ToF-SIMS and Machine Learning for Single-Pixel Molecular Discrimination of an Acrylate Polymer Microarray

© 2020 American Chemical Society. Combinatorial approaches to materials discovery offer promising potential for the rapid development of novel polymer systems. Polymer microarrays enable the high-throughput comparison of material physical and chemical properties - such as surface chemistry and properties like cell attachment or protein adsorption - in order to identify correlations that can progress materials development. A challenge for this approach is to accurately discriminate between highly similar polymer chemistries or identify heterogeneities within individual polymer spots. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) offers unique potential in this regard, capable of describing the chemistry associated with the outermost layer of a sample with high spatial resolution and chemical sensitivity. However, this comes at the cost of generating large scale, complex hyperspectral imaging data sets. We have demonstrated previously that machine learning is a powerful tool for interpreting ToF-SIMS images, describing a method for color-tagging the output of a self-organizing map (SOM). This reduces the entire hyperspectral data set to a single reconstructed color similarity map, in which the spectral similarity between pixels is represented by color similarity in the map. Here, we apply the same methodology to a ToF-SIMS image of a printed polymer microarray for the first time. We report complete, single-pixel molecular discrimination of the 70 unique homopolymer spots on the array while also identifying intraspot heterogeneities thought to be related to intermixing of the polymer and the pHEMA coating. In this way, we show that the SOM can identify layers of similarity and clusters in the data, both with respect to polymer backbone structures and their individual side groups. Finally, we relate the output of the SOM analysis with fluorescence data from polymer-protein adsorption studies, highlighting how polymer performance can be visualized within the context of the global topology of the data set

Ab initio RAFT emulsion polymerization mediated by small cationic RAFT agents to form polymers with low molar mass dispersity

We report on low molar mass cationic RAFT agents that provide predictable molar mass and low molar mass dispersities (Đm) in ab initio emulsion polymerization. Thus RAFT emulsion polymerization of styrene in the presence of the protonated RAFT agent, ((((cyanomethyl)thio)carbonothioyl)(methyl)amino)pyridin-1-ium toluenesulfonate (4), and the analogous methyl-quaternized RAFT agents, 4-((((cyanomethyl)thio)carbonothioyl)(methyl)amino)-1-methylpyridin-1-ium dodecyl sulfate (6), provide low dispersity polystyrene with Đm 1.2–1.4 for Mn ∼ 20 000. We postulate that the success of ab initio emulsion polymerization with 4 is due to the hydrophilicity of the pyridinium group, which is such that the water soluble RAFT agent partitions predominantly into the aqueous phase under the conditions of the experiment and that 4 provides little retardation. With 6, when the counterion is dodecyl sulfate, we can achieve “surfactant-free” RAFT emulsion polymerization to provide a low Đm polystyrene. However, the RAFT end-group is lost on isolation of the polymer. Preliminary results show that this class of RAFT agent is broadly applicable in ab initio emulsion polymerization of other more-activated monomers (e.g., butyl acrylate, butyl methacrylate). Furthermore, cyanomethyl(pyridin-4-yl)carbamodithioate (3, the RAFT agent in neutral form) provides molar mass control and Đm < 1.8 in ab initio emulsion polymerization of less activated monomers, specifically, the vinyl esters, vinyl acetate and vinyl benzoate.Published onlin

Survival of Migrating Salmon Smolts in Large Rivers With and Without Dams

The mortality of salmon smolts during their migration out of freshwater and into the ocean has been difficult to measure. In the Columbia River, which has an extensive network of hydroelectric dams, the decline in abundance of adult salmon returning from the ocean since the late 1970s has been ascribed in large measure to the presence of the dams, although the completion of the hydropower system occurred at the same time as large-scale shifts in ocean climate, as measured by climate indices such as the Pacific Decadal Oscillation. We measured the survival of salmon smolts during their migration to sea using elements of the large-scale acoustic telemetry system, the Pacific Ocean Shelf Tracking (POST) array. Survival measurements using acoustic tags were comparable to those obtained independently using the Passive Integrated Transponder (PIT) tag system, which is operational at Columbia and Snake River dams. Because the technology underlying the POST array works in both freshwater and the ocean, it is therefore possible to extend the measurement of survival to large rivers lacking dams, such as the Fraser, and to also extend the measurement of survival to the lower Columbia River and estuary, where there are no dams. Of particular note, survival during the downstream migration of at least some endangered Columbia and Snake River Chinook and steelhead stocks appears to be as high or higher than that of the same species migrating out of the Fraser River in Canada, which lacks dams. Equally surprising, smolt survival during migration through the hydrosystem, when scaled by either the time or distance migrated, is higher than in the lower Columbia River and estuary where dams are absent. Our results raise important questions regarding the factors that are preventing the recovery of salmon stocks in the Columbia and the future health of stocks in the Fraser River

