Characterization of pysio-chemical properties of novel one stop chemical method in preparations of copper nanofluids and possible explanations

Nanofluid is a dilute suspension containing particles in nanometer sized which are dispersed in the base fluid like ethylene glycol or water. Nanofluid is one of the crucial discovery in modern science which found to be having better thermal properties compared with conventional fluids like water or ethylene glycol thus makes it ideal to be applied and utilized in many areas in heat transfer area such as cooling, utilized as fluid for heat echangers and etc. Besides, the nanofluid with the improved thermal properties could solve the problem faced by various industries in the area of heat transfer. For example, in the semiconductor industry, the needs of superior cooling coolant are very crucialJn this paper, presents about preparation of copper nanofluid using novel one stop chemical method by reducing copper sulphate pentahydrate using reduction agent which is sodium hypophosphite in ethylene glycol as base fluids. The obtained nanofluid by using this novel one stop method is more stable besides cheaper and faster compared with two stop method whereby in the two step method, the production of the nanoparticles and the nanofluids are isolated. The process of drying, storage and transportation of the nanoparticles that takes place in two step method have cause the agglomeration and sedimentation of the nanofluids. As the result, the agglomeration could cause the settlement and clogging in the microchannel besides reduce the thermal conductivity. Therefore in the novel one stop method the production of the nanoparticles and the nanofluids are combined and not separated to avoid the process of drying, storage and transportation of nanoparticles. Meanwhile the nanofluid that obtained were analyzed using Transmission Electron Microscopy (TEM), UV-Vis Spectrophotometer, Viscometer and Fourier Transform Infared Spectroscopy (FTIR). The effect and influences of pH and dilution to the reaction rate and properties of nanofluid were also investigated

Quantifying Uncertainties in Sequential Chemical Extraction of Soil Phosphorus Using XANES Spectroscopy.

Sequential chemical extraction has been widely used to study soil phosphorus (P) dynamics and inform nutrient management, but its efficacy for assigning P into biologically meaningful pools remains unknown. Here, we evaluated the accuracy of the modified Hedley extraction scheme using P K-edge X-ray absorption near-edge structure (XANES) spectroscopy for nine carbonate-free soil samples with diverse chemical and mineralogical properties resulting from different degrees of soil development. For most samples, the extraction markedly overestimated the pool size of calcium-bound P (Ca-P, extracted by 1 M HCl) due to (1) P redistribution during the alkaline extractions (0.5 M NaHCO3 and then 0.1 M NaOH), creating new Ca-P via formation of Ca phosphates between NaOH-desorbed phosphate and exchangeable Ca2+ and/or (2) dissolution of poorly crystalline Fe and Al oxides by 1 M HCl, releasing P occluded by these oxides into solution. The first mechanism may occur in soils rich in well-crystallized minerals and exchangeable Ca2+ regardless of the presence or absence of CaCO3, whereas the second mechanism likely operates in soils rich in poorly crystalline Fe and Al minerals. The overestimation of Ca-P simultaneously caused underestimation of the pools extracted by the alkaline solutions. Our findings identify key edaphic parameters that remarkably influenced the extractions, which will strengthen our understanding of soil P dynamics using this widely accepted procedure

Vulnerability of Physically Protected Soil Organic Carbon to Loss Under Low Severity Fires

Soil aggregate degradation during medium and high severity fires is often identified as the main mechanism that leads to loss of soil organic matter (SOM) due to fire. Low severity fires, however, are considered not to cause aggregate degradation assuming that temperatures <250°C, as occurring during low-severity burns, have only limited effects on the stability of the soil organic binding agents. Recent studies suggest that low severity burns may cause soil aggregate degradation due to rapid vaporization of soil pore water that can induce pressure on the soil aggregates beyond their yield stress. Such pressure-driven degradation of soil aggregates may expose physically protected organic carbon to decomposition. Our study investigated the effect of a low-severity fire on soil organic matter (SOM), water extractable organic C, and N as well as respiration for two initial soil moisture conditions undergoing three “heating regimes” using aggregates from a California forest and a Nevada shrubland soil. We found that initially moist soil aggregates that were rapidly heated up degraded the most, showing increased cumulative carbon mineralization when compared to aggregates that were not heated, aggregates that were dry before being heated, and initially moist soil aggregates that were slowly heated. Our results suggest that exposure of previously physically protected organic carbon within the soil aggregates to oxidative conditions was the most likely cause of increased rates of decomposition of organic matter after low-severity burns. Additionally, we show that for a shrubland soil, aggregates with relatively low organic carbon content, low severity burns increased cumulative carbon mineralization. We hypothesized that this was due to decomposition of cytoplasmic material from lysed microbes. Our results suggest that low severity burns can accelerate decomposition of soil organic carbon (SOC) protected in soil aggregates

