Simultaneous determination of pigments of spinach (Spinacia oleracea L.) leaf for quality inspection using hyperspectral imaging and multi-task deep learning regression approaches.

Rapid and accurate determination of pigment content is important for quality inspection of spinach leaves during storage. This study aimed to use hyperspectral imaging at two spectral ranges (visible/near-infrared, VNIR: 400-1000 nm; NIR: 900-1700 nm) to simultaneously determine the pigment (chlorophyll a, chlorophyll b, total chlorophyll, and carotenoids) content in spinach stored at different durations and conditions (unpackaged and packaged). Partial least squares (PLS), back propagation neural network (BPNN) and convolutional neural network (CNN) were used to establish single-task and multi-task regression models. Single-task CNN (STCNN) models and multi-task CNN (MTCNN) models obtained better performances than the other models. The models using VNIR spectra were superior to those using NIR spectra. The overall results indicated that hyperspectral imaging with multi-task learning could predict the quality attributes of spinach simultaneously for spinach quality inspection under various storage conditions. This research will guide food quality inspection by simultaneously inspecting multiple quality attributes.

Bispecific CAR-T cells targeting FAP and GPC3 have the potential to treat hepatocellular carcinoma.

Chimeric antigen receptor (CAR) T cell therapy has demonstrated robust efficacy against hematological malignancies, but there are still some challenges regarding treating solid tumors, including tumor heterogeneity, antigen escape, and an immunosuppressive microenvironment. Here, we found that SNU398, a hepatocellular carcinoma (HCC) cell line, exhibited high expression levels of fibroblast activation protein (FAP) and Glypican 3 (GPC3), which were negatively correlated with patient prognosis. The HepG2 HCC cell line highly expressed GPC3, while the SNU387 cell line exhibited high expression of FAP. Thus, we developed bispecific CAR-T cells to simultaneously target FAP and GPC3 to address tumor heterogeneity in HCC. The anti-FAP-GPC3 bispecific CAR-T cells could recognize and be activated by FAP or GPC3 expressed by tumor cells. Compared with anti-FAP CAR-T cells or anti-GPC3 CAR-T cells, bispecific CAR-T cells achieved more robust activity against tumor cells expressing FAP and GPC3 in vitro. The anti-FAP-GPC3 bispecific CAR-T cells also exhibited superior antitumor efficacy and significantly prolonged the survival of mice compared with single-target CAR-T cells in vivo. Overall, the use of anti-FAP-GPC3 bispecific CAR-T cells is a promising treatment approach to reduce tumor recurrence caused by tumor antigen heterogeneity.

A multivalent polyphenol-metal-nanoplatform for cascade amplified chemo-chemodynamic therapy.

