Metabolite Profiling Analysis of the Tongmai Sini Decoction in Rats after Oral Administration through UHPLC-Q-Exactive-MS/MS.

Tongmai Sini decoction (TSD), the classical prescriptions of traditional Chinese medicine, consisting of three commonly used herbal medicines, has been widely applied for the treatment of myocardial infarction and heart failure. However, the absorbed components and their metabolism in vivo of TSD still remain unknown. In this study, a reliable and effective method using ultra-performance liquid chromatography coupled with hybrid quadrupole-Orbitrap mass spectrometry (UHPLC-Q-Exactive-MS/MS) was employed to identify prototype components and metabolites in vivo (rat plasma and urine). Combined with mass defect filtering (MDF), dynamic background subtraction (DBS), and neutral loss filtering (NLF) data-mining tools, a total of thirty-two major compounds were selected and investigated for their metabolism in vivo. As a result, a total of 82 prototype compounds were identified or tentatively characterized in vivo, including 41 alkaloids, 35 phenolic compounds, 6 saponins. Meanwhile, A total of 65 metabolites (40 alkaloids and 25 phenolic compounds) were tentatively identified. The metabolic reactions were mainly hydrogenation, demethylation, hydroxylation, hydration, methylation, deoxylation, and sulfation. These findings will be beneficial for an in-depth understanding of the pharmacological mechanism and pharmacodynamic substance basis of TSD.

Combined PET/CT and Autoantibody Detection in Lung Nodules Diagnosis.

Objective: To assess the usefulness of combining positron emission tomography/computed tomography (PET/CT) with lung cancer autoantibody detection in identifying and managing lung nodules. Methods: The researchers identified 160 patients with pulmonary nodules admitted to their hospital between January 2018 and January 2021. These patients were designated as the experimental group. Additionally, 60 healthy individuals without pulmonary nodules were admitted to the hospital during the same period. The individuals constituted the control group. All study participants underwent digital PET/CT detection and had their lung cancer autoantibody levels determined through enzyme-linked immunosorbent assay. Further testing, such as puncture or surgical pathology, was performed for patients with lung nodules. The aim was to evaluate the significance of combining PET/CT with autoantibody detection in diagnosing and treating lung nodules. Results: The study found that testing multiple autoantibodies together increased sensitivity and accuracy compared to testing individual autoantibodies. Combining PET/CT screening with autoantibody detection improved the diagnostic rate for identifying lung nodules, including benign and suspected malignant ones. Several autoantibodies were significantly higher in the experimental group compared to the control group. Testing for multiple autoantibodies showed higher sensitivity and accuracy than testing for one. Pathological examination confirmed 129 benign nodules and 31 malignant nodules. The median SUVmax values were measured at 0.7 for benign nodules and 4.8 for malignant nodules. The diagnostic efficacy of PET/CT combined with autoantibodies was determined through comparison with pathology testing and was as follows: PET/CT combined with autoantibody detection > PET/CT > autoantibody detection. Conclusion: Combining PET/CT with the detection of autoantibodies enhances the positive diagnostic rate and accuracy of lung nodules in the case of lung cancer. The SUVmax also shows excellent potential as a supplement in diagnosing both benign and malignant lung nodules, providing valuable guidance in determining the pathological types.

Use of MRI-based deep learning radiomics to diagnose sacroiliitis related to axial spondyloarthritis.

OBJECTIVES: This study aimed to evaluate the performance of a deep learning radiomics (DLR) model, which integrates multimodal MRI features and clinical information, in diagnosing sacroiliitis related to axial spondyloarthritis (axSpA). MATERIAL & METHODS: A total of 485 patients diagnosed with sacroiliitis related to axSpA (n = 288) or non-sacroiliitis (n = 197) by sacroiliac joint (SIJ) MRI between May 2018 and October 2022 were retrospectively included in this study. The patients were randomly divided into training (n = 388) and testing (n = 97) cohorts. Data were collected using three MRI scanners. We applied a convolutional neural network (CNN) called 3D U-Net for automated SIJ segmentation. Additionally, three CNNs (ResNet50, ResNet101, and DenseNet121) were used to diagnose axSpA-related sacroiliitis using a single modality. The prediction results of all the CNN models across different modalities were integrated using a stacking method based on different algorithms to construct ensemble models, and the optimal ensemble model was used as DLR signature. A combined model incorporating DLR signature with clinical factors was developed using multivariable logistic regression. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: Automated deep learning-based segmentation and manual delineation showed good correlation. ResNet50, as the optimal basic model, achieved an area under the curve (AUC) and accuracy of 0.839 and 0.804, respectively. The combined model yielded the highest performance in diagnosing axSpA-related sacroiliitis (AUC: 0.910; accuracy: 0.856) and outperformed the best ensemble model (AUC: 0.868; accuracy: 0.825) (all P < 0.05). Moreover, the DCA showed good clinical utility in the combined model. CONCLUSION: We developed a diagnostic model for axSpA-related sacroiliitis by combining the DLR signature with clinical factors, which resulted in excellent diagnostic performance.

