Adsorption entropy is a fundamental thermodynamic quantity governing molecular organization at solid surfaces, yet its physicochemical significance remains considerably less explored than adsorption enthalpy and free energy. In this work, a generalized five-parameter Lewis acid–base entropy model is proposed for the quantitative interpretation of the polar adsorption entropy of organic molecules on solid surfaces. The model introduces five entropy-derived thermodynamic parameters, ωA, ωD, ω, ω2A, and ω2D, associated with first-order acidic and basic contributions, amphoteric donor–acceptor coupling, and nonlinear second-order effects.The model was applied to adsorption entropy data obtained by inverse gas chromatography at infinite dilution (IGC-ID) for Rh/H-Beta zeolites containing 0–2 wt% rhodium, MgY and NH4Y zeolites, and oxide surfaces including silica, ZnO, Zn, alumina, titania, and MgO. Statistical comparisons among different entropy formulations demonstrated that higher-order Lewis acid–base contributions are frequently required to accurately describe adsorption entropy. Depending on the investigated material, the optimal representation was provided by the 3-P, 4.2-P, 4.3-P, or generalized 5-P models, highlighting the importance of amphoteric and nonlinear organizational effects.A statistical thermodynamic interpretation was further developed through the introduction of an organizational statistical probability, Porg, which quantitatively relates adsorption entropy to molecular organization at solid interfaces. Strong quadratic relationships were established between adsorption entropy, entropy-derived Lewis acid–base parameters, and specific surface area, revealing highly regular structural trends across zeolites and oxides.The proposed framework establishes adsorption entropy as a powerful tool for surface characterization and provides the foundations of an Entropic Lewis Acid–Base Surface Chemistry applicable to adsorption, inverse gas chromatography, catalysis, porous materials, and interfacial phenomena.
High-throughput and highly sensitive detection of small-molecule metabolites are the key in in vitro diagnosis (IVD). Laser desorption/ionization mass spectrometry (LDI MS), as a rapid analytical technology, has attracted much attention in IVD. The regulation of the LDI MS matrix can significantly enhance the throughput and sensitivity of small-molecular detection. Metal-organic frameworks (MOFs), characterized by their tunable porosity, high surface area, and structural diversity, have emerged as promising nanomatrices for LDI MS. However, pristine MOFs have insufficient light absorption, low conductivity, and poor energy transfer, which limit their capability as LDI MS matrices for detection of metabolites. Engineered MOFs address these limitations but systematic summaries are lacked. This work comprehensively reviewed the design principles and classification of engineered MOFs as LDI MS matrices, with a focus on modulating the adsorption and photoelectronic/photothermal conversion properties. Recent targeted and untargeted IVD applications using engineered MOFs matrices are also discussed. Finally, the further trends and challenges of engineered MOFs-assisted LDI MS for small-molecule detection are outlined. We believe this review can provide a forward-looking perspective on engineered MOFs as next-generation LDI MS matrices, and provides effective engineered strategies of nanomaterials to promote their application in clinical metabolic analysis and precision diagnosis.
The chemical complexity of medicinal plants poses a significant challenge for metabolomic profiling and chemotaxonomic discrimination. In this study, we applied an integrated pipeline combining Global Natural Products Social Molecular Networking (GNPS) platform with SIRIUS-based in silico annotation to systematically profile and compare the metabolomes of three medicinal Polygonaceae plants: Rheum palmatum, Polygonum cuspidatum, and Polygonum multiflorum. Beyond routine library matching, we implemented a data-mining strategy combining spectral networking and in silico structure annotation to extract characteristic MS/MS fragmentation features with chemotaxonomic relevance. Our results reveal distinct metabolic fingerprints among the three species and demonstrate that this integrated approach provides an efficient platform for comparative phytochemical studies, supporting chemotaxonomic classification and quality control of medicinal plants.
Triazine herbicides are widely used in agricultural production. However, their environmental persistence and mobility pose potential threats to ecological safety and human health. Herein, a novel analytical method integrating stir bar sorptive extraction (SBSE) with airflow-assisted thermal desorption dielectric barrier discharge ionization mass spectrometry (AFA-TD-DBDI-MS), an ambient ionization mass spectrometry (AMS), was developed for the sensitive determination of triazine herbicides. A hydroxyl-functionalized covalent organic framework (COF-HF) was synthesized and used to fabricate COF-HF-coated stir bars via physical adhesion. The as-prepared stir bars exhibited outstanding extraction performance toward triazine herbicides, and the underlying adsorption mechanism was systematically investigated. The established COF-HF-based SBSE-AFA-TD-DBDI-MS method demonstrated good linearity (R² ≥ 0.9957), satisfactory recoveries (91.31%-98.81%), good repeatability (RSD ≤ 8.49%), and low limits of detection (0.0061-0.0312 ng/mL). This study provides a rapid and efficient strategy for monitoring trace triazine pollutants in environmental water and serum matrices.