Chemodynamic therapy (CDT), as an emerging therapeutic strategy, kills cancer cells by converting intracellular hydrogen peroxide (H2O2) into cytotoxic oxidizing hydroxyl radicals (⋅OH). However, the therapeutic efficiency of CDT is compromised due to the insufficient endogenous H2O2 and metal catalysts in tumor cells. The use of multivalent polyphenols with multiple hydroxyl functions provides a facile yet robust means for efficient CDT augmentation. For this purpose, we reported herein the construction of polyphenol-metal nanoparticles (NPs) via a phenol-metal coordination strategy. The uniqueness of this study is the preparation of only one polymer construct with multivalency that can afford various supramolecular interactions for simultaneous "one-pot" loading of different therapeutic species, i.e., doxorubicin (DOX), glucose oxidases (GOD), and Fe3+ and further co-self-assembly into a stabilized nanomedicine for cascade amplified chemo-chemodynamic therapy. Specifically, the tumor intracellular acidic pH-triggered DOX release could serve for chemotherapy as well as enhance the intracellular H2O2 level. Together with the extra H2O2 and gluconic acid produced by the GOD-triggered glucose consumption, DOX@POAD-Fe@GOD NPs promoted Fe3+participation in the Fe-mediated Fenton reaction for cascade amplified chemo-chemodynamic therapy. Notably, this formulation displayed a greater anti-tumor effect with a tumor inhibition ratio 1.6-fold higher than that of free DOX in a BALB/c mice model bearing 4T1 tumors. Overall, the multivalent polyphenol-metal nanoplatform developed herein integrates chemotherapy, starvation therapy, and CDT for synergistic enhanced anticancer efficiency, which shows great potential for clinical translations. STATEMENT OF SIGNIFICANCE: Chemodynamic therapy (CDT) generally suffers from compromised therapeutic efficiency due to insufficient endogenous H2O2 and metal catalysts in tumor cells. To develop a facile yet robust strategy for efficient CDT augmentation, we reported herein construction of a multivalent polyphenol-metal nanoplatform, DOX@POAD-Fe@GOD nanoparticles (NPs) via a phenol-metal coordination strategy. This nanoplatform integrates multiple supramolecular dynamic interactions not only for simultaneously safe encapsulation of doxorubicin (DOX), Fe3+, and glucose oxidases (GOD), but also for cascade amplified chemo-chemodynamic therapy. Specifically, the intracellular acidic pH-triggered dissociation of DOX@POAD-Fe@GOD NPs promoted the release of Fe3+, DOX, and GOD for significantly increased ROS levels that can accelerate Fenton reactions for cascaded chemotherapy, starvation therapy, and CDT with amplified antitumor efficiency in vivo.

Aqueous green synthesis of organic/inorganic nanohybrids with an unprecedented synergistic mechanism for enhanced near-infrared photothermal performance.

Silver sulfide (Ag2S) nanoparticles (NPs) represent one of the most popular inorganic reagents for near-infrared (NIR) photothermal therapy (PTT). However, the extensive biomedical applications of Ag2S NPs are greatly compromised by the hydrophobicity of the NPs prepared in organic solvents, their low photothermal conversion efficiency, certain surface modification-induced damage to their intrinsic properties and short circulation time. To develop a facile yet efficient green approach to overcome these shortcomings for improved properties and performance of Ag2S NPs, we report herein the construction of Ag2S@polydopamine (PDA) nanohybrids via a "one-pot" organic-inorganic hybridization strategy, which produces uniform Ag2S@PDA nanohybrids with well-modulated sizes in the range of 100-300 nm via the self-polymerization of dopamine (DA) and subsequent synergistic assembly of PDA with Ag2S NPs in a three-phase mixed medium containing water, ethanol and trimethylbenzene (TMB). Integration of dual photothermal moieties, i.e., Ag2S and PDA at a molecular level, endows Ag2S@PDA nanohybrids with synergistically enhanced NIR photothermal properties that are much better than those of either PDA or Ag2S NPs due to calculated combination indexes (CIs) of 0.3-0.7 between Ag2S NPs and PDA based on a modified Chou-Talalay method. Therefore, this study not only developed a facile "one-pot" green approach toward producing uniform Ag2S@PDA nanohybrids with well-modulated dimensions, but also revealed an unprecedented synergistic mechanism for organic/inorganic nanohybrids that is based on dual photothermal moieties providing enhanced near-infrared photothermal performance.

Dual gatekeepers-modified mesoporous organic silica nanoparticles for synergistic photothermal-chemotherapy of breast cancer.

HYPOTHESIS: Construction of dual gatekeepers-functionalized mesoporous organic silica nanoparticles (MONs) with both physical and chemical mechanisms for modulated drug delivery properties provides one solution to the extracellular stability vs. intracellular high therapeutic efficiency of MONs that hold great potential for clinical translations. EXPERIMENTS: We reported herein facile construction of diselenium-bridged MONs decorated with dual gatekeepers, i.e., azobenzene (Azo)/polydopamine (PDA) for both physical and chemical modulated drug delivery properties. Specifically, Azo can act as a physical barrier to block DOX in the mesoporous structure of MONs for extracellular safe encapsulation. The PDA outer corona serves not only as a chemical barrier with acidic pH-modulated permeability for double insurance of minimized DOX leakage in the extracellular blood circulation but also for inducing a PTT effect for synergistic PTT and chemotherapy of breast cancer. FINDINGS: An optimized formulation, DOX@(MONs-Azo3)@PDA resulted in approximately 1.5 and 2.4 fold lower IC50 values than DOX@(MONs-Azo3) and (MONs-Azo3)@PDA controls in MCF-7 cells, respectively, and further mediated complete tumor eradication in 4T1 tumor-bearing BALB/c mice with insignificant systematic toxicity due to the synergistic PTT and chemotherapy with enhanced therapeutic efficiency.