Establishment of a prognosis prediction model for lung squamous cell carcinoma related to PET/CT: basing on immunogenic cell death-related lncRNA.

BACKGROUND: Immunogenic cell death (ICD) stimulates adaptive immunity and holds significant promise in cancer therapy. Nevertheless, the influence of ICD-associated long non-coding RNAs (lncRNAs) on the prognosis of patients with lung squamous cell carcinoma (LUSC) remains unexplored. METHODS: We employed data from the The Cancer Genome Atlas (TCGA)database to identify ICD-related lncRNAs associated with the prognosis of LUSC using univariate Cox regression analysis. Subsequently, we utilized the LOSS regression model to construct a predictive risk model for assessing the prognosis of LUSC patients based on ICD-related lncRNAs. Our study randomly allocated187 TCGA patients into a training group and 184 patients for testing the predictive model. Furthermore, we conducted quantitative polymerase chain reaction (qPCR) analysis on 43 tumor tissues from LUSC patients to evaluate lncRNA expression levelsPearson correlation analysis was utilized to analyze the correlation of risk scores with positron emission tomography/computed tomography (PET/CT) parameters among LUSC patients. RESULTS: The findings from the univariate Cox regression revealed 16 ICD-associated lncRNAs linked to LUSC prognosis, with 12 of these lncRNAs integrated into our risk model utilizing the LOSS regression. Survival analysis indicated a markedly higher overall survival time among patients in the low-risk group compared to those in the high-risk group. The area under the Receiver operating characteristic (ROC) curve to differentiate high-risk and low-risk patients was 0.688. Additionally, the overall survival rate was superior in the low-risk group compared to the high-risk group. Correlation analysis demonstrated a positive association between the risk score calculated based on the ICD-lncRNA risk model and the maximum standard uptake value (SUVmax) (r = 0.427, P = 0.0043) as well as metabolic volume (MTV)of PET-CT (r = 0.360, P = 0.0177) in 43 LUSC patients. CONCLUSION: We have successfully developed a risk model founded on ICD-related lncRNAs that proves effective in predicting the overall survival of LUSC patients.

Carbon-doped boron nitride nanosheets as an efficient metal-free catalyst for the selective oxidation of H2S.

We report the first use of carbon-doped boron nitride (BCN) for H2S-selective catalytic oxidation. The obtained carbon-doped BN with an ultrathin layer structure exhibits outstanding H2S elimination and high S yield. In particular, BN doped carbon nanosheets display better catalytic performance than traditional catalysts, such as iron- and carbon-based catalysts. The findings of the present work shed a new light on metal-free catalysts for efficient catalytic removal of toxic H2S.

Automatic Image Segmentation and Grading Diagnosis of Sacroiliitis Associated with AS Using a Deep Convolutional Neural Network on CT Images.

