[Imaging for displaying lymphatic vessels in whole-mount aorta adventitia of ApoE-/- mice by immunofluorescent histochemical staining].

Objective To explore a simple and feasible method for whole-mount immunofluorescence staining of lymphatic vessels in the ApoE-/- mouse model of atherosclerosis. Methods Aortic specimens were carefully excised from the ApoE-/- mouse model. Following immunostaining with specific antibodies against smooth muscle actin (SMA) and lymphatic vessel endothelial receptor 1 (LYVE1), the aortas, including the aortic root, were subjected to a 30-minute treatment with 5 g/L Sudan Black B solution. This step was instrumental in minimizing the autofluorescent background of the tissue. Thereafter, the aortas were processed through a clearing protocol and imaged within a purpose-built chamber under a fluorescence microscope. Results The pretreatment with 5 g/L Sudan Black B effectively suppressed the autofluorescent signals emanating from the vascular structures, thereby enhancing the contrast and clarity of the specific fluorescence signals associated with the lymphatic vessels. This enhancement in signal quality did not compromise the integrity or specificity of the immunofluorescent markers. Conclusion A facile, highly specific, and effective approach for the visualization of lymphatic vessels in whole-mount aortic preparations from ApoE-/- mice is established.

Interpreting chemisorption strength with AutoML-based feature deletion experiments.

The chemisorption energy of reactants on a catalyst surface, [Formula: see text], is among the most informative characteristics of understanding and pinpointing the optimal catalyst. The intrinsic complexity of catalyst surfaces and chemisorption reactions presents significant difficulties in identifying the pivotal physical quantities determining [Formula: see text]. In response to this, the study proposes a methodology, the feature deletion experiment, based on Automatic Machine Learning (AutoML) for knowledge extraction from a high-throughput density functional theory (DFT) database. The study reveals that, for binary alloy surfaces, the local adsorption site geometric information is the primary physical quantity determining [Formula: see text], compared to the electronic and physiochemical properties of the catalyst alloys. By integrating the feature deletion experiment with instance-wise variable selection (INVASE), a neural network-based explainable AI (XAI) tool, we established the best-performing feature set containing 21 intrinsic, non-DFT computed properties, achieving an MAE of 0.23 eV across a periodic table-wide chemical space involving more than 1,600 types of alloys surfaces and 8,400 chemisorption reactions. This study demonstrates the stability, consistency, and potential of AutoML-based feature deletion experiment in developing concise, predictive, and theoretically meaningful models for complex chemical problems with minimal human intervention.

Revealing the anti-sintering phenomenon on silica-supported nickel catalysts during CO2 hydrogenation.

The CO2 catalytic hydrogenation represents a promising approach for gas-phase CO2 utilization in a direct manner. Due to its excellent hydrogenation ability, nickel has been widely studied and has shown good activities in CO2 hydrogenation reactions, in addition to its high availability and low price. However, Ni-based catalysts are prone to sintering under elevated temperatures, leading to unstable catalytic performance. In the present study, various characterization techniques were employed to study the structural evolution of Ni/SiO2 during CO2 hydrogenation. An anti-sintering phenomenon is observed for both 9% Ni/SiO2 and 1% Ni/SiO2 during CO2 hydrogenation at 400°C. Results revealed that Ni species were re-dispersed into smaller-sized nanoparticles and formed Ni0 active species. While interestingly, this anti-sintering phenomenon leads to distinct outcomes for two catalysts, with a gradual increase in both reactivity and CH4 selectivity for 9% Ni/SiO2 presumably due to the formation of abundant surface Ni° from redispersion, while an apparent decreasing trend of CH4 selectivity for 1% Ni/SiO2 sample, presumably due to the formation of ultra-small nanoparticles that diffuse and partially filled the mesoporous pores of the silica support over time. Finally, the redispersion phenomenon was found relevant to the H2 gas in the reaction environment and enhanced as the H2 concentration increased. This finding is believed to provide in-depth insights into the structural evolution of Ni-based catalysts and product selectivity control in CO2 hydrogenation reactions.

Single-Atom Platinum Catalyst for Efficient CO2 Conversion via Reverse Water Gas Shift Reaction.

