Microplastics in sewers: Hidden catalysts for methane emissions?
Peer-Reviewed Publication
Updates every hour. Last Updated: 26-Oct-2026 07:16 ET (26-Oct-2026 11:16 GMT/UTC)
Microplastics, the persistent pollutants already known to threaten ecosystems and human health, have a previously unrecognized dark side inside the world's urban sewer networks.
In data center networks, the growth rate of switch buffer size is much smaller than that of network bandwidth capacity. This results in frequent network congestion, which requires congestion control. Currently, the classification of end-to-end congestion control protocols is based on single dimensions such as time or congestion arbitration subjects.
A Chinese research team has achieved a breakthrough in improving the training efficiency of Graph Neural Networks (GNNs). They introduced an innovative architecture named "Decentralized Hypercube Collaborative Framework," addressing long-standing challenges such as high memory overhead, low computational efficiency, and underutilized hardware resources in traditional Graph Convolutional Network (GCN) training.
HUST researchers introduce DMCGuard to mitigate security risks in multi-channel IoT management.
Serverless computing typically uses secure containers encapsulated within lightweight microVMs to isolate function invocations across different tenants. However, to achieve high security guarantees, the Single-Container-Per-VM (SCPV) model can lead to significant memory inefficiency, as each microVM includes a separate guest OS, even when the container instances are identical.
Micro/nanomotors (MNMs) hold immense potential for advancing fundamental science and enabling a diverse range of applications, including targeted therapeutic interventions and environmental remediation. MOF-based motors have demonstrated excellent performance in wastewater treatment, leveraging the MOF tunable chemistry, high porosity, and large surface area for enhanced interactions with target species.
Advances in precision oncology necessitate tools capable of effectively imaging and treating deep-seated tumours. Radioactive quantum dots (RQDs) fulfil this requirement by creating self-luminescent probes through the unique fusion of quantum dots’ (QDs) tunable optical properties with the radiological characteristics of radioactive isotopes. This design enables multimodal imaging and synergistic therapy within deep tissues, overcoming the penetration depth limitations of conventional light sources.
A multiscale-coupling triple confinement strategy was developed to construct high-performance room-temperature phosphorescent (RTP) carbon dots (CDs) that display significantly prolonged RTP lifetime and enhanced quantum yield, and show great potential in multifunctional applications including information encryption, fingerprint recognition, dynamic display, and warm white light-emitting.