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions.

The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.

Identification of Potential Parkinson's Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network.

Parkinson's disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson's disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision-recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.

Identification of Chemical-Disease Associations Through Integration of Molecular Fingerprint, Gene Ontology and Pathway Information.

The identification of chemical-disease association types is helpful not only to discovery lead compounds and study drug repositioning, but also to treat disease and decipher pathomechanism. It is very urgent to develop computational method for identifying potential chemical-disease association types, since wet methods are usually expensive, laborious and time-consuming. In this study, molecular fingerprint, gene ontology and pathway are utilized to characterize chemicals and diseases. A novel predictor is proposed to recognize potential chemical-disease associations at the first layer, and further distinguish whether their relationships belong to biomarker or therapeutic relations at the second layer. The prediction performance of current method is assessed using the benchmark dataset based on ten-fold cross-validation. The practical prediction accuracies of the first layer and the second layer are 78.47% and 72.07%, respectively. The recognition ability for lead compounds, new drug indications, potential and true chemical-disease association pairs has also been investigated and confirmed by constructing a variety of datasets and performing a series of experiments. It is anticipated that the current method can be considered as a powerful high-throughput virtual screening tool for drug researches and developments.

Novel Potential Small Molecule-MiRNA-Cancer Associations Prediction Model Based on Fingerprint, Sequence, and Clinical Symptoms.

As an important biomarker in organisms, miRNA is closely related to various small molecules and diseases. Research on small molecule-miRNA-cancer associations is helpful for the development of cancer treatment drugs and the discovery of pathogenesis. It is very urgent to develop theoretical methods for identifying potential small molecular-miRNA-cancer associations, because experimental approaches are usually time-consuming, laborious, and expensive. To overcome this problem, we developed a new computational method, in which features derived from structure, sequence, and symptoms were utilized to characterize small molecule, miRNA, and cancer, respectively. A feature vector was construct to characterize small molecule-miRNA-cancer association by concatenating these features, and a random forest algorithm was utilized to construct a model for recognizing potential association. Based on the 5-fold cross-validation and benchmark data set, the model achieved an accuracy of 93.20 ± 0.52%, a precision of 93.22 ± 0.51%, a recall of 93.20 ± 0.53%, and an F1-measure of 93.20 ± 0.52%. The areas under the receiver operating characteristic curve and precision recall curve were 0.9873 and 0.9870. The real prediction ability and application performance of the developed method have also been further evaluated and verified through an independent data set test and case study. Some potential small molecules and miRNAs related to cancer have been identified and are worthy of further experimental research. It is anticipated that our model could be regarded as a useful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Multi-class-SMMCA.

Strained Ultralong Silver Nanowires for Enhanced Electrocatalytic Oxygen Reduction Reaction in Alkaline Medium.

Many noble metals are efficient catalysts for oxygen reduction reaction (ORR), including silver (Ag). Among all these noble metals, Ag is the most affordable because of its relative abundance. Surface energy has been proven to play a crucial role in the catalytic process, and straining is an effective operation to raise the surface energy over electrocatalysts. In this work, sonication was utilized to induce strain in Ag nanowires (NWs) through lattice deformation. A 0.18 J/m2 improvement of the surface energy around the stacking faults area has been calculated via density functional theory. The diffusion-limiting current density was evaluated and increases by >20% (from -4.98 to -6.00 mA/cm2) after sonication straining. Meanwhile, the onset potential remains almost constant (i.e., 0.95 V vs RHE). The results show that induction of strain has a strong impact on the diffusion-limiting current density and significantly improves the ORR catalytic performance of Ag NWs.