The need to tackle CO2 emissions arising from the continuously rising combustion of fossil fuels has sparked considerable interest in investigating the reverse water gas shift (RWGS) reaction. This reaction holds great promise as an alternative technique for the conversion and utilization of CO2. In this study, a scalable method was employed to synthesize a single-atom Pt catalyst, uniformly dispersed on SiC, where up to 6.4 wt% Pt1 was loaded onto a support based on ligand modification and UV photoreduction. This Pt1/SiC catalyst exhibited a high selectivity (100%) towards the RWGS reaction; 54% CO2 conversion was observed at 900 °C with a H2/CO2 feed-in ratio of 1:1, significantly higher than the conventional Pt nanoparticle counterparts. Moreover, Pt1/SiC displayed a robust stability during the long-term test. The activation energy with as-synthesized Pt1/SiC was further calculated to be 61.6 ± 6.4 kJ/mol, which is much lower than the 91.6 ± 15.9 kJ/mol of the Pt nanoparticle counterpart and other Pt-based catalysts reported so far. This work offers new insights into the utilization of diverse single-atom catalysts for the RWGS reaction and other crucial catalytic processes, paving the way for the further exploration and application of SACs in various industrial endeavors.

Predictors of cancer screening behavior of the working population in China based on the information-motivation-behavioral skills model.

Background: The cancer screening rate in the working population is very low in China. Information-motivation-behavioral skills (IMB) model has been applied to elucidate screening behavior for various chronic diseases but has not been investigated in analyzing cancer screening behavior. This study aimed to examine factors influencing cancer screening behavior and their linkages based on the IMB model. Methods: A cross-sectional study was conducted in Shanghai, China from August to October 2021. Data were obtained through an anonymous questionnaire. Predictive relationships between variables in the IMB model and cancer screening behavior were evaluated. Structural equation modeling (SEM) was constructed to demonstrate the utility of the IMB model. Results: Among the 556 participants included in the analysis, 34.4% of participants had ever done a cancer screening. The construct validation analysis supported that the measure items included were acceptable. SEM found that knowledge of cancer warning signs and symptoms (ß = 0.563, p < 0.001) and cancer screening behavioral skills (ß = 0.264, p = 0.003) were related to participation in cancer screening, whereas cancer screening motivation was not directly influenced the participation in cancer screening (ß = - 0.075, p = 0.372). Conclusion: The cancer screening rate was found to be lower than expected in the working population. The IMB model could be used to make decisions in implementing behavioral interventions to participate in cancer screening among the Chinese working population. Enhancing the knowledge of cancer warning signs and symptoms and strengthening behavioral skills should be focused on to improve participation in cancer screening.

The effectiveness of optimal exercise-based strategy for patients with hip fracture: a systematic review and Bayesian network meta-analysis.

The implementation of exercise intervention (EI) presents a promising and economical way for patients with hip fracture. However, the optimal type of EI remains unclear. The objective of this study is to evaluate the efficacy of various EI approaches and identify the optimal intervention for improving the prognosis of patients with hip fracture. A comprehensive search of Medline (via PubMed), Web of Science, Embase, Cochrane Central Register of Controlled Trials, CINAHL, CNKI, Wan Fang, VIP, and CBM was conducted from their earliest records to June 2022. The included randomized controlled trials (RCTs) included at least one type of exercise for patients with hip fracture. The methodological quality of these trials was assessed using the Cochrane Collaboration Risk of Bias Tool. All direct and indirect comparisons were analyzed by Stata 14.0 and OpenBUGS 3.2.3 software. The primary outcome was hip function, and the secondary outcomes were activity of daily living (ADL), walking capacity and balance ability of patients. Based on the ranking probabilities, resistance exercise (RE) was ranked as the most effective among all exercise interventions (surface under cumulative ranking curve values [SUCRA]: 94.8%, [MD]: - 11.07, [Crl]: - 15.07 to - 7.08) in improving the efficacy of patients' hip function, followed by balance exercise (BE) ([SUCRA]:81.1%, [MD]: - 8.79, [Crl]: - 13.41 to - 4.18) and muscle strength exercise ([SUCRA]:57.6%, [MD]: - 5.35, [Crl]: - 9.70 to - 0.95). For the improvement of ADL for patients with hip fracture, BE ([SUCRA]:98.4%, [MD]: - 17.38, [Crl]: - 23.77 to - 11.04) may be the best EI. The findings of this study indicate that RE and BE might be the best approach to improve prognosis for patients with hip fracture. However, further rigorous and meticulously planned RCTs are required to substantiate the conclusions drawn from this study.

