Separation Identification Research Articles

Sorbent‐Based Sampling With Two‐Stage Trapping/Desorption Coupled to Comprehensive Two‐Dimensional Gas Chromatography and Mass Spectrometry for Terpenoids Profiling in Cannabis

ABSTRACTCannabis inflorescences represent an important source of many high‐value bioactive specialized metabolites, among which the family of terpenes or terpenoids that are the largest classes of natural products known. Besides their biological activities either alone or synergistic with other terpenoids and/or cannabinoids, they are responsible for their distinctive flavour. In this study, we exploited the separation power and identification capabilities of comprehensive two‐dimensional gas chromatography coupled to mass spectrometry (GC×GC‐MS) for the profiling of terpenes and terpenoids in cannabis inflorescences. The dynamic headspace (DHS) used herein for the extraction was chosen for its sensitivity, portability, suitability, as well as its versatility of sampling various natural products, including plant raw materials and different plant parts. The enrichment method and the following desorption into the GC were developed and optimized on both standards and real samples considering different sorbent traps (i.e. Tenax‐TA, Carbotrap T420, Carbotrap 202), and evaluating key performance values. Analyte coverage, recovery and response reproducibility were used for the evaluation of the best performing thermal desorption tube. Considering terpenoids profiling on cannabis inflorescences, satisfactory extraction performance was observed with both Tenax‐TA and Carbotrap T420. However, Tenax‐TA provided a wider analyte coverage beyond the class of terpenoids, thus can be better suited for non‐targeted analysis. On the other hand, peak width, peak height, peak quality and resolution were considered for the optimization of the chromatographic process, and more specifically the injection process, demonstrating the benefit of a secondary trapping/desorption stage with a cryotrap. Finally, considering the final DHSE‐TD‐GC×GC‐MS conditions, terpenes and terpenoids were profiled in real‐world cannabis inflorescences, highlighting the differences among the chemovars.

Addressing catastrophic forgetting in payload parameter identification using incremental ensemble learning.

Collaborative robots (cobots) are increasingly integrated into Industry 4.0 dynamic manufacturing environments that require frequent system reconfiguration due to changes in cobot paths and payloads. This necessitates fast methods for identifying payload inertial parameters to compensate the cobot controller and ensure precise and safe operation. Our prior work used Incremental Ensemble Model (IEM) to identify payload parameters, eliminating the need for an excitation path and thus removing the separate identification step. However, this approach suffers from catastrophic forgetting. This paper introduces a novel incremental ensemble learning method that addresses the problem of catastrophic forgetting by adding a new weak learner to the ensemble model for each new training bag. Moreover, it proposes a new classification model that assists the ensemble model in identifying which weak learner provides the most accurate estimation for new input data. The proposed method incrementally updates the identification model while the cobot navigates any task path, maintaining accuracy on old weak learner even after updating with new data. Validation performed on the Franka Emika cobot showcases the model's superior accuracy and adaptability, effectively eliminating the problem of catastrophic forgetting.

End-to-end multiple object tracking in high-resolution optical sensors of drones with transformer models

Drone aerial imaging has become increasingly important across numerous fields as drone optical sensor technology continues to advance. One critical challenge in this domain is achieving both accurate and efficient multi-object tracking. Traditional deep learning methods often separate object identification from tracking, leading to increased complexity and potential performance degradation. Conventional approaches rely heavily on manual feature engineering and intricate algorithms, which can further limit efficiency. To overcome these limitations, we propose a novel Transformer-based end-to-end multi-object tracking framework. This innovative method leverages self-attention mechanisms to capture complex inter-object relationships, seamlessly integrating object detection and tracking into a unified process. By utilizing end-to-end training, our approach simplifies the tracking pipeline, leading to significant performance improvements. A key innovation in our system is the introduction of a trajectory detection label matching technique. This technique assigns labels based on a comprehensive assessment of object appearance, spatial characteristics, and Gaussian features, ensuring more precise and logical label assignments. Additionally, we incorporate cross-frame self-attention mechanisms to extract long-term object properties, providing robust information for stable and consistent tracking. We further enhance tracking performance through a newly developed self-characteristics module, which extracts semantic features from trajectory information across both current and previous frames. This module ensures that the long-term interaction modules maintain semantic consistency, allowing for more accurate and continuous tracking over time. The refined data and stored trajectories are then used as input for subsequent frame processing, creating a feedback loop that sustains tracking accuracy. Extensive experiments conducted on the VisDrone and UAVDT datasets demonstrate the superior performance of our approach in drone-based multi-object tracking.

