Palladium nanoparticles by electrospinning from poly(acrylonitrile-co-acrylic acid)-PdCl2 solutions. Relations between preparation conditions, particle size, and catalytic activity

Catalytic palladium (Pd) nanoparticles on electrospun copolymers of acrylonitrile and acrylic acid (PAN-AA) mats were produced via reduction of PdCl2 with hydrazine. Fiber mats were electrospun from homogeneous solutions of PAN-AA and PdCl2 in dimethylformamide (DMF). Pd cations were reduced to Pd metals when fiber mats were treated in an aqueous hydrazine solution at room temperature. Pd atoms nucleate and form small crystallites whose sizes were estimated from the peak broadening of X-ray diffraction peaks. Two to four crystallites adhere together and form agglomerates. Agglomerate sizes and fiber diameters were determined by scanning and transmission electron microscopy. Spherical Pd nanoparticles were dispersed homogeneously on the electrospun nanofibers. The effects of copolymer composition and amount of PdCl2 on particle size were investigated. Pd particle size mainly depends on the amount of acrylic acid functional groups and PdCl2 concentration in the spinning solution. Increasing acrylic acid concentration on polymer chains leads to larger Pd nanoparticles. In addition, Pd particle size becomes larger with increasing PdCl2 concentration in the spinning solution. Hence, it is possible to tune the number density and the size of metal nanoparticles. The catalytic activity of the Pd nanoparticles in electrospun mats was determined by selective hydrogenation of dehydrolinalool (3,7-dimethyloct-6- ene-1-yne-3-ol, DHL) in toluene at 90 °C. Electrospun fibers with Pd particles have 4.5 times higher catalytic activity than the current Pd/Al2O3 catalyst

On the role of soil water retention characteristic on aerobic microbial respiration

Soil water status is one of the most important environmental factors that control microbial activity and rate of soil organic matter (SOM) decomposition. Its effect can be partitioned into effect of water energy status (water potential) on cellular activity, effect of water volume on cellular motility, and aqueous diffusion of substrate and nutrients, as well as the effect of air content and gas-diffusion pathways on concentration of dissolved oxygen. However, moisture functions widely used in SOM decomposition models are often based on empirical functions rather than robust physical foundations that account for these disparate impacts of soil water. The contributions of soil water content and water potential vary from soil to soil according to the soil water characteristic (SWC), which in turn is strongly dependent on soil texture and structure. The overall goal of this study is to introduce a physically based modeling framework of aerobic microbial respiration that incorporates the role of SWC under arbitrary soil moisture status. The model was tested by comparing it with published datasets of SOM decomposition under laboratory conditions.</p

Metal oxide–zeolite composites in transformation of methanol to hydrocarbons : do iron oxide and nickel oxide matter?

The methanol-to-hydrocarbon (MTH) reaction has received considerable attention as utilizing renewable sources of both value-added chemicals and fuels becomes a number one priority for society. Here, for the first time we report the development of hierarchical zeolites (ZSM-5) containing both iron oxide and nickel oxide nanoparticles. By modifying the iron oxide (magnetite, Fe3O4) amounts, we are able to control the catalyst activity and the product distribution in the MTH process. At the medium Fe3O4 loading, the major fraction is composed of C9–C11 hydrocarbons (gasoline fraction). At the higher Fe3O4 loading, C1–C4 hydrocarbons prevail in the reaction mixture, while at the lowest magnetite loading the major component is the C5–C8 hydrocarbons. Addition of Ni species to Fe3O4–ZSM-5 leads to the formation of mixed Ni oxides (NiO/Ni2O3) positioned either on top of or next to Fe3O4 nanoparticles. This modification allowed us to significantly improve the catalyst stability due to diminishing coke formation and disordering of the coke formed. The incorporation of Ni oxide species also leads to a higher catalyst activity (up to 9.3 g(methanol)/(g(ZSM-5) × h)) and an improved selectivity (11.3% of the C5–C8 hydrocarbons and 23.6% of the C9–C11 hydrocarbons), making these zeolites highly promising for industrial applications