Ankylosing spondylitis (AS) is a chronic inflammatory disease that causes inflammatory low back pain and may even limit activity. The grading diagnosis of sacroiliitis on imaging plays a central role in diagnosing AS. However, the grading diagnosis of sacroiliitis on computed tomography (CT) images is viewer-dependent and may vary between radiologists and medical institutions. In this study, we aimed to develop a fully automatic method to segment sacroiliac joint (SIJ) and further grading diagnose sacroiliitis associated with AS on CT. We studied 435 CT examinations from patients with AS and control at two hospitals. No-new-UNet (nnU-Net) was used to segment the SIJ, and a 3D convolutional neural network (CNN) was used to grade sacroiliitis with a three-class method, using the grading results of three veteran musculoskeletal radiologists as the ground truth. We defined grades 0-I as class 0, grade II as class 1, and grades III-IV as class 2 according to modified New York criteria. nnU-Net segmentation of SIJ achieved Dice, Jaccard, and relative volume difference (RVD) coefficients of 0.915, 0.851, and 0.040 with the validation set, respectively, and 0.889, 0.812, and 0.098 with the test set, respectively. The areas under the curves (AUCs) of classes 0, 1, and 2 using the 3D CNN were 0.91, 0.80, and 0.96 with the validation set, respectively, and 0.94, 0.82, and 0.93 with the test set, respectively. 3D CNN was superior to the junior and senior radiologists in the grading of class 1 for the validation set and inferior to expert for the test set (P < 0.05). The fully automatic method constructed in this study based on a convolutional neural network could be used for SIJ segmentation and then accurately grading and diagnosis of sacroiliitis associated with AS on CT images, especially for class 0 and class 2. The method for class 1 was less effective but still more accurate than that of the senior radiologist.

Dual-energy CT Virtual Non-Calcium Image is Beneficial for the Detection of Non-displaced Knee Fractures.

BACKGROUND: Early and accurate diagnosis is vital for avoiding the development of nondisplaced fractures to displaced fractures. Dual-energy CT (Computed Tomography) can detect bone marrow edema (BME), which may help to detect non-displaced fractures. AIM: To evaluate the value of DECT (Dual-Energy Computed Tomography) VNCa (Virtual noncalcium) images for improving diagnostic performance and confidence in acute non-displaced knee fractures. METHODS: 125 patients with clinical suspicion of knee fractures underwent both DECT and MR. Conventional linear-blended CT and VNCa images were obtained from DECT. First, five readers with varying levels of experience evaluated the presence of fractures on conventional linear-blended CT and graded their diagnostic confidence on a scale of 1 to 10. Then BME with VNCa images was evaluated and compared with MR. Finally, the VNCa images combined with conventional linear-blended CT images were used to reassess the presence of fractures and diagnostic confidence. Diagnostic performance and matched pair analyses were performed. RESULTS: 20 non-displaced knee fractures were detected. The consistency test of VNCa images and MR by five radiologists showed Kappa values are 0.76, 0.79, 0.81,0.85,and 0.90,respectively. The diagnostic performance of all readers was improved when using VNCa images combined with conventional linear-blended CT compared with that with conventional linear-blended CT alone. Diagnostic confidence was improved with combined conventional linear-blended CT and VNCa images (median score:8,8,9,9, and 10, respectively) compared with conventional linear-blended CT alone (median score:7,7,8,9, and 9). CONCLUSION: DECT VNCa images could improve the radiologists' diagnostic performance and confidence with varying levels of experience in the detection of non-displaced knee fractures.

Efficacy of Camrelizumab in Advanced Non-Small-Cell Lung Cancer and Prognostic Analysis of Different PET/CT Features.

Objective: To evaluate the efficacy of the PD-1 inhibitor camrelizumab plus chemotherapy in the first-line treatment of advanced non-small-cell lung cancer (NSCLC) and the prognostic differences of patients with different PET/CT features. Methods: Between December 2018 and October 2020, 100 patients with NSCLC assessed for eligibility treated in our institution were recruited and randomly assigned (1 : 1) to receive either the TC regimen chemotherapy (control group) or the TC regimen chemotherapy plus camrelizumab (study group). The primary endpoints were clinical efficacy, progression-free survival (PFS), and overall survival (OS). A decrease of max standard uptake value (SUVmax) of >30% in primary lung cancer was considered as metabolic remission. The prognostic differences of the eligible patients with different PET/CT features were assessed. Survival data were analyzed using the Kaplan-Meier method to obtain the survival rate and calculate the median survival time. Results: The metabolic remission rate and objective remission rate were significantly higher with chemotherapy plus camrelizumab versus chemotherapy alone. The study group had significantly higher CD3+ and CD4+ T-cell ratios and CD4+/CD8+ ratio and significantly lower CD8+ T-cell ratio than the control group after treatment. PFS (10 months versus 4 months) and OS (HR = 37.094, P ≤ 0.001) were better with camrelizumab plus chemotherapy versus stand-alone chemotherapy. The incidence of adverse events (AE) was similar between the two groups. The patients in the study group were stratified into metabolic remission and metabolic nonremission based on PET/CT results. Intersubgroup analysis showed significantly better PFS and OS in the metabolic remission group than in the nonmetabolic remission group. Conclusion: The camrelizumab plus chemotherapy as a first-line treatment option for NSCLC significantly increases the survival benefit. Metabolic status shown by PET/CT correlates with long-term prognosis and demonstrates a great potential for early assessment of efficacy to support the choice of treatment regimens.