Identification methodology for MIMO Hammerstein nonlinear model with process noise

In this paper, we present a methodology for identifying the multi-input–multi-output (MIMO) Hammerstein nonlinear model under colored noise. The Hammerstein model presented comprises neural fuzzy models (NFMs) as its static nonlinear block and rational transfer functions (RTF) model as its dynamic linear block. The hybrid signals consisting of separable signals and random signals are utilized to deal with the MIMO Hammerstein model identification issue, and the separable signals to implement separation identification of MIMO Hammerstein model is introduced, that is, the two blocks are separately identified. First, parameters of the linear block are estimated applying correlation function-based least squares method in the presence of measurable input–output of Gaussian signals, which can efficiently weaken the process noise interference. Second, estimate of noise parameters vector is introduced to solve the unknown noise vector in the information matrix, then a recursive extended least squares method is developed for identifying parameters of nonlinear block and colored noise model based on available input–output of random signals. The validity and precision of the presented methodology are demonstrated applying a numerical simulation and a nonlinear process, and it is known from the research results that compared with existing identification techniques, the methodology utilized achieved higher identification accuracy.

Determination of bioequivalence between generic and reference drugs using laser-induced breakdown spectroscopy

BackgroundIn vitro bioequivalence studies are strictly limited to the comparison of dissolution performance to a reference drug. These studies are performed without considering the chemical similarity between the generic and reference drug formulations. This work has focused on developing a groundbreaking method based on the laser-induced breakdown spectroscopy (LIBS) technique for the in vitro bioequivalence determination of immediate-release solid oral dosage form generic drugs and as an alternative method for establishing the biowaiver of in vivo generic drug studies. ResultsThe novel LIBS-based methodology to determine in vitro bioequivalence is fast, easy to perform, and can be carried out without the requirement of tedious and complicated sample pre-treatment, nor expensive instrumentals and reagents, almost directly on the drug samples. Furthermore, the proposed methodology demonstrated that it is enough to identify the spectrochemical similarity of the formulation between generic drugs to a reference drug through the chemometric study of their LIBS spectra, based on the determination of the differentiation and similarity factors, f1 and f2, respectively, used in the pharmaceutical industry in this purpose. After analysing their LIBS spectra, the generic drugs selected for this work have all been shown to be in vitro bioequivalent, given their f1 values of less than 15 and f2 values greater than 50, according to the technical regulations on which the American and European medicines agencies are based for the approval of registration for generic immediate-release solid oral dosage form drugs. This has been evidenced even for drugs from Class III and Class IV of the biopharmaceutical classification system, whose active principle nominal concentration is very low as 0.1 and 0.25 mg/tablet, respectively. Significancefor the first time the LIBS technique has been successfully used in an advanced application for the pharmaceutical industry. The proposed method constitutes a reliable and specialized methodology for the establishment of formulation similarity between two drugs, without the requirement of separate identification of each of their components, which is a new and potential tool to determine the in vitro bioequivalence for generic immediate-release solid oral dosage form drugs.

Optimization design and key-term separation identification of parallel six-dimensional pose sensor with high sensitivity and high precision

The integrated bearing-positioning parallel manipulator is an important mean for ground testing of space optical telescope before being launched into space. To ensure positioning accuracy, the manipulator requires a pose sensor for the end-effector measurement with six degrees of freedom (DOF), real-time, high-precision, and operation in a vacuum environment. However, it is challenging for the current pose sensor to simultaneously meet all these requirements. In this paper, a novel concept of six-dimensional pose sensor based on parallel mechanism is proposed. A new mechanism design approach is presented to achieve the large measurement range, real-time measurement performance, and high accuracy measurement based on performance atlas. First, the GF sets synthesis is developed to optimize the configuration of pose sensor. Next, two new design indicators are proposed to evaluate the real-time and high accuracy performance. Mechanism parameters are optimized by combining with mechanism singularity analysis. To guarantee high measurement accuracy, a key-term separation identification method with double neural networks is presented. Experiments on 6-UPS pose sensor demonstrate the effectiveness of the proposed mechanism design and key-term identification approach.