Inhibitory effect of Xiaochuan pill on obstinate cough and its role in regulation of TLR4-MyD88-NF-κBp65 signaling pathway. / å“®å–˜ä¸¸æŠ‘åˆ¶é¡½å›ºæ€§å’³å—½åŠå ¶å¯¹TLR4-MyD88-NF-κBp65信号通路的调控作用.

OBJECTIVES: Cough variant asthma (CVA) is the main cause of obstinate cough. This study aimed to observe the therapeutic effect of Xiaochuan pill on CVA in a rat model, and to explore the mechanisms. METHODS: The rats were sensitized and challenged with 4% ovaibumin (OA) and 2% Al(OH)3 to establish the CVA models. They were treated with Xiaochuan pill (at the dose of 0.9, 1.8, 3.6 g/kg) or montelukast sodium once a day for 14 days. After 7 and 14 days of intervention, 5 and 10 rats were randomly selected from each group to collect bronchoalveolar lavage fluid (BALF), trachea, and lungs. The number of white blood cells (WBC) and eosinophils (EOS), and the levels of IL-1ß, TNF- α, and IFN-γ in BALF were detected. Histopathological examination of lung tissue was performed to observe the histomorphological changes. The expressions of TLR4, MyD88, NF-κBp65, and p-p65 in lung tissue were detected by Western blotting. RESULTS: The numbers of WBC and EOS in BALF of CVA rats were significantly decreased by Xiaochuan pill (P<0.05 or P<0.01). The hyperplasia of tracheal, bronchial mucosa and the infiltration of inflammatory cells in lung were alleviated obviously. After 14 d of intervention, high dose of Xiaochuan pill significantly increased the level of IFN- γ (P<0.01), reduced the levels of IL-1ß (P<0.05) and TNF-α (P<0.05), and decreased the expressions of TLR4, MyD88, p65, and p-p65 (P<0.05 or P<0.01). CONCLUSIONS: Xiaochuan pill exerts the significant therapeutic effect on obstinate cough in rats. The mechanism of action may be related to the regulation of TLR4-MyD88-NF-κBp65 signaling pathway as well as the inflammation and immune response.

Molecular-scale cage-confinement pyrolysis route to size-controlled molybdenum carbide nanoparticles for electrochemical sensor.

Herein, size-controllable molybdenum carbide nanoparticles (Mo2C NPs) encapsulated by N, P-codoped carbon shells which simultaneously wrapping on the surface of carbon nanotube (Mo2C@NPC/CNT) is synthesized through a molecular-scale cage-confinement pyrolysis route. Such confinement achieves a good coating and protection of Mo2C and the effective control over the size of Mo2C NPs ranging from 2.5 to 10 nm facilitates a rational investigation into their electrochemical sensor behavior at nanometer scales. The optimized structure consisting of Mo2C nanoparticles with size of ~5 nm showed an outstanding electrochemical response toward dopamine (DA) and acetaminophen (AC) with detection limits (S/N = 3) of 0.008 µM for AC and 0.01 µM for DA.

Confinement of single polyoxometalate clusters in molecular-scale cages for improved flexible solid-state supercapacitors.

Herein, we realized the supramolecular confinement of a single polyoxometalate (POM) cluster precisely in a polypyrrole (PPy) hydrogel-wrapped CNT framework with molecular-scale cages. This hybrid hydrogel framework demonstrated an ultra-high loading (67.5 wt%) and extremely uniform dispersion of individual of H3[P(Mo3O10)4] (PMo12) molecules, as demonstrated by sub-ångström-resolution HAADF-STEM. Consequently, it exhibited a better supercapacitor performance than that of the conventional composite system. The flexible solid-state supercapacitor exhibited a high energy density of 67.5 µW h cm-2 at a power density of 700 µW cm-2 and delivered a high capacitance retention of 85.7% after 3000 cycles. Moreover, the flexible device exhibited excellent mechanical stability. Density functional theory calculations revealed that the wrapped "fishnet-like" hydrogel creates a cage structure with a size of 1.8 nm for the precise storage of the PMo12 molecule (diameter = 1.05 nm), leading to the mono-dispersion of single PMo12 molecules on the hybrid hydrogel. The "caging" effect also activates the PMo12 molecule to enhance its charging/discharging performance by introducing new reactive sites for proton transfer. We believe that this design for suitable cage structures can be used for the construction of other POM-based hybrid hydrogels, thereby achieving mono-dispersity and performance enhancement.

