Fuel-oxidizer mixtures: a lab and field study

ABSTRACT

Improvised explosive may be as primitive as a fuel and oxidizer mixture; yet not all fuel-oxidizer mixtures are explosive. Predicting explosive potential from laboratory-scale tests is desirable to screen a large matrix of potential threats in varying concentrations. Herein the properties of various fuel/oxidizer mixtures were measured at small scale (2 g) with bomb calorimetry and on large scale (5 kg) with high speed photography for detonation velocity and with piezoelectric pressure probes for TNT air blast equivalence. Potassium nitrate (KN), potassium chlorate (KC), potassium permanganate (KMnO₄), potassium iodate (KIO₃), ammonium nitrate (AN), and ammonium perchlorate (AP) were prepared with sucrose (Su) and aluminum (Al) fuel. Results were compared to each other as well as predictions from Cheetah thermochemical code.     

Characterizing pathological imperfections in macromolecular crystals: lattice disorders and modulations

Macromolecular crystal structure determination can be complicated or brought to a halt by crystal imperfections. These issues motivated us to write up what we affectionately call ‘The Definitive Hitchhiker’s Guide to Pathological Macromolecular Crystals: Lattice Disorders and Modulations’. Perhaps the most challenging imperfections are lattice order–disorder phenomena and positional modulations. Many of these types of crystals have been solved, and progress has been made on the more challenging forms. Diagnostic tools and how to solve many of these problem crystal structures are reviewed. New avenues are provided for approaching the solution of incommensurately modulated crystals. There are a good number of case studies in the literature of lattice order–disorder phenomena and crystallographic modulations that make it timely to write a review. This review concludes with a projected pathway for solving incommensurately modulated crystals, personal views of future directions and needs of the crystallographic community.     

Discovery of the Tiancilactone Antibiotics by Genome Mining of Atypical Bacterial Type II Diterpene Synthases

Although genome mining has advanced the identification, discovery, and study of microbial natural products, the discovery of bacterial diterpenoids continues to lag behind. Herein, we report the identification of 66 putative producers of novel bacterial diterpenoids, and the discovery of the tiancilactone (TNL) family of antibiotics, by genome mining of type II diterpene synthases that do not possess the canonical DXDD motif. The TNLs, which are broad‐spectrum antibiotics with moderate activities, are produced by both Streptomyces sp. CB03234 and Streptomyces sp. CB03238 and feature a highly functionalized diterpenoid skeleton that is further decorated with chloroanthranilate and γ‐butyrolactone moieties. Genetic manipulation of the tnl gene cluster resulted in TNL congeners, which provided insights into their biosynthesis and structure–activity relationships. This work highlights the biosynthetic potential that bacteria possess to produce diterpenoids and should inspire continued efforts to discover terpenoid natural products from bacteria.     

Nitrogen-doped graphene-supported zinc sulfide nanorods as efficient Pt-free for visible-light photocatalytic hydrogen production

Nitrogen-doped graphene-ZnS composite (NG-ZnS) was synthesized by thermal treatment of graphene-ZnS composite (G-ZnS) in NH₃ medium. In the second step, the as-synthesized samples were deposited on indium tin oxide glass (ITO) by electrophoretic deposition for photocatalytic hydrogen evolution reaction. The as-prepared NG-ZnS-modified ITO electrode displayed excellent photocatalytic activity, rapid transient photocurrent response, superior stability and high recyclability compared to the pure ZnS and G-ZnS-modified ITO electrode due to the synergy between the photocatalytic activity of ZnS nanorods and the large surface area and high conductivity of N-graphene.     

Methyltransferase Contingencies in the Pathway of Everninomicin D Antibiotics and Analogues

Everninomicins are orthoester oligosaccharide antibiotics with potent activity against multidrug-resistant bacterial pathogens. Everninomicins act by disrupting ribosomal assembly in a distinct region in comparison to clinically prescribed drugs. We employed microporous intergeneric conjugation with Escherichia coli to manipulate Micromonospora for targeted gene-replacement studies of multiple putative methyltransferases across the octasaccharide scaffold of everninomicin effecting the A₁, C, F, and H rings. Analyses of gene-replacement and genetic complementation mutants established the mutability of the everninomicin scaffold through the generation of 12 previously unreported analogues and, together with previous results, permitted assignment of the ten methyltransferases required for everninomicin biosynthesis. The in vitro activity of A₁- and H-ring-modifying methyltransferases demonstrated the ability to catalyze late-stage modification of the scaffold on an A₁-ring phenol and H-ring C-4’ hydroxy moiety. Together these results establish the potential of the everninomicin scaffold for modification through mutagenesis and in vitro modification of advanced biosynthetic intermediates.     

A Unified Analysis of Structured Sonar-Terrain Data Using Bayesian Functional Mixed Models

Sonar emits pulses of sound and uses the reflected echoes to gain information about target objects. It offers a low cost, complementary sensing modality for small robotic platforms. Although existing analytical approaches often assume independence across echoes, real sonar data can have more complicated structures due to device setup or experimental design. In this article, we consider sonar echo data collected from multiple terrain substrates with a dual-channel sonar head. Our goals are to identify the differential sonar responses to terrains and study the effectiveness of this dual-channel design in discriminating targets. We describe a unified analytical framework that achieves these goals rigorously, simultaneously, and automatically. The analysis was done by treating the echo envelope signals as functional responses and the terrain/channel information as covariates in a functional regression setting. We adopt functional mixed models that facilitate the estimation of terrain and channel effects while capturing the complex hierarchical structure in data. This unified analytical framework incorporates both Gaussian models and robust models. We fit the models using a full Bayesian approach, which enables us to perform multiple inferential tasks under the same modeling framework, including selecting models, estimating the effects of interest, identifying significant local regions, discriminating terrain types, and describing the discriminatory power of local regions. Our analysis of the sonar-terrain data identifies time regions that reflect differential sonar responses to terrains. The discriminant analysis suggests that a multi- or dual-channel design achieves target identification performance comparable with or better than a single-channel design. Supplementary materials for this article are available online.