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Flory-Huggins (F-H) model has been widely used to analyze the thermodynamic behavior of polymeric membrane solution during the formation of membrane structure in a phase inversion process. The F-H model involves concentration and binary interaction parameter of components in membrane solution. Those parameters are used to calculate the composition of membrane solution at equilibrium which is then plotted on ternary diagram known as a binodal curve. The binodal curve is used to determine the type of demixing of the polymeric membrane solution and to predict the structure of resulted membrane. Several modifications of F-H model have been conducted in relation to composition (ternary or quaternary) and temperature of the membrane solution. In this paper, the development of F-H model used in designing polymeric membrane by phase inversion method, particularly immersion precipitation method, will be reviewed. The structure and performance of membranes that are governed by the thermodynamic property of the membrane solution based on F-H model will be discussed.

Accurate and precise estimates of population size are critical for effective management but can be particularly difficult to achieve for small populations of large carnivores. We approached this challenge by integrating multiple noninvasive data sources into a DNA-based mark—recapture framework to estimate the abundance of the small and endangered Apennine brown bear population. To improve sample size and coverage, we collected hair samples from June to September 2011 by concurrently using 4 noninvasive sampling methods: intensive hair-snagging (forty-three 5×5-km cells and five 12-day sampling sessions) plus secondary sampling methods (bear rub trees, alpine buckthorn aggregations, and incidental sampling). Following marker selection based on tissue samples from 55 Apennine bears, we used 13 microsatellites (plus gender) and quality assurance protocols to identify multilocus genotypes from hair samples. We used Huggins closed models in program MARK to estimate population size, which allowed us to account for spatial, temporal, and demographic components of heterogeneity in secondary sampling methods. Based on 529 analyzed hair samples, 80.5% of which yielded high-confidence scores for all markers, we achieved a rather precise (CV = 7.9%) population estimate of 51 bears (95% CI = 47–66) including cubs. Compared to a previous survey in 2008, our results provide evidence that the Apennine brown bear population has not been declining in recent years. Additionally, the relatively high (closure corrected) density (39.7 bears/1,000 km2; 95% CI = 36.6–51.4) indicates that habitat productivity within the core range is currently adequate for bears and that effective conservation of this small bear population should aim to expand the bears' range across a larger portion of the central Apennines. We examined if a reduction in sampling effort would affect the precision of our population estimates. Reduced sample coverage, small sample size, and low hair-trap-capture probability preclude the adoption of a single sampling method or a subset of such to survey small bear populations if a comparable level of precision is required.

The conservation status of the 2 threatened grizzly bear (Ursus arctos) populations in the Cabinet-Yaak Ecosystem (CYE) of northern Montana and Idaho had remained unchanged since designation in 1975; however, the current demographic status of these populations was uncertain. No rigorous data on population density and distribution or analysis of recent population genetic structure were available to measure the effectiveness of conservation efforts. We used genetic detection data from hair corral, bear rub, and opportunistic sampling in traditional and spatial capture—recapture models to generate estimates of abundance and density of grizzly bears in the CYE. We calculated mean bear residency on our sampling grid from telemetry data using Huggins and Pledger models to estimate the average number of bears present and to correct our superpopulation estimates for lack of geographic closure. Estimated grizzly bear abundance (all sex and age classes) in the CYE in 2012 was 48–50 bears, approximately half the population recovery goal. Grizzly bear density in the CYE (4.3–4.5 grizzly bears/1,000 km2) was among the lowest of interior North American populations. The sizes of the Cabinet (n = 22–24) and Yaak (n = 18–22) populations were similar. Spatial models produced similar estimates of abundance and density with comparable precision without requiring radio-telemetry data to address assumptions of geographic closure. The 2 populations in the CYE were demographically and reproductively isolated from each other and the Cabinet population was highly inbred. With parentage analysis, we documented natural migrants to the Cabinet and Yaak populations by bears born to parents in the Selkirk and Northern Continental Divide populations. These events supported data from other sources suggesting that the expansion of neighboring populations may eventually help sustain the CYE populations. However, the small size, isolation, and inbreeding documented by this study demonstrate the need for comprehensive management designed to support CYE population growth and increased connectivity and gene flow with other populations. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

