Thermodynamics and Phase Transitions of Electrolytes on Lattices with Different Discretization Parameters

Lattice models are crucial for studying thermodynamic properties in many physical, biological and chemical systems. We investigate Lattice Restricted Primitive Model (LRPM) of electrolytes with different discretization parameters in order to understand thermodynamics and the nature of phase transitions in the systems with charged particles. A discretization parameter is defined as a number of lattice sites that can be occupied by each particle, and it allows to study the transition from the discrete picture to the continuum-space description. Explicit analytic and numerical calculations are performed using lattice Debye-H\"{u}ckel approach, which takes into account the formation of dipoles, the dipole-ion interactions and correct lattice Coulomb potentials. The gas-liquid phase separation is found at low densities of charged particles for different types of lattices. The increase in the discretization parameter lowers the critical temperature and the critical density, in agreement with Monte Carlo computer simulations results. In the limit of infinitely large discretization our results approach the predictions from the continuum model of electrolytes. However, for the very fine discretization, where each particle can only occupy one lattice site, the gas-liquid phase transitions are suppressed by order-disorder phase transformations.Comment: Submitted to Molecular Physic

The role of snow cover affecting boreal-arctic soil freeze–thaw and carbon dynamics

Northern Hemisphere permafrost affected land areas contain about twice as much carbon as the global atmosphere. This vast carbon pool is vulnerable to accelerated losses through mobilization and decomposition under projected global warming. Satellite data records spanning the past 3 decades indicate widespread reductions (~ 0.8–1.3 days decade−1) in the mean annual snow cover extent and frozen-season duration across the pan-Arctic domain, coincident with regional climate warming trends. How the soil carbon pool responds to these changes will have a large impact on regional and global climate. Here, we developed a coupled terrestrial carbon and hydrology model framework with a detailed 1-D soil heat transfer representation to investigate the sensitivity of soil organic carbon stocks and soil decomposition to climate warming and changes in snow cover conditions in the pan-Arctic region over the past 3 decades (1982–2010). Our results indicate widespread soil active layer deepening across the pan-Arctic, with a mean decadal trend of 6.6 ± 12.0 (SD) cm, corresponding to widespread warming. Warming promotes vegetation growth and soil heterotrophic respiration particularly within surface soil layers (≤ 0.2 m). The model simulations also show that seasonal snow cover has a large impact on soil temperatures, whereby increases in snow cover promote deeper (≥ 0.5 m) soil layer warming and soil respiration, while inhibiting soil decomposition from surface (≤ 0.2 m) soil layers, especially in colder climate zones (mean annual T ≤ −10 °C). Our results demonstrate the important control of snow cover on northern soil freeze–thaw and soil carbon decomposition processes and the necessity of considering both warming and a change in precipitation and snow cover regimes in characterizing permafrost soil carbon dynamics

Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska

An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ∼ 50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higherpermafrost-probability (PP ≥ 70 %) areas (n = 33; R = 0.60; mean bias = 1.58 cm; RMSE = 20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend = 0.32 ± 1.18 cm yr−1 ) and much larger increases (> 3 cm yr−1 ) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R = 0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain

Surface plasmon enhanced light scattering biosensing: Size dependence on the gold nanoparticle tag

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Surface plasmon enhanced light scattering (SP-LS) is a powerful new sensing SPR modality that yields excellent sensitivity in sandwich immunoassay using spherical gold nanoparticle (AuNP) tags. Towards further improving the performance of SP-LS, we systematically investigated the AuNP size effect. Simulation results indicated an AuNP size-dependent scattered power, and predicted the optimized AuNPs sizes (i.e., 100 and 130 nm) that afford extremely high signal enhancement in SP-LS. The maximum scattered power from a 130 nm AuNP is about 1700-fold higher than that obtained from a 17 nm AuNP. Experimentally, a bio-conjugation protocol was developed by coating the AuNPs with mixture of low and high molecular weight PEG molecules. Optimal IgG antibody bioconjugation conditions were identified using physicochemical characterization and a model dot-blot assay. Aggregation prevented the use of the larger AuNPs in SP-LS experiments. As predicted by simulation, AuNPs with diameters of 50 and 64 nm yielded significantly higher SP-LS signal enhancement in comparison to the smaller particles. Finally, we demonstrated the feasibility of a two-step SP-LS protocol based on a gold enhancement step, aimed at enlarging 36 nm AuNPs tags. This study provides a blue-print for the further development of SP-LS biosensing and its translation in the bioanalytical field

