Magnetic nanofluid: synthesis and characterization

The application of deep eutectic solvent-based magnetic nanofluid in analytical sample preparation

The pursuit of green analytical chemistry has led to the exploration of deep eutectic solvents (DESs) as green solvents in sample preparation processes. DESs, formed by hydrogen bond donor and acceptor components, exhibit unique properties such as low toxicity, biodegradability, and designable structures that make them ideal for extraction technologies. However, no comprehensive assessment of the utilization of DES-based magnetic nanofluid for analytical sample pretreatment has been performed. This review summarized the preparation methods of DES-based magnetic nanofluids and their application in various microextraction technologies, including vortex-assisted, ultrasonic-assisted, dispersive, and microfluidic device-based approaches, highlighting their role in enhancing the efficiency and sustainability of analytical methods. The paper underscored the importance of the stability of magnetic nanofluids in sample pretreatment and the advantages of using DESs, such as reduced organic solvent usage and compatibility with green chemistry principles. Key findings from recent research on the application of DES-based magnetic nanofluids in microextraction were presented, demonstrating their high extraction recoveries, low detection limits, and applicability to a wide range of analytes and matrices. The outlook suggests potential directions for future research, including the refinement of DES-based magnetic nanofluids for improved performance in analytical sample preparation. This review provides a valuable reference for researchers and practitioners in the field of analytical chemistry, showcasing the potential of DES-based magnetic nanofluids as a sustainable and efficient tool for sample preparation and microextraction.

Porous liquids: a novel porous medium for efficient carbon dioxide capture

Porous liquids (PLs) are the combination of porous solid material and flowing liquid, which provides alternative options to solve difficulties in the development of porous solids. With the booming development of PLs since 2015, plenty of syntheses and applications have been reported with a specific focus on gas adsorption. Given the lack of a comprehensive review, this paper reviews the application of PLs in CO2 capture. To start with, ground-breaking case studies are reviewed to help understand the progress of PLs research. Then, as a major part of this paper, studies of PLs for CO2 capture are reviewed separately. Moreover, five basic properties of porous liquids, including stability, viscosity, selectivity, porosity, capacity, and the influencing factors are systemically reviewed respectively. Furthermore, gas storage and release mechanisms in PLs are briefly outlined, and potential processing methods of PLs used for CO2 capture are discussed.

On-Demand 3D Spatial Distribution of Magnetic Permeability Based on Fe3 O4 Nanoparticle Liquid Toward Micro-Cavity Detectors

The change of 3D spatial distribution of magnetic permeability can lead to the generation of introduced electrical signals. However, present studies can only achieve rough regulation by simple shape deformation of magnetic elastomers such as compression, bending, or stretching. Accurate control of the 3D spatial distribution of magnetic permeability is still an open question. In this study, an on-demand 3D spatial distribution of magnetic permeability by controlled flowing of Fe3 O4 nanoparticle liquid (FNL) is demonstrated. The flowing routes of FNL are tuned by a 3D-printed cage with pre-designed hollow structure, thus changing the 3D spatial distribution of magnetic permeability. Then, eight symmetrically distributed coils under cage are used to receive characteristic induction voltage signals. Maxwell numerical simulation reveals the working mechanism of signal generation. Notably, those eight coils can detect FNL flowing status in eight directions, allowing recognition of up to 255 different FNL flowing combinations. By introducing machine learning, the micro-cavity detector based on FNL can distinguish nine kinds of micro-cavity structures with an accuracy of 98.77%. This work provides a new strategy for the adjustment of the 3D spatial distribution of the magnetic permeability and expands the application of FNL in the field of space exploration.