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The topsoil of a clayey slope is easily washed off by rain due to its loose structure. To protect the slope surface, in recent years, several types of non-traditional soil additives have been used by means of mixing with soil. In this work, a new organic polymer soil stabilizer, named aqua-dispersing-nano-binder (ADNB), was sprayed on the soil surface to stabilize the topsoil of a clayey slope. To understand the interaction between the polymer and soil particles during the infiltration process as well as the stabilization mechanism, infiltration tests, water stability tests and scanning electron microscopy (SEM) analyses were performed with different polymer contents. The infiltration tests showed that the infiltration rate of the polymer stabilizer in the soil was slower than that of water due to its characteristics of easy adhesion to soil particles, poor fluidity and large molecular volume. The maximum effective infiltration depth was achieved in the specimen treated with 2% ADNB, and the minimum was achieved in the specimen treated with 5% ADNB. The water stability of the soil increased with the content of the soil stabilizer in the soil aggregates with diameters of either 5&ndash, 10 mm or 10&ndash, 20 mm. The SEM analysis showed that the quantity of polymer decreased with infiltration depth, a polymer membrane was formed on the surface of the topsoil and chains were formed inside. The amelioration of the soil water stability may have been due to the bonding between soil particles and polymers generated after evaporation of water in the emulsion. The polymer stabilizer could be applied to improve the erosion resistance of the slope topsoil and reduce soil loss.

The spatial distribution of soil constituents at the micrometer scale is of great importance to understand processes controlling the formation of micro-aggregates and the stabilization of organic carbon. Here, the spatial distribution of organic and mineral constituents in Podzol horizons is studied by concerted measurements of (i) the content of various forms of Fe, Al, Si and C determined by selective extraction in the fine earth fraction of soil (f < 2 mm); (ii) the elemental composition of the clay fraction (f < 2 um) with lateral resolution using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS), and with surface selectivity using X-ray photoelectron spectroscopy (XPS); (iii) the specific surface area (SSA) of fine earth and clay fractions by krypton physisorption. The SSA of the fine earth in illuvial horizons is predominantly due to finely divided Fe oxides, including goethite, characterized by an equivalent particle size of about 10 mu m. Kaolinite platelets of about 2 gm size account for a large volume proportion in the clay fraction but have a minor contribution to SSA. Fe oxides and organic matter (OM) are intimately associated. Heterogeneity at the um scale is created by local variations in the relative amounts of kaolinite and Fe-OM associations. These two kinds of physical entities are in random mixture. Moreover, variation of C/Fe atomic ratios reveals sub-mu m scale heterogeneity. The latter is due to variation in the relative proportion of organic compounds and Fe oxides, indicating that aggregation of nanoparticles, and not only mere adsorption or pore filling, plays a role in these associations. In this regard, our results highlight that OM associated with Fe protects Fe oxides against physical displacement and that part of this associated OM is oxidizable by NaOCl treatment. These findings demonstrate that the concept of OM stabilization through association with Fe must be revisited when considering the sub-mu m scale level because fine Fe oxide particles can be easily dispersed during oxidation of associated carbon. Combination of physical fractionation and microanalysis (e.g. SEM-EDS, vibrational spectroscopy) offer promising perspectives to clarify the relationship between chemical composition and sub-inn scale architecture, and to better understand soil processes.

Surface modification with linear polymethacrylic acid (20 kDa), linear and branched polyethylenimine (25 kDa), and branched oligoethylenimine (800 Da) is commonly used to improve the function of magnetite nanoparticles (MNPs) in many biomedical applications. These polymers were shown herein to have different adsorption capacity and anticipated conformations on the surface of MNPs due to differences in their functional groups, architectures, and molecular weight. This in turn affects the interaction of MNPs surfaces with biological serum proteins (fetal bovine serum). MNPs coated with 25 kDa branched polyethylenimine were found to attract the highest amount of serum protein while MNPs coated with 20 kDa linear polymethacrylic acid adsorbed the least. The type and amount of protein adsorbed, and the surface conformation of the polymer was shown to affect the size stability of the MNPs in a model biological media (RPMI-1640). A moderate reduction in r 2 relaxivity was also observed for MNPs suspended in RPMI-1640 containing serum protein compared to the same particles suspended in water. However, the relaxivities following protein adsorption are still relatively high making the use of these polymer-coated MNPs as Magnetic Resonance Imaging (MRI) contrast agents feasible. This work shows that through judicious selection of functionalization polymers and elucidation of the factors governing the stabilization mechanism, the design of nanoparticles for applications in biologically relevant conditions can be improved. © 2012 American Chemical Society., ARC (DP0985848); Spanish ISCIII-MSPS (CD09/00030)

While the influence of climate, vegetation, management and abiotic site factors on total carbon budgets and turn-over is intensively assessed, the influences of these ecosystem properties on the chemical complexity of soil organic matter (SOM) remains poorly understood. This study addresses the chemical composition of NaOH-extracted SOM from maritime temperate forest sites in Flanders (Belgium) by pyrolysis-GC/MS. The studied forests were chosen based on dominant tree species (Pinus sylvestris, Fagus sylvatica, Quercus robur and Populus spp.), soil texture and soil-moisture conditions. Differences in extractable-SOM pyrolysis products were correlated to site variables including dominant tree species, management of the woody biomass, site history, soil properties, total carbon stocks and indicators for microbial activity. Despite of a typical high intercorrelation between these site variables, the influence of the dominant tree species is prominent. The extractable-SOM composition is strongly correlated to litter quality and available nutrients. In nutrient-poor forests with low litter quality, the decomposition of relatively recalcitrant compounds (i.e. short and mid-chain alkanes/alkenes and aromatic compounds) appears hampered, causing a relative accumulation of these compounds in the soil. However, if substrate quality is favorable, no accumulations of recalcitrant compounds were observed, not even under high soil-moisture conditions. Former heathland vegetation still had a profound influence on extractable-SOM chemistry of young pine forests after a minimum of 60 years.

Accurate estimation of mineralizable nitrogen (N) from soil organic matter is essential to improve fertilizer management in agricultural systems. Mineralizable N refers to the amount of N in soil that is released during a certain period (ranging from 1 week to the length of a growing season). It has been estimated from increases in inorganic N during incubation or from N uptake by plants grown in a greenhouse or field. Many chemical soil tests measuring extractable organic N (EON) fractions have been proposed to predict mineralizable N. We evaluated the predictive value of these soil tests, using 2068 observations from 218 papers. Meta-analysis was used to find the best soil test, to analyse differences between field and laboratory experiments, and to determine whether their predictive value is affected by extraction intensity (% of total soil N that is extracted). The concentration of EON was positively related to mineralizable N, explaining on average 47% of the variation. It did not, however, explain more of the variation than total N. Best predictions (57%