Polysaccharide Conjugates' contribution to mellow and thick taste of Pu-erh ripe tea, besides Theabrownin

Mellow and thick taste (MTT) is considered to be a typical taste characteristic of high-quality Pu-erh ripe tea. However, the role of polysaccharide conjugates remains unclear. In this study, the infusion of different grades of Pu-erh ripe tea was isolated to fractions by sensory-guided ultrafiltration technology and the key taste substances of MTT in Pu-erh ripe tea were identified and confirmed in the sensory reconstruction experiment. Further separation, purification and structural identification of the polysaccharide conjugates were carried out. Involving in aggregation morphology, the ultrafiltration fraction exhibited obvious MTT than other fractions. The main MTT compound (PRTPS-5), mainly composed of the rhamnose, galactose, arabinose and mannose, had a molecular weight of 22.93 kDa. The main chain of PRTPS-5 comprised α-L-Araf-(1→, →2,4)-α-L-Rhap-(1→, →2)-α-L-Rhap-(1→, α-D-Galp-(1→, →4)-α-D-GalpA-6-OMe-(1→, →4)-α-D-Manp-(1→, →3,6)-β-D-Galp-(1 → and →5)-α-L-Araf-(1 → and contained multiple pectic characteristic peaks. This result had scientific guiding significance for the quality enhancement of Pu-erh ripe tea.

Detection of trace levels of selected allergic chemicals in commercial hair dyes in local market of Jeddah, Saudi Arabia

Hair dyes are now commonly used in Saudi Arabia, and other nations across the world, to cover up grey hairs and/or to modify the natural color of the hair. The principal ingredients of hair dyes are aromatic amines and phenols and which most likely cause allergic reactions, irritated skin, and even cancer in certain cases. Thus, the current study reports the separation and subsequent identification of four chemical constituents namely p-Phenylenediamine (PPD), 3-aminophenol (3AP), resorcinol (RES), and 2-methylresorcinol (2MRS) in 26 commercial hair dyes that represent ten famous brands available in the local market of Jeddah city, Saudi Arabia. High-performance liquid chromatography (HPLC) technique was successfully employed to identify the four chemical compounds using methanol-aqueous solution (10:90 v/v) containing diethanolamine (0.05 %) and ammonium acetate (0.02 M) at pH 5.2 as a proper mobile phase. The column was 150 × 4.6 mm ID and 5 µm Eclipse Plus C18 column and the retention times were 1.26, 3.16, 4.99 and 6.01 min for PPD, 3AP, RES, and 2MRS, respectively. The four standards PPD, 3AP, RES, and 2MRS at 254 nm displayed excellent linear calibration plots in the concentration range 5–100 µg/mL with acceptable correlation coefficients (R2) of 0.9964, 0.9980, 0.9952 and 0.9961, respectively. The low limits of detection (LOD) and quantification (LOQ) based on the standard deviation of the response and the slopes of the plots were found to be equal 7.17, 5.39, 8.33, and 7.47 µg/mL and 23.91, 17.97, 27.75, 24.89 µg/mL for PPD, 3AP, RES, and 2MRS, respectively.

A protein pre-trained model-based approach for the identification of the liquid-liquid phase separation (LLPS) proteins