Multiple models and experiments underscore large uncertainty in soil carbon dynamics

Soils contain more carbon than plants or the atmosphere, and sensitivities of soil organic carbon (SOC) stocks to changing climate and plant productivity are a major uncertainty in global carbon cycle projections. Despite a consensus that microbial degradation and mineral stabilization processes control SOC cycling, no systematic synthesis of long-term warming and litter addition experiments has been used to test process-based microbe-mineral SOC models. We explored SOC responses to warming and increased carbon inputs using a synthesis of 147 field manipulation experiments and five SOC models with different representations of microbial and mineral processes. Model projections diverged but encompassed a similar range of variability as the experimental results. Experimental measurements were insufficient to eliminate or validate individual model outcomes. While all models projected that CO efflux would increase and SOC stocks would decline under warming, nearly one-third of experiments observed decreases in CO flux and nearly half of experiments observed increases in SOC stocks under warming. Long-term measurements of C inputs to soil and their changes under warming are needed to reconcile modeled and observed patterns. Measurements separating the responses of mineral-protected and unprotected SOC fractions in manipulation experiments are needed to address key uncertainties in microbial degradation and mineral stabilization mechanisms. Integrating models with experimental design will allow targeting of these uncertainties and help to reconcile divergence among models to produce more confident projections of SOC responses to global changes. 2

Divergent controls of soil organic carbon between observations and process-based models

The storage and cycling of soil organic carbon (SOC) are governed by multiple co-varying factors, including climate, plant productivity, edaphic properties, and disturbance history. Yet, it remains unclear which of these factors are the dominant predictors of observed SOC stocks, globally and within biomes, and how the role of these predictors varies between observations and process-based models. Here we use global observations and an ensemble of soil biogeochemical models to quantify the emergent importance of key state factors – namely, mean annual temperature, net primary productivity, and soil mineralogy – in explaining biome- to global-scale variation in SOC stocks. We use a machine-learning approach to disentangle the role of covariates and elucidate individual relationships with SOC, without imposing expected relationships a priori. While we observe qualitatively similar relationships between SOC and covariates in observations and models, the magnitude and degree of non-linearity vary substantially among the models and observations. Models appear to overemphasize the importance of temperature and primary productivity (especially in forests and herbaceous biomes, respectively), while observations suggest a greater relative importance of soil minerals. This mismatch is also evident globally. However, we observe agreement between observations and model outputs in select individual biomes – namely, temperate deciduous forests and grasslands, which both show stronger relationships of SOC stocks with temperature and productivity, respectively. This approach highlights biomes with the largest uncertainty and mismatch with observations for targeted model improvements. Understanding the role of dominant SOC controls, and the discrepancies between models and observations, globally and across biomes, is essential for improving and validating process representations in soil and ecosystem models for projections under novel future conditions

The dosimetric effects of limited elective nodal irradiation in volumetric modulated arc therapy treatment planning for locally advanced non-small cell lung cancer

Objective—Contemporary radiotherapy guidelines for locally advanced non-small cell lung carcinoma (LA-NSCLC) recommend omitting elective nodal irradiation, despite the fact that evidence supporting this came primarily from older reports assessing comprehensive nodal coverage using 3D conformal techniques. Herein, we evaluated the dosimetric implications of the addition of limited elective nodal irradiation (LENI) to standard involved field irradiation (IFI) using volumetric modulated arc therapy (VMAT) planning. Method—Target volumes and organs-at-risk (OARs) were delineated on CT simulation images of 20 patients with LA-NSCLC. Two VMAT plans (IFI and LENI) were generated for each patient. Involved sites were treated to 60 Gy in 30 fractions for both IFI and LENI plans. Adjacent uninvolved nodal regions, considered high risk based on the primary tumor site and extent of nodal involvement, were treated to 51 Gy in 30 fractions in LENI plans using a simultaneous integrated boost approach. Results—All planning objectives for PTVs and OARs were achieved for both IFI and LENI plans. LENI resulted in significantly higher esophagus Dmean (15.3 vs. 22.5 Gy, p \u3c 0.01), spinal cord Dmax (34.9 vs. 42.4 Gy, p = 0.02) and lung Dmean (13.5 vs. 15.9 Gy, p = 0.02), V20 (23.0 vs. 27.9%, p = 0.03), and V5 (52.6 vs. 59.4%, p = 0.02). No differences were observed in heart parameters. On average, only 32.2% of the high-risk nodal volume received an incidental dose of 51 Gy when untargeted in IFI plans. Conclusion—The addition of LENI to VMAT plans for LA-NSCLC is feasible, with only modestly increased doses to OARs and marginal expected increase in associated toxicity

