Congli Li

Efficient separation of carbon monoxide (CO) from nitrogen (N₂) is vital for recycling steel industry by-product gases and advancing carbon-neutral chemical production. However, current Cu(I)-based π-complexation adsorbents suffer from excessive binding strength and low working capacity. Here we report thermally-dispersed open metal sites (OMS) with two-coordinated Cu(I) units, ( ≡ C–O)CuCl, anchored onto zeolite imidazolate frameworks (ZIFs) derived oxygen-containing porous carbons (ZPC), where the Cu···CO interaction strength is precisely moderated. The optimized adsorbent, 15Cu(I)/ZPC-1, with 15 mmol g⁻¹ Cu(I) dispersed in 10–30 Å pores, delivers a high CO working capacity of 3.21 mmol g⁻¹ (0.1–1 bar, 298 K), the highest among reported recyclable Cu(I)-based adsorbents. The analyses reveal that a low-symmetric trigonal ( ≡ C–O)CuCl···CO coordination geometry weakens π-backbonding, enhances working capacity. Process simulations predict that 15Cu(I)/ZPC-1 enables high productivity and low energy consumption. This work establishes OMS regulation in porous carbon as an effective strategy for efficient CO/N₂ separation.

The distributed energy resources (DERs) in the distribution network include distributed generation, energy storage, electric vehicles, flexible loads, etc. Most of these DERs are located behind the meter and are not visible to the system operators. With the increased penetration of DERs, the system operators wish to take the behine-the-meter (BTM) DER into the distribution restoration scheme to enhance the resilience of the distribution network. This anticipation will be based on the correct equivalence of the BTM DER at the boundary of distribution automation. A method for modeling the BTM DER is proposed, to train the BTM DER model based on the Advanced Metering Infrastructure (AMI) meter readings with a generative adversarial network, and to modify the equivalent model based on Distribution Automation (DA) measurements. The wind and photovoltaic power profile from NREL integration data sets and the residential dataset from the reference energy disaggregation data set are used to demonstrate the performance of the proposed method.

Polarization imaging has advantages to reveal objective characteristics in foggy conditions. Quality assessment of polarization analysis images in imaging is rarely reported. A quality assessment method suitable to polarization analysis images in foggy conditions is proposed. Firstly, polarization characteristics in foggy conditions are analyzed. Three types of quality factors are selected, which are contrast factor based on brightness, the moment of inertia factor based on structure, and MSCN (Mean Subtracted Contrast Normalized) factor based on Stokes parameters. Corresponding assessment model is constructed, and pooling strategies are designed to map quality score of intensity of polarization image I and degree of polarization image P. Experiments on images captured by polarization camera in simulation foggy environment and generated by fog simulation of Photoshop. Results show that the proposed method is well consistent with subjective observation and analysis.