Construction of a Pd(PdO)/Co3O4@SiO2 core-shell structure for efficient low-temperature methane combustion.

Catalytic combustion is a promising way to remove trace amounts of CH4 to alleviate serious environmental concerns. However, the reactivity of a catalyst at low temperature is usually limited because of the difficulty to activate the C-H bond of methane. Herein, we design a Pd(PdO)/Co3O4@SiO2 bimetallic oxide core-shell catalyst which shows much higher activity in the methane combustion reaction compared with Pd(PdO)/SiO2 and Co3O4@SiO2 catalysts without a core-shell structure. The T50% and T90% of Pd(PdO)/Co3O4@SiO2 are 357 °C and 445 °C, respectively, which decrease by 67 °C and 55 °C in comparison with those of Pd(PdO)/SiO2. Extensive characterization demonstrates that the bimetallic oxide core-shell structure can effectively enhance the metal interaction between Pd and Co, which can weaken the strength of the Co-O bond in Pd(PdO)/Co3O4@SiO2. The weakening of the Co-O bond could promote the release of more lattice oxygen species to participate in the C-H breaking, resulting in superior catalytic performance in methane combustion at low temperature.

Diagnostic accuracy of dual-energy CT virtual non-calcium images with different related contrast material values for the detection of bone marrow edema in knee.

PURPOSE: The purpose of this study was to evaluate the diagnostic accuracy of different related contrast material (Rel.CM) values in dual-energy computed tomography (DECT) virtual non-calcium (VNCa) images for the detection of bone marrow edema (BME) in knee. METHOD: This prospective study was approved by the institutional research ethics board, and written informed consent was obtained from all participants. Twenty-three patients (24 knees) who underwent dual-energy CT and MRI within three weeks from July 2018 to June 2019 with a definite history of trauma were enrolled. Each knee was divided into 12 regions. First, MR images served as the reference standard, Receiver operating characteristic (ROC) curve was used and diagnostic accuracy of VNCa images corresponding to different Rel.CM values (1.25, 1.35, 1.45, 1.55, 1.65, 1.75) were analyzed, aimed to select an optimal Rel.CM value of VNCa images for detecting BME. Then, CT values of the normal areas and BME areas were measured on the VNCa images corresponding to the optimal Rel.CM value for preliminary quantitative analysis. The rank-sum test was used to compare the differences of CT values between BME areas and normal bone marrow areas on the VNCa images. RESULTS: The 24 knees were divided into 288 areas. MR Imaging showed BME in 121 areas. The areas under the ROC curve with different Rel.CM values (1.25, 1.35, 1.45, 1.55, 1.65, and 1.75) were 0.633, 0.674, 0.882, 0.684, 0.651, and 0.649, respectively. On the VNCa images of Rel.CM = 1.45, the diagnostic accuracy was the highest (up to 89.2 %), the CT values of the BME area and the normal area were -67.9 (1.7∼-100.1) HU and -94.5 (-69.7∼-144.9) HU, respectively, with statistical significance (Z=-9.804, P < 0.05). CONCLUSIONS: The VNCa images with a Rel.CM value of 1.45 is optimal for the detection of BME in knee.

[Radiomics models based on non-enhanced MRI can differentiate chondrosarcoma from enchondroma].