Gender recognition from a partial view of the face using local feature vectors

This paper proposes a gender recognition scheme focused on local appearance-based features to describe the top half of the face. Due to the fact that only the top half of the face is used, this is a feasible approach in those situations where the bottom half is hidden. In the experiments, several face detection methods with different precision levels are used in order to prove the robustness of the scheme with respect to variations in the accuracy level of the face detection proces

Graphene based superconducting quantum point contacts

We investigate the Josephson effect in the graphene nanoribbons of length $L$ smaller than the superconducting coherence length and an arbitrary width $W$. We find that in contrast to an ordinary superconducting quantum point contact (SQPC) the critical supercurrent $I_c$ is not quantized for the nanoribbons with smooth and armchair edges. For a low concentration of the carriers $I_c$ decreases monotonically with lowering $W/L$ and tends to a constant minimum for a narrow nanoribbon with $W\lesssim L$. The minimum $I_c$ is zero for the smooth edges but $e\Delta_{0}/\hbar$ for the armchair edges. At higher concentrations of the carriers this monotonic variation acquires a series of peaks. Further analysis of the current-phase relation and the Josephson coupling strength $I_cR_N$ in terms of $W/L$ and the concentration of carriers revels significant differences with those of an ordinary SQPC. On the other hand for a zigzag nanoribbon we find that, similar to an ordinary SQPC, $I_c$ is quantized but to the half-integer values $(n+1/2)4e\Delta_{0}/\hbar$.Comment: 8 pages, 5 figure

Towards an Automated Classification of Transient Events in Synoptic Sky Surveys

We describe the development of a system for an automated, iterative, real-time classification of transient events discovered in synoptic sky surveys. The system under development incorporates a number of Machine Learning techniques, mostly using Bayesian approaches, due to the sparse nature, heterogeneity, and variable incompleteness of the available data. The classifications are improved iteratively as the new measurements are obtained. One novel feature is the development of an automated follow-up recommendation engine, that suggest those measurements that would be the most advantageous in terms of resolving classification ambiguities and/or characterization of the astrophysically most interesting objects, given a set of available follow-up assets and their cost functions. This illustrates the symbiotic relationship of astronomy and applied computer science through the emerging discipline of AstroInformatics.Comment: Invited paper, 15 pages, to appear in Statistical Analysis and Data Mining (ASA journal), ref. proc. CIDU 2011 conf., eds. A. Srivasatva & N. Chawla, in press (2011

A bi-objective robust inspection planning model in a multi-stage serial production system

In this paper, a bi-objective mixed-integer linear programming (BOMILP) model for planning of an inspection process used to detect nonconforming products and malfunctioning processors in a multi-stage serial production system is presented. The model involves two inter-related decisions: 1) which quality characteristics need what kind of inspections (i.e., which-what decision) and 2) when the inspection of these characteristics should be performed (i.e., when decision). These decisions require a trade-off between the cost of manufacturing (i.e., production, inspection and scrap costs) and the customer satisfaction. Due to inevitable variations in the manufacturing systems, a global robust BOMILP (RBOMILP) is developed to tackle the inherent uncertainty of the concerned parameters (i.e., production and inspection times, errors type I and II, misadjustment and dispersion of the process). In order to optimally solve the presented RBOMILP model, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is combined with the Taguchi and Monte Carlo methods. The proposed model and solution algorithm are validated through a real industrial case from a leading automotive industry in France