A Physical Model for the Quasar Luminosity Function Evolution between Cosmic Dawn and High Noon

Modeling the evolution of the number density distribution of quasars through the quasar luminosity function (QLF) is critical to improving our understanding of the connection between black holes, galaxies, and their halos. Here we present a novel semiempirical model for the evolution of the QLF that is fully defined after the specification of a free parameter, the internal duty cycle, ε DC, along with minimal other assumptions. All remaining model parameters are fixed upon calibration against the QLF at two redshifts, z = 4 and z = 5. Our modeling shows that the evolution at the bright end results from the stochasticity in the median quasar luminosity versus halo mass relation, while the faint end shape is determined by the evolution of the halo mass function (HMF) with redshift. Additionally, our model suggests the overall quasar density is determined by the evolution of the HMF, irrespective of the value of ε DC. The z ≥ 4 QLFs from our model are in excellent agreement with current observations for all ε DC, with model predictions suggesting that observations at z ≳ 7.5 are needed to discriminate between different ε DC. We further extend the model at z ≤ 4, successfully describing the QLF between 1 ≤ z ≤ 4, albeit with additional assumptions on Σ and ε DC. We use the existing measurements of quasar duty cycle from clustering to constrain ε DC, finding ε DC ∼ 0.01 or ε DC ≳ 0.1 dependent on observational data sets used for reference. Finally, we present forecasts for future wide-area surveys with promising expectations for the Nancy Grace Roman Telescope to discover N ≳ 10, bright, m UV < 26.5 quasars at z ∼ 8.

Predicting the Swelling Behavior of Acrylic Superabsorbent Polymers Used in Diapers

Acrylic polymer is a superabsorbent for water and widely used in diapers, in which its swelling behavior can be significantly affected by several factors, i.e., the time, temperature, pH, and salt concentration, and thus the product performance in the applications. In this work, the water absorption behavior of acrylic superabsorbent polymers by each of these individual factors was investigated. The results showed that the water absorbency increases with the pH in the range of 2 to ~7 and decreases when the pH continues to increase. However, it decreases with the increases in NaCl concentration in the solution. Moreover, more water can be absorbed by the acrylic polymers at the higher temperature. Based on a previously developed kinetic swelling model and the information from the above investigations, a semiempirical model for predicting the swelling behavior of superabsorbent polymers (SAPs) under different conditions has been developed. Data showed that the model can predict (with a relative error of <4.5%) the amount of water absorbed by acrylic SAPs under different swelling conditions. The model would be very helpful to the practical application in both product design and its performance evaluation.

A Semiempirical Model for Predicting Celestite Scale Formation and Inhibition in Oilfield Operating Conditions

Mineral scale formation has always been a serious problem during production. Most scales can be treated by adding threshold scale inhibitors. Several crystallization and inhibition models have previously been reported to predict the minimum inhibitor concentration (MIC) needed to control the barite and calcite scale. Recently, more attentions have been paid to the formation of celestite scale in the oilfield. However, no related models have been developed to help determine the MIC needed for the celestite scale control. Therefore, in this study, the crystallization and inhibition kinetics data of celestite under a wide range of celestite saturation index (SI = 0.7 – 2.6), temperature (T = 25 – 90 °C), ionic strength (IS = 1.075 – 3.075 M) and pH (4 – 6.7) with one phosphonate inhibitor (diethylenetriamine penta(methylene phosphonic acid, DTPMP) and two polymeric inhibitors (phophinopolycarboxylate, PPCA and polyvinyl sulfonate, PVS) were measured by laser apparatus or collected from previous studies. Then, based on the results, the celestite crystallization and inhibition models were established accordingly. Good agreements between the experimental results and calculated results from the models can be found. By using these newly developed models, the MIC needed for three commonly seen inhibitors, DTPMP, PPCA and PVS on celestite scale control can be predicted under extensive production conditions. The developed models can fill in the blank in scaling management strategies for high Sr2+ and SO42- concentrations in the produced waters.

Photovoltaic Cleaning Optimization: A Simplified Theoretical Approach for Air to Water Generator (AWG) System Employment

Photovoltaic panel efficiency can be heavily affected by soiling, due to dust and other airborne particles, which can determine up to 50% of energy production loss. Generally, it is possible to reduce that impact by means of periodic cleaning, and one of the most efficient cleaning solutions is the use of demineralized water. As pauperization of traditional water sources is increasing, new technologies have been developed to obtain the needed water amount. Water extracted from the air using air to water generator (AWG) technology appears to be particularly suitable for panel cleaning, but its effective employment presents issues related to model selection, determining system size, and energy efficiency. To overcome such issues, the authors proposed a method to choose an AWG system for panel cleaning and to determine its size accordingly, based on a cleaning time optimization procedure and tailored to AWG peculiarities, with an aim to maximize energy production. In order to determine the energy loss due to soiling, a simplified semiempirical model (i.e., the DIrt method) was developed as well. The methodology, which also allows for energy saving due to an optimal cleaning frequency, was applied to a case study. The results show that the choice of the most suitable AWG model could prevent 83% of energy loss related to soling. These methods are the first example of a design tool for panel cleaning planning involving AWG technology.

A Semiempirical Model for Barium-Strontium-Sulfate Solid Solution Scale Crystallization and Inhibition Kinetics at Oilfield Conditions

Summary Scale inhibitors have been widely used as one of the most efficient methods for sulfate-scale control. To accurately predict the required minimum inhibitor concentration (MIC), we have previously developed several crystallization and inhibition models for pure sulfate scales, including barite, celestite, and gypsum. However, disregarding the wide existence of barium-strontium-sulfate (Ba-Sr-SO4) solid solution in the oil field, no related models have been developed that would lead to large errors in MIC determination. In this study, the induction time of Ba-Sr-SO4 solid solution was measured by laser apparatus with or without different concentrations of scale inhibitor diethylenetriamine penta(methylene phosphonic acid) (DTPMP) at the conditions of barite saturation index (SI) from 1.5 to 1.8, temperature (T) from 40 to 70°C, and [Sr2+]/[Ba2+] ratios from 0 to 15 with celestite SI &lt; 0. The results showed that the Ba-Sr-SO4 solid solution’s induction time increases with the [Sr2+]/[Ba2+] ratio at a fixed barite SI, T, and DTPMP dosage. That means the MIC will be overestimated if it is calculated by the previous semiempirical pure barite crystallization and inhibition models without considering the presence of Sr2+. To resolve such deviations, the novel quantitative Ba-Sr-SO4 solid solution crystallization and inhibition models were developed for the first time. The novel models are in good agreement with the experimental data. They can be used to predict the induction time and MIC more accurately at these common Ba2+ and Sr2+ coexisting scenarios. The observations and new models proposed in this study will significantly improve the barite scale management while Ba2+ and Sr2+ coexist in the oil field. NOTE: Supplementary materials are available in support of this paper and have been published online under Supplementary Data at https://doi.org/10.2118/205367-PA.