Detection Limits of Organic Compounds Achievable with Intense, Short-Pulse Lasers

Many organic molecules have strong absorption bands which can be accessed by ultraviolet short pulse lasers to produce efficient ionization. This resonant multiphoton ionization scheme has already been exploited as an ionization source in time-of-flight mass spectrometers used for environmental trace analysis. In the present work we quantify the ultimate potential of this technique by measuring absolute ion yields produced from the interaction of 267 nm femtosecond laser pulses with the organic molecules indole and toluene, and gases Xe, N2 and O2. Using multiphoton ionization cross sections extracted from these results, we show that the laser pulse parameters required for real-time detection of aromatic molecules at concentrations of one part per trillion in air and a limit of detection of a few attomoles are achievable with presently available commercial laser systems. The potential applications for the analysis of human breath, blood and tissue samples are discussed.

Repurposing medicinal compounds for blood cancer treatment

Drug development is being continuously scrutinised for its lack of productivity. Novel drug development is associated with high costs, high failure rates and lengthy development process. These downfalls combined with a huge demand in blood cancer for new therapeutic treatments have led many to consider the method of drug repurposing. Finding new therapeutic indications for already established drug substances is known as redirecting, repositioning, reprofiling, or repurposing of drugs. Off-patent and on-patent drugs can be screened for additional targets and new indications thus bringing them to clinical trials at a faster pace. This approach offers smaller research groups, such as those that are academic based, into the drug development industry. Drug repurposing can make use of previously published data concerning dosage, toxicology and mechanism of activity.

Microstructure and high cycle fatigue fracture surface of a Ti-5Al-5Mo-5V-1Cr-1Fe titanium alloy

Because of the requirements for the damage tolerance and fatigue life of commercial aircraft components, the high cycle fatigue (HCF) properties of Ti–5Al–5Mo–5V–1Cr–1Fe titanium alloy forgings are important. The effects of microstructure types of the α+β titanium alloy on fatigue properties need to be understood. In this paper, by analysing the fracture surfaces of the titanium alloy having four types of microstructure, the effects of microstructure are investigated. The differences of initiation areas and crack propagation among different microstructures were studied. It was found that the area of the initiation region decreases in the order of coarse basketweave, fine basketweave, Widmanstätten, and bimodal microstructure.

Stress and Strain Distributions During Compressive Deformation of Titanium Alloy Affected by Microstructure

In the research of the microstructural influence on dynamic compression, an assumption that the α and the β phases in titanium alloys were linearly strengthened was proposed, and a two-dimensional model using ANSYS (ANSYS, Inc., Canonsburg, PA) focusing on the role of microgeometrical structure was developed. By comparing the stress and strain distributions of different microstructures, the roles of cracks and phase boundaries in titanium compression were studied.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

Assessment of lux-marked Pseudomonas fluorescens for reporting on organic carbon compounds

Carbon-flow from plant roots to the rhizosphere provides a major source of nutrients for the soil microbial population. However, quantification of carbon-flow is problematic due to its complex composition. This study investigated the potential of lux-marked Pseudomonas fluorescens to discriminate between forms of carbon present in the rhizosphere by measuring the light response to a range of carbon compounds. Results indicate that bioluminescence of short-term carbon-starved P. fluorescens is dependent upon the source and concentration of carbon. This system, therefore, has the potential to both quantify and qualify organic acids present in rhizodeposits.

Do leaves of plants on phosphorus-impoverished soils contain high concentrations of phenolic defence compounds?

Prominent theories of plant defence have predicted that plants growing on nutrient-poor soils produce more phenolic defence compounds than those on richer soils. Only recently has the Protein Competition Model (PCM) of phenolic allocation suggested that N and P limitation could have different effects because the nutrients are involved in different cellular metabolic processes. 2. We extend the prediction of the PCM and hypothesize that N will have a greater influence on the production of phenolic defensive compounds than P availability, because N limitation reduces protein production and thus competition for phenylalanine, a precursor of many phenolic compounds. In contrast, P acts as a recyclable cofactor in these reactions, allowing protein and hence phenolic production to continue under low P conditions. 3. We test this hypothesis by comparing the foliar concentrations of phenolic compounds in (i) phenotypes of 21 species growing on P-rich alluvial terraces and P-depleted marine terraces in southern New Zealand, and (ii) 87 species growing under similar climates on comparatively P-rich soils in New Zealand vs. P-depleted soils in Tasmania. 4. Foliar P concentrations of plants from the marine terraces were about half those of plants from alluvial soils, and much lower in Tasmania than in New Zealand. However, foliar concentrations of N and phenolic compounds were similar across sites in both comparisons, supporting the hypothesis that N availability is a more important determinant of plant investment in phenolic defensive compounds than P availability. We found no indication that reduced soil P levels influenced plant concentrations of phenolic compounds. There was wide variation in the foliar N and P concentrations among species, and those with low foliar nutrient concentrations produced more phenolics (including condensed tannins). 5. Our study is the first trait comparison extending beyond standard leaf economics to include secondary metabolites related to defence in forest plants, and emphasizes that N and P have different influences on the production of phenolic defence compounds. © 2009 British Ecological Society.