Chemical Degradation of End-of-Life Poly(lactic acid) into Methyl Lactate by a Zn(II) Complex.

The catalyzed methanolysis of end-of-life poly(lactic acid) (PLA) products by an ethylenediamine Zn(II) complex to form biodegradable methyl lactate was studied experimentally at 70, 90, and 110 °C. The PLA samples consisted of typical consumer waste materials, including a cup, a toy, and a three-dimensional (3D) printing material. High selectivities and yields (>94%) were possible depending on temperature and reaction time. Additionally, and to develop a predictive kinetic model, kinetic parameters (pre-exponential factor and activation energies) of the PLA transesterification reaction were first obtained from virgin PLA. These parameters were subsequently used to estimate the conversion of PLA, selectivity, and yield of methyl lactate after 1 and 4 h of the reaction, and the results were compared with the experimental values of the end-of-life PLA. Despite the presence of unknown additives in the PLA waste material and uncontrolled particle size, the model was able to predict the overall conversion, selectivity, and yield to an average deviation of 5, 7, and 12%, respectively. A greater agreement between the model and experimental values is observed for the higher temperatures and the longer reaction time. Larger deviations were observed for the PLA toy, which we attribute to the presence of additives, since despite its lower molecular weight, it possessed a higher structural strength.

Enhancing photosynthetic production of glycogen-rich biomass for use as a fermentation feedstock.

Current sources of fermentation feedstocks, i.e. corn, sugar cane, or plant biomass, fall short of demand for liquid transportation fuels and commodity chemicals in the United States. Aquatic phototrophs including cyanobacteria have the potential to supplement the supply of current fermentable feedstocks. In this strategy, cells are engineered to accumulate storage molecules including glycogen, cellulose, and/or lipid oils that can be extracted from harvested biomass and fed to heterotrophic organisms engineered to produce desired chemical products. In this manuscript, we examine the production of glycogen in the model cyanobacteria, Synechococcus sp. strain PCC 7002, and subsequent conversion of cyanobacterial biomass by an engineered Escherichia coli to octanoic acid as a model product. In effort to maximize glycogen production, we explored the deletion of catabolic enzymes and overexpression of GlgC, an enzyme that catalyzes the first committed step towards glycogen synthesis. We found that deletion of glgP increased final glycogen titers when cells were grown in diurnal light. Overexpression of GlgC led to a temporal increase in glycogen content but not in an overall increase in final titer or content. The best strains were grown, harvested, and used to formulate media for growth of E. coli. The cyanobacterial media was able to support the growth of an engineered E. coli and produce octanoic acid at the same titer as common laboratory media.

Supporting fingerprint identification assessments using a skin stretch model - A preliminary study.

To support fingerprint expert opinion, this research proposes an approach that combines subjective human analysis (as currently applied by fingerprint practitioners) with a statistical test of the result. This approach relies on the hypothesis that there are limits to the distortion caused by skin stretch. Such limits can be modelled by applying a multivariate normal probability density function to the distances and angle formed by a marked ridge characteristic and the two closest neighbouring minutiae. This study presents a model tested on 5 donors in total. The "expected range" of distortion in a within-source comparison using 10 minutiae was determined and compared to between-source comparisons. The expected range of log probability densities for within-source comparisons marked with 10 minutiae was determined to be from -33.4 to -60.0, with all between-source data falling outside this range, between -83 and -305. These results suggest that the proposed generated metric could be a powerful tool for the assessment of fingerprint expert opinion in operational casework.

Modern statistical models for forensic fingerprint examinations: a critical review.

Over the last decade, the development of statistical models in support of forensic fingerprint identification has been the subject of increasing research attention, spurned on recently by commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. Such models are increasingly seen as useful tools in support of the fingerprint identification process within or in addition to the ACE-V framework. This paper provides a critical review of recent statistical models from both a practical and theoretical perspective. This includes analysis of models of two different methodologies: Probability of Random Correspondence (PRC) models that focus on calculating probabilities of the occurrence of fingerprint configurations for a given population, and Likelihood Ratio (LR) models which use analysis of corresponding features of fingerprints to derive a likelihood value representing the evidential weighting for a potential source.

Spatial analysis of corresponding fingerprint features from match and close non-match populations.

The development of statistical models for forensic fingerprint identification purposes has been the subject of increasing research attention in recent years. This can be partly seen as a response to a number of commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. In addition, key forensic identification bodies such as ENFSI [1] and IAI [2] have recently endorsed and acknowledged the potential benefits of using statistical models as an important tool in support of the fingerprint identification process within the ACE-V framework. In this paper, we introduce a new Likelihood Ratio (LR) model based on Support Vector Machines (SVMs) trained with features discovered via morphometric and spatial analyses of corresponding minutiae configurations for both match and close non-match populations often found in AFIS candidate lists. Computed LR values are derived from a probabilistic framework based on SVMs that discover the intrinsic spatial differences of match and close non-match populations. Lastly, experimentation performed on a set of over 120,000 publicly available fingerprint images (mostly sourced from the National Institute of Standards and Technology (NIST) datasets) and a distortion set of approximately 40,000 images, is presented, illustrating that the proposed LR model is reliably guiding towards the right proposition in the identification assessment of match and close non-match populations. Results further indicate that the proposed model is a promising tool for fingerprint practitioners to use for analysing the spatial consistency of corresponding minutiae configurations.