High-solid-content hydroxyapatite slurry for the production of bone substitute scaffolds

Key to various bone substitute scaffold production techniques is the development of free-flowing ceramic slurry with optimum theological properties. The aim is to achieve a colloidal suspension with as high a solid content as possible while maintaining a low viscosity which easily penetrates the pores of relevant sacrificial templates. The following investigation describes the optimization of a hydroxyapatite slip and demonstrates its potential application in scaffold production. Using predominantly spherical particles of hydroxyapatite of between 0.82 mu m and 16.2 mu m, coupled with a 2 wt % addition of the anionic polyelectrolyte, ammonium polyacrylate, an 80 wt % (55.9 vol %) hydroxyapatite solid loaded slip with a viscosity of approximately 126 mPa s has been developed. Its ability to infiltrate and replicate porous preforms has been shown using polyurethane foam. The enhanced particle packing achieved has allowed for the production of scaffolds with highly dense and uniform grain structures. The results represent a significant improvement in current slurry production techniques and can be utilized to develop high-density ceramic bone substitute scaffolds.

The effect of feedstock cost on biofuel cost as exemplified by biomethane production from grass silage.

Abstract: The potential variance in feedstock costs can have signifi cant implications for the cost of a biofuel and the fi nancial viability of a biofuel facility. This paper employs the Grange Feed Costing Model to assess the cost of on-farm biomethane production using grass silages produced under a range of management scenarios. These costs were compared with the cost of wheat grain and sugarbeet roots for ethanol production at an industrial scale. Of the three feedstocks examined, grass silage represents the cheapest feedstock per GJ of biofuel produced. At a production cost of €27/tonne (t) feedstock (or €150/t volatile solids (VS)), the feedstock production cost of grass silage per gigajoule (GJ) of biofuel (€12.27) is lower than that of sugarbeet (€16.82) and wheat grain (€18.61). Grass biomethane is also the cheapest biofuel when grass silage is costed at the bottom quartile purchase price of silage of €19/t (€93/t VS). However, when considering the production costs (full-costing) of the three feedstocks, the total cost of grass biomethane (€32.37/GJ of biofuel; intensive 2-cut system) from a small on-farm facility ranks between that of sugarbeet (€29.62) and wheat grain ethanol (€34.31) produced in large industrial facilities. The feedstock costs for the above three biofuels represent 0.38, 0.57, and 0.54 of the total biofuel cost. The importance of feedstock cost on biofuel cost is further highlighted by the 0.43 increase in the cost of biomethane when grass silage is priced at the top quartile (€46/t or €232/t VS) compared to the bottom quartile purchase price.

Total arsenic, inorganic arsenic, and other elements concentrations in Italian rice grain varies with origin and type

Rice is comparatively efficient at assimilating inorganic arsenic (As _i), a class-one, non-threshold carcinogen, into its grain, being the dominant source of this element to mankind. Here it was investigated how the total arsenic (As_t) and As_i content of Italian rice grain sourced from market outlets varied by geographical origin and type. Total Cr, Cd Se, Mg, K, Zn, Ni were also quantified. As_t concentration on a variety basis ranged from means of 0.18 mg kg^-1 to 0.28 mg kg ^-1, and from 0.11 mg kg^-1 to 0.28 mg kg^-1 by production region. For As_i concentration, means ranged from 0.08 mg kg^-1 to 0.11 mg kg^-1 by variety and 0.10 mg kg ^-1 to 0.06 mg kg^-1 by region. There was significant geographical variation for both As_t and As_i; total Se and Ni concentration; while the total concentration of Zn, Cr, Ni and K were strongly influenced by the type of rice.

Sprinkler irrigation of rice fields reduces grain arsenic but enhances cadmium

Previous studies have demonstrated that rice cultivated under flooded conditions has higher concentrations of arsenic (As) but lower cadmium (Cd) compared to rice grown in unsaturated soils. To validate such effects over long terms under Mediterranean conditions a field experiment, conducted over 7 successive years was established in SW Spain. The impact of water management on rice production and grain arsenic (As) and cadmium (Cd) was measured, and As speciation was determined to inform toxicity evaluation. Sprinkler irrigation was compared to traditional flooding.

Both irrigation techniques resulted in similar grain yields (similar to 3000 kg grain ha(-1)). Successive sprinkler irrigation over 7 years decreased grain total As to one-sixth its initial concentration in the flooded system (0.55 to 0.09 mg As kg(-1)), while one cycle of sprinkler irrigation also reduced grain total As by one-third (0.20 mg kg(-1)). Grain inorganic As concentration increased up to 2 folds under flooded conditions compared to sprinkler irrigated fields while organic As was also lower in sprinkler system treatments, but to a lesser extent. This suggests that methylation is favored under water logging. However, sprinkler irrigation increased Cd transfer to grain by a factor of 10, reaching 0.05 mg Cd kg(-1) in 7 years. Sprinlder systems in paddy fields seem particularly suited for Mediterranean climates and are able to mitigate against excessive As accumulation, but our evidence shows that an increased Cd load in rice grain may result.

ALEA: Fine-Grain Energy Profiling with Basic Block Sampling

Energy efficiency is an essential requirement for all contemporary computing systems. We thus need tools to measure the energy consumption of computing systems and to understand how workloads affect it. Significant recent research effort has targeted direct power measurements on production computing systems using on-board sensors or external instruments. These direct methods have in turn guided studies of software techniques to reduce energy consumption via workload allocation and scaling. Unfortunately, direct energy measurements are hampered by the low power sampling frequency of power sensors. The coarse granularity of power sensing limits our understanding of how power is allocated in systems and our ability to optimize energy efficiency via workload allocation.
We present ALEA, a tool to measure power and energy consumption at the granularity of basic blocks, using a probabilistic approach. ALEA provides fine-grained energy profiling via sta- tistical sampling, which overcomes the limitations of power sens- ing instruments. Compared to state-of-the-art energy measurement tools, ALEA provides finer granularity without sacrificing accuracy. ALEA achieves low overhead energy measurements with mean error rates between 1.4% and 3.5% in 14 sequential and paral- lel benchmarks tested on both Intel and ARM platforms. The sampling method caps execution time overhead at approximately 1%. ALEA is thus suitable for online energy monitoring and optimization. Finally, ALEA is a user-space tool with a portable, machine-independent sampling method. We demonstrate two use cases of ALEA, where we reduce the energy consumption of a k-means computational kernel by 37% and an ocean modelling code by 33%, compared to high-performance execution baselines, by varying the power optimization strategy between basic blocks.