Impacts of Surface Depletion on the Plasmonic Properties of Doped Semiconductor Nanocrystals

Degenerately doped semiconductor nanocrystals (NCs) exhibit a localized surface plasmon resonance (LSPR) in the infrared range of the electromagnetic spectrum. Unlike metals, semiconductor NCs offer tunable LSPR characteristics enabled by doping, or via electrochemical or photochemical charging. Tuning plasmonic properties through carrier density modulation suggests potential applications in smart optoelectronics, catalysis, and sensing. Here, we elucidate fundamental aspects of LSPR modulation through dynamic carrier density tuning in Sn-doped Indium Oxide NCs. Monodisperse Sn-doped Indium Oxide NCs with various doping level and sizes were synthesized and assembled in uniform films. NC films were then charged in an in situ electrochemical cell and the LSPR modulation spectra were monitored. Based on spectral shifts and intensity modulation of the LSPR, combined with optical modeling, it was found that often-neglected semiconductor properties, specifically band structure modification due to doping and surface states, strongly affect LSPR modulation. Fermi level pinning by surface defect states creates a surface depletion layer that alters the LSPR properties; it determines the extent of LSPR frequency modulation, diminishes the expected near field enhancement, and strongly reduces sensitivity of the LSPR to the surroundings

Numerical results for network capacity problem with SAB method at <i>α</i> = 1.

<p>Numerical results for network capacity problem with SAB method at <i>α</i> = 1.</p

Network capacity with probit-based stochastic user equilibrium problem

<div><p>Among different stochastic user equilibrium (SUE) traffic assignment models, the Logit-based stochastic user equilibrium (SUE) is extensively investigated by researchers. It is constantly formulated as the low-level problem to describe the drivers’ route choice behavior in bi-level problems such as network design, toll optimization et al. The Probit-based SUE model receives far less attention compared with Logit-based model albeit the assignment result is more consistent with drivers’ behavior. It is well-known that due to the identical and irrelevant alternative (IIA) assumption, the Logit-based SUE model is incapable to deal with route overlapping problem and cannot account for perception variance with respect to trips. This paper aims to explore the network capacity with Probit-based traffic assignment model and investigate the differences of it is with Logit-based SUE traffic assignment models. The network capacity is formulated as a bi-level programming where the up-level program is to maximize the network capacity through optimizing input parameters (O-D multiplies and signal splits) while the low-level program is the Logit-based or Probit-based SUE problem formulated to model the drivers’ route choice. A heuristic algorithm based on sensitivity analysis of SUE problem is detailed presented to solve the proposed bi-level program. Three numerical example networks are used to discuss the differences of network capacity between Logit-based SUE constraint and Probit-based SUE constraint. This study finds that while the network capacity show different results between Probit-based SUE and Logit-based SUE constraints, the variation pattern of network capacity with respect to increased level of travelers’ information for general network under the two type of SUE problems is the same, and with certain level of travelers’ information, both of them can achieve the same maximum network capacity.</p></div

Probability of choosing route 1 with different <i>α</i> and <i>ς</i>.

<p>Probability of choosing route 1 with different <i>α</i> and <i>ς</i>.</p

O-D demands and network capacity at Logit-based SUE conditions with different <i>θ</i>.

<p>O-D demands and network capacity at Logit-based SUE conditions with different <i>θ</i>.</p

Input data to the example network.

<p>Input data to the example network.</p

Numerical network for example 2.

<p>Numerical network for example 2.</p

Reversible Switch between Bulk MgCO₃·3H₂O and Mg(OH)₂ Micro/Nanorods Induces Continuous Selective Preconcentration of Anionic Dyes

It is still a big challenge to treat large amount of water with low concentration of pollutant. In this study, a hierarchical (micro/nano) structured Mg­(OH)₂ adsorbent was introduced by the in situ hydration of porous MgO in the dye solution. The adsorbent showed high selective adsorption capacity (<i>Q</i>₀ ≈ 155 mg/g for acid alizarine blue) and fast adsorption rate for the removal of anionic dyes down to the mg/L levels. Moreover, the adsorbed dye was successfully desorbed by carbonation, resulting in a ∼4000 fold enrichment of the dye solution. It was demonstrated that by establishing a reversible switch between the Mg­(OH)₂ micro/nanorod and the bulk MgCO₃·3H₂O, a continuous preconcentration of low-concentration dye wastewater could be achieved

O-D demands and network capacity at Probit-based SUE conditions with different <i>α</i>.

<p>O-D demands and network capacity at Probit-based SUE conditions with different <i>α</i>.</p

Numerical results for problem (5) with different <i>α</i>.

<p>Numerical results for <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171158#pone.0171158.e007" target="_blank">problem (5)</a> with different <i>α</i>.</p