Polyethylene terephthalate (PET) derived terephthalic acid (TPA) has recently emerged as a sustainable alternative precursor for the synthesis of metal organic frameworks (MOFs), offering a promising strategy for converting waste plastic into high value functional materials. PET-derived TPA-MOFs have demonstrated morphological, physicochemical, and photoluminescent properties comparable to those of conventionally synthesised TPA-MOFs, highlighting their potential for electrochemical and fluorescence sensing applications. This review provides a comprehensive comparison between PET-derived and conventional TPA-MOFs, with emphasis on synthesis strategies, functional properties, and their sensing performance. First, we discuss routes for PET valorisation for TPA recovery, MOF synthesis strategies, and the integration of carbon/polymer materials to develop hybrid materials. Furthermore, we discussed the sensing performance of PET, conventional TPA-MOFs, and hybrids in terms of sensitivity, selectivity, LOD, and practical applications for environmental pollutants. Finally, we take a closer look at the current challenges and prospects of PET-derived MOFs, highlighting these materials as an important bridge between circular economy principles and next-generation functional sensing technologies.
Red yeast rice dietary supplements are marketed for cholesterol management but show variability in the content of monacolin K (lovastatin), the main active ingredient. Together with changing legislation, this creates the need for rapid analytical methods capable of pre-screening red yeast products. This study evaluates dielectric barrier discharge ionization mass spectrometry (DBDI-MS) as a rapid, separation-free screening method for total lovastatin equivalents in dietary supplements.The ionization behavior of lovastatin and its active form, lovastatin hydroxy acid, was investigated under DBDI-MS conditions and compared with electrospray ionization. Because both forms produced overlapping signals in DBDI-MS, direct quantification without chromatographic separation proved impractical. Alkaline conversion was therefore performed to quantitatively convert lovastatin to lovastatin hydroxy acid prior to analysis, enabling determination of total lovastatin equivalents using a single isotopically labelled internal standard. Method performance was evaluated for repeatability, matrix effects, calibration, and applicability to commercial dietary supplements. Results were compared with a validated LC-UV reference method.The alkaline conversion improved the repeatability across six representative matrices and also decreased matrix effects after isotope-dilution correction. However, calibration performance and accuracy did not meet the requirements for reliable quantification. The method was therefore applied as a threshold-based screening tool using a practical analytical detection limit. In eleven supplements, DBDI-MS correctly distinguished samples with detectable lovastatin from those below the detection limit, with positive findings confirmed by LC-UV. Instrumental analysis required approximately 3 min for calibration and 1.5 min per sample.DBDI-MS provides a rapid first-line screening approach for identifying dietary supplements that require confirmatory chromatographic analysis. Although unsuitable for definitive quantification, its speed and compatibility with high-throughput workflows make it promising for quality-control screening of dietary supplements containing lovastatin or red yeast rice.
Serotonin (5-hydroxytryptamine, 5-HT) is an important neurotransmitter and a potential biomarker for neurological and psychiatric disorders, necessitating sensitive and reliable analytical methods for its determination in biological samples. Herein, a mixed-mode monolithic pipette-tip micro-solid-phase extraction (PT µ-SPE) platform coupled with HPLC–UV was developed for the rapid determination of urinary serotonin. A poly(methacrylic acid-co-ethylene dimethacrylate) monolith was synthesized in situ within a surface-modified polypropylene pipette tip by UV-initiated polymerization, providing synergistic cation-exchange and hydrophobic interactions for selective serotonin extraction. Under optimized conditions, the proposed PT µ-SPE achieved a 12-fold pre-concentration factor with an extraction efficiency of 91%. The mixed-mode monolith exhibited a dynamic breakthrough capacity approximately 200-fold higher than that of a hydrophobic monolith and substantially higher than those of commercial C18 SPE sorbents, enabling highly efficient single-pass extraction. The PT µ-SPE–HPLC–UV method demonstrated excellent analytical performance, with limits of detection and quantification of 1.70 and 5.67 ng mL⁻1, respectively, recoveries of 95–104%, and good repeatability (RSD < 3.42%). Under the selected conditions (2 mL sample volume and 150 µL elution volume), the complete PT µ-SPE–HPLC–UV workflow required approximately 10 min, comprising 5 min for extraction and 5 min for analysis. The method was successfully applied to the determination of urinary serotonin in healthy volunteers. The proposed mixed-mode PT µ-SPE platform provides a simple, rapid, and cost-effective sample preparation strategy using conventional HPLC–UV instrumentation and demonstrates the potential of mixed-mode monolithic sorbents for the routine laboratory analysis of polar basic compounds in complex biological matrices.