Liquid-liquid phase separation (LLPS) regulates many biological processes including RNA metabolism, chromatin rearrangement, and signal transduction. Aberrant LLPS potentially leads to serious diseases. Therefore, the identification of the LLPS proteins is crucial. Traditionally, biochemistry-based methods for identifying LLPS proteins are costly, time-consuming, and laborious. In contrast, artificial intelligence-based approaches are fast and cost-effective and can be a better alternative to biochemistry-based methods. Previous research methods employed word2vec in conjunction with machine learning or deep learning algorithms. Although word2vec captures word semantics and relationships, it might not be effective in capturing features relevant to protein classification, like physicochemical properties, evolutionary relationships, or structural features. Additionally, other studies often focused on a limited set of features for model training, including planar π contact frequency, pi-pi, and β-pairing propensities. To overcome such shortcomings, this study first constructed a reliable dataset containing 1206 protein sequences, including 603 LLPS and 603 non-LLPS protein sequences. Then a computational model was proposed to efficiently identify the LLPS proteins by perceiving semantic information of protein sequences directly; using an ESM2-36 pre-trained model based on transformer architecture in conjunction with a convolutional neural network. The model could achieve an accuracy of 85.68% and 89.67%, respectively on training data and test data, surpassing the accuracy of previous studies. The performance demonstrates the potential of our computational methods as efficient alternatives for identifying LLPS proteins.

Derivative spectrophotometry-assisted determination of tryptophan metabolites emerges host and intestinal flora dysregulations during sepsis

Sepsis is a life-threatening condition characterized by organ dysfunction resulting from a dysregulated host response to infection. Dysregulated tryptophan (TRP) metabolites serve as significant indicators for endogenous immune turnovers and abnormal metabolism in the intestinal microbiota during sepsis. Therefore, a high coverage determination of TRP and its metabolites in sepsis is beneficial for the diagnosis and prognosis of sepsis, as well as for understanding the underlying mechanism of sepsis development. However, similar structures in TRP metabolites make it challenging for separation and metabolite identification. Here, high-performance liquid chromatography coupled with a diode array detector (HPLC-DAD) was developed to determine TRP metabolites in rat serum. The first-order derivative spectrophotometry of targeted metabolites in the serum was investigated and proved to be promising for chromatographic peak annotation across different columns and systems. The established method separating the targeted metabolites was optimized and validated to be sensitive and accurate. Application of the method revealed dysregulated TRP metabolites, associated with immune disorders and NAD + metabolism in both the host and gut flora in septic rats. Our findings indicate that the derivative spectrophotometry-assisted method enhances metabolite identifications for the chromatographic systems based on DAD detectors and holds promise for precision medicine in sepsis.

Assessment of fishes, sediment and water from some inland rivers across the six geopolitical zones in Nigeria for microplastics.

In Nigeria, limited research has been conducted on Microplastics (MPs) in inland rivers, necessitating a comprehensive assessment to understand the extent of contamination. This study aimed to assess the abundance, distribution, and composition of MPs in fishes, sediment, and water from inland rivers across Nigeria's six geopolitical zones. Samples were collected from selected rivers in each geopolitical zone (Rivers Yauri, Benue, Argungu, Jamare, Ogun, Ethiope and Orashi). MPs were isolated using a combination of filtration, density separation, and visual identification. MPs abundance, distribution, shapes, colors, and chemical composition were determined using microscopy and Fourier-transform infrared spectroscopy. The study presents the first report of MPs in six in land rivers in Nigeria and found that MPs were present in all the fishes, sediments and river waters studied across all the rivers. The abundance and composition of MPs varied among the different sample types, with fibers being the most abundant shape in both water and fish samples. PET, PP, and PE were the most prevalent types of plastics found in fish samples, while PE/PA/Nylon, PVA, and PVC were predominant in water samples. PA/Nylon, PUR, PVC, and PET were the most common in sediment samples. Source analysis by Principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the presence of MPs was mainly influenced by local anthropogenic activities. However, estimated daily intakes are generally low, indicating that daily consumption of the samples is not likely to be harmful. The widespread presence of MPs in inland rivers across Nigeria highlights the urgent need for effective waste management strategies and environmental conservation efforts to mitigate plastic pollution.

Blow flies (Diptera: Calliphoridae) of the Baja California Peninsula.