OBJECTIVE: To develop and validate radiomics models based on non-enhanced magnetic resonance (MR) imaging for differentiating chondrosarcoma from enchondroma. METHODS: We retrospectively evaluated a total of 68 patients (including 27 with chondrosarcoma and 41 with enchondroma), who were randomly divided into training group (n=46) and validation group (n=22). Radiomics features were extracted from T1WI and T2WI-FS sequences of the whole tumor by two radiologists independently and selected by Low Variance, Univariate feature selection, and least absolute shrinkage and selection operator (LASSO). Radiomics models were constructed by multivariate logistic regression analysis based on the features from T1WI and T2WI-FS sequences. The receiver-operating characteristics (ROC) curve and intraclass correlation coefficient (ICC) analyses of the radiomics models and conventional MR imaging were performed to determine their diagnostic accuracy. RESULTS: The ICC value for interreader agreement of the radiomics features ranged from 0.779 to 0.923, which indicated good agreement. Ten and 11 features were selected from the T1WI and T2WI-FS sequences to construct radiomics models, respectively. The areas under the curve (AUCs) of T1WI and T2WI-FS models were 0.990 and 0.925 in training group and 0.915 and 0.855 in the validation group, respectively, showing no significant differences between the two sequence-based models (P>0.05). In all the cases, the AUCs of the two radiomics models based on T1WI and T2WI-FS sequences and conventional MR imaging were 0.955, 0.901 and 0.569, respectively, demonstrating a significantly higher diagnostic accuracy of the two sequence-based radiomics models than conventional MR imaging (P<0.01). CONCLUSIONS: The radiomics models based on T1WI and T2WI-FS non-enhanced MR imaging can be used for the differentiation of chondrosarcoma from enchondroma.

Geometric and electronic modification of the active Fe3+ sites of α-Fe2O3 for highly efficient toluene combustion.

In the present study, catalytically inactive or low-active Ti4+ (d0) or Zn2+ (d10) ions were doped to α-Fe2O3 to tune the geometric and electronic engineering for Fe active center. X-ray absorption near edge structure (XANES) and Powder X-ray diffraction (XRD) analyses coupled with density functional theory (DFT) calculation show that the added of Ti4+ could occupy the interstitial octahedral or tetrahedral sites, resulting in surface Fe2+ species are oxidized to octahedrally coordinated Fe3+. As a result, more oxygen vacancies are generated, which improve the catalytic performance for toluene combustion. On the other hand, Fe2+ was substituted by Zn2+ ion could result in the partial destruction of hematite crystal structure, forming an additional phase of ZnFe2O4, and meanwhile part of Zn2+ ions replace the octahedrally coordinated Fe3+ sites, and therefore significantly decreasing the toluene catalytic performance. Moreover, our studies demonstrate that the combustions of toluene over Fe-based catalysts involve both the MvK and L-H mechanisms.

Stimuli-Responsive Aggregation-Induced Delayed Fluorescence Emitters Featuring the Asymmetric D-A Structure with a Novel Diarylketone Acceptor Toward Efficient OLEDs with Negligible Efficiency Roll-Off.

Multifunctional luminescent materials with aggregation-induced delayed fluorescence (AIDF) are capable of suppressing concentration-caused emission quenching and exciton annihilation when used as organic light-emitting diode (OLED) emitters. In this contribution, three stimuli-responsive AIDF luminogens, pipd-BZ-PXZ, pipd-BZ-PTZ, and pipd-BZ-DMAC, featuring a D-A asymmetric framework based on a fused N-heterocycle diarylketone acceptor (imid-azo[1,2-a]pyridin-2-yl(phenyl)methanone pipd) are designed and synthesized. Interestingly, pipd-BZ-PTZ forms two different kinds of crystals (G-crystal and O-crystal) with distinct intermolecular interactions between pipd moieties. The G-crystal with a looser packing mode presents significant morphology-dependent stimuli-responsive behavior with a shifted emission wavelength of 56 nm. Generated by a strong intramolecular charge transfer effect, pipd-BZ-PXZ and pipd-BZ-PTZ exhibit orange to red emission in solution and neat films. Both nondoped and doped devices are fabricated for comparison. Nondoped devices present moderate performance with external quantum efficiencies and current efficiency that reach 7.04% and 19.86 cd A-1, respectively, and the corresponding efficiency roll off at 1000 cd m-2 is as small as 2.3%, which is among the best records of AIDF-OLEDs with an emission wavelength over 570 nm. Doped devices show better performance with corresponding efficiencies of up to 55.41 cd A-1 and 15.77%.

Anti-interleukin-5-neutralizing antibody attenuates caradiac injury and cadiac dysfunction by aggravating the inflammatory response in doxorubicin-treated mice.