This study aimed to develop an innovative and efficient magnetic sorbent based on urea-modified magnetic TMU-16-NH₂ for magnetic solid-phase extraction (MSPE). Urea was selected as a suitable surface modifier for TMU-16-NH₂ because of its availability and functional groups capable of providing additional interaction sites for the target analytes, thereby enhancing the adsorption capability of the sorbent. In this regard, the proposed sorbent was used for the extraction of diazinon, teflubenzuron, and hexaflumuron. The extracted analytes were separated and quantified by high-performance liquid chromatography-ultraviolet detection (HPLC-UV). The experimental parameters including sorbent amount, salt concentration, pH of sample solution, eluent volume, desorption time, and extraction time, were systematically studied and optimized. Under the optimal condition, the developed method exhibited good linearity in the concentration range of 0.50–200.00 μg L-1 for diazinon, 1.00–200.00 μg L-1 for hexaflumuron, and 2.00–200.00 μg L-1 for teflubenzuron. The limits of detection (LODs) and limits of quantification (LOQs) were in the range of 0.13–0.61 μg L⁻¹ and 0.43 to 2.01 μg L⁻¹, respectively. The enrichment factors (EFs) and absolute recoveries (%ARs) were also in the range of 255.00 to 310.00 and 63.25% to 77.50%, respectively. The prepared sorbent was able to be reused 9 times. The BAGI and AGREEprep scores were calculated to be 80.00 and 0.76, indicating favorable greenness characteristics of the analytical procedure. The validated method was applied for the analysis of selected real samples, including eggplant, bell pepper, orange, and potato, with acceptable relative and spiking recoveries (above 90%), indicating its reliability and applicability for the analysis of complex matrices.
Background Small extracellular vesicles (sEVs) have emerged as pivotal mediators in liquid biopsy, carrying molecular cargo that reflects the pathological status of their origin cells; however, their clinical translation is often hindered by the gap between computational discovery and experimental validation. Current sEVs isolation methods frequently lack the necessary purity and throughput required for reliable biomarker verification. Here, we implement a sequential workflow that integrates in silico biomarker identification with our high-performance microfluidic capture system to streamline the diagnostic pipeline for sEVs. Methods Our workflow comprises two core modules: (1) Data-Driven Discovery: A machine learning-assisted meta-analysis was performed on multi-center transcriptomic datasets to establish a sEV-Related Signature (SRS), which identified Lipocalin-2 (LCN2) as a robust candidate for pancreatic cancer diagnosis. (2) Hardware-Enabled Validation: A centrifugal microfluidic platform was engineered for the standardized, high-purity recovery of sEVs. Results The workflow demonstrated high fidelity in translating bioinformatic predictions into experimental results. Targeted validation via Western blot and label-free proteomic profiling confirmed that LCN2 was significantly enriched in the isolated sEVs, echoing the initial machine learning-based predictions. Gene Ontology (GO) analysis further validated that the captured proteome was intrinsically linked to vesicle-mediated transport and tumor-specific signaling. Conclusion By coupling machine learning mining with microfluidic isolation, this stepwise workflow provides a robust, standardized framework for the discovery and rapid validation of sEV-derived biomarkers. This approach offers a scalable solution for cancer diagnostics and supports the development of precision diagnostic tools.