Recent progress of catalytic methane combustion over transition metal oxide catalysts.

Methane (CH4) is one of the cleanest fossil fuel resources and is playing an increasingly indispensable role in our way to carbon neutrality, by providing less carbon-intensive heat and electricity worldwide. On the other hand, the atmospheric concentration of CH4 has raced past 1,900 ppb in 2021, almost triple its pre-industrial levels. As a greenhouse gas at least 86 times as potent as carbon dioxide (CO2) over 20 years, CH4 is becoming a major threat to the global goal of deviating Earth temperature from the +2°C scenario. Consequently, all CH4-powered facilities must be strictly coupled with remediation plans for unburned CH4 in the exhaust to avoid further exacerbating the environmental stress, among which catalytic CH4 combustion (CMC) is one of the most effective strategies to solve this issue. Most current CMC catalysts are noble-metal-based owing to their outstanding C-H bond activation capability, while their high cost and poor thermal stability have driven the search for alternative options, among which transition metal oxide (TMO) catalysts have attracted extensive attention due to their Earth abundance, high thermal stability, variable oxidation states, rich acidic and basic sites, etc. To date, many TMO catalysts have shown comparable catalytic performance with that of noble metals, while their fundamental reaction mechanisms are explored to a much less extent and remain to be controversial, which hinders the further optimization of the TMO catalytic systems. Therefore, in this review, we provide a systematic compilation of the recent research advances in TMO-based CMC reactions, together with their detailed reaction mechanisms. We start with introducing the scientific fundamentals of the CMC reaction itself as well as the unique and desirable features of TMOs applied in CMC, followed by a detailed introduction of four different kinetic reaction models proposed for the reactions. Next, we categorize the TMOs of interests into single and hybrid systems, summarizing their specific morphology characterization, catalytic performance, kinetic properties, with special emphasis on the reaction mechanisms and interfacial properties. Finally, we conclude the review with a summary and outlook on the TMOs for practical CMC applications. In addition, we also further prospect the enormous potentials of TMOs in producing value-added chemicals beyond combustion, such as direct partial oxidation to methanol.

Spatially-directed magnetic molecularly imprinted polymers with good anti-interference for simultaneous enrichment and detection of dual disease-related bio-indicators.

As the changes of biomarkers directly reflect the occurrence of degenerative diseases, accurate detection of biomarkers is of great significance for disease diagnosis and control. However, single index detection has high uncertainties to accurately reflect the pathological characteristics because of the complexity of the human internal environment and the extremely trace concentration of indicators. To this end, a method for simultaneous detection of dual-biomarkers based on anti-interference magnetic molecularly imprinted polymers (D-mag-MIPs) is thereby proposed, and successfully applied in human urine analysis for the detection of Parkinson's disease bio-indicators 4-dihydroxyphenylacetic acid (DOPAC) and dopamine (DA). In this work, carboxyl functionalized ferric oxide served as a magnetic core, laying a solid foundation for batch detection. Hyperbranched polyethylenimine, whose abundant amino groups can provide multiple interaction forces to templates with high affinity, is employed as a functional monomer. Relative to single-template MIPs, D-mag-MIPs achieve the detection of dual bio-indicators in a one-time test, reducing the false positive result probability and enhancing the detection accuracy. The proposed methodology has been evaluated to exhibit good anti-interference, satisfactory precision, low detection limits, wide linear ranges and fast batch detection for DA and DOPAC. This work thus offers an alternative and efficient pathway for convenient batch detection of dual bio-indicators from biofluids at once.

Aggregation and Binding-Directed FRET Modulation of Conjugated Polymer Materials for Selective and Point-of-Care Monitoring of Serum Albumins.

Nonspecific interactions of conjugated polymers (CPs) with various proteins prove to be a major impediment for researchers when designing a suitable CP-based probe for the amplified and selective recognition of particular proteins in complex body fluids. Herein, a new strategy is presented for the precise and specific monitoring of clinically important serum albumin (SA) proteins at the nanomolar level using fluorescence resonance energy transfer (FRET)-modulated CP-surfactant ensembles as superior sensing materials. In brief, the newly designed color-tunable CP PF-DBT-Im undergoes intense aggregation with the surfactant sodium dodecyl sulfate (SDS), enabling drastic change in the emission color from violet to deep red due to intermolecular FRET. The emission of PF-DBT-Im/SDS ensembles then changed from deep red to magenta specifically on addition of SAs owing to the exclusive reverse FRET facilitated by synergistic effects of electrostatic interactions, hydrophobic forces, and the comparatively high intrinsic quantum yield of SAs. Interestingly, PF-DBT-Im itself could not differentiate SAs from other proteins, demonstrating the superiority of the PF-DBT-Im/SDS self-assembly over PF-DBT-Im. Finally, an affordable smartphone-integrated point-of-care (PoC) device is also fabricated as a proof-of-concept for the on-site and rapid monitoring of SAs, validating the potential of the system in long-term clinical applications.

