Modelling the thermal stability of precursor nanoparticles in zeolite synthesis

We have modelled the thermal stability of silica nanoparticles commonly observed as precursors in the synthesis of zeolites. We performed canonical Monte Carlo and parallel tempering simulations on a lattice model that describes the self-assembly of nanoparticles under conditions at which they are observed experimentally. The effect of heating on the relative stability of the phases of the model was analysed by running simulations at various temperatures. At low temperature, the model yields a metastable multi-particle phase with a characteristic size distribution, which is separated by an energy barrier from the true equilibrium phase, a dense silica solid. As temperature increases, the system enters a transition region and eventually reaches the bulk phase. This transition is reminiscent of the experimentally observed transition from nanoparticles to zeolite. The transition temperature scales with the inverse of the system volume, approaching an asymptotic value for large system sizes. This indicates the transition temperature is a reproducible macroscopic property of the system. The transition temperature in the model is within the range of temperatures at which nanoparticles form zeolite crystals in experiments

Evaluation Context Impacts Neuropsychological Performance of OEF/OIF Veterans with Reported Combat-Related Concussion

Although soldiers of Operations Iraqi Freedom (OIF) and Enduring Freedom (OEF) encounter combat-related concussion at an unprecedented rate, relatively few studies have examined how evaluation context, insufficient effort, and concussion history impact neuropsychological performances in the years following injury. The current study explores these issues in a sample of 119 U.S. veterans (OEF/OIF forensic concussion, n = 24; non-OEF/OIF forensic concussion, n = 20; OEF/OIF research concussion, n = 38; OEF/OIF research without concussion, n = 37). The OEF/OIF forensic concussion group exhibited significantly higher rates of insufficient effort relative to the OEF/OIF research concussion group, but a comparable rate of insufficient effort relative to the non-OEF/OIF forensic concussion group. After controlling for effort, the research concussion and the research non-concussion groups demonstrated comparable neuropsychological performance. Results highlight the importance of effort assessment among OEF/OIF and other veterans with concussion history, particularly in forensic contexts

Neuropsychological evaluation of blast-related concussion: Illustrating the challenges and complexities through OEF/OIF case studies

Background/objective: Soldiers of Operations Enduring Freedom (OEF) and Iraqi Freedom (OIF) sustain blast-related mild traumatic brain injury (concussion) with alarming regularity. This study discusses factors in addition to concussion, such as co-morbid psychological difficulty (e.g. post-traumatic stress) and symptom validity concerns that may complicate neuropsychological evaluation in the late stage of concussive injury. Case report: The study presents the complexities that accompany neuropsychological evaluation of blast concussion through discussion of three case reports of OEF/OIF personnel. Discussion: The authors emphasize uniform assessment of blast concussion, the importance of determining concussion severity according to acute-injury characteristics and elaborate upon non-concussion-related factors that may impact course of cognitive limitation. The authors conclude with a discussion of the need for future research examining the impact of blast concussion (particularly recurrent concussion) and neuropsychological performance

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the “Zeolite Sorting Hat”—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si–O distances around 3.2–3.4 Å as the key discriminatory factor

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme—the “Zeolite Sorting Hat”—that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si–O distances around 3.2–3.4 Å as the key discriminatory factor

On the geometric dilation of closed curves, graphs, and point sets

The detour between two points u and v (on edges or vertices) of an embedded planar graph whose edges are curves is the ratio between the shortest path in in the graph between u and v and their Euclidean distance. The maximum detour over all pairs of points is called the geometric dilation. Ebbers-Baumann, Gruene and Klein have shown that every finite point set is contained in a planar graph whose geometric dilation is at most 1.678, and some point sets require graphs with dilation at least pi/2 = 1.57... We prove a stronger lower bound of 1.00000000001*pi/2 by relating graphs with small dilation to a problem of packing and covering the plane by circular disks. The proof relies on halving pairs, pairs of points dividing a given closed curve C in two parts of equal length, and their minimum and maximum distances h and H. Additionally, we analyze curves of constant halving distance (h=H), examine the relation of h to other geometric quantities and prove some new dilation bounds.Comment: 31 pages, 16 figures. The new version is the extended journal submission; it includes additional material from a conference submission (ref. [6] in the paper

Neuropsychological Outcomes of U.S. Veterans with Report of Remote Blast-Related Concussion and Current Psychopathology

This study explored whether remote blast-related MTBI and/or current Axis I psychopathology contribute to neuropsychological outcomes among OEF/OIF veterans with varied combat histories. OEF/OIF veterans underwent structured interviews to evaluate history of blast-related MTBI and psychopathology and were assigned to MTBI (n = 18), Axis I (n = 24), Co-morbid MTBI/Axis I (n = 34), or post-deployment control (n = 28) groups. A main effect for Axis I diagnosis on overall neuropsychological performance was identified (F(3,100) = 4.81; p = .004), with large effect sizes noted for the Axis I only (d = .98) and Co-morbid MTBI/Axis I (d = .95) groups relative to the control group. The latter groups demonstrated primary limitations on measures of learning/memory and processing speed. The MTBI only group demonstrated performances that were not significantly different from the remaining three groups. These findings suggest that a remote history of blast-related MTBI does not contribute to objective cognitive impairment in the late stage of injury. Impairments, when present, are subtle and most likely attributable to PTSD and other psychological conditions. Implications for clinical neuropsychologists and future research are discussed. (JINS, 2012, 18, 1–11

A Comparison of the TempO-Seq S1500+ Platform to RNA-Seq and Microarray Using Rat Liver Mode of Action Samples

The TempO-SeqTM platform allows for targeted transcriptomic analysis and is currently used by many groups to perform high-throughput gene expression analysis. Herein we performed a comparison of gene expression characteristics measured using 45 purified RNA samples from the livers of rats exposed to chemicals that fall into one of five modes of action (MOAs). These samples have been previously evaluated using AffymetrixTM rat genome 230 2.0 microarrays and Illumina® whole transcriptome RNA-Seq. Comparison of these data with TempO-Seq analysis using the rat S1500+ beta gene set identified clear differences in the platforms related to signal to noise, root mean squared error, and/or sources of variability. Microarray and TempO-Seq captured the most variability in terms of MOA and chemical treatment whereas RNA-Seq had higher noise and larger differences between samples within a MOA. However, analysis of the data by hierarchical clustering, gene subnetwork connectivity and biological process representation of MOA-varying genes revealed that the samples clearly grouped by treatment as opposed to gene expression platform. Overall these findings demonstrate that the results from the TempO-Seq platform are consistent with findings on other more established approaches for measuring the genome-wide transcriptome