The blow fly fauna of the Baja California peninsula, Mexico has been sparsely documented. This study incorporates and lists recent and historical records of blow flies from collecting trips throughout the peninsula, visits to major southern California museums for historical collection records, and a literature search. Seven genera and 16 species are reported from the two peninsular Mexican States, Baja California, and Baja California Sur. Three species are recorded that are not included in the 2020 Catalog of Mexican Calliphoridae by Jaume-Schinkel, & Ibanez-Bernal (2020), they are: Calliphora livida, Calliphora terraenovae, and Calliphora vomitoria. One blow fly species, Cochliomyia hominivorax (the primary Screw-Worm Fly) is not included here but is found in museum collections. Once present in Baja, it is considered eradicated from continental North America as far south as the Panama Canal by the U.S. Department of Agriculture (USDA) however, it is occasionally reintroduced (Skoda et al. 2018) and is still present in some Caribbean islands. It is noteworthy that all calliphorid species from this mountainous desert peninsula are also found in the State of California, USA which supports 11 genera and 47 species in this family (Whitworth 2006) primarily because of its more varied climate and terrain. The Baja peninsula can be considered a southern extension of the State of California, with reduced species representation due to reduced climate and terrain variation, so no separate identification keys are necessary. Forensic entomologists and taxonomists can use the blow fly keys for North America by Whitworth, 2006, and 2017 for this region. The latest key by Jones, Whitworth, and Marshall (2019) is revolutionary and much welcome in taxonomy in that it is an online computer-aided key that has actual macro-photos of species identification characters and is intended to be used with a computer screen next to your microscope for identifications in place of printed material.

Abnormal User Identification in Substation Area Based on Restricted Ridge Regression Model

The rapid development of the smart grid affects every aspect of people’s lives at all times. Among them, the identification accuracy of power metering anomalies is a serious problem that needs to be solved urgently. To further improve the identification accuracy of power metering anomalies, a user identification method for substation area anomalies is designed based on the restricted ridge regression model. First, a clustering algorithm is designed using high-frequency voltage data of users in the substation area to conduct phase separation and terminal branch identification for each user metering point in the substation area. Then establish a substation area energy conservation model equation set, and merge the end branch users based on genetic algorithm clustering results, thereby reducing the unknown quantity dimension. Then, a line resistance-constrained condition is constructed using the topological structure, and the model equation set is solved using quadratic programming based on the constrained ridge regression method to obtain the measurement point error results of users in the substation area. The experimental results show that this recognition method’s clustering effect and recognition accuracy are high, with a recognition rate of 98.21%. It can be seen that this abnormal user identification method has application value.

Physics-informed deep operator network for predicting martensite evolution in superelastic shape memory alloys through cyclic tensile tests

Superelastic shape memory alloy (SMA) wires and rods possess unique deformation and energy dissipation capabilities. For the assessment of their stress response, commonly cyclic tensile tests are conducted. An important but subtle parameter in this procedure is the martensite evolution. In scenarios where conducting thermal experiments is impractical, inverse modeling from cyclic tests serves as a viable alternative. However, employing constitutive models in this process presents distinct challenges, such as parameter identification and calibration, or numerical stability issues. To address these challenges, this paper proposes a data-driven method based on a physics-informed deep operator network (DeepONet) to estimate the martensite evolution. Constraint with a stress equation, the network requires only strain–stress data for training and provides stress responses in addition to the martensite evolution. From the training data, the network learns to consider the effects included in the response. The DeepONet can be coupled with experiments to provide online estimates from noisy sensor-based strain inputs, while remaining numerically stable. Moreover, this approach avoids the need for separate parameter identification or calibration. This paper details this method and evaluates its performance through experiments conducted on superelastic SMA wires. Furthermore, as an alternative approach, training using a constitutive model is provided.

Rapid Separation and API Grades Identification of Base Oil in Low Viscosity Gasoline Engine Oil SN 0W-16.

Using low viscosity engine oil is one of the most economical and easily achievable ways to improve fuel economy. Base oil is a main component in low viscosity engine oils, and therefore, the separation and identification of its are of great significance for oil product developers to prepare high-performance lubricants. However, the extraction methods reported for base oils mainly adopt membrane dialysis, which not only fails to completely separate the base oil but also wastes a large amount of solvent. The reason for this result is that the concentration of substances inside and outside the membrane cannot always be in an imbalanced state of permeation resulting from manual operation. Additionally, most studies primarily focus on the characterization of base oil components, while there are few reports on grade identification. For the above reasons, an economically effective separation technique of base oil from low viscosity gasoline engine oil SN 0W-16 is successfully established by combining improved Soxhlet extraction and a column chromatography separation method. By applying this method, the yield of extracting base oil generally exceeds 96%, and the solvent can also save more than 3 times. Besides, an exclusion method is built through several simple characterization steps including viscosity index (VI), FT-IR, size-exclusion chromatography (SEC), and hydrocarbon composition, which can quickly identify the American Petroleum Institute (API) grade and brand of the base oils.