The Application and Comparison of Machine Learning Models for the Prediction of Breast Cancer Prognosis: Retrospective Cohort Study.

BACKGROUND: Over the recent years, machine learning methods have been increasingly explored in cancer prognosis because of the appearance of improved machine learning algorithms. These algorithms can use censored data for modeling, such as support vector machines for survival analysis and random survival forest (RSF). However, it is still debated whether traditional (Cox proportional hazard regression) or machine learning-based prognostic models have better predictive performance. OBJECTIVE: This study aimed to compare the performance of breast cancer prognostic prediction models based on machine learning and Cox regression. METHODS: This retrospective cohort study included all patients diagnosed with breast cancer and subsequently hospitalized in Fudan University Shanghai Cancer Center between January 1, 2008, and December 31, 2016. After all exclusions, a total of 22,176 cases with 21 features were eligible for model development. The data set was randomly split into a training set (15,523 cases, 70%) and a test set (6653 cases, 30%) for developing 4 models and predicting the overall survival of patients diagnosed with breast cancer. The discriminative ability of models was evaluated by the concordance index (C-index), the time-dependent area under the curve, and D-index; the calibration ability of models was evaluated by the Brier score. RESULTS: The RSF model revealed the best discriminative performance among the 4 models with 3-year, 5-year, and 10-year time-dependent area under the curve of 0.857, 0.838, and 0.781, a D-index of 7.643 (95% CI 6.542, 8.930) and a C-index of 0.827 (95% CI 0.809, 0.845). The statistical difference of the C-index was tested, and the RSF model significantly outperformed the Cox-EN (elastic net) model (C-index 0.816, 95% CI 0.796, 0.836; P=.01), the Cox model (C-index 0.814, 95% CI 0.794, 0.835; P=.003), and the support vector machine model (C-index 0.812, 95% CI 0.793, 0.832; P<.001). The 4 models' 3-year, 5-year, and 10-year Brier scores were very close, ranging from 0.027 to 0.094 and less than 0.1, which meant all models had good calibration. In the context of feature importance, elastic net and RSF both indicated that TNM staging, neoadjuvant therapy, number of lymph node metastases, age, and tumor diameter were the top 5 important features for predicting the prognosis of breast cancer. A final online tool was developed to predict the overall survival of patients with breast cancer. CONCLUSIONS: The RSF model slightly outperformed the other models on discriminative ability, revealing the potential of the RSF method as an effective approach to building prognostic prediction models in the context of survival analysis.

All-optical spatial terahertz modulator with surface-textured and passivated silicon.

For a Si-based all-optical spatial terahertz modulator (STM), an enhanced modulation efficiency under low illumination density would be of great significance to exploit the competence of THz technology in real-world applications. We presented here an implementation of such a device by microtexturing and passivating the Si surface, forming a truncated pyramidal array (TPA). This TPA structure with SiO2 passivating coatings not only decreases light reflectance and expands the active area for THz modulation but also remarkably increases the photogenerated carrier lifetime. These 3-fold benefits render Si-TPA superior to bare-Si with respect to the achievable modulation efficiency, especially at low irradiation power. Furthermore such a Si-TPA device is also more applicable than its counterpart that is only passivated by SiO2 nanocoatings, even though the Si-SiO2 has a slightly increased modulation efficiency. These periodically aligned pyramids resembled as a mesa array significantly suppress the lateral diffusion induced by longer diffusion, resulting in an equivalent resolution of bare-Si. This novel Si-TPA based STM is highly desired for realizing a high-performance THz imager and provides a feasible approach to breaking the trade-off between resolution and modulation efficiency.

Novel bayberry-and-honeycomb-like magnetic surface molecularly imprinted polymers for the selective enrichment of rutin from Sophora japonica.

