Recycled water: potential health risks from volatile organic compounds and use of 1,4-dichlorobenzene as treatment performance indicator

Characterisation of the concentrations and potential health risks of chemicals in recycled water is important if this source of water is to be safely used to supplement drinking water sources. This research was conducted to: (i) determine the concentration of volatile organic compounds (VOCs) in secondary treated effluent (STE) and, post-reverse osmosis (RO) treatment and to; (ii) assess the health risk associated with VOCs for indirect potable reuse (IPR). Samples were examined pre and post-RO in one full-scale and one pilot plant in Perth, Western Australia. Risk quotients (RQ) were estimated by expressing the maximum and median concentration as a function of the health value. Of 61 VOCs analysed over a period of three years, twenty one (21) were detected in STE, with 1,4-dichlorobenzene (94%); tetrachloroethene (88%); carbon disulfide (81%) and; chloromethane (58%) most commonly detected. Median concentrations for these compounds in STE ranged from 0.81 μg/L for 1,4-dichlorobenzene to 0.02 μg/L for carbon disulphide. After RO, twenty six (26) VOCs were detected, of which 1,4-dichlorobenzene (89%); acrylonitrile (83%) chloromethane (63%) and carbon disulfide (40%) were the more frequently detected. RQ(max) were all below health values in the STE and after RO. Median removal efficiency for RO was variable, ranging from −77% (dichlorodifluoromethane) to 91.2% (tetrachloroethene). The results indicate that despite the detection of VOCs in STE and after RO, their human health impact in IPR is negligible due to the low concentrations detected. The results indicate that 1,4-dichlorobenzene is a potential treatment chemical indicator for assessment of VOCs in IPR using RO treatment.     

Estimates of echo correlation and measurement bias in acoustic radiation force impulse imaging

Acoustic radiation force impulse (ARFI) imaging is a novel imaging modality in which pulses from a diagnostic ultrasound scanner are used to displace tissue and track its motion. The region displaced has lateral and elevational dimensions of similar scale to the ultrasound beams used to track the motion. Therefore, there is a range of tissue displacements present within the tracking beam, leading to decorrelation of the echo signal. Expressions are derived for the expected value of the displacement estimate and the cross-correlation at the expected displacement. Numerical simulations confirm the analytical model.     

Synthesis of porous oxide ceramics using a soft responsive scaffold

Ceramic materials have significant utility. Developing synthetic protocols that are facile and provide low energy alternatives to traditional methods remains a major driver in materials synthesis. We present here the adaptation of a method recently developed in our group for the synthesis of porous silica using a non-ionic emulsion template. The silicate materials are porous on both the nanometre and micrometre length scales and surface-to-volume ratios may be readily modified by altering the volume fraction of the emulsion template. Switching the silica precursor for an alumina or titania precursor resulted in the formation of porous alumina and titania materials which were prepared as thin films or monoliths. The pores formed in the amorphous alumina materials were ~0.8 μm and ~50 nm, with a primary particle size of 50–100 nm. The titania materials had pores on one length scale only: ~0.8 μm, with a smaller primary particle size of 20–60 nm. As-synthesized materials were investigated using scanning electron microscopy and X-ray diffraction.     

Could Intelligent Speed Adaptation make overtaking unsafe?

This driving simulator study investigated how mandatory and voluntary ISA might affect a driver's overtaking decisions on rural roads, by presenting drivers with a variety of overtaking scenarios designed to evaluate both the frequency and safety of the manoeuvres. In half the overtaking scenarios, ISA was active and in the remainder ISA was switched off. A rural road was modelled with a number of 2 + 1 road sections, thus allowing drivers a protected overtaking opportunity. The results indicate that drivers became less inclined to initiate an overtaking manoeuvre when the mandatory ISA was active and this was particularly so when the overtaking opportunity was short. In addition to this, when ISA was activated drivers were more likely to have to abandon an overtaking, presumably due to running out of road. They also spent more time in the critical hatched area—a potentially unsafe behaviour. The quality of the overtaking manoeuvre was also affected when mandatory ISA was active, with drivers pulling out and cutting back in more sharply. In contrast, when driving with a voluntary ISA, overtaking behaviour remained mostly unchanged: drivers disengaged the function in approximately 70% of overtaking scenarios. The results of this study suggest that mandatory ISA could affect the safety of overtaking manoeuvres unless coupled with an adaptation period or other driver support functions that support safe overtaking.     

