Fast Water Transport Through Sub-5 nm Polyamide Nanofilms: The New Upper-Bound of the Permeance-Selectivity Trade-Off in Nanofiltration

2021 ◽  
Author(s):  
Pulak Sarkar ◽  
Solagna Modak ◽  
Santanu Ray ◽  
Vasista Adupa ◽  
K. Anki Reddy ◽  
...  
Keyword(s):  
Water Transport ◽  
Composite Membrane ◽  
Upper Bound ◽  
Polymer Membranes ◽  
Liquid Transport ◽  
Trade Off ◽  
Commercial Exploitation

Liquid transport through the composite membrane is inversely proportional to the thickness of its separation layer. While the scalable fabrication of ultrathin polymer membranes is sought for their commercial exploitation,...

A Review on Glassy Polymeric Membranes for Gas Separation

2014 ◽  
Vol 625 ◽  
pp. 701-703 ◽  
Author(s):  
Marjan Farnam ◽  
Hilmi Mukhtar ◽  
Azmi Mohd Shariff
Keyword(s):  
Gas Separation ◽  
Upper Bound ◽  
Review Paper ◽  
Glassy Polymer ◽  
Polymer Membranes ◽  
Polymeric Membranes ◽  
Polymeric Membrane ◽  
Mixed Matrix Membrane ◽  
Mixed Matrix ◽  
Trade Off

Polymeric membranes are widely used for gas separation purposes but their performance is restricted by the upper bound trade-off discovered by Robeson in 1991. The polymeric membrane can be glassy, rubbery or a blend of these two polymers. This review paper discusses the properties of glassy polymer membranes and their performance in gas separation. The area of improvement for glassy membrane with development of mixed matrix membrane is also highlighted.

scholarly journals Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model

2020 ◽  
Vol 24 (21) ◽  
pp. 15937-15949
Author(s):  
Giorgio Gnecco ◽  
Federico Nutarelli ◽  
Daniela Selvi
Keyword(s):  
Panel Data ◽  
Sample Size ◽  
Upper Bound ◽  
Data Model ◽  
Fixed Effects ◽  
Conditional Variance ◽  
Panel Data Model ◽  
Generalization Error ◽  
Trade Off ◽  
Selection Probabilities

Abstract This paper is focused on the unbalanced fixed effects panel data model. This is a linear regression model able to represent unobserved heterogeneity in the data, by allowing each two distinct observational units to have possibly different numbers of associated observations. We specifically address the case in which the model includes the additional possibility of controlling the conditional variance of the output given the input and the selection probabilities of the different units per unit time. This is achieved by varying the cost associated with the supervision of each training example. Assuming an upper bound on the expected total supervision cost and fixing the expected number of observed units for each instant, we analyze and optimize the trade-off between sample size, precision of supervision (the reciprocal of the conditional variance of the output) and selection probabilities. This is obtained by formulating and solving a suitable optimization problem. The formulation of such a problem is based on a large-sample upper bound on the generalization error associated with the estimates of the parameters of the unbalanced fixed effects panel data model, conditioned on the training input dataset. We prove that, under appropriate assumptions, in some cases “many but bad” examples provide a smaller large-sample upper bound on the conditional generalization error than “few but good” ones, whereas in other cases the opposite occurs. We conclude discussing possible applications of the presented results, and extensions of the proposed optimization framework to other panel data models.

Structural organization in palm stems of Roystonea regia and Archontophoenix alexandrae

IAWA Journal ◽  
2020 ◽  
pp. 1-26
Author(s):  
Amy Ny Aina Aritsara ◽  
Kun-Fang Cao
Keyword(s):  
Water Transport ◽  
Structural Organization ◽  
Spatial Organization ◽  
Mechanical Support ◽  
Trade Off ◽  
Breast Height ◽  
Mechanical Constraints ◽  
Fiber Fraction ◽  
Trade Offs ◽  
Palm Species

Abstract Having a high stature subjects palms to the same constraints as trees, but the lack of cambial growth urges them to adopt a different strategy. We aimed to characterize the spatial organization of xylem tissues and their potential functions in two palm stems: a 30 cm diameter at breast height (DBH) royal palm (Roystonea regia) and a 12 cm DBH Alexandra king palm (Archontophoenix alexandrae). Macroscopic and microscopic anatomical characteristics were assessed at five vertical locations and 5–6 radial locations at each height. Over 9600 vessels across the two stems were drawn manually and measured based on histological sections. Vertically, a hydraulic bottleneck was identified at the first meter, and both stems showed conduit tapering from 4 m to the top. Radially, most water transport and mechanical support were achieved within 2–5 cm below the bark. The larger stem diameter of royal palm may have improved its water transport, storage, and potential for mechanical support compared to king palm. There was a strong trade-off between ground parenchyma and the fiber fraction. However, the correlation between theoretical hydraulic conductivity (Kt) and the ground parenchyma fraction, and between Kt and the fiber fraction, shifted from positive or non-significant below the bark, to strongly negative close to the center. These changes reflect the functional sectoriality of the palm stems, which may reduce the constraint of trade-offs between water transport, storage, and mechanical support. To conclude, functional sectoriality may have helped both palm species to withstand the hydraulic and mechanical constraints due to high stature.