Previous studies have demonstrated that interleukins (ILs) are closely associated with doxorubicin (DOX)-induced cardiac injury. IL-5 is an important member of the IL family, and this study was performed to investigate whether IL-5 affects DOX-induced cardiac injury and its underlying mechanisms. The cardiac IL-5 expression was first detected and the results showed that cardiac IL-5 levels were significantly lower in DOX-treated mice, and IL-5 was mainly derived from cardiac macrophage (Mø). In addition, some DOX-treated mice received an injection of anti-IL-5-neutralizing antibody (nAb), and we found that treatment with a mouse anti-IL-5 nAb significantly upregulated the levels of myocardial injury markers, aggravated cardiac dysfunction, increased M1 macrophage (Mø1) and decreased M2 macrophage (Mø2) differentiation, and promoted apoptotic marker expression. Furthermore, the effect of mouse IL-5 nAb on DOX-induced Mø differentiation and its role on mouse cardiomyocyte (MCM) cells apoptosis were detected in vitro, and the results exhibited that mouse IL-5 nAb promoted Mø1 differentiation but inhibited Mø2 differentiation in vitro and alleviated apoptosis in MCM cells. Our results found a mouse anti-IL-5 nAb-aggravated DOX-induced cardiac injury and dysfunction by alleviating the inflammatory response and myocardial cell apoptosis.

Porous polyimide particle-coated adsorptive microextraction bar combined with thermal desorption-gas chromatography for rapid determination of parabens in condiments.

Bar adsorptive microextraction using polyimide (PI) particles as the extraction phase followed by thermal desorption and gas chromatography-mass spectrometry (BAµE/TD-GC-MS) was developed to detect parabens in condiments, such as soy sauce, vinegar, and cooking wine. The PI particles were prepared by pneumatic spray combined with the immersion-precipitation phase transformation method. The prepared particles have highly porous surfaces, on which the 10-60 nm open nanopores are closely packed. Particles between 250-500 µm were then sieved out and used as extraction phase for BAµE. In contrast to the smooth and dense surface of the conventional PI phase transformation bar, the macroscopic rough surface of the PI particle bar and its microscopic porous particle surfaces provided larger extraction interfaces and more surface adsorption sites, both of which enhanced the extraction mass flux. With an extraction time of 2 min, the absolute recoveries of parabens by the PI particle bar were 1.9˜2.7 times those obtained by the conventional PI phase transformation bar. The intrabatch and interbatch precisions of the PI particle bars were less than 4.6% and 7.5%, respectively, and the PI particle bar exhibited a long lifetime of more than 50 extraction/desorption cycles. To realize rapid determination of parabens, the extraction time was fixed at 2 min. The analytical performance for standard water samples showed wide linearity (0.14-50 µg/L) with good correlation coefficients (r > 0.9980), good precision (RSD < 5.6%), appropriate detection limits (0.005-0.008 µg/L), and high enrichment factors (305-626). For the analysis of parabens in diluted condiments, the relative recoveries were between 86.1% and 109.0% with RSDs ranging from 0.1%-8.7%.

Facile fabrication of Ce-decorated composition-tunable Ce@ZnCo2O4 core-shell microspheres for enhanced catalytic propane combustion.

The design of heterogeneous catalysts of high efficiency for complete oxidation of volatile organic compounds (VOCs) is a challenge. In the present study, propane is adopted as a VOC representative, and core-shell structured ZnCo2O4@CeO2 catalysts with Ce decoration were synthesized and tested for propane combustion. Through SEM, STEM, and EDX analyses, the structure of the ZnCo2O4@CeO2 catalysts was characterized. The results of activity evaluation demonstrate that the presence of Ce can significantly promote catalytic performance, and the most suitable Ce content has been verified. Furthermore, the optimized ZnCo2O4@CeO2 catalyst exhibits excellent thermal stability and strong resistance toward water. The superior catalytic performance over the optimized ZnCo2O4@CeO2 catalyst is attributed to the high concentration of surface lattice oxygen (O2-) and the presence of strong interactions between Ce and Co.

Inhibition of water adsorption into polar solid-phase microextraction materials with ultrathin polydimethylsiloxane coating for thermal desorption-gas chromatography analysis.