Reflections on Earth surface research

To celebrate the first anniversary of Nature Reviews Earth & Environment, we asked six researchers investigating Earth surface processes to outline notable developments within their discipline and provide thoughts on important work yet to be done

Synergy between compost and cover crops in a Mediterranean row crop system leads to increased subsoil carbon storage

Subsoil carbon (C) stocks are a prime target for efforts to increase soil C storage for climate change mitigation. However, subsoil C dynamics are not well understood, especially in soils under long-term intensive agricultural management. We compared subsoil C storage and soil organic matter (SOM) composition in tomato-corn rotations after 25 years of differing C and nutrient management in the California Central Valley: CONV (mineral fertilizer), CONV+WCC (mineral fertilizer and cover crops), and ORG (composted poultry manure and cover crops). The cover crop mix used in these systems is a mix of oat (Avena sativa L.), faba bean (Vicia faba L.), and hairy vetch (Vicia villosa Roth). Our results showed a ∼19Mgha-1 increase in soil organic C (SOC) stocks down to 1m under ORG systems, no significant SOC increases under CONV+WCC or CONV systems, and an increased abundance of carboxyl-rich C in the subsoil (60-100cm) horizons of ORG and CONV+WCC systems. Our results show the potential for increased subsoil C storage with compost and cover crop amendments in tilled agricultural systems and identify potential pathways for increasing C transport and storage in subsoil layers. Copyright

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

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

Global ecological predictors of the soil priming effect.

Identifying the global drivers of soil priming is essential to understanding C cycling in terrestrial ecosystems. We conducted a survey of soils across 86 globally-distributed locations, spanning a wide range of climates, biotic communities, and soil conditions, and evaluated the apparent soil priming effect using 13C-glucose labeling. Here we show that the magnitude of the positive apparent priming effect (increase in CO2 release through accelerated microbial biomass turnover) was negatively associated with SOC content and microbial respiration rates. Our statistical modeling suggests that apparent priming effects tend to be negative in more mesic sites associated with higher SOC contents. In contrast, a single-input of labile C causes positive apparent priming effects in more arid locations with low SOC contents. Our results provide solid evidence that SOC content plays a critical role in regulating apparent priming effects, with important implications for the improvement of C cycling models under global change scenarios

Effect of cover crop on carbon distribution in size and density separated soil aggregates

Increasing soil organic carbon (SOC) stocks in agricultural soils can contribute to stabilizing or even lowering atmospheric greenhouse gas (GHG) concentrations. Cover crop rotation has been shown to increase SOC and provide productivity benefits for agriculture. Here we used a split field design to evaluate the short-term effect of cover crop on SOC distribution and chemistry using a combination of bulk, isotopic, and spectroscopic analyses of size-and density-separated soil aggregates. Macroaggregates (\u3e250 µm) incorporated additional plant material with cover crop as evidenced by more negative δ13C values (−25.4%∘ with cover crop compared to −25.1%∘without cover crop) and increased phenolic (plant-like) resonance in carbon NEXAFS spectra. Iron EXAFS data showed that the Fe pool was composed of 17–21% Fe oxide with the remainder a mix of primary and secondary minerals. Comparison of oxalate and dithionite extractions suggests that cover crop may also increase Fe oxide crystallinity, especially in the dense (\u3e2.4 g cm−3) soil fraction. Cover crop δ13C values were more negative across density fractions of bulk soil, indicating the presence of less processed organic carbon. Although no significant difference was observed in bulk SOC on a mass per mass basis between cover and no cover crop fields after one season, isotopic and spectroscopic data reveal enhanced carbon movement between aggregates in cover crop soil