scholarly journals Thin-film composite membrane breaking the trade-off between conductivity and selectivity for a flow battery

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Qing Dai ◽  
Zhiqiang Liu ◽  
Ling Huang ◽  
Chao Wang ◽  
Yuyue Zhao ◽  
...  
Keyword(s):  
Thin Film ◽  
Power Density ◽  
Composite Membrane ◽  
Ion Selectivity ◽  
Flow Battery ◽  
Selective Layer ◽  
Trade Off ◽  
Film Composite ◽  
Thin Film Composite Membrane ◽  
Thin Film Composite

AbstractA membrane with both high ion conductivity and selectivity is critical to high power density and low-cost flow batteries, which are of great importance for the wide application of renewable energies. The trade-off between ion selectivity and conductivity is a bottleneck of ion conductive membranes. In this paper, a thin-film composite membrane with ultrathin polyamide selective layer is found to break the trade-off between ion selectivity and conductivity, and dramatically improve the power density of a flow battery. As a result, a vanadium flow battery with a thin-film composite membrane achieves energy efficiency higher than 80% at a current density of 260 mA cm−2, which is the highest ever reported to the best of our knowledge. Combining experiments and theoretical calculation, we propose that the high performance is attributed to the proton transfer via Grotthuss mechanism and Vehicle mechanism in sub-1 nm pores of the ultrathin polyamide selective layer.

scholarly journals Observe Before Play: Multi-Armed Bandit with Pre-Observations

2020 ◽  
Vol 34 (04) ◽  
pp. 7023-7030
Author(s):  
Jinhang Zuo ◽  
Xiaoxi Zhang ◽  
Carlee Joe-Wong
Keyword(s):  
Upper Bound ◽  
Synthetic Data ◽  
Wireless Channel ◽  
Confidence Bound ◽  
Trade Off ◽  
Greedy Strategy ◽  
Optimal Policies ◽  
Centralized Algorithm ◽  
Upper Confidence Bound

We consider the stochastic multi-armed bandit (MAB) problem in a setting where a player can pay to pre-observe arm rewards before playing an arm in each round. Apart from the usual trade-off between exploring new arms to find the best one and exploiting the arm believed to offer the highest reward, we encounter an additional dilemma: pre-observing more arms gives a higher chance to play the best one, but incurs a larger cost. For the single-player setting, we design an Observe-Before-Play Upper Confidence Bound (OBP-UCB) algorithm for K arms with Bernoulli rewards, and prove a T-round regret upper bound O(K2log T). In the multi-player setting, collisions will occur when players select the same arm to play in the same round. We design a centralized algorithm, C-MP-OBP, and prove its T-round regret relative to an offline greedy strategy is upper bounded in O(K4/M2log T) for K arms and M players. We also propose distributed versions of the C-MP-OBP policy, called D-MP-OBP and D-MP-Adapt-OBP, achieving logarithmic regret with respect to collision-free target policies. Experiments on synthetic data and wireless channel traces show that C-MP-OBP and D-MP-OBP outperform random heuristics and offline optimal policies that do not allow pre-observations.

ON THE DESCRIPTIONAL COMPLEXITY OF THE WINDOW SIZE FOR DELETING RESTARTING AUTOMATA

2013 ◽  
Vol 24 (06) ◽  
pp. 831-846 ◽  
Author(s):  
MARTIN KUTRIB ◽  
FRIEDRICH OTTO
Keyword(s):  
Upper Bound ◽  
Recursive Function ◽  
Window Size ◽  
Input Word ◽  
Trade Off ◽  
Descriptional Complexity ◽  
The Impact ◽  
Current Word ◽  
Restarting Automaton

The restarting automaton was inspired by the technique of ‘analysis by reduction’ from linguistics. A restarting automaton processes a given input word through a sequence of cycles. In each cycle the current word on the tape is scanned from left to right and a single local simplification (a rewrite) is executed. One of the essential parameters of a restarting automaton is the size of its read/write window. Here we study the impact of the window size on the descriptional complexity of several types of deterministic and nondeterministic restarting automata. For all k ≥ 4, we show that the savings in the economy of descriptions of restarting automata that can only delete symbols but not rewrite them (that is, the so-called R- and RR-automata) cannot be bounded by any recursive function, when changing from window size k to window size k + 1. This holds for deterministic as well as for nondeterministic automata, and for k ≥ 5, it even holds for the stateless variants of these automata. However, the trade-off between window sizes two and one is recursive for deterministic devices. In addition, a polynomial upper bound is given for the trade-off between RRWW-automata with window sizes k + 1 and k for all k ≥ 2.

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