Rutin (RT), a widely distributed natural flavonoid compound, has been generally utilized as an important active ingredient owing to its considerable biomedical and economic value. Inspired by the structure features of densely-packed bayberry and well-orientated honeycomb, a novel type of magnetic molecularly imprinted polymers (HB-TI-MMIPs) with abundant high-affinity and uniformly-distributed binding sites was rationally constructed for the selective enrichment of RT from Sophora japonica. The polymerization conditions, physicochemical properties, and adsorption performance of the imprinted nanomaterials were systematically investigated. The optimized HB-TI-MMIPs display a high adsorption capacity, fast adsorption rate, and satisfactory selectivity towards RT. Meanwhile, the proposed analytical methodology using HPLC, with HB-TI-MMIPs as adsorbents, successfully applied to enrich and detect RT from Sophora japonica with high recoveries (87.2-94.6%) and good RSDs (lower than 4.3%). Therefore, the fabricated HB-TI-MMIPs with a fast magnetic responsivity and desirable adsorption performance would be attractive in plant active ingredients extraction fields.

Endogenous asymmetric dimethylarginine accumulation precipitates the cardiac and mitochondrial dysfunctions in type 1 diabetic rats.

Myocardial mitochondrial function and biogenesis are suppressed in diabetes, but the mechanisms are unclear. Increasing evidence suggests that asymmetric dimethylarginine (ADMA) is associated with diabetic cardiovascular complications. This study was to determine whether endogenous ADMA accumulation contributes to cardiac and mitochondrial dysfunctions of diabetic rats and elucidate the potential mechanisms. Diabetic rat was induced by single intraperitoneal injection of streptozotocin (50 mg/kg). N-acetylcysteine was given (250 mg/kg/d) by gavage for 12w. Cardiac function was detected by echocardiography. Left ventricle papillary muscles were isolated to examine myocardial contractility. Myocardial ATP and mitochondrial DNA contents were measured to evaluate mitochondrial function and biogenesis. Endogenous ADMA accumulation was augmented resulting in decreased nitric oxide (NO) production and increased oxidative stress, suggesting NO synthase (NOS) uncoupling in the myocardium of T1DM rats compared with control rats. ADMA augmentation was associated with cardiac and mitochondrial dysfunctions along with myocardial uncoupling protein-2 (UCP2) upregulation and peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) downregulation in T1DM rats. Exogenous ADMA could directly inhibit myocardial contractility, mitochondrial function and biogenesis in parallel with decreasing NO content and PGC-1α expression while increasing oxidative stress and UCP2 expression in papillary muscles and cardiomyocytes. Treatment with antioxidant N-acetylcysteine, also an inhibitor of NOS uncoupling, either ameliorated ADMA-associated cardiac and mitochondrial dysfunctions or reversed ADMA-induced NO reduction and oxidative stress enhance in vivo and in vitro. These results indicate that myocardial ADMA accumulation precipitates cardiac and mitochondrial dysfunctions in T1DM rats. The underlying mechanism may be related to NOS uncoupling, resulting in NO reduction and oxidative stress increment, ultimate PGC-1α down-regulation and UCP2 up-regulation.

A coating strategy to achieve effective local charge separation for photocatalytic coevolution.

Semiconductors of narrow bandgaps and high quantum efficiency have not been broadly utilized for photocatalytic coevolution of H2 and O2 via water splitting. One prominent issue is to develop effective protection strategies, which not only mitigate photocorrosion in an aqueous environment but also facilitate charge separation. Achieving local charge separation is especially challenging when these reductive and oxidative sites are placed only nanometers apart compared to two macroscopically separated electrodes in a photoelectrochemical cell. Additionally, the driving force of charge separation, namely the energetic difference in the barrier heights across the two type of sites, is small. Herein, we used conformal coatings attached by nanoscale cocatalysts to transform two classes of tunable bandgap semiconductors, i.e., CdS and GaInP2, into stable and efficient photocatalysts. We used hydrogen evolution and redox-mediator oxidation for model study, and further constructed a two-compartment solar fuel generator that separated stoichiometric H2 and O2 products. Distinct from the single charge-transfer direction reported for conventional protective coatings, the coating herein allows for concurrent injection of photoexcited electrons and holes through the coating. The energetic difference between reductive and oxidative catalytic sites was regulated by selectivity and local kinetics. Accordingly, the charge separation behavior was validated using numerical simulations. Following this design principle, the CdS/TiO2/Rh@CrOx photocatalysts evolved H2 while oxidizing reversible polysulfide redox mediators at a maximum rate of 90.6 µmol⋅h-1⋅cm-2 by stacking three panels. Powered by a solar cell, the redox-mediated solar water-splitting reactor regenerated the polysulfide repeatedly and achieved solar-to-hydrogen efficiency of 1.7%.