Kinetics-based inference of environment-dependent microbial interactions and their dynamic variation.

Microbial communities in nature are dynamically evolving as member species change their interactions subject to environmental variations. Accounting for such context-dependent dynamic variations in interspecies interactions is critical for predictive ecological modeling. In the absence of generalizable theoretical foundations, we lack a fundamental understanding of how microbial interactions are driven by environmental factors, significantly limiting our capability to predict and engineer community dynamics and function. To address this issue, we propose a novel theoretical framework that allows us to represent interspecies interactions as an explicit function of environmental variables (such as substrate concentrations) by combining growth kinetics and a generalized Lotka-Volterra model. A synergistic integration of these two complementary models leads to the prediction of alterations in interspecies interactions as the outcome of dynamic balances between positive and negative influences of microbial species in mixed relationships. The effectiveness of our method was experimentally demonstrated using a synthetic consortium of two Escherichia coli mutants that are metabolically dependent (due to an inability to synthesize essential amino acids) but competitively grow on a shared substrate. The analysis of the E. coli binary consortium using our model not only showed how interactions between the two amino acid auxotrophic mutants are controlled by the dynamic shifts in limiting substrates but also enabled quantifying previously uncharacterizable complex aspects of microbial interactions, such as asymmetry in interactions. Our approach can be extended to other ecological systems to model their environment-dependent interspecies interactions from growth kinetics.IMPORTANCEModeling environment-controlled interspecies interactions through separate identification of positive and negative influences of microbes in mixed relationships is a new capability that can significantly improve our ability to understand, predict, and engineer the complex dynamics of microbial communities. Moreover, the prediction of microbial interactions as a function of environmental variables can serve as valuable benchmark data to validate modeling and network inference tools in microbial ecology, the development of which has often been impeded due to the lack of ground truth information on interactions. While demonstrated against microbial data, the theory developed in this work is readily applicable to general community ecology to predict interactions among macroorganisms, such as plants and animals, as well as microorganisms.

Aza-Prilezhaev Aziridination-Enabled Multidimensional Analysis of Isomeric Lipids via High-Resolution U-Shaped Mobility Analyzer-Mass Spectrometry.

Unsaturated lipids constitute a significant portion of the lipidome, serving as players of multifaceted functions involving cellular signaling, membrane structure, and bioenergetics. While derivatization-assisted liquid chromatography tandem mass spectrometry (LC-MS/MS) remains the gold standard technique in lipidome, it mainly faces challenges in efficiently labeling the carbon-carbon double bond (C═C) and differentiating isomeric lipids in full dimension. This presents a need for new orthogonal methodologies. Herein, a metal- and additive-free aza-Prilezhaev aziridination (APA)-enabled ion mobility mass spectrometric method is developed for probing multiple levels of unsaturated lipid isomerization with high sensitivity. Both unsaturated polar and nonpolar lipids can be efficiently labeled in the form of N-H aziridine without significant side reactions. The signal intensity can be increased by up to 3 orders of magnitude, achieving the nM detection limit. Abundant site-specific fragmentation ions indicate C═C location and sn-position in MS/MS spectra. Better yet, a stable monoaziridination product is dominant, simplifying the spectrum for lipids with multiple double bonds. Coupled with a U-shaped mobility analyzer, identification of geometric isomers and separation of different lipid classes can be achieved. Additionally, a unique pseudo MS3 mode with UMA-QTOF MS boosts the sensitivity for generating diagnostic fragments. Overall, the current method provides a comprehensive solution for deep-profiling lipidomics, which is valuable for lipid marker discovery in disease monitoring and diagnosis.

