Highly Selective Recovery of Gold by In Situ Magnetic Field-Assisted Fe/Co-MOF@PDA/NdFeB Double Network Gel.

There are enormous economic benefits to conveniently increasing the selective recovery capacity of gold. Fe/Co-MOF@PDA/NdFeB double-network organogel (Fe/Co-MOF@PDA NH) is synthesized by aggregation assembly strategy. The package of PDA provides a large number of nitrogen-containing functional groups that can serve as adsorption sites for gold ions, resulting in a 21.8% increase in the ability of the material to recover gold. Fe/Co-MOF@PDA NH possesses high gold recovery capacity (1478.87 mg g-1) and excellent gold selectivity (Kd = 5.71 mL g-1). With the assistance of an in situ magnetic field, the gold recovery capacity of Fe/Co-MOF@PDA NH is increased from 1217.93 to 1478.87 mg g-1, and the recovery rate increased by 24.7%. The above excellent performance is attributed to the efficient reduction of gold by FDC/FC+, Co2+/Co3+ double reducing couple, and the optimization of the reduction reaction by the magnetic field. After the samples are calcined, high-purity gold (95.6%, 22K gold) is recovered by magnetic separation. This study proposes a forward-looking in situ energy field-assisted strategy to enhance precious metal recovery, which has a guiding role in the development of low-carbon industries.

Cross comparison representation learning for semi-supervised segmentation of cellular nuclei in immunofluorescence staining.

The morphological analysis of cells from optical images is vital for interpreting brain function in disease states. Extracting comprehensive cell morphology from intricate backgrounds, common in neural and some medical images, poses a significant challenge. Due to the huge workload of manual recognition, automated neuron cell segmentation using deep learning algorithms with labeled data is integral to neural image analysis tools. To combat the high cost of acquiring labeled data, we propose a novel semi-supervised cell segmentation algorithm for immunofluorescence-stained cell image datasets (ISC), utilizing a mean-teacher semi-supervised learning framework. We include a "cross comparison representation learning block" to enhance the teacher-student model comparison on high-dimensional channels, thereby improving feature compactness and separability, which results in the extraction of higher-dimensional features from unlabeled data. We also suggest a new network, the Multi Pooling Layer Attention Dense Network (MPAD-Net), serving as the backbone of the student model to augment segmentation accuracy. Evaluations on the immunofluorescence staining datasets and the public CRAG dataset illustrate our method surpasses other top semi-supervised learning methods, achieving average Jaccard, Dice and Normalized Surface Dice (NSD) indicators of 83.22%, 90.95% and 81.90% with only 20% labeled data. The datasets and code are available on the website at https://github.com/Brainsmatics/CCRL.

Enhanced adsorption performance of alginate/MXene/CoFe2O4 for antibiotic and heavy metal under rotating magnetic field.

Improving the adsorption performance with simple and strong applicability has always been a research hotspot in water treatment. This study introduces sodium alginate/MXene/CoFe2O4 (SA/MX/CFO) as functional composite materials by sodium alginate cross-linking and as adsorbents in the removal of contaminations with an external magnetic field (MF). SA/MX/CFO beads exhibited excellent mechanical properties, with fracture stress of 1.64 MPa at 73.4%, and an elastic modulus of 2.23 MPa. The isotherm fitting chose Cu2+ and CIP as the model pollutants, the isotherm fitting shows that the adsorption capacity of CIP is significantly improved by 24.2%.The magnetic effect of the adsorption capacity of Cu2+ is not obvious, which indicated the selectivity for adsorption; however, the adsorption rates of CIP and Cu2+ are greatly improved by 359.76% and 371% respectively. Promoting materials transfer rate, changing of hydrogen bond, and surface functional group reactivity is the key to determine the adsorption enhancement with a rotating magnetic field (RMF). The combination of the external magnetic field and the inherent magnetic properties of the adsorbent can adjust the adsorption process and the selectivity of pollutants. It also provides an innovative and practical method for MF to remediate contaminants in the magnetically enhanced adsorption system.

Magnetic field assisted adsorption of pollutants from an aqueous solution: A review.

The magnetic field is a special substance that exists objectively and transmits the magnetic force between objects. Magnetic fields (MFs) are gradually attracting attention as a facile, universal adsorption enhancement method, especially under the condition of low concentration of pollutants. By adjusting the type and parameters of the magnetic field, enhancement of adsorption capacity, rate and selectivity can be targeted. Many studies have focused on the adsorbent separation based on magnetic properties under MF assistant, and no review have come up in recent years on the pollution enhanced-adsorption technique using MFs. The present review brings out a series of magnetic field and summarizes adsorption-assisted enhancement mechanism of MFs in different situations. This review article aimed at helping researchers obtain quick ideas of MFs and application for pollutant adsorption.