A two-metal ion mechanism operates in the hammerhead ribozyme-mediated cleavage of an RNA substrate

Evidence for a two-metal ion mechanism for cleavage of the HH16 hammerhead ribozyme is provided by monitoring the rate of cleavage of the RNA substrate as a function of La3+ concentration in the presence of a constant concentration of Mg2+. We show that a bell-shaped curve of cleavage activation is obtained as La3+ is added in micromolar concentrations in the presence of 8 mM Mg2+, with a maximal rate of cleavage being attained in the presence of 3 microM La3+. These results show that two-metal ion binding sites on the ribozyme regulate the rate of the cleavage reaction and, on the basis of earlier estimates of the Kd values for Mg2+ of 3.5 mM and > 50 mM, that these sites bind La3+ with estimated Kd values of 0.9 and > 37.5 microM, respectively. Furthermore, given the very different effects of these metal ions at the two binding sites, with displacement of Mg2+ by La3+ at the stronger (relative to Mg2+) binding site activating catalysis and displacement of Mg2+ by La3+ at the weaker (relative to Mg2+) (relative to Mg2+) binding site inhibiting catalysis, we show that the metal ions at these two sites play very different roles. We argue that the metal ion at binding site 1 coordinates the attacking 2'-oxygen species in the reaction and lowers the pKa of the attached proton, thereby increasing the concentration of the attacking alkoxide nucleophile in an equilibrium process. In contrast, the role of the metal ion at binding site 2 is to catalyze the reaction by absorbing the negative charge that accumulates at the leaving 5'-oxygen in the transition state. We suggest structural reasons why the Mg(2+)-La3+ ion combination is particularly suited to demonstrating these different roles of the two-metal ions in the ribozyme cleavage reaction.

Observations on catalysis by hammerhead ribozymes are consistent with a two-divalent-metal-ion mechanism

Significant cleavage by hammerhead ribozymes requires activation by divalent metal ions. Several models have been proposed to account for the influence of metal ions on hammerhead activity. A number of recent papers have presented data that have been interpreted as supporting a one-metal-hydroxide-ion mechanism. In addition, a solvent deuterium isotope effect has been taken as evidence against a proton transfer in the rate-limiting step of the cleavage reaction. We propose that these data are more easily explained by a two-metal-ion mechanism that does not involve a metal hydroxide, but does involve a proton transfer in the rate-limiting step.

Virtual prototyping for planning bridge construction

4D simulation, building information modeling, virtual construction, computer simulation and virtual prototyping are emerging topics in the building construction industry. These techniques not only relate to the buildings themselves, but can also be applied to other forms of construction, including bridges. Since bridge construction is a complex process involving multiple types of plant and equipment, applying such virtual methods benefits the understanding of all parties in construction practice. This paper describes the relationship between temporary platforms, plant and equipment resources and a proposed-built model in the construction planning and use of Virtual Prototyping Simulation (VPS) to implement different construction scenarios in order to help planners identify an optimal construction plan. A case study demonstrates the use of VPS integrated with temporary platform design and plant and equipment-resource allocation to generate different construction scenarios.

Driving selective aerobic oxidation of alkyl aromatics by sunlight on alcohol grafted metal hydroxides

It is very difficult to selectively oxidise stable compounds such as toluene and xylenes to useful chemicals with molecular oxygen (O 2) under moderate conditions. To achieve high conversion and less over-oxidised products, a new class of photocatalysts, metal hydroxide nanoparticles grafted with alcohols, is devised. They can efficiently oxidise alkyl aromatic compounds with O 2 using visible or ultraviolet light or even sunlight to generate the corresponding aldehydes, alcohols and acids at ambient temperatures and give very little over-oxidation. For example toluene can be oxidised with a 23% conversion after a 48-hour exposure to sunlight with 85% of the product being benzaldehyde, and only a trace of CO 2.The surface complexes grafted onto metal hydroxides can absorb light, generating free radicals on the surface, which then initiate aerobic oxidation of the stable alkyl aromatic molecules with high product selectivity. This mechanism is distinctly different from those of any known catalysts. The use of the new photocatalysts as a controlled means to generate surface radicals through light excitation allows us to drive the production of fine organic chemicals at ambient temperatures with sunlight. The process with the new photocatalysts is especially valuable for temperature-sensitive syntheses and a greener process than many conventional thermal reactions. © 2012 The Royal Society of Chemistry.