Coupling thin layer chromatography with mass spectrometry for detection and identification of sertraline and its metabolite in the urine

The aim of the study was to detect sertraline and its metabolites in urine under standardized thin layer chromatography (TLC) screening conditions and identify the metabolites using the mass spectrometry method. Materials and methods. Urine samples, collected within 30 hours in portions of 20–50 mL, commenced from the seventh hour after the administration of a single therapeutic dose of the drug. The sample preparation process involved dilute acid hydrolysis, followed by the extraction of the native compound and metabolites with chloroform at a pH of 8–9. Thin layer chromatography studies of the extracts were conducted using four unified TLC systems recommended by The International Association of Forensic Toxicologists for general drug screening. Chromatograms were subjected to color reactions with a variety of chromogenic reagents. For the analysis of eluates from chromatograms, a Varian 1200 L mass spectrometer (Netherlands) equipped with a dual quadrupole mass analyzer was employed. Identification was performed through direct sample introduction into the ion chamber, electron-impact ionization (70 eV), and full ion scanning mode. Results. The spot of the native drug on the chromatogram was identified by the Rf, value. Metabolite of sertraline was identified as N-desmethyltrihydroxysertraline by the molecular ion peak in the mass spectrum. Conclusions. The study demonstrated the ability of TLC to detect sertraline and its metabolite, N-desmethyltrihydroxysertraline, in urine after the administration of a single therapeutic dose of the drug. The chromatographic mobility of the native compound and N-desmethyltrihydroxysertraline in the unified TLC screening systems, along with the results of their visualization using chromogenic reagents for toxicological drug screening, was determined. Furthermore, the potential of coupling TLC with mass spectrometry for the separation, detection, and confirmatory identification of sertraline and its metabolic products in urine was established.

Identification and evaluation of bed separation induced by mining based on beam-column and stress-balanced arch theory

Identification and evaluation of bed separation induced by mining in the overburden is critical to prevent secondary disasters during coal mining. Traditional theoretical methods ignore the effect of horizontal stress in bed separation and fail to accurately identify bed separation and evaluate its dynamic evolution process. In this study, based on the beam-column theory, the deflection of composite beam is determined, in which the horizontal stress is considered. On this basis, by combining beam-column and stress-balanced arch theory (BS theory), the BS model is proposed for identification and evaluation. The identification results from the BS model agree well with the field measurement results with a high accuracy rate of 88.9%. Furthermore, one working face in a coal mine is selected to evaluate the dynamic evolution process of bed separation by the numerical simulation and the BS model. It is found that the bed separation shows evident stages of growth and extinction during mining. The BS model has a wide range of applications for the identification and evaluation of bed separation induced by mining and can be used to ensure the safety of mining operations.

Liquid electron ionization-mass spectrometry as a novel strategy for integrating normal-phase liquid chromatography with low and high-resolution mass spectrometry.

Normal-phase liquid chromatography (NPLC) plays a pivotal role in the rapid separation of non-polar compounds, facilitating isomer separation and finding applications in various crucial areas where aprotic solvents are necessary. Similar to reversed-phase liquid chromatography (RPLC), NPLC requires a robust and sensitive detector to unequivocally identify the analytes, such as a mass spectrometer. However, coupling NPLC with mass spectrometry (MS) poses challenges due to the incompatibility between the non-polar solvents used as the mobile phase and the primary ionization techniques employed in MS. Several analytical methods have been developed to combine NPLC with electrospray ionization (ESI), but these methods are restricted to the analysis of polar compounds. In most cases, atmospheric pressure chemical ionization (APCI) becomes necessary to expand the range of analysis applications. To overcome these limitations and fully realize the potential of NPLC-MS coupling, a technique termed liquid electron ionization-mass spectrometry (LEI-MS) can be used. LEI-MS offers a straightforward solution by enabling the effective coupling of NPLC with both low and high-resolution MS. LEI allows for the comprehensive analysis of non-polar compounds and provides a powerful tool for isomer separation and precise identification of analytes. Optimal separations, mass spectral qualities, and matches with the NIST library were obtained in both configurations, demonstrating the potential of the proposed approach.