information delivery, closed-loop neuromodulation and biointegrated neural interfaces.

Brain–computer interfaces are increasingly being explored as a way to restore lost functions or provide new channels for communication between the nervous system and electronic devices. While much BCI research focuses on reading neural signals, ICMS provides the complementary ability to write information back into the brain.

“ICMS has emerged as a prominent invasive modality capable of activating intracortical neuronal populations with high precision,” the researchers write. The technology can potentially replace or supplement natural sensory inputs, probe

dysfunctional neural circuits and influence behavior.

Multiphoton microscopy (MPM), with its excellent optical sectioning capability and deep tissue penetration, has become an important tool for neuroscience and biomedical imaging. However, efficient axial information acquisition remains a major challenge for high-speed volumetric imaging of dynamic biological processes. Recent advances in optical devices, light-field control, information encoding, and intelligent computing have expanded the approaches to axial imaging, ranging from single-point scanning and multifocal parallel imaging to continuous-beam coverage. This review summarizes these developments from the perspective of axial information acquisition, and further discusses light-field control for improving 3D imaging performance, providing an overview of the field and its future directions toward faster, deeper, and higher-quality volumetric MPM. Meanwhile, the focal control, temporal encoding, structured light fields, and computational reconstruction methods discussed in this review are highly transferable and can be extended to other laser-scanning microscopy modalities, such as coherent Raman and photoacoustic microscopy.

Quantum random number generators play a crucial role in securing high-demand information contexts by producing true random numbers. Nevertheless, the large volume and high cost limit their widespread use. Here, a system on-chip is proposed that fully leverages the advantages of different photonic integrated platforms, where the interference optical paths and photodiodes are integrated on a standard silicon process, while the laser source on-chip is realized on a III–V platform. Using micro-lens coupling package technology with coupling loss lower than 2 dB, the components on different platforms are combined and packaged with the amplifier circuits in a 42 mm × 24 mm footprint in a butterfly form. This miniaturized and cost-effective entropy source enables outputting a vacuum noise signal with a 3-dB bandwidth of over 500 MHz. After sampling and post-processing, a random number generation rate of up to 6.57 Gbps is achieved.

With the increasingly urgent demand for non-toxic, high-specific-impulse propellants in space propulsion systems, ammonium dinitramide (ADN)-based liquid propellants have attracted extensive attention as a new-generation green alternative to hydrazine-based fuels. However, existing ADN-based thrusters all adopt the catalytic ignition technology route, which suffers from inherent drawbacks: the catalyst cannot withstand temperatures exceeding 1500 K, long preheating times are required prior to startup (in the Prisma mission, in-flight preheating lasted 600–720 s with a single-event energy consumption of 25 kJ), and insufficient preheating may lead to "hard start" or even explosion—these issues severely constrain the rapid response capability and operational safety of the thrusters. Although active ignition methods such as resistive ignition and laser ignition have been validated at the single-droplet level, how to achieve catalyst-free electric ignition at the thruster system level and systematically evaluate its combustion characteristics remains a critical engineering problem urgently to be addressed in the field of green space propulsion.

Plasma bubbles in the ionosphere over equatorial and low-latitude regions are a common nighttime plasma density depletion structure that can cause severe scintillation interference on high-frequency communications, making them an important target of space weather monitoring. Traditional detection methods rely on statistical thresholds derived from temporal fluctuations in ion density; however, plasma bubbles exhibit significant variability in spatiotemporal scales (ranging from kilometers to hundreds of kilometers), and their occurrence frequency is strongly modulated by solar activity and seasonal factors. Consequently, fixed-threshold methods struggle to accommodate this diversity, with miss rates reaching up to 40% during periods of low solar activity. Although deep learning has demonstrated powerful capabilities in automatically extracting and recognizing complex waveform features in fields such as medical image segmentation and signal peak detection, its systematic application to ionospheric plasma bubble detection—and thus addressing the adaptability deficiencies of traditional threshold methods—remains underexplored.