In this study, the influence of NaCl on the cloud point of polyethoxylate surfactants from the family of lauryl alcohol polyethoxylates (C12EOn ) and nonylphenol polyethoxylates (NPEOn ) was investigated. Liquid-liquid equilibrium curves of the aforementioned aqueous surfactants system with the presence of NaCl were plotted and thermodynamic parameters based on the Flory-Huggins model were obtained. The visual method was used to determine the cloud point. Solutions containing surfactant concentrations between 0.5 and 20% (by weight) and NaCl between 4.9 and 12.1% (by weight) were prepared. Salt had a salting-out effect, decreasing surfactant solubility in water. Furthermore, the cloud point decreased with an increase of NaCl concentration. The Flory-Huggins model satisfactorily described the experimental data for all surfactant + NaCl aqueous mixtures studied.

Biodegradable polymers are emerging polymeric materials with application in the biomedical, pharmaceutical and packaging fields. Blending is a simple and effective route to develop new materials with tailored properties, and this review reports the advances in the field of biodegradable polymer blends with both natural and synthetic polymers. First, the theoretical background necessary to understand the miscibility behaviors observed in real polymer blends are provided. The simple but highly flexible Flory‐Huggins theory is examined, incorporating the regular solution theory to account for the enthalpic contribution. This simple theoretical model is extended with the appropriate approaches to systems presenting specific interactions, providing a quite detailed overall picture of the miscibility behavior of polymer blends. The second section reviews the structure, preparation, miscibility and properties of different biodegradable polymer blends investigated up to now. Particularly, the biodegradable blends based on polylactides (PLAs), poly(ϵ‐caprolactone) (PCL), poly(3‐hydroxybutyrate) (PHB), poly(p‐dioxanone) (PPDO) and polyglycolide (PGA) have been reviewed.

Program MARK provides > 65 data types in a common configuration for the estimation of population parameters from mark-encounter data. Encounter information from live captures, live resightings, and dead recoveries can be incorporated to estimate demographic parameters. Available estimates include survival (S or [Greek Phi symbol]), rate of population change (λ), transition rates between strata (Ψ), emigration and immigration rates, and population size (N). Although N is the parameter most often desired by biologists, N is one of the most difficult parameters to estimate precisely without bias for a geographically and demographically closed population. The set of closed population estimation models available in Program MARK incorporate time (t) and behavioral (b) variation, and individual heterogeneity (h) in the estimation of capture and recapture probabilities in a likelihood framework. The full range of models from M ₀ (null model with all capture and recapture probabilities equal) to M tbh are possible, including the ability to include temporal, group, and individual covariates to model capture and recapture probabilities. Both the full likelihood formulation of Otis et al. (1978) and the conditional model formulation of Huggins (1989, 1991) and Alho (1990) are provided in Program MARK, and all of these models are incorporated into the robust design (Kendall et al. 1995, 1997; Kendall and Nichols 1995) and robust-design multistrata (Hestbeck et al. 1991, Brownie et al. 1993) data types. Model selection is performed with AICc (Burnham and Anderson 2002) and model averaging (Burnham and Anderson 2002) is available in Program MARK to provide estimates of N with standard error that reflect model selection uncertainty.

