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.

Design of deep eutectic solvents for multiple perfluoroalkyl substances removal: Energy-based screening and mechanism elucidation.

The current landscape of perfluoroalkyl substances (PFAS) extraction methodologies presents significant challenges, particularly for multiple PFAS with different carbon chain lengths. This study introduced an energy-driven strategic approach for screening deep eutectic solvents (DESs) to effectively remove a diverse range of PFAS, including perfluoroalkylcarboxylic acids (PFCAs), perfluoroalkanesulfonic acids (PFSAs), and perfluoroalkyl amides (FAAs), from contaminated environments (total 13 target compounds). Utilizing energy-based screening, we identified DES candidates with high affinity for a spectrum of PFAS compounds from 1234 potential starting materials of eutectic systems. Key findings revealed the superior removal efficiency of tributylphosphineoxide/2-methylpiperazine system, exceeding 99 % for various PFAS with different carbon chain lengths in real environmental water samples. Additionally, we elucidated the molecular interactions between DESs and PFAS through ab initio molecular dynamics (AIMD) simulations, providing valuable insights into the mechanisms governing the removal process. The mechanism of extraction involves hydrogen bond network topology and structural organization, with DESs capable of extracting PFAS while maintaining a weakly aggregated state of target molecules and minimizing the impact on the intrinsic structures of DES. The proposed system forms a dynamic, complementary, and flexible non-covalent interaction network structure with PFAS. The study advances the understanding of DES as a designable, effective, and sustainable alternative to conventional solvents for PFAS remediation, offering a significant contribution to environmental chemistry and green technology.

Simultaneous size and defect control of metal-organic framework by deep eutectic solvent for efficient perfluoroalkyl substances adsorption: Delving into mechanism.

This study reports an environment-friendly protocol to prepare a metal-organic framework (MOF) with simultaneously controlled particle size and open metal site for adsorption removal of perfluoroalkyl substances (PFASs). The successful preparation of UiO-66 with defect and crystal size modulation was achieved using a green and straightforward method, adjusting the components and molar ratios of ammonium salt/glycolic acid deep eutectic solvents (DESs). The corresponding modulation mechanism primarily relied on the combined regulation of the deprotonation and competitive coordination abilities of the eutectic solvent components. The adsorption process was thoroughly examined using spectral analyses, adsorption behavior profiling, and ab initio molecular dynamics simulations. The results revealed that PFAS adsorption is driven by combined capturing effects, such as CF-π, acid/base coordination, C-F⋯Zr, hydrogen bonding, and hydrophobic interactions. Our findings were not thus that the smaller the crystal size of MOF and the higher the defect concentration in the material, the better the PFAS adsorption performance. The result demonstrated the combined effect of these adsorbent features on PFAS mixtures. Furthermore, they revealed unique differences in sorption properties between these targets with different carbon chain lengths. Extensive defects in DES-based UiO-66 led to larger pores, increasing the availability of many adsorption sites and aiding in PFAS adsorption and diffusion. Nevertheless, the surplus of larger pores in the substance increased the competitive adsorption, reducing the total quantity of PFASs absorbed. Furthermore, various interactions and a less restrictive configuration increased the contact of functional groups with adsorbates, substantially enhancing the adsorption. This study investigates the basic questions about how PFAS molecules are adsorbed on DES-based MOFs and the relationship among the structure, properties, and performance to improve the efficiency of this novel adsorbent.

A systematic evaluation and meta-analysis of early prediction of post-thrombotic syndrome.

Objective: Post-thrombotic syndrome (PTS) is the most common long-term complication in patients with deep venous thrombosis, and the prevention of PTS remains a major challenge in clinical practice. Some studies have explored early predictors and constructed corresponding prediction models, whereas their specific application and predictive value are controversial. Therefore, we conducted this systematic evaluation and meta-analysis to investigate the incidence of PTS and the feasibility of early prediction. Methods: We systematically searched databases of PubMed, Embase, Cochrane and Web of Science up to April 7, 2023. Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included articles, and the OR values of the predictors in multi-factor logistic regression were pooled to assess whether they could be used as effective independent predictors. Results: We systematically included 20 articles involving 8,512 subjects, with a predominant onset of PTS between 6 and 72 months, with a 2-year incidence of 37.5% (95% CI: 27.8-47.7%). The results for the early predictors were as follows: old age OR = 1.840 (95% CI: 1.410-2.402), obesity or overweight OR = 1.721 (95% CI: 1.245-2.378), proximal deep vein thrombosis OR = 2.335 (95% CI: 1.855-2.938), history of venous thromboembolism OR = 3.593 (95% CI: 1.738-7.240), history of smoking OR = 2.051 (95% CI: 1.305-3.224), varicose veins OR = 2.405 (95% CI: 1.344-4.304), and baseline Villalta score OR = 1.095(95% CI: 1.056-1.135). Meanwhile, gender, unprovoked DVT and insufficient anticoagulation were not independent predictors. Seven studies constructed risk prediction models. In the training set, the c-index of the prediction models was 0.77 (95% CI: 0.74-0.80) with a sensitivity of 0.75 (95% CI: 0.68-0.81) and specificity of 0.69 (95% CI: 0.60-0.77). In the validation set, the c-index, sensitivity and specificity of the prediction models were 0.74(95% CI: 0.69-0.79), 0.71(95% CI: 0.64-0.78) and 0.72(95% CI: 0.67-0.76), respectively. Conclusions: With a high incidence after venous thrombosis, PTS is a complication that cannot be ignored in patients with venous thrombosis. Risk prediction scoring based on early model construction is a feasible option, which helps to identify the patient's condition and develop an individualized prevention program to reduce the risk of PTS.