Developing the Pressure-Temperature-Magnetic Field Phase Diagram of Multiferroic [(CH3)2NH2]Mn(HCOO)3.

We combined Raman scattering and magnetic susceptibility to explore the properties of [(CH3)2NH2]Mn(HCOO)3 under compression. Analysis of the formate bending mode reveals a broad two-phase region surrounding the 4.2 GPa critical pressure that becomes increasingly sluggish below the order-disorder transition due to the extensive hydrogen-bonding network. Although the paraelectric and ferroelectric phases have different space groups at ambient-pressure conditions, they both drive toward P1 symmetry under compression. This is a direct consequence of how the order-disorder transition changes under pressure. We bring these findings together with prior magnetization work to create a pressure-temperature-magnetic field phase diagram, unveiling entanglement, competition, and a progression of symmetry-breaking effects that underlie functionality in this molecule-based multiferroic. That the high-pressure P1 phase is a subgroup of the ferroelectric Cc suggests the possibility of enhanced electric polarization as well as opportunity for strain control.

Biocatalytic degradation of pollutants.

Microbial reactions play key roles in biocatalysis and biodegradation. The recent genome sequencing of environmentally relevant bacteria has revealed previously unsuspected metabolic potential that could be exploited for useful purposes. For example, oxygenases and other biodegradative enzymes are benign catalysts that can be used for the production of industrially useful compounds. In conjunction with their biodegradative capacities, bacterial chemotaxis towards pollutants might contribute to the ability of bacteria to compete with other organisms in the environment and to be efficient agents for bioremediation. In addition to the bacterial biomineralization of organic pollutants, certain bacteria are also capable of immobilizing toxic heavy metals in contaminated aquifers, further illustrating the potential of microorganisms for the removal of pollutants.

A square-planar hydrated cationic tetrakis(methimazole)gold(III) complex.

The cationic pseudo-square-planar complex tetrakis(1-methyl-2,3-dihydro-1H-imidazole-2-thione-κS)gold(III) trichloride sesquihydrate, [Au(C4H6N2S)4]Cl3·1.5H2O, was isolated as dark-red crystals from the reaction of chloroauric acid trihydrate (HAuCl4·3H2O) with four equivalents of methimazole in methanol. The Au(III) atoms reside at the corners of the unit cell on an inversion center and are bound by the S atoms of four methimazole ligands in a planar arrangement, with S-Au-S bond angles of approximately 90°.

Polydiacetylene nanovesicles as carriers of natural phenylpropanoids for creating antimicrobial food-contact surfaces.

The ultimate goal of this study was developing antimicrobial food-contact materials based on natural phenolic compounds using nanotechnological approaches. Among the methyl-ß-cyclodextrin-encapsulated phenolics tested, curcumin showed by far the highest activity toward Escherichia coli with a minimum inhibitory concentration of 0.4 mM. Curcumin was enclosed in liposome-type polydiacetylene/phosholipid nanovesicles supplemented with N-hydroxysuccinimide and glucose. The fluorescence spectrum of the nanovesicles suggested that curcumin was located in their bilayer region. Free-suspended nanovesicles tended to bind to the bacterial surface and demonstrated bactericidal activity toward Gram-negative (E. coli) and vegetative cells of Gram-positive (Bacillus cereus) bacteria reducing their counts from 5 log CFU mL(-1) to an undetectable level within 8 h. The nanovesicles were covalently bound to silanized glass. Incubation of E. coli and B. cereus with nanovesicle-coated glass resulted in a 2.5 log reduction in their counts. After optimization this approach can be used for controlling microbial growth, cross-contamination, and biofilm formation on food-contacting surfaces.

A study of factors affecting highway accident rates using the random-parameters tobit model.

A large body of previous literature has used a variety of count-data modeling techniques to study factors that affect the frequency of highway accidents over some time period on roadway segments of a specified length. An alternative approach to this problem views vehicle accident rates (accidents per mile driven) directly instead of their frequencies. Viewing the problem as continuous data instead of count data creates a problem in that roadway segments that do not have any observed accidents over the identified time period create continuous data that are left-censored at zero. Past research has appropriately applied a tobit regression model to address this censoring problem, but this research has been limited in accounting for unobserved heterogeneity because it has been assumed that the parameter estimates are fixed over roadway-segment observations. Using 9-year data from urban interstates in Indiana, this paper employs a random-parameters tobit regression to account for unobserved heterogeneity in the study of motor-vehicle accident rates. The empirical results show that the random-parameters tobit model outperforms its fixed-parameters counterpart and has the potential to provide a fuller understanding of the factors determining accident rates on specific roadway segments.

A multivariate tobit analysis of highway accident-injury-severity rates.

Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on all roadway segments during the period over which data are collected). This censoring may arise from a number of sources, one of which being the possibility that less severe crashes may be under-reported and thus may be less likely to appear in crash databases. Traditional tobit-regression analyses have dealt with the overall accident rate (all crashes regardless of injury severity), so the issue of censoring by the severity of crashes has not been addressed. However, a tobit-regression approach that considers accident rates by injury-severity level, such as the rate of no-injury, possible injury and injury accidents per distance traveled (as opposed to all accidents regardless of injury-severity), can potentially provide new insights, and address the possibility that censoring may vary by crash-injury severity. Using five-year data from highways in Washington State, this paper estimates a multivariate tobit model of accident-injury-severity rates that addresses the possibility of differential censoring across injury-severity levels, while also accounting for the possible contemporaneous error correlation resulting from commonly shared unobserved characteristics across roadway segments. The empirical results show that the multivariate tobit model outperforms its univariate counterpart, is practically equivalent to the multivariate negative binomial model, and has the potential to provide a fuller understanding of the factors determining accident-injury-severity rates on specific roadway segments.