Plants of the genus Curcuma are vital medicinal resources; however, their highly similar chemical profiles and morphological features present substantial challenges for accurate species authentication. Here, we established a comprehensive analytical strategy for the precise differentiation of eight medicinal Curcuma species and one counterfeit by integrating untargeted gas chromatography-mass spectrometry (GC-MS) profiling with rapid in situ portable mass spectrometry (PMS) and machine learning. GC-MS analysis tentatively identified ten predominant volatile components, whose potential biological targets and signaling pathways were elucidated via network pharmacology. Chemometric analysis of GC-MS metabolic profiles further enabled the screening of core differential markers driving species discrimination. For rapid on-site detection, in situ PMS fingerprints were acquired and processed using a characteristic ion-based binarization strategy following base peak normalization. When coupled with advanced machine learning algorithms, particularly Ensemble and Efficient Linear classifiers, the system achieved 100% classification accuracy with high computational efficiency. Together, this dual-platform approach provides an effective, and broadly applicable method for quality control and rapid authentication of multi-origin traditional medicines.
In this study, we developed a systematic validation framework for non-targeted analysis (NTA) and applied it to complementary analytical workflows using comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GC × GC-TOFMS) and liquid chromatography-trapped ion mobility spectrometry-quadrupole time-of-flight mass spectrometry (LC-TIMS-QTOFMS) for broad profiling of cigarette mainstream smoke constituents. The validation framework evaluated instrumental detection and annotation performance, method-specific library-search thresholds, the accuracy of semi-quantitative concentration estimates, repeatability and intermediate precision, and analytical coverage across two GC × GC-TOFMS methods and three LC-TIMS-QTOFMS methods. An operational reporting threshold of 100 ng per cigarette was selected as the lowest evaluated level at which the mean true-positive rate across the analytical methods was at least 90% and no individual method showed a true-positive rate below 80%. The method-specific library-similarity-score thresholds used to define high-confidence annotations ranged from 700 to 800. For the GC × GC-TOFMS methods, the accuracy of semi-quantitative concentration estimates for the quality control (QC) compounds ranged from 41% to 128%. In the LC-TIMS-QTOFMS methods, the initial semi-quantitative concentration estimates showed substantial compound-dependent positive bias; application of predicted relative response factors reduced this bias, although the accuracy of the adjusted semi-quantitative estimates still ranged from 67% to 428%. The coefficients of variation for repeatability and intermediate precision were below 30% for all evaluated QC compounds across all analytical methods. The summed semi-quantitative concentration estimates obtained using the NTA workflows, together with separately determined amounts of major constituents, including nicotine, water, propylene glycol, glycerin, and triacetin, represented 39.6 mg/cig. This amount corresponded to an apparent mass ratio of 91.0% relative to the gravimetrically determined total particulate matter (TPM) yield. The annotated constituents also demonstrated broad and complementary chemical space coverage in terms of molecular weight and calculated log P. Application of the evaluated workflows to mainstream smoke from the Kentucky reference cigarette (1R6F) resulted in 3590 reported analytical features after consolidation of duplicate "Confirmed" identifications across analytical methods. These results demonstrate that the proposed framework provides a systematic basis for evaluating and further developing NTA workflows for cigarette smoke and other complex chemical mixtures.
Autographa californica multiple nucleopolyhedrovirus, known as Baculovirus, is a widely used platform for producing therapeutic proteins and viral vectors. The purity and infectious activity of Baculovirus stocks determine the quality and productivity of recombinant products produced through this system. Current purification strategies suffer from major limitations: centrifugation lacks productivity and scalability; ion-exchange chromatography affords limited selectivity and purity; and the only commercial affinity resin requires harsh elution conditions that significantly reduce functional product recovery. To overcome these limitations, this study introduces the first peptide affinity ligands targeting the baculoviral envelope glycoprotein GP64 for the purification of active Baculovirus particles. We implemented a combinatorial selection workflow based on dual-fluorescence screening of solid-phase peptide libraries to identify 12-mer sequences that bind GP64 and elute Baculovirus under mild conditions (pH 8.5). As the selected ligands are enriched in histidine and tyrosine residues, product release is effected by the combined modulation of pH and ionic strength. Eight candidate peptides (SB1-SB8) were evaluated on Toyopearl and POROS chromatographic resins, demonstrating that matrix chemistry, pore size, and ligand density govern purification performance. The lead peptide SB4 conjugated to POROS resin at ∼10 µmol/mL achieved 81% recovery of infectious virions (transducing units), robust host cell protein reduction (LRV 1.65), and a dynamic binding capacity (DBC10%) of 1.9 × 1010 vg/mL resin. Transmission electron microscopy and multi-angle light scattering confirmed the integrity of purified particles (200 × 50 nm rods with intact nucleocapsids), compared to BacuClear eluates that showed collapsed morphology. The SB4-POROS resin demonstrated storage stability and ∼80% retention of binding capacity over ten purification–regeneration cycles with caustic cleaning. Integration into a three-step downstream process (clarification, affinity capture, and polishing) raised product purity 1,528-fold, from 6.22 × 106 to 9.50 × 109 viral genomes per µg of HCP, while reducing the total HCP burden 1,698-fold, at a cumulative transducing-unit yield of ∼69% relative to the feedstock, establishing SB4-POROS as a promising technology with a favorable projected cost structure for Baculovirus purification.