Highly Strained Au Nanoparticles for Improved Electrocatalysis of Ethanol Oxidation Reaction.

Au is an ideal noble metal for use as an electrocatalyst for the ethanol oxidation reaction owing to its high performance-to-cost ratio. The catalyst usually exists as nanoparticles (NPs) for high surface area-to-volume ratio. In the present work, a nontraditional physical approach has been developed to fabricate ultrasmall and homogeneous single-crystalline Au NPs by ion bombardment in a precision ion polishing system. Transmission electron microscopy characterizations show that the Au NPs produced with 5 keV Ar+ are highly strained to form twinned crystals, which accumulate a large amount of surface energy, and this was found to be an underlying reason causing strong catalysis. Electrochemistry tests reveal that in alkaline medium the C1 pathway occurs much more preferentially with the strained Au NPs than the normal Au NPs. The surface area-to-volume ratio is no longer the only factor that affects the performance; instead, surface energy might play a more important role in enhancing the catalytic activities.

Extraordinary catalysis induced by titanium foil cathode plasma for degradation of water pollutant.

The present paper reports a rapid and cost-effective bifunctional approach to the degradation of organic pollutants in the aqueous solution. This in situ hybrid induced photocatalytic method involves the advanced oxidation process, and photocatalytic process induced by ultraviolet radiated from the plasma discharge to improve the degradation efficiency. This powerful plasma allows the organic molecules to be cleaved either in the plasma zone or on the plasma/solution interface through hydrogen abstraction and electron transfer. Four parallel metal foil electrodes (i.e. Ta, Cu, Ti and Au coated Ti), used as cathodes in the two-electrode system, were evaluated in terms of their degradable performance to organic pollutants. It was found that the degradation rates are dependent on the electrical conduction of metal cathodes. During the discharge process, the Ti-based foil produces TiO2 particles, which then act as catalyst in the electrolyte and perform the photocatalytic process along with the plasma discharge process to degrade organic pollutants. It is of particular interest that gold nanoparticles, generated from Au coated Ti foil film during electrode discharging, are less than 5 nm in size and further enhance the TiO2 photocatalytic activity. In fact, this bifunctional plasma discharge process to the degradation of water pollutant provides an insight into more applications such as chemical conversion, water purification and dust pollution.

Nanostructured CuO/C Hollow Shell@3D Copper Dendrites as a Highly Efficient Electrocatalyst for Oxygen Evolution Reaction.

Adoption of bare metal oxides as catalytic materials shows inferior electrochemical activity because of their poor electrical conductivity. Although synthetic strategies for the employment of conductive substrates are well-established, the rational design and fabrication of hollow metal oxides nanostructures on the robust matrix with a high surface area and conductivity remains challenging. In the present research work, a strategy that transforms a metal-organic framework thin layer into a nanostructured CuO/C hollow shell to coat on the 3D nano-dendritic Cu foams as an electrode was successfully developed. This electrode is claimed to provide an extraordinary electrocatalysis for oxygen evolution reaction (OER) in alkaline media. The hierarchical complex presents fast electronic transmission networks and rich redox sites, leading to the significant enhancement in electrocatalytic OER efficiency. Furthermore, the spherical porous structure and robust architecture facilitate the high-speed diffusion of O2 bubbles in a long-term operation. The results of this study may serve as a reference for the designing of novel class 3D metal/metal oxide hierarchical structures for gas-involved (i.e., O2, H2, and CO2) electrocatalytic applications and beyond.

