Zinc ion thermal charging cell for low-grade heat conversion and energy storage

Converting low-grade heat from environment into electricity shows great sustainability for mitigating the energy crisis and adjusting energy configurations. However, thermally rechargeable devices typically suffer from poor conversion efficiency when a semiconductor is employed. Breaking the convention of thermoelectric systems, we propose and demonstrate a new zinc ion thermal charging cell to generate electricity from low-grade heat via the thermo-extraction/insertion and thermodiffusion processes of insertion-type cathode (VO2-PC) and stripping/plating behaviour of Zn anode. Based on this strategy, an impressively high thermopower of ~12.5 mV K−1 and an excellent output power of 1.2 mW can be obtained. In addition, a high heat-to-current conversion efficiency of 0.95% (7.25% of Carnot efficiency) is achieved with a temperature difference of 45 K. This work, which demonstrates extraordinary energy conversion efficiency and adequate energy storage, will pave the way towards the construction of thermoelectric setups with attractive properties for high value-added utilization of low-grade heat.

Sizing Optimization of District Energy Systems Considering Meteorological, Demand, and Electricity Emission Factor Uncertainties

In this study, a framework is developed to perform two-stage stochastic programming in a district energy system. This framework optimizes the sizing of energy components to minimize the total cost and operating $CO_2$ emissions. Uncertainties in electricity demand, solar irradiance, wind speed, and electricity emissions are considered. A group of buildings at University of Utah is used as the case study to test the optimization framework. This study is novel by forming an open-source framework, considering electricity emissions with more details compared to previous studies in the literature, and performing the optimization for a campus in the U.S. This study’s results show the trade-off between cost and emissions when different energy configurations are used for three electricity purchasing cases. This framework can help facility managers to evaluate the optimum sizing of their district energy system to minimize the cost and emissions.

Evolutionary computing and machine learning for discovering of low-energy defect configurations

Density functional theory (DFT) has become a standard tool for the study of point defects in materials. However, finding the most stable defective structures remains a very challenging task as it involves the solution of a multimodal optimization problem with a high-dimensional objective function. Hitherto, the approaches most commonly used to tackle this problem have been mostly empirical, heuristic, and/or based on domain knowledge. In this contribution, we describe an approach for exploring the potential energy surface (PES) based on the covariance matrix adaptation evolution strategy (CMA-ES) and supervised and unsupervised machine learning models. The resulting algorithm depends only on a limited set of physically interpretable hyperparameters and the approach offers a systematic way for finding low-energy configurations of isolated point defects in solids. We demonstrate its applicability on different systems and show its ability to find known low-energy structures and discover additional ones as well.

Performance Assessment of District Energy Systems with Common Elements for Heating and Cooling

District energy systems, especially those integrating renewables or low exergy sources, have multiple elements for generating heating and cooling. Some of these elements might be used for both purposes: heating and cooling, either simultaneously or alternatively. This makes it more complex to separate the assessment and have a clear picture on performance of cooling service on one side, and heating services on the other, in terms of energy, environmental, and economic results. However, a correct comparison between different district energy configurations or among district energy and conventional solutions requires split assessment of each service. The paper presents a methodology for calculating different district heating and cooling system key performance indicators (KPIs), distinguishing between heating and cooling ones. A total of eleven indicators are organized under four categories: energy, environment, economy and socio-economy. Each KPI is defined for heating service and for cooling service. According to this, the methodology proposes a demand-based and an investment-based share factors that facilitate the heating and cooling KPI calculation.

Thermal Stability of Nanocrystalline Gradient Inconel 718 Alloy

Gradient structures containing nanograins in the surface layer have been introduced into Inconel 718 (IN718) nickel-based alloy using the surface mechanical grinding treatment technique. The thermal stability of the gradient IN718 alloy was investigated. Annealing studies reveal that nanograins with a grain size smaller than 40 nm exhibited significantly better thermal stability than those with larger grain size. Transmission electron microscopy analyses reveal that the enhanced thermal stability was attributed to the formation of grain boundaries with low energy configurations. This study provides new insight on strategies to improve the thermal stability of nanocrystalline metals.

Simulation-Based Evaluation of Zeolite Adsorbents for the Removal of Emerging Contaminants

<p>A number of experimental studies have evaluated the potential of hydrophobic high-silica zeolites for the adsorptive removal of emerging organic contaminants, such as pharmaceuticals and personal care products, from water. Despite the widespread use of molecular modelling techniques in various other fields of zeolite science, the adsorption of pharmaceuticals and related pollutants has hardly been studied computationally. In this work, inexpensive molecular simulations using a literature force field (DREIDING) were performed to study the interaction of 21 emerging contaminants with two all-silica zeolites, mordenite (MOR topology) and zeolite Y (FAU topology). The selection of adsorbents and adsorbates was based on a previous experimental investigation of organic contaminant removal using high-silica zeolites (Rossner et al., <i>Water Res.</i> <b>2009</b>, <i>43</i>, 3787–3796). An analysis of the lowest-energy configurations revealed a good correspondence between calculated interaction energies and experimentally measured removal efficiencies (strong interaction – high removal), despite a number of inherent simplifications. This indicates that such simulations could be used as a screening tool to identify promising zeolites for adsorption-based pollutant removal prior to experimental investigations. To illustrate the predictive capabilities of the method, additional calculations were performed for acetaminophen adsorption in 11 other zeolite frameworks, as neither mordenite nor zeolite Y remove this pharmaceutical efficiently. Furthermore, the lowest-energy configurations were analysed for selected adsorbent-adsorbate combinations in order to explain the observed differences in affinity.</p>

Simulation-Based Evaluation of Zeolite Adsorbents for the Removal of Emerging Contaminants

<p>A number of experimental studies have evaluated the potential of hydrophobic high-silica zeolites for the adsorptive removal of emerging organic contaminants, such as pharmaceuticals and personal care products, from water. Despite the widespread use of molecular modelling techniques in various other fields of zeolite science, the adsorption of pharmaceuticals and related pollutants has hardly been studied computationally. In this work, inexpensive molecular simulations using a literature force field (DREIDING) were performed to study the interaction of 21 emerging contaminants with two all-silica zeolites, mordenite (MOR topology) and zeolite Y (FAU topology). The selection of adsorbents and adsorbates was based on a previous experimental investigation of organic contaminant removal using high-silica zeolites (Rossner et al., <i>Water Res.</i> <b>2009</b>, <i>43</i>, 3787–3796). An analysis of the lowest-energy configurations revealed a good correspondence between calculated interaction energies and experimentally measured removal efficiencies (strong interaction – high removal), despite a number of inherent simplifications. This indicates that such simulations could be used as a screening tool to identify promising zeolites for adsorption-based pollutant removal prior to experimental investigations. To illustrate the predictive capabilities of the method, additional calculations were performed for acetaminophen adsorption in 11 other zeolite frameworks, as neither mordenite nor zeolite Y remove this pharmaceutical efficiently. Furthermore, the lowest-energy configurations were analysed for selected adsorbent-adsorbate combinations in order to explain the observed differences in affinity.</p>