Mir-21-Sox2 Axis Delineates Glioblastoma Subtypes with Prognostic Impact.

UNLABELLED: Glioblastoma (GBM) is the most aggressive human brain tumor. Although several molecular subtypes of GBM are recognized, a robust molecular prognostic marker has yet to be identified. Here, we report that the stemness regulator Sox2 is a new, clinically important target of microRNA-21 (miR-21) in GBM, with implications for prognosis. Using the MiR-21-Sox2 regulatory axis, approximately half of all GBM tumors present in the Cancer Genome Atlas (TCGA) and in-house patient databases can be mathematically classified into high miR-21/low Sox2 (Class A) or low miR-21/high Sox2 (Class B) subtypes. This classification reflects phenotypically and molecularly distinct characteristics and is not captured by existing classifications. Supporting the distinct nature of the subtypes, gene set enrichment analysis of the TCGA dataset predicted that Class A and Class B tumors were significantly involved in immune/inflammatory response and in chromosome organization and nervous system development, respectively. Patients with Class B tumors had longer overall survival than those with Class A tumors. Analysis of both databases indicated that the Class A/Class B classification is a better predictor of patient survival than currently used parameters. Further, manipulation of MiR-21-Sox2 levels in orthotopic mouse models supported the longer survival of the Class B subtype. The MiR-21-Sox2 association was also found in mouse neural stem cells and in the mouse brain at different developmental stages, suggesting a role in normal development. Therefore, this mechanism-based classification suggests the presence of two distinct populations of GBM patients with distinguishable phenotypic characteristics and clinical outcomes. SIGNIFICANCE STATEMENT: Molecular profiling-based classification of glioblastoma (GBM) into four subtypes has substantially increased our understanding of the biology of the disease and has pointed to the heterogeneous nature of GBM. However, this classification is not mechanism based and its prognostic value is limited. Here, we identify a new mechanism in GBM (the miR-21-Sox2 axis) that can classify ∼50% of patients into two subtypes with distinct molecular, radiological, and pathological characteristics. Importantly, this classification can predict patient survival better than the currently used parameters. Further, analysis of the miR-21-Sox2 relationship in mouse neural stem cells and in the mouse brain at different developmental stages indicates that miR-21 and Sox2 are predominantly expressed in mutually exclusive patterns, suggesting a role in normal neural development

Identification of a CpG Island Methylator Phenotype that Defines a Distinct Subgroup of Glioma

SummaryWe have profiled promoter DNA methylation alterations in 272 glioblastoma tumors in the context of The Cancer Genome Atlas (TCGA). We found that a distinct subset of samples displays concerted hypermethylation at a large number of loci, indicating the existence of a glioma-CpG island methylator phenotype (G-CIMP). We validated G-CIMP in a set of non-TCGA glioblastomas and low-grade gliomas. G-CIMP tumors belong to the proneural subgroup, are more prevalent among lower-grade gliomas, display distinct copy-number alterations, and are tightly associated with IDH1 somatic mutations. Patients with G-CIMP tumors are younger at the time of diagnosis and experience significantly improved outcome. These findings identify G-CIMP as a distinct subset of human gliomas on molecular and clinical grounds