Phase controllable fabrication of zinc cobalt sulfide hollow polyhedra as high-performance electrocatalysts for the hydrogen evolution reaction.

Structure and phase modulations allow the development of highly active, cost-effective and stable electrocatalysts for the hydrogen evolution reaction (HER) but are rather challenging. In this paper, Zn-Co-S hollow/porous polyhedra with controllable phases were fabricated via solvent-based sulfidation at room temperature followed by thermal annealing. The obtained hollow structure Zn-Co-S-300 with an amorphous phase exhibits excellent electrocatalytic HER activity, which is higher than crystalline Zn-Co sulfides annealed at a higher temperature. Zn-Co-S-300 also shows a long-term working stability (91.7% current density retention over 10 hours) in alkaline media. This work provides a feasible approach for the fabrication of homogeneous ternary transition metal sulfide (TMS) electrocatalysts via the Kirkendall effect towards high-efficiency HER applications.

In Situ Formation of Decavanadate-Intercalated Layered Double Hydroxide Films on AA2024 and their Anti-Corrosive Properties when Combined with Hybrid Sol Gel Films.

A layered double hydroxide (LDH) film was formed in situ on aluminum alloy 2024 through a urea hydrolysis method, and a decavanadate-intercalated LDH (LDH-V) film fabricated through the dip coating method. The microstructural and morphological characteristics were investigated by scanning electron microscopy (SEM). The corrosion-resistant performance was analyzed by electrochemical impedance spectroscopy (EIS), scanning electrochemical microscopy (SECM), and a salt-spray test (SST).The SEM results showed that a complete and defect-free surface was formed on the LDH-VS film. The anticorrosion results revealed that the LDH-VS film had better corrosion-resistant properties than the LDH-S film, especially long-term corrosion resistance. The mechanism of corrosion protection was proposed to consist of the self-healing effect of the decavanadate intercalation and the shielding effect of the sol-gel film.

Enhanced CO2 permeability of membranes by incorporating polyzwitterion@CNT composite particles into polyimide matrix.

In this study, polyzwitterion is introduced into a CO2 separation membrane. Composite particles of polyzwitterion coated carbon nanotubes (SBMA@CNT) are prepared via a precipitation polymerization method. Hybrid membranes are fabricated by incorporating SBMA@CNT in polyimide matrix and utilized for CO2 separation. The prepared composite particles and hybrid membranes are characterized by transmission electron microscopy (TEM) with element mapping, field emission scanning electron microscopy (FESEM), Fourier transform infrared (FTIR) spectra, differential scanning calorimetry (DSC) and an electronic tensile machine. Water uptake and water state of membranes are measured to probe the relationship among water uptake, water state and CO2 transport behavior. Hybrid membranes show significantly enhanced CO2 permeability compared to an unfilled polyimide membrane at a humidified state. A hybrid membrane with 5 wt % SBMA@CNT exhibits the maximum CO2 permeability of 103 Barrer with a CO2/CH4 selectivity of 36. The increase of CO2 permeability is attributed to the incorporation of the SBMA@CNT composite particles. First, SBMA@CNT form interconnected channels for CO2 transport due to the facilitated transport effect of the quaternary ammonium in repeat unit of pSBMA. Second, SBMA@CNT improve water uptake and adjust water state of membrane, which further increases CO2 permeability. Meanwhile, the variation of CO2/CH4 selectivity is dependent on the bound water portion in the membrane. A gas permeation test at a dry state and a pressure test are conducted to further probe the membrane separation performance.

Surface dynamics of noisy viscoelastic films by adiabatic approximation.

Surface dynamics is sometimes used to determine the rheological properties of soft materials. In typical data analyses, surface capillary waves are included without incorporating thermal noise. A phenomenological expression for the time-dependent power spectral density has been proposed to account for thermal noise and shown to agree well with experiment. In this paper, we investigate the surface dynamics of viscoelastic films with thermal noise by using an adiabatic approximation involving fast quasi-equilibrium elastic vibrations to derive the power spectral density. Our result justifies the use of the phenomenological expression.