A sensual philology for Anglo-Saxon England

What forgotten forms can philology assume anew? Reassessing how early medieval writers loved words differently than we do reveals significant gaps between past and presence senses of the physical phenomena words can index. In the early medieval language of Old English texts there remains a largely uncharted capacity for less linguistically driven aspects of expression, formed through a network of words, sounds, bodies and media: how the mute sound of a bell and the crook of a silent finger come together in medieval sign language, or how the Old English word for ring becomes a weeping, poetic gasp within a heaving breast. Such early medieval moments of communication survive because of language and in spite of language, and qualify the visualist framework through which we predictably reconstitute the medieval past, calling, /sotto voce/, for more than lovely words

Microparticles Decorated with Cell‐Instructive Surface Chemistries Actively Promote Wound Healing

Wound healing is a complex biological process involving close crosstalk between various cell types. Dysregulation in any of these processes, such as in diabetic wounds, results in chronic non-healing wounds. Fibroblasts are a critical cell type involved in the formation of granulation tissue, essential for effective wound healing. We screened 315 different polymer surfaces to identify candidates which actively drove fibroblasts towards either pro- or anti-proliferative functional phenotypes. Fibroblast-instructive chemistries were identified, which we synthesized into surfactants to fabricate easy to administer microparticles for direct application to diabetic wounds. The pro-proliferative microfluidic derived particles were able to successfully promote neovascularisation, granulation tissue formation and wound closure after a single application to the wound bed. These active novel 3D bio-instructive microparticles show great potential as a route to reducing the burden of chronic wounds

Exploring the Relationship between Polymer Surface Chemistry and Bacterial Attachment Using ToF-SIMS and Self-Organizing maps

Biofilm formation is a major cause of hospital-acquired infections. Research into biofilm-resistant materials is therefore critical to reduce the frequency of these events. Polymer microarrays offer a high-throughput approach to enable the efficient discovery of novel biofilm-resistant polymers. Herein, bacterial attachment and surface chemistry are studied for a polymer microarray to improve the understanding of Pseudomonas aeruginosa biofilm formation on a diverse set of polymeric surfaces. The relationships between time-of-flight secondary ion mass spectrometry (ToF-SIMS) data and biofilm formation are analyzed using linear multivariate analysis (partial least squares [PLS] regression) and a nonlinear self-organizing map (SOM). The SOM models revealed several combinations of fragment ions that are positively or negatively associated with bacterial biofilm formation, which are not identified by PLS. With these insights, a second PLS model is calculated, in which interactions between key fragments (identified by the SOM) are explicitly considered. Inclusion of these terms improved the PLS model performance and shows that, without such terms, certain key fragment ions correlated with bacterial attachment may not be identified. The chemical insights provided by the combination of PLS regression and SOM will be useful for the design of materials that support negligible pathogen attachment

Generation and Characterization of a Library of Novel Biologically Active Functional Surfactants (Surfmers) Using Combined High-Throughput Methods

We report the first successful combination of three distinct high-throughput techniques to deliver the accelerated design, synthesis, and property screening of a library of novel, bio-instructive, polymeric, comb-graft surfactants. These three-dimensional, surface-active materials were successfully used to control the surface properties of particles by forming a unimolecular deep layer on the surface of the particles via microfluidic processing. This strategy deliberately utilizes the surfactant to both create the stable particles and deliver a desired cell-instructive behavior. Therefore, these specifically designed, highly functional surfactants are critical to promoting a desired cell response. This library contained surfactants constructed from 20 molecularly distinct (meth)acrylic monomers, which had been pre-identified by HT screening to exhibit specific, varied, and desirable bacterial biofilm inhibitory responses. The surfactant's self-assembly properties in water were assessed by developing a novel, fully automated, HT method to determine the critical aggregation concentration. These values were used as the input data to a computational-based evaluation of the key molecular descriptors that dictated aggregation behavior. Thus, this combination of HT techniques facilitated the rapid design, generation, and evaluation of further novel, highly functional, cell-instructive surfaces by application of designed surfactants possessing complex molecular architectures

Expected Performance of the ATLAS Experiment - Detector, Trigger and Physics

A detailed study is presented of the expected performance of the ATLAS detector. The reconstruction of tracks, leptons, photons, missing energy and jets is investigated, together with the performance of b-tagging and the trigger. The physics potential for a variety of interesting physics processes, within the Standard Model and beyond, is examined. The study comprises a series of notes based on simulations of the detector and physics processes, with particular emphasis given to the data expected from the first years of operation of the LHC at CERN