Removal of toxic metals in a multi metal system using sorbents for potential application to urban stormwater treatment

In the context of increasing demand for potable water and the depletion of water resources, stormwater is a logical alternative. However, stormwater contains pollutants, among which metals are of particular interest due to their toxicity and persistence in the environment. Hence, it is imperative to remove toxic metals in stormwater to the levels prescribed by drinking water guidelines for potable use. Consequently, various techniques have been proposed, among which sorption using low cost sorbents is economically viable and environmentally benign in comparison to other techniques. However, sorbents show affinity towards certain toxic metals, which results in poor removal of other toxic metals. It was hypothesised in this study that a mixture of sorbents that have different metal affinity patterns can be used for the efficient removal of a range of toxic metals commonly found in stormwater. The performance of six sorbents in the sorption of Al, Cr, Cu, Pb, Ni, Zn and Cd, which are the toxic metals commonly found in urban stormwater, was investigated to select suitable sorbents for creating the mixtures. For this purpose, a multi criteria analytical protocol was developed using the decision making methods: PROMETHEE (Preference Ranking Organisation METHod for Enrichment Evaluations) and GAIA (Graphical Analysis for Interactive Assistance). Zeolite and seaweed were selected for the creation of trial mixtures based on their metal affinity pattern and the performance on predetermined selection criteria. The metal sorption mechanisms employed by seaweed and zeolite were defined using kinetics, isotherm and thermodynamics parameters, which were determined using the batch sorption experiments. Additionally, the kinetics rate-limiting steps were identified using an innovative approach using GAIA and Spearman correlation techniques developed as part of the study, to overcome the limitation in conventional graphical methods in predicting the degree of contribution of each kinetics step in limiting the overall metal removal rate. The sorption kinetics of zeolite was found to be primarily limited by intraparticle diffusion followed by the sorption reaction steps, which were governed mainly by the hydrated ionic diameter of metals. The isotherm study indicated that the metal sorption mechanism of zeolite was primarily of a physical nature. The thermodynamics study confirmed that the energetically favourable nature of sorption increased in the order of Zn < Cu < Cd < Ni < Pb < Cr < Al, which is in agreement with metal sorption affinity of zeolite. Hence, sorption thermodynamics has an influence on the metal sorption affinity of zeolite. On the other hand, the primary kinetics rate-limiting step of seaweed was the sorption reaction process followed by intraparticle diffusion. The boundary layer diffusion was also found to limit the metal sorption kinetics at low concentration. According to the sorption isotherm study, Cd, Pb, Cr and Al were sorbed by seaweed via ion exchange, whilst sorption of Ni occurred via physisorption. Furthermore, ionic bonding is responsible for the sorption of Zn. The thermodynamics study confirmed that sorption by seaweed was energetically favourable in the order of Zn < Cu < Cd < Cr . Al < Pb < Ni. However, this did not agree with the affinity series derived for seaweed suggesting a limited influence of sorption thermodynamics on metal affinity for seaweed. The investigation of zeolite-seaweed mixtures indicated that mixing sorbents have an effect on the kinetics rates and the sorption affinity. Additionally, the theoretical relationships were derived to predict the boundary layer diffusion rate, intraparticle diffusion rate, the sorption reaction rate and the enthalpy of mixtures based on that of individual sorbents. In general, low coefficient of determination (R2) for the relationships between theoretical and experimental data indicated that the relationships were not statistically significant. This was attributed to the heterogeneity of the properties of sorbents. Nevertheless, in relative terms, the intraparticle diffusion rate, sorption reaction rate and enthalpy of sorption had higher R2 values than the boundary layer diffusion rate suggesting that there was some relationship between the former set of parameters of mixtures and that of sorbents. The mixture, which contained 80% of zeolite and 20% of seaweed, showed similar affinity for the sorption of Cu, Ni, Cd, Cr and Al, which was attributed to approximately similar sorption enthalpy of the metal ions. Therefore, it was concluded that the seaweed-zeolite mixture can be used to obtain the same affinity for various metals present in a multi metal system provided the metal ions have similar enthalpy during sorption by the mixture.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Influence of organic matter in road deposited particulates in heavy metal accumulation and transport

The research study discussed in the paper investigated the influence of organic matter on heavy metal adsorption for different particle size ranges of build-up solids. Samples collected from road surfaces were assessed for organic matter content, mineral composition, particle size distribution and effective cation exchange capacity. It was found that the organic matter plays a key role in >75µm particles in the adsorption of Zinc, Lead, Nickel and Copper, which are generated by traffic activities. Clay forming minerals and metal oxides of Iron, Aluminium and Manganese was found to be important for heavy metal adsorption to <75µm particles. It was also found that heavy metals adsorbed to organic matter are strongly bound to particles and these metal ions will not be bio-available if the chemical quality of the media remains stable.

Beat phenomena in metal nanowires, and their implications for resonance-based elastic property measurements

The elastic properties of 1D nanostructures such as nanowires are often measured experimentally through actuation of the nanowire at its resonance frequency, and then relating the resonance frequency to the elastic stiffness using elementary beam theory. In the present work, we utilize large scale molecular dynamics simulations to report a novel beat phenomenon in [110]oriented Ag nanowires. The beat phenomenon is found to arise from the asymmetry of the lattice spacing in the orthogonal elementary directions of the [110] nanowire, i.e. the [-110] and [001] directions, which results in two different principal moments of inertia. Because of this, actuations imposed along any other direction are found to decompose into two orthogonal vibrational components based on the actuation angle relative to these two elementary directions, with this phenomenon being generalizable to <110> FCC nanowires of different materials (Cu, Au, Ni, Pd and Pt). The beat phenomenon is explained using a discrete moment of inertia model based on the hard sphere assumption, the model is utilized to show that surface effects enhance the beat phenomenon, while the effect is reduced with increasing nanowires cross-sectional size or aspect ratio. Most importantly, due to the existence of the beat phenomena, we demonstrate that in resonance experiments only a single frequency component is expected to be observed, particularly when the damping ratio is relatively large or very small. Furthermore, for a large range of actuation angles, the lower frequency is more likely to be detected than the higher one, which implies that experimental predictions of Young’s modulus obtained from resonance may in fact be under predictions. The present study therefore has significant implications for experimental interpretations of Young’s modulus as obtained via resonance testing.

Characterising metal build-up on urban road surfaces

Reliable approaches for predicting pollutant build-up are essential for accurate urban stormwater quality modelling. Based on the in-depth investigation of metal build-up on residential road surfaces, this paper presents empirical models for predicting metal loads on these surfaces. The study investigated metals commonly present in the urban environment. Analysis undertaken found that the build-up process for metals primarily originating from anthropogenic (copper and zinc) and geogenic (aluminium, calcium, iron and manganese) sources were different. Chromium and nickel were below detection limits. Lead was primarily associated with geogenic sources, but also exhibited a significant relationship with anthropogenic sources. The empirical prediction models developed were validated using an independent data set and found to have relative prediction errors of 12-50%, which is generally acceptable for complex systems such as urban road surfaces. Also, the predicted values were very close to the observed values and well within 95% prediction interval.

Intercalation of kaolins by alkaline earth metal salts

A suite of new materials, based on chemical modification of kaolins, has been successfully prepared via manipulation of the kaolin structure and subsequent intercalation by CaCl2 and MgCl2. A standard kaolinite(KGa-1)and a commercially available halloysite (New Zealand china clay) were used for this study. The kaolins are given several cycles of intercalation and deintercalation using a common intercalant such as potassium acetate. The number of cycles given depends on the type of kaolin. After this treatment, both kaolinite and halloysite hydrate show considerable broadening of the (00l) reflections which indicate extensive exfoliation of the layers. In the case of kaolinite, exfoliated layers roll to form tubes similar to proper halloysite. Kaolins modified by the above treatment readily intercalate MgCl2 and CaCl2 from saturated solutions of these salts. On intercalation with CaCl2 and MgCl2, kaolinite layers expand to 10A and 9.8A, and those of halloysite to 12.8A and 15.5A, respectively. To our knowledge, this is the first report of successful intercalation of alkaline-earth halides by kaolins.

Modeling as a bridge between real world problems and school mathematics

This article argues for an interdisciplinary approach to mathematical problem solving at the elementary school, one that draws upon the engineering domain. A modeling approach, using engineering model eliciting activities, might provide a rich source of meaningful situations that capitalize on and extend students’ existing mathematical learning. The study reports on the developments of 48 twelve-year old students who worked on the Bridge Design activity. Results revealed that young students, even before formal instruction, have the capacity to deal with complex interdisciplinary problems. A number of students created quite appropriate models by developing the necessary mathematical constructs to solve the problem. Students’ difficulties in mathematizing the problem, and in revising and documenting their models are presented and analysed, followed by a discussion on the appropriateness of a modeling approach as a means for introducing complex problems to elementary school students.

First-principles prediction of metal-free magnetism and intrinsic half-metallicity in graphitic carbon nitride

Transition metal-free magnetism and half-metallicity recently has been the subject of intense research activity due to its potential in spintronics application. Here we, for the first time, demonstrate via density functional theory that the most recently experimentally realized graphitic carbon nitride (g-C4N3) displays a ferromagnetic ground state. Furthermore, this novel material is predicted to possess an intrinsic half-metallicity never reported to date. Our results highlight a new promising material toward realistic metal-free spintronics application.

Synthetic rating system for railway bridge management

Railway bridges deteriorate with age. Factors such as environmental effects on different materials of a bridge, variation of loads, fatigue, etc will reduce the remaining life of bridges. Bridges are currently rated individually for maintenance and repair actions according to the structural conditions of their elements. Dealing with thousands of bridges and several factors that cause deterioration, makes the rating process extremely complicated. Current simplified but practical rating methods are not based on an accurate structural condition assessment system. On the other hand, the sophisticated but more accurate methods are only used for a single bridge or particular types of bridges. It is therefore necessary to develop a practical and accurate system which will be capable of rating a network of railway bridges. This paper introduces a new method for rating a network of bridges based on their current and future structural conditions. The method identifies typical bridges representing a group of railway bridges. The most crucial agents will be determined and categorized to criticality and vulnerability factors. Classification based on structural configuration, loading, and critical deterioration factors will be conducted. Finally a rating method for a network of railway bridges that takes into account the effects of damaged structural components due to variations in loading and environmental conditions on the integrity of the whole structure will be proposed. The outcome of this research is expected to significantly improve the rating methods for railway bridges by considering the unique characteristics of different factors and incorporating the correlation between them.

Synthetic rating system for railway bridge management

Railway bridges deteriorate with age. Factors such as environmental effects on different materials of a bridge, variation of loads, fatigue, etc. will reduce the remaining life of bridges. Dealing with thousands of bridges and several factors that cause deterioration, makes the rating process extremely complicated. Current simplified but practical methods of rating a network of bridges are not based on an accurate structural condition assessment system. On the other hand, the sophisticated but more accurate methods are only used for a single bridge or particular types of bridges. It is therefore necessary to develop a practical and accurate system, which will be capable of rating a network of railway bridges. This article introduces a new method to rate a network of bridges based on their current and future structural conditions. The method identifies typical bridges representing a group of railway bridges. The most crucial agents will be determined and categorized to criticality and vulnerability factors. Classification based on structural configuration, loading, and critical deterioration factors will be conducted. Finally a rating method for a network of railway bridges that takes into account the effects of damaged structural components due to variations in loading and environmental conditions on the integrity of the whole structure will be proposed. The outcome of this article is expected to significantly improve the rating methods for railway bridges by considering the unique characteristics of different factors and incorporating the correlation among them.