Process for removing metals from hydrocarbons.

This invention relates to a process for removing metals, particularly mercury, from hydrocarbon streams by use of an ionic liq., where in the metal-contg. hydrocarbon stream is contacted with an ionic liq. to produce a product hydrocarbon stream having reduced mercury content. [on SciFinder(R)]

Do ectomycorrhizal fungi exhibit adaptive tolerance to potentially toxic metals in the environment?

The effects of potentially toxic metals on ectomycorrhizal (ECM) fungi and their higher plant hosts are examined in this review. Investigations at a species and community level have revealed wide inter- and intraspecific variation in sensitivity to metals. Adaptive and constitutive mechanisms of ECM tolerance are proposed and discussed in relation to proven tolerance mechanisms in bacteria, yeasts and plants. Problems with methodology and research priorities are highlighted. These include the need for a detailed understanding of the genetic basis of tolerance in the ECM symbiosis, and for studies of ECM community dynamics in polluted sites.

Heavy metals as markers for assessing environmental pollution from chemical warehouse and plastics fires

Case histories of large, accidental fires are presented to illustrate that heavy metals may be used as markers to assess the extent of localized environmental contamination resulting from fires. Due to the complexity of fire chemistry with respect to organic pollutants, determination of organic pollutants in the environment following a fire would be time consuming and expensive. Using heavy metals as markers on the other hand is much cheaper and can be done very rapidly. © 1995.

Major and trace elements in milk and Halloumi cheese as markers for authentication of goat feeding regimes and geographical origin

Sixty samples of milk, Halloumi cheese and local grazing plants (i.e. shrubs) were collected over a year from dairy farms located on three different locations of Cyprus. Major and trace elements were quantified using inductively coupled plasma-atomic emission spectroscopy (ICP-AES). Milk and Halloumi cheese produced in different geographical locations presented significant differences in the concentration of some of the elements analysed. Principal component analysis showed grouping of samples according to the region of production for both milk and cheese samples. These findings show that the assay of elements can provide useful fingerprints for the characterisation of dairy products.

Trace elements as predictors of preeclampsia in type 1 diabetic pregnancy

Preeclampsia (PE) affects approximately 5% of all pregnancies, but is increased several-fold in women with pre-gestational type 1 diabetes mellitus (T1DM). Increased oxidative stress and altered maternal plasma trace elements that modulate the antioxidant system have been implicated in PE. In non-diabetic women, increased plasma copper and iron and decreased manganese, selenium, and zinc have been associated with PE in cross-sectional studies. In a longitudinal study, we hypothesized that plasma levels of trace elements differ between T1DM women with vs. without subsequent PE. Samples were collected during the first (gestation 12.2 ± 1.9 weeks, [mean ± SD]), second (21.6 ± 1.5 weeks), and third (31.5 ± 1.7 weeks) trimesters of pregnancy, all before the onset of PE. We compared 23 T1DM women who subsequently developed PE with 24 T1DM women who remained normotensive; and we included 19 non-diabetic (non-DM) normotensive pregnant women as reference controls. Trace elements were measured using inductively coupled plasma mass spectroscopy. In T1DM women with subsequent PE vs normotensive, only plasma zinc was significantly higher at the first trimester, while copper:zinc and copper:high-density lipoprotein cholesterol ratios were higher throughout gestation (all P < .05). These findings persisted after adjustment for covariates. Higher copper:zinc ratios may contribute to oxidative stress in T1DM women who develop PE. Ratios of pro- to anti-oxidant factors may predict risk for PE in diabetic pregnancies more effectively than individual trace element levels.

Tailoring Plasmonic Metamaterials for Ultrasenstive Detection of Mercury Trace Contaminants via Molecular Logic Gates

Structural and functional information encoded in DNA combined with unique properties of nanomaterials could be of use for the construction of novel biocomputational circuits and intelligent biomedical nanodevices. However, at present their practical applications are still limited by either low reproducibility of fabrication, modest sensitivity, or complicated handling procedures. Here, we demonstrate the construction of label-free and switchable molecular logic gates (AND, INHIBIT, and OR) that use specific conformation modulation of a guanine- and thymine-rich DNA, while the optical readout is enabled by the tunable metamaterials which serve as a substrate for surface enhanced Raman spectroscopy (MetaSERS). Our MetaSERS-based DNA logic is simple to operate, highly reproducible, and can be stimulated by ultra-low concentration of the external inputs, enabling an extremely sensitive detection of mercury ions down to 2×10-4 ppb, which is four orders of magnitude lower than the exposure limit allowed by United States Environmental Protection Agency

Influence of trace substances on methanation catalysts used in dynamic biogas upgrading

The aim of this work was to study the possible deactivation effects of biogas trace ammonia concentrations on methanation catalysts. It was found that small amounts of ammonia led to a slight decrease in the catalyst activity. A decrease in the catalyst deactivation by carbon formation was also observed, with ammonia absorbed on the active catalyst sites. This was via a suppression of the carbon formation and deposition on the catalyst, since it requires a higher number of active sites than for the methanation of carbon oxides. From the paper findings, no special pretreatment for ammonia removal from the biogas fed to a methanation process is required.

Availability and transfer to grain of As, Cd, Cu, Ni, Pb and Zn in a barley agri-system: Impact of biochar, organic and mineral fertilizers

With biochar becoming an emerging soil amendment and a tool to mitigate climate change, there are only a few studies documenting its effects on trace element cycling in agriculture. Zn and Cu are deficient in many human diets, whilst exposures to As, Pb and Cd need to be decreased. Biochar has been shown to affect many of them mainly at a bench or greenhouse scale, but field research is not available. In our experiment we studied the impact of biochar, as well as its interactions with organic (compost and sewage sludge) and mineral fertilisers (NPK and nitrosulfate), on trace element mobility in a Mediterranean agricultural field (east of Madrid, Spain) cropped with barley. At harvesting time, we analysed the soluble fraction, the available fraction (assessed with the diffusive gradients in thin gels technique, DGT) and the concentration of trace elements in barley grain. No treatment was able to significantly increase Zn, Cu or Ni concentration in barley grain, limiting the application for cereal fortification. Biochar helped to reduce Cd and Pb in grain, whereas As concentration slightly increased. Overall biochar amendments demonstrated a potential to decrease Cd uptake in cereals, a substantial pathway of exposure in the Spanish population, whereas mineral fertilisation and sewage sludge increased grain Cd and Pb. In the soil, biochar helped to stabilise Pb and Cd, while marginally increasing As release/mobilisation. Some of the fertilisation practises or treatments increased toxic metals and As solubility in soil, but never to an extent high enough to be considered an environmental risk. Future research may try to fortify Zn, Cu and Ni using other combinations of organic amendments and different parent biomass to produce enriched biochars.

Detecting obfuscated malware using reduced opcode set and optimised runtime trace

The research presented, investigates the optimal set of operational codes (opcodes) that create a robust indicator of malicious software (malware) and also determines a program’s execution duration for accurate classification of benign and malicious software. The features extracted from the dataset are opcode density histograms, extracted during the program execution. The classifier used is a support vector machine and is configured to select those features to produce the optimal classification of malware over different program run lengths. The findings demonstrate that malware can be detected using dynamic analysis with relatively few opcodes.