Solid-phase microextraction (SPME) coupled with thermal desorption-gas chromatography (TD-GC) has become a powerful analysis tool for volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) in water samples. However, water adsorption into polar microextraction phase is usually unavoidable during the extraction process, and the burst of large amounts of water vapour during thermal desorption will cause serious problems to GC separation and detectors. Pawliszyn's group had demonstrated that the tens of micron-thick, defect-free polydimethylsiloxane (PDMS) coating could act as a perfect barrier for water adsorption and offer much better compatibility in complex matrices. However, the PDMS overcoat largely decreased the uptake rate of polar analytes into the inner sorbent. In order to quantify the effect of PDMS coating thickness on water adsorption amount and the extraction kinetics, ultrathin PDMS layer was used to coat the polar extraction phase with polyimide (PI) as a model in this work. It was surprising to find that the PDMS coating with the thickness less than one micron can decrease the water adsorption by 96%, while the extraction efficiency for polar analytes (phenolic compounds and nitroaromatic explosives) was decreased by less than 20% at the extraction time of 30 min. Moreover, the kinetic data showed that the thinner the PDMS coating was, the less the uptake rate of polar analytes into PI extraction phase decreased. Finally, polar poly (phthalazine ether sulfone ketone) (PPESK) extraction phase was also coated with ultrathin PDMS coating to verify the universality of the strategy. Generally, the water adsorption problem in polar SPME was overcome to a great extent, and the extraction efficiency of polar analytes was mainly preserved with this ultrathin PDMS coating, which could broaden the application of SPME in the environmental field.

Electrospun nanofiber cloud for ultrafast solid phase micro-extraction of trace organics in water samples.

In order to achieve ultrafast solid phase micro-extraction (SPME) of trace organics in water samples, the robust polyimide/polyvinylpyrrolidone (PI/PVP) composite nanofiber was prepared by electrospinning. Unlike the nonwoven electrospun membrane bulk widely used for SPME, the hydrophilic PI/PVP nanofibers here can be easily dispersed into and recovered from water sample like a cloud, so nanofiber cloud SPME (NC-SPME) was proposed in this work. The extraction performance of the NC-SPME method was evaluated using phthalates and organochlorine pesticides as model analytes. The extracted analytes were then desorbed by acetone and eventually quantified by gas chromatography-mass spectrometry (GC-MS). The extraction equilibrium was reached in 30 s for most of the phthalates and 2 min for organochlorine pesticides. While for bar-SPME, the equilibrium time was much longer than 2 h. At the extraction time of 5 min, the recoveries of phthalates and organochlorine pesticides were more than 34% and 52% by the NC-SPME method while they were less than 15% and 10% respectively by the bar-SPME method. In a word, the NC-SPME method presented significant advantages such as fast equilibrium and high recoveries compared to bar-SPME. For the phthalates analysis of real samples by NC-SPME, 5 mL of water sample was used for a 5-min extraction, linear range covered 1-2 orders of magnitude with correlation coefficients (r) higher than 0.99 and limits of detection (LODs) at ng/L levels for all of the tested water samples. This method will be useful for the rapid micro-extraction of other kinds of organics in water samples even biological fluid samples due to the stable composition, ultra-hydrophilic surface of PI/PVP nanofiber, and the biocompatible nature of PVP.

Mg-Al hydrotalcite-supported Pd catalyst for low-temperature CO oxidation: effect of Pdn+ species and surface hydroxyl groups.

Hydrotalcite-like compounds (HTlcs) are promising supports or catalyst precursors for heterogeneous catalysts. Herein, MgAl-HTlcs-supported Pd catalyst was fabricated, and two Pd catalysts supported on Mg(OH)2 and Al(OH)3 were prepared for comparison. The presence of hydroxyl groups (OH-) in the support is important for obtaining uniform Pd nanoparticles with small sizes. We found that Pdn+ species are more active than Pd0 in low temperature CO oxidation due to their lower barrier in CO activation. The Pd/MgAl-HT catalyst shows the most stable Pdn+ at a temperature lower than 90 °C, leading to the highest catalytic activity towards CO oxidation. Pdn+ in the Pd/Al(OH)3 catalyst is more stable than that in Pd/Mg(OH)2 at low temperature, which is ascribed to its smaller temperature hysteresis (Thysteresis) between the oxidation and re-reduction cycles. The effect of hydroxyl groups on stabilizing Pd species is related to the stability of Pd catalyst in CO oxidation reaction.