Predicting conductivities of alkali borophosphate glasses based on site energy distributions derived from network former unit concentrations

For ion transport in network glasses, it is a great challenge to predict conductivities specifically based on structural properties. To this end it is necessary to gain an understanding of the energy landscape where the thermally activated hopping motion of the ions takes place. For alkali borophosphate glasses, a statistical mechanical approach was suggested to predict essential characteristics of the distribution of energies at the residence sites of the mobile alkali ions. The corresponding distribution of site energies was derived from the chemical units forming the glassy network. A hopping model based on the site energy landscape allowed to model the change of conductivity activation energies with the borate to phosphate mixing ratio. Here we refine and extend this general approach to cope with minimal local activation barriers and to calculate dc-conductivities without the need of performing extensive Monte-Carlo simulations. This calculation relies on the mapping of the many-body ion dynamics onto a network of local conductances derived from the elementary jump rates of the mobile ions. Application of the theoretical modelling to three series of alkali borophosphate glasses with the compositions 0.33Li2O–0.67[xB2O3–(1 − x)P2O5], 0.35Na2O–0.65[xB2O3–(1 − x)P2O5] and 0.4Na2O–0.6[xB2O3–(1 − x)P2O5] shows good agreement with experimental data.

Humic Acid Promotes the Adsorption of Lead onto PSMPs: Site Energy Distribution Theory and Fluorescence Quenching Analysis

Microplastics (MPs) have a great potential to adsorb heavy metal pollutants such as Pb2+ and the dissolved organic matter(DOM) in the aquatic environment will affect this adsorption behavior. In this study, batch experiments were performed to investigate the adsorption characteristics of Pb2+ onto polystyrene microplastics (PSMPs) in the presence and absence of HA(a kind of representative DOM). The adsorption kinetics of Pb2+ onto PSMPs conformed to the pseudo-second order model, and the adsorption isotherms were well fitted by the Langmuir model. With the increase of HA concentration, the Pb2+ adsorption onto PSMPs increased. Site energy distribution analysis showed that the presence of HA increased the adsorption site energy of PSMPs, thus enhancing the adsorption capacity for Pb2+. The fluorescence quenching analysis of HA further indicated that part of HA were adsorbed onto PSMPs, which increased additional binding sites on the surface of PSMPs. This was attributed to the abundant functional groups that could react with Pb2+ of HA. The pH and ionic strength of solution changed the structure of HA and the adsorption sites of PSMPs, which influenced the adsorption capacity of PSMPs for Pb2+. This study illustrated the effect of HA on the process and mechanism of Pb2+ adsorption onto PSMPs, and provided more information for the evaluation of environmental behavior and toxicological effects of microplastics in aquatic environments.

COMPUTER SIMULATION OF LOCAL STRUCTURE AND DIFFUSION MECHANISM THROUGH VORONOI POLYHEDRONS IN SODIUM SILICATE GLASS

The Na2O.SiO2 glass has been studied at different temperatures from 300 K to 1173 K by using molecular dynamics simulations. The Voronoi Si and O polyhedrons are analyzed to evaluate the displacement of sodium between these polyhedrons. The results show that more than 90% of the total number of Na is located in NBF polyhedrons that contain non-bridging (NBO) and free oxygen (FO) polyhedrons. The site energy for Na atoms located in NBF is smaller than one in BO polyhedron. The diffusion process of Na atoms is occurred in two ways: the first one is the alone jumping of Na in BO polyhedrons and the second one is the mixed alone jumping of Na and the cooperative movement of Na in NBF polyhedrons. The calculation of the average time between two consecutive jumps and the visiting time for Na atoms leads to the correlation effect for the diffusion of Na atoms. This effect depends on the temperature of the samples.

Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio

Buildings account for 40% of the energy consumption and 31% of the CO2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption).