Three-dimensional model study of the Arctic ozone loss in 2002/2003 and comparison with 1999/2000 and 2003/2004

We have used the SLIMCAT 3-D off-line chemical transport model (CTM) to quantify the Arctic chemical ozone loss in the year 2002/2003 and compare it with similar calculations for the winters 1999/2000 and 2003/2004. Recent changes to the CTM have improved the model's ability to reproduce polar chemical and dynamical processes. The updated CTM uses σ-θ as a vertical coordinate which allows it to extend down to the surface. The CTM has a detailed stratospheric chemistry scheme and now includes a simple NAT-based denitrification scheme in the stratosphere. In the model runs presented here the model was forced by ECMWF ERA40 and operational analyses. The model used 24 levels extending from the surface to ~55km and a horizontal resolution of either 7.5° x 7.5° or 2.8° x 2.8°. Two different radiation schemes, MIDRAD and the CCM scheme, were used to diagnose the vertical motion in the stratosphere. Based on tracer observations from balloons and aircraft, the more sophisticated CCM scheme gives a better representation of the vertical transport in this model which includes the troposphere. The higher resolution model generally produces larger chemical O3 depletion, which agrees better with observations. The CTM results show that very early chemical ozone loss occurred in December 2002 due to extremely low temperatures and early chlorine activation in the lower stratosphere. Thus, chemical loss in this winter started earlier than in the other two winters studied here. In 2002/2003 the local polar ozone loss in the lower stratosphere was ~40% before the stratospheric final warming. Larger ozone loss occurred in the cold year 1999/2000 which had a persistently cold and stable vortex during most of the winter. For this winter the current model, at a resolution of 2.8° x 2.8°, can reproduce the observed loss of over 70% locally. In the warm and more disturbed winter 2003/2004 the chemical O3 loss was generally much smaller, except above 620K where large losses occurred due to a period of very low minimum temperatures at these altitudes

Analysis and prediction of defects in UV photo-initiated polymer microarrays

Polymer microarrays are a key enabling technology for the discovery of novel materials. This technology can be further enhanced by expanding the combinatorial space represented on an array. However, not all materials are compatible with the microarray format and materials must be screened to assess their suitability with the microarray manufacturing methodology prior to their inclusion in a materials discovery investigation. In this study a library of materials expressed on the microarray format are assessed by light microscopy, atomic force microscopy and time-of-flight secondary ion mass spectrometry to identify compositions with defects that cause a polymer spot to exhibit surface properties significantly different from a smooth, round, chemically homogeneous ‘normal’ spot. It was demonstrated that the presence of these defects could be predicted in 85% of cases using a partial least square regression model based upon molecular descriptors of the monomer components of the polymeric materials. This may allow for potentially defective materials to be identified prior to their formation. Analysis of the PLS regression model highlighted some chemical properties that influenced the formation of defects, and in particular suggested that mixing a methacrylate and an acrylate monomer and/or mixing monomers with long and linear or short and bulky pendant groups will prevent the formation of defects. These results are of interest for the formation of polymer microarrays and may also inform the formulation of printed polymer materials generally.Burroughs Wellcome Fund (grant number 085245)Royal Society (Great Britain) (Wolfson Research Merit Award

High throughput discovery of thermo-responsive materials using water contact angle measurements and time-of-flight secondary ion mass spectrometry

Switchable materials that alter their chemical or physical properties in response to external stimuli allow for temporal control of material-biological interactions, thus, are of interest for many biomaterial applications. Our interest is the discovery of new materials suitable to the specific requirements of certain biological systems. A high throughput methodology has been developed to screen a library of polymers for thermo-responsiveness, which has resulted in the identification of novel switchable materials. To elucidate the mechanism by which the materials switch, time-of-flight secondary ion mass spectrometry has been employed to analyse the top 2 nm of the polymer samples at different temperatures. The surface enrichment of certain molecular fragments has been identified by time-of-flight secondary ion mass spectrometry analysis at different temperatures, suggesting an altered molecular conformation. In one example, a switch between an extended and collapsed conformation is inferred

High throughput discovery of thermo-responsive materials using water contact angle measurements and time-of-flight secondary ion mass spectrometry

Switchable materials that alter their chemical or physical properties in response to external stimuli allow for temporal control of material-biological interactions, thus, are of interest for many biomaterial applications. Our interest is the discovery of new materials suitable to the specific requirements of certain biological systems. A high throughput methodology has been developed to screen a library of polymers for thermo-responsiveness, which has resulted in the identification of novel switchable materials. To elucidate the mechanism by which the materials switch, time-of-flight secondary ion mass spectrometry has been employed to analyse the top 2 nm of the polymer samples at different temperatures. The surface enrichment of certain molecular fragments has been identified by time-of-flight secondary ion mass spectrometry analysis at different temperatures, suggesting an altered molecular conformation. In one example, a switch between an extended and collapsed conformation is inferred

Modelling and prediction of bacterial attachment to polymers

Infection by pathogenic bacteria on implanted and indwelling medical devices during surgery causes large morbidity and mortality worldwide. Attempts to ameliorate this important medical issue have included development of antimicrobial surfaces on materials, ‘no touch’ surgical procedures, and development of materials with inherent low pathogen attachment. The search for new materials is increasingly being carried out by high throughput methods. Efficient methods for extracting knowledge from these large data sets are essential. We used data from a large polymer microarray exposed to three clinical pathogens to derive robust and predictive machine-learning models of pathogen attachment. The models could predict pathogen attachment for the polymer library quantitatively. The models also successfully predicted pathogen attachment for a second-generation library, and identified polymer surface chemistries that enhance or diminish pathogen attachment

Strategies for MCR image analysis of large hyperspectral data-sets

Polymer microarrays are a key enabling technology for high throughput materials discovery. In this study, multivariate image analysis, specifically multivariate curve resolution (MCR), is applied to the hyperspectral time of flight secondary ion mass spectroscopy (ToF-SIMS) data from eight individual microarray spots. Rather than analysing the data individually, the data-sets are collated and analysed as a single large data-set. Desktop computing is not a practical method for undertaking MCR analysis of such large data-sets due to the constraints of memory and computational overhead. Here, a distributed memory High-Performance Computing facility (HPC) is used. Similar to what is achieved using MCR analysis of individual samples, the results from this consolidated data-set allow clear identification of the substrate material; furthermore, specific chemistries common to different spots are also identified. The application of the HPC facility to the MCR analysis of ToF-SIMS hyperspectral data-sets demonstrates a potential methodology for the analysis of macro-scale data without compromising spatial resolution (data ‘binning’

Strategies for MCR image analysis of large hyperspectral data-sets

Polymer microarrays are a key enabling technology for high throughput materials discovery. In this study, multivariate image analysis, specifically multivariate curve resolution (MCR), is applied to the hyperspectral time of flight secondary ion mass spectroscopy (ToF-SIMS) data from eight individual microarray spots. Rather than analysing the data individually, the data-sets are collated and analysed as a single large data-set. Desktop computing is not a practical method for undertaking MCR analysis of such large data-sets due to the constraints of memory and computational overhead. Here, a distributed memory High-Performance Computing facility (HPC) is used. Similar to what is achieved using MCR analysis of individual samples, the results from this consolidated data-set allow clear identification of the substrate material; furthermore, specific chemistries common to different spots are also identified. The application of the HPC facility to the MCR analysis of ToF-SIMS hyperspectral data-sets demonstrates a potential methodology for the analysis of macro-scale data without compromising spatial resolution (data ‘binning’

Discovery of Novel Materials with Broad Resistance to Bacterial Attachment Using Combinatorial Polymer Microarrays

A new class of bacteria-attachment-resistant materials is discovered using a multi-generation polymer microarray methodology that reduces bacterial attachment by up to 99.3% compared with a leading commercially available silver hydrogel anti-bacterial material. The coverage of three bacterial species, Pseudomonas aeruginosa, Staphylococcus aureus, and uropathogenic Escherichia coli is assessed.National Institutes of Health (U.S.) (Grant R01 DE016516

Bacterial attachment to polymeric materials correlates with molecular flexibility and hydrophilicity

A new class of material resistant to bacterial attachment has been discovered that is formed from polyacrylates with hydrocarbon pendant groups. In this study, the relationship between the nature of the hydrocarbon moiety and resistance to bacteria is explored, comparing cyclic, aromatic, and linear chemical groups. A correlation is shown between bacterial attachment and a parameter derived from the partition coefficient and the number of rotatable bonds of the materials' pendant groups. This correlation is applicable to 86% of the hydrocarbon pendant moieties surveyed, quantitatively supporting the previous qualitative observation that bacteria are repelled from poly (meth)acrylates containing a hydrophilic ester group when the pendant group is both rigid and hydrophobic. This insight will help inform and predict the further development of polymers resistant to bacterial attachment

Polymers with hydro-responsive topography identified using high throughput AFM of an acrylate microarray

Atomic force microscopy has been applied to an acrylate polymer microarray to achieve a full topographic characterisation. This process discovered a small number of hydro-responsive materials created from monomers with disparate hydrophilicities that show reversibility between pitted and protruding nanoscale topographies.Wellcome Trust (London, England) (Grant number 085245/Z/08/Z