Asymmetrical dimethylarginine induces dysfunction of insulin signal transduction via endoplasmic reticulum stress in the liver of diabetic rats.

AIMS: Endoplasmic reticulum stress (ERS) as an emerging factor is involved in insulin resistance (IR), which is the pathological basis of diabetes mellitus. Accumulation of asymmetric dimethylarginine (ADMA), an endogenous inhibitor of nitric oxide synthase is associated with IR, but the underlying mechanisms have not been elucidated. This study was to reveal the important role of ADMA in IR and determine whether endogenous ADMA accumulation contributes to hepatic IR via ERS in diabetic rats and hepatocytes. MATERIALS AND METHODS: Diabetic rat model was induced by a single intraperitoneal injection of streptozotocin (50 mg/kg). Phosphorylation of insulin receptor substrate 1 (IRS1) and protein kinase B (Akt) was detected to evaluate IR. The protein kinase PKR-like ER kinase (PERK) and eukaryotic initiation factor 2α kinase (eIF2α) phosphorylation, x-box binding protein-1 (XBP-1) splicing, glucose-regulated protein 78 (GRP78) and C/EBP homologues protein (CHOP) expressions were measured to assess ERS. KEY FINDINGS: Endogenous ADMA content was significantly increased and positively correlated with either IR as evidenced by increased IRS1 at serine and reduced Akt phosphorylation or ERS as indicated by upregulations of PERK and eIF2α phosphorylation, XBP-1 splicing, GRP78 and CHOP expressions in the liver of diabetic rats compared with control rats. Exogenous ADMA directly caused IR and ERS in dose- and time-dependent manners in primary mouse hepatocytes. Pretreatment with ERS inhibitor 4-phenylbutyrate or ADMA antagonist L-arginine not only improved ADMA-associated or -induced hepatic IR but also attenuated ADMA-associated or -induced ERS in diabetic rats or hepatocytes. SIGNIFICANCE: These findings indicate that endogenous ADMA accumulation contributes to hepatic IR via ERS in diabetic rats.

In Situ Identification of Reaction Intermediates and Mechanistic Understandings of Methane Oxidation over Hematite: A Combined Experimental and Theoretical Study.

Effective methane utilization for either clean power generation or value-added chemical production has been a subject of growing attention worldwide for decades, yet challenges persist mostly in relation to methane activation under mild conditions. Here, we report hematite, an earth-abundant material, to be highly effective and thermally stable to catalyze methane combustion at low temperatures (<500 °C) with a low light-off temperature of 230 °C and 100% selectivity to CO2. The reported performance is impressive and comparable to those of precious-metal-based catalysts, with a low apparent activation energy of 17.60 kcal·mol-1. Our theoretical analysis shows that the excellent performance stems from a tetra-iron center with an antiferromagnetically coupled iron dimer on the hematite (110) surface, analogous to that of the methanotroph enzyme methane monooxygenase that activates methane at ambient conditions in nature. Isotopic oxygen tracer experiments support a Mars van Krevelen redox mechanism where CH4 is activated by reaction with a hematite surface oxygen first, followed by a catalytic cycle through a molecular-dioxygen-assisted pathway. Surface studies with in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory (DFT) calculations reveal the evolution of reaction intermediates from a methoxy CH3-O-Fe, to a bridging bidentate formate b-HCOO-Fe, to a monodentate formate m-HCOO-Fe, before CO2 is eventually formed via a combination of thermal hydrogen-atom transfer (HAT) and proton-coupled electron transfer (PCET) processes. The elucidation of the reaction mechanism and the intermediate evolutionary profile may allow future development of catalytic syntheses of oxygenated products from CH4 in gas-phase heterogeneous catalysis.

Shape-Dependent Interactions of Manganese Oxide Nanomaterials with Lipid Bilayer Vesicles.