Multifunctional mesoporous silica nanospheres with cleavable Gd(III) chelates as MRI contrast agents: synthesis, characterization, target-specificity, and renal clearance

Mesoporous silica nanospheres (MSNs) are a promising material for magnetic resonance imaging (MRI) contrast agents. In this paper multifunctional MSNs with cleavable Gd(III) chelates are synthesized and characterized, and their applicability as MRI contrast agents is demonstrated both in vitro and in vivo. The MSNs contain Gd(III) chelates that are covalently linked via a redox-responsive disulfide moiety. The MSNs are further functionalized with polyethylene glycol (PEG) and an anisamide ligand to improve their biocompatibility and target specificity. The effectiveness of MSNs as an MRI imaging contrast agent and their targeting ability are successfully demonstrated in vitro using human colon adenocarcinoma and pancreatic cancer cells. Finally, the capability of this platform as an in vivo MRI contrast agent is tested using a 3T scanner. The Gd(III) chelate was quickly cleaved by the blood pool thiols and eliminated through the renal excretion pathway. Further tuning of the Gd(III) chelate release kinetics is needed before the MSN system can be used as target-specific MRI contrast agents in vivo.     

Prediction of MS/MS data. 1. A focus on pharmaceuticals containing carboxylic acids

Metabolite identification is a necessary step in developing safe and effective drugs. Metabolite analysis typically involves rapid identification of the chemical composition of the metabolite by automated HPLC-MS methods, followed by the laborious process of identifying the structure of the metabolite. Since MS is typically utilized to identify the metabolite, it is logical to utilize MS/MS to structurally characterize the sample. However, interpretation of MS/MS data may not provide sufficient information, as fragmentation pathways are not well understood or predictable. Therefore, other more time-consuming methods of analysis are often undertaken. If the dissociation rules for low-energy MS/MS experiments were clearly defined for all classes of compounds, more information would be obtained from MS/MS data, and metabolite identification would proceed more rapidly. We are currently developing methods to define these fragmentation rules. By screening approximately 100 carboxylic acids at a time and applying knowledge of physical-organic chemistry, predictive rules are under development that describe how compounds dissociate under low-energy collision-induced dissociation conditions. Studies of carboxylic acid dissociation demonstrate that this approach is practical and reliable. Dissociation rules were predicted with a 90% success rate, when tested on acid-containing pharmaceuticals. This predictive power cannot be matched by any commercially available software. This study, and others like it, will be used to develop algorithms that more rapidly identify drug metabolites and degradation products, based on MS/MS data. Such algorithms will benefit drug development for all types of pharmaceuticals.     

Calibration and validation of coarse-grained models of atomic systems: application to semiconductor manufacturing

Coarse-grained models of atomic systems, created by aggregating groups of atoms into molecules to reduce the number of degrees of freedom, have been used for decades in important scientific and technological applications. In recent years, interest in developing a more rigorous theory for coarse graining and in assessing the predictivity of coarse-grained models has arisen. In this work, Bayesian methods for the calibration and validation of coarse-grained models of atomistic systems in thermodynamic equilibrium are developed. For specificity, only configurational models of systems in canonical ensembles are considered. Among major challenges in validating coarse-grained models are (1) the development of validation processes that lead to information essential in establishing confidence in the model’s ability predict key quantities of interest and (2), above all, the determination of the coarse-grained model itself; that is, the characterization of the molecular architecture, the choice of interaction potentials and thus parameters, which best fit available data. The all-atom model is treated as the “ground truth,” and it provides the basis with respect to which properties of the coarse-grained model are compared. This base all-atom model is characterized by an appropriate statistical mechanics framework in this work by canonical ensembles involving only configurational energies. The all-atom model thus supplies data for Bayesian calibration and validation methods for the molecular model. To address the first challenge, we develop priors based on the maximum entropy principle and likelihood functions based on Gaussian approximations of the uncertainties in the parameter-to-observation error. To address challenge (2), we introduce the notion of model plausibilities as a means for model selection. This methodology provides a powerful approach toward constructing coarse-grained models which are most plausible for given all-atom data. We demonstrate the theory and methods through applications to representative atomic structures and we discuss extensions to the validation process for molecular models of polymer structures encountered in certain semiconductor nanomanufacturing processes. The powerful method of model plausibility as a means for selecting interaction potentials for coarse-grained models is discussed in connection with a coarse-grained hexane molecule. Discussions of how all-atom information is used to construct priors are contained in an appendix.