Solid-phase extraction (SPE) constitutes one of the most common techniques for cleanup of sample extracts in analytical chemistry. Traditionally, SPE cleanup involves 100-1000 mg of one sorbent phase housed in cartridges placed onto vacuum manifolds for manual performance of several steps. However, this format has several drawbacks regarding convenience, versatility, speed, cost, labor, and performance, among other facets. To overcome several of these limitations, dispersive (d)-SPE has become more commonly applied in which extracts are simply mixed with sorbents in a versatile, quick, easy, and cheap approach that often yields high recoveries of analytes. The main disadvantage of d-SPE, however, is that quality of cleanup is often sacrificed for its other benefits. Over time, other commercial SPE approaches and formats have become available, such as mini-column SPE (or µ-SPE) using either a robotic or centrifugal platform to avoid the inconveniences of traditional SPE with a vacuum manifold. In this way, the conveniences of d-SPE can be merged with the better cleanup of columnar SPE. In Part I of this study, a new commercial centrifugal mini-SPE product using different spin rates was compared with d-SPE in a SpinFiltr format. QECh*All (formerly known as QuEChERSER) extracts of 9 commodities (apple, orange, kale, grains, catfish, egg, infant formula, cashew, and coffee) were cleaned up at the same time using both techniques with the same sorbents from the same source with equivalent sample/sorbent ratios. Low-pressure gas chromatography - high-resolution mass spectrometry with orbital ion trap detection (LPGC-orbitrap) was used for analysis of 47 pesticides and environmental contaminants with LogP ranging from -1 to 8. Practical aspects, degree of cleanup, and analytical performance were compared. Centrifugal mini-SPE using 16-177 rcf was found to remove up to 99% of the fatty acids, cholesterol, phytosterols, and other matrix components from the extracts for the same or better sample throughput, cost, and ease compared to d-SPE. It also provided better and more consistent analyte recoveries, especially for the complex fatty matrices, except coffee which required more extensive cleanup than achieved by either approach.
An emulsive liquid‑liquid microextraction (ELLME) technique based on deep eutectic solvents (DES) was developed for the extraction of three sulfonamide antibiotics (SAs) in canine and feline urine, followed by their determination using high‑performance liquid chromatography (HPLC) with a diode array detector (DAD). The DES synthesized from thymol and hexanoic acid was used as an extractant to form an emulsion with water for the efficient extraction of SAs. Without using auxiliary instruments or chemical reagents, the emulsion undergoes spontaneous demulsification upon static standing. No auxiliary equipment, such as vortex mixers, ultrasonic baths, or centrifuges, is required for sample pretreatment. This simplifies operational procedures and shortens the extraction time. The linear range is 0.005-0.5 mg L-1, with detection and quantitation limits of 0.002 and 0.005 mg L-1, respectively. The extraction recovery ranges from 85.3 % to 97.2 %, with a relative standard deviation <2.7 %. Seven evaluation tools were employed to assess the greenness and practical applicability of the analytical procedure. This simple and eco-friendly DES-ELLME-HPLC-DAD method provides a novel and reliable technical strategy for rapid screening and accurate quantification of veterinary drugs in urine samples.
The ion transport mechanism is a key factor influencing the analytical properties of detectors used in ion mobility spectrometry. In this study, the transport properties of hydrated chloride, bromide, and iodide ions were investigated using drift tube ion mobility spectrometer (DT IMS) and differential mobility spectrometer (DMS) under controlled humidity and temperature conditions. The objective of this work was to compare the effects of temperature and electric field strength on the transport properties of hydrated halide ions. DT IMS measurements were performed in nitrogen at atmospheric pressure over the temperature range of 318-363 K and water vapor concentrations up to approximately 1000 ppm. For all halide ions, increasing humidity resulted in systematic decreases in reduced mobility, whereas increasing temperature promoted declustering and increased mobility. DMS measurements revealed positive field-dependent mobility behavior for all hydrated halide ions. Increasing electric field strength causes declustering, leading to mobility changes analogous to those observed in DT IMS with increasing temperature. The results indicate that the commonly used concept of effective temperature does not adequately describe the transport of hydrated ions in DT IMS and DMS, where clustering and declustering processes modify the composition of the ion population. Comparison of DT IMS and DMS data indicates that a more appropriate description can be obtained using the concept of an equivalent temperature defined by the degree of ion hydration. Because hydration equilibria depend on water vapor concentration, the equivalent temperature is also humidity-dependent and cannot be represented by a single universal value.