Although the brown bear (Ursus arctos) population in Abruzzo (central Apennines, Italy) suffered high mortality during the past 30 years and is potentially at high risk of extinction, no formal estimate of its abundance has been attempted. In 2004, the Italian Forest Service and Abruzzo National Park applied DNA-based techniques to hair-snag samples from the Apennine bear population. Even though sampling and theoretical limitations prevented estimating population size from being the objective of these first applications, we extracted the most we could out of the 2004 data to produce the first estimate of population size. To overcome the limitations of the sampling strategies (systematic grid, opportunistic sampling at buckthorn [Rhamnus alpina] patches, incidental sampling during other field activities), we used a multiple data-source approach and Huggins closed models implemented in program MARK. To account for model uncertainty, we averaged plausible models using Akaike weights and estimated an unconditional population size of 43 bears (95% CI  =  35–67). We urge caution in interpreting these results because other expected but undefined sources of heterogeneity (i.e., gender) may have biased this estimate. The low capture probability obtained through the systematic grid prevented the use of this sampling technique as a stand-alone tool to estimate the Apennine bear population size. Therefore, further applications in this direction will require a substantial improvement of field procedures, the use of a multiple data-source approach, or both. In this perspective, we used Monte Carlo simulations to compare the relative performance of the 3 sampling approaches and discuss their feasibility to overcome the problem of small and sparse DNA data that often prevent reliable capture–mark–recapture applications in small bear populations.

Interior Alaska, USA, is the least-studied region in Alaska for breeding shorebirds because of challenging accessibility and expectations of low densities and abundances. We estimated lowland and upland shorebird population sizes on 370,420 ha of military lands in interior Alaska boreal forest from May–July 2016 and 2017. We modified the Program for Regional and International Shorebird Monitoring (PRISM) protocol used elsewhere in Alaska and incorporated a probability-based sampling design and dependent double-observer methods. We pooled all lowland shorebird and all upland shorebird observations and estimated abundance using Huggins closed captures models in Program MARK. Estimated abundances of all lowland and upland shorebirds were 42,239 ± 13,431 (SE) and 3,523 ± 494, respectively. The survey area is important for shorebirds in Alaska. We estimate that military lands in interior Alaska support 45,762 ± 13,925 shorebirds, including 7 species of conservation concern. Higher abundance of lowland shorebirds was best explained by lower elevation, lower percent scrub canopy, and higher percent water on plots. Higher abundance of upland shorebirds was best explained by higher elevation and increased distance to wetland. Our modified Arctic PRISM protocol was effective for surveys in the boreal forest and we recommend continued use of method modifications for future shorebird surveys in boreal forests. Identifying baseline abundances of shorebirds using interior Alaska is an important step in monitoring distributional shifts and potential future population declines.

Neutron scattering is among the most important experimental tools in polymer and related soft matter research, probing length‐scales ranging from one to several hundred nanometers. Small‐angle neutron scattering (SANS) is particularly useful. The ability to scatter neutrons is very different for the two hydrogen isotopes 1 H and 2 H (deuterium). This provides a tool for highlighting specific molecules or molecular subunits, making them visible in the scattering experiment. SANS has therefore been used extensively to study structural features of soft materials such as polymers, biomolecules, and microemulsions. The present chapter describes the foundation for using neutron scattering for studying the phase behavior of polymer blends and also of block copolymer systems. The chapter describes the basics of scattering methods, and the relationship between scattering and thermodynamics are also described, and some illustrative examples are given.

We present the first rigorous estimate of grizzly bear (Ursus arctos) population density and distribution in and around Glacier National Park (GNP), Montana, USA. We used genetic analysis to identify individual bears from hair samples collected via 2 concurrent sampling methods: 1) systematically distributed, baited, barbed-wire hair traps and 2) unbaited bear rub trees found along trails. We used Huggins closed mixture models in Program MARK to estimate total population size and developed a method to account for heterogeneity caused by unequal access to rub trees. We corrected our estimate for lack of geographic closure using a new method that utilizes information from radiocollared bears and the distribution of bears captured with DNA sampling. Adjusted for closure, the average number of grizzly bears in our study area was 240.7 (95% CI = 202–303) in 1998 and 240.6 (95% CI = 205–304) in 2000. Average grizzly bear density was 30 bears/1,000 km2, with 2.4 times more bears detected per hair trap inside than outside GNP. We provide baseline information important for managing one of the few remaining populations of grizzlies in the contiguous United States.