Interactions of transition-metal-oxide nanomaterials with biological membranes have important environmental implications and applications in ecotoxicity and life-cycle assessment analysis. In this study, we quantitatively assess the impact of MnO2 nanomaterial morphology-one-dimensional (1D) nanowires, 2D nanosheets, and 3D nanoflowers-on their interaction with phospholipid vesicles as a model for biological membranes. Confocal microscopy suggests visual evidence for the interaction of undisrupted vesicles with dispersed MnO2 nanomaterials of different morphologies, and it further supports the observation that minimal dye leakage of the vesicle inner solution was detected during the interaction with MnO2 nanomaterials during the dye leakage assay. Upon titration of vesicles to dispersions of MnO2 nanowires, nanosheets, and nanoflowers, each roughly 10 times larger than the vesicles, dynamic light scattering reveals two diffusive time scales associated with aggregates in the mixture. While the longer time scale corresponds to the dispersed MnO2 control population, the appearance of a shorter timescale with vesicle addition indicates interaction between the dispersed metal oxide nanomaterials and the vesicles. The interaction is shape-dependent, being more pronounced for MnO2 nanowires than for nanosheets and nanoflowers. Furthermore, the shorter diffusive time scale is intermediate between the vesicle and nanomaterial controls, which may suggest a degree of metal oxide aggregate breakup. Vesicle adsorption isotherms and zeta potential measurements during titration corroborate vesicle attachment on the nanomaterials. Our results suggest that the dispersed nanomaterial shape plays an important role in mediating nondestructive vesicle-nanomaterial interactions and that lipid vesicles act as efficient surfactants for MnO2 nanomaterials.

Catalytic manganese oxide nanostructures for the reverse water gas shift reaction.

Understanding the fundamental structure-property relationships of nanomaterials is critical for many catalytic applications as they comprise of the catalyst designing principles. Here, we develop efficient synthetic methods to prepare various MnO2 structures and investigate their catalytic performance as applied to the reverse Water Gas Shift (rWGS) reaction. We show that the support-free MnO derived from MnO2 1D, 2D and 3D nanostructures are highly selective (100% CO2 to CO), thermally stable catalysts (850 °C) and differently effective in the rWGS. Up to 50% conversion is observed, with a H2/CO2 feed-in ratio of 1 : 1. From both experiments and DFT calculations, we find the MnO2 morphology plays a critical role in governing the catalytic behaviors since it affects the predominant facets exposed under reaction conditions as well as the intercalation of K+ as a structural building block, substantially affecting the gas-solid interactions. The relative adsorption energy of reactant (CO2) and product (CO), ΔE = Eads(CO2) -Eads(CO), is found to correlate linearly with the catalytic activity, implying a structure-function relationship. The strong correlation found between Eads(CO2) -Eads(CO), or more generally, Eads(R) -Eads(P), and catalytic activity makes ΔE a useful descriptor for characterization of efficient catalysts involving gas-solid interactions beyond the rWGS.

Metabolic engineering of Bacillus amyloliquefaciens LL3 for enhanced poly-γ-glutamic acid synthesis.

Poly-γ-glutamic acid (γ-PGA) is a biocompatible and biodegradable polypeptide with wide-ranging applications in foods, cosmetics, medicine, agriculture and wastewater treatment. Bacillus amyloliquefaciens LL3 can produce γ-PGA from sucrose that can be obtained easily from sugarcane and sugar beet. In our previous work, it was found that low intracellular glutamate concentration was the limiting factor for γ-PGA production by LL3. In this study, the γ-PGA synthesis by strain LL3 was enhanced by chromosomally engineering its glutamate metabolism-relevant networks. First, the downstream metabolic pathways were partly blocked by deleting fadR, lysC, aspB, pckA, proAB, rocG and gudB. The resulting strain NK-A6 synthesized 4.84 g l-1 γ-PGA, with a 31.5% increase compared with strain LL3. Second, a strong promoter PC 2up was inserted into the upstream of icd gene, to generate strain NK-A7, which further led to a 33.5% improvement in the γ-PGA titre, achieving 6.46 g l-1 . The NADPH level was improved by regulating the expression of pgi and gndA. Third, metabolic evolution was carried out to generate strain NK-A9E, which showed a comparable γ-PGA titre with strain NK-A7. Finally, the srf and itu operons were deleted respectively, from the original strains NK-A7 and NK-A9E. The resulting strain NK-A11 exhibited the highest γ-PGA titre (7.53 g l-1 ), with a 2.05-fold improvement compared with LL3. The results demonstrated that the approaches described here efficiently enhanced γ-PGA production in B. amyloliquefaciens fermentation.