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Development Trends of New Energy Materials and Devices
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Development Trends of New Energy Materials and Devices

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 5345

Special Issue Editors

Dr. Juan Bai

E-Mail Website
Guest Editor
School of Chemistry & Physics, Queensland University of Technology, Brisbane, QLD 4000, Austrilia
Interests: design and synthesis of functional nanostructured materials and their applications in electrochemistry and energy conversion device
Dr. Jun Mei

E-Mail Website
Guest Editor
School of Chemistry and Physics, Queensland University of Technology, Brisbane, QLD 4000, Australia
Interests: rational design of advanced nanomaterials for energy storage and green catalysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to submit your latest research to this Special Issue on “Development Trends of New Energy Materials and Devices”.

Petroleum, coal and refined products currently account for the largest parts of global fuel consumption, which has dramatically increased over the past few decades. This has caused serious environmental pollution, climate change, and other energy crises. In response to these problems, the development of clean fuels and new energy devices has been regarded as one promising solution.

This special issue aims to focusing on the development of new energy materials and devices. Both original research articles and reviews are welcome. Research areas may include (but not limited to) the following topics:

  • Design of new energy devices and Synthesis of energy materials
  • Rechargeable batteries and Supercapacitors
  • Electrocatalysis and Fuel cells
  • Solar-related sustainable applications
  • Energy policies and development trends
  • Theoretical insights on energy-based topics
  • Other energy applications

Dr. Juan Bai
Dr. Jun Mei
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • batteries
  • fuel cells
  • electrocatalysis
  • nanomaterials
  • hydrogen production

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

18 pages, 5803 KiB  
Article
A Novel Numerical Method for Geothermal Reservoirs Embedded with Fracture Networks and Parameter Optimization for Power Generation
by Xufeng Yan, Kangsheng Xue, Xiaobo Liu and Xiaolou Chi
Sustainability 2023, 15(12), 9744; https://doi.org/10.3390/su15129744 - 19 Jun 2023
Cited by 1 | Viewed by 1366
Abstract
Geothermal recovery involves a coupled thermo-hydro-mechanical (THM) process in fractured rocks. A fluid transient equilibrium equation, considering thermal conduction, convection, and heat exchange, is established. The evolution of the reservoir permeability and the variance in the fracture aperture due to a change in [...] Read more.
Geothermal recovery involves a coupled thermo-hydro-mechanical (THM) process in fractured rocks. A fluid transient equilibrium equation, considering thermal conduction, convection, and heat exchange, is established. The evolution of the reservoir permeability and the variance in the fracture aperture due to a change in the stress field are derived simultaneously. THM coupling is accomplished through iterative hydromechanical and thermo-hydro processes. To overcome the difficulty of geometric discretization, a three-dimensional THM coupler model embedded with discrete fracture networks, using a zero-thickness surface and line elements to simulate fractures and injection/production wells, is established to evaluate the geothermal production. The reliability of the method is verified by a case study. Then, this method is applied to evaluate the influence of the geometric topological characteristics of fracture networks and the fracture aperture on the reservoir temperature evolution and heat extraction effectiveness. The results show that the power generation efficiency and geothermal depletion rate are significantly affected by the injection–production pressure. Injection wells and production wells with pressures higher than the initial fluid pressure in the fractures can be used to significantly increase power generation, but the consumption of geothermal energy and loss of efficiency are significant and rapid. To achieve better benefits for the geothermal recovery system, an optimization algorithm based on simultaneous perturbation stochastic approximation (SPSA) is proposed; it takes the power generation efficiency as the objective function, and the corresponding program is developed using MATLAB to optimize the position and pressure values for each production well. The results show that the heat transfer for the entire EGS reservoir becomes more uniform after optimization, and the heat transfer efficiency is greatly improved. Full article
(This article belongs to the Special Issue Development Trends of New Energy Materials and Devices)
►▼ Show Figures

Figure 1

Figure 1
<p>Schematic of the geothermal recovery system before and after geometric simplification.</p> Full article ">Figure 2
<p>Meshing of simplified and unreduced models: (<b>a</b>) simplified model, 31,274 mesh elements; (<b>b</b>) unreduced model, 15,750,953 mesh elements.</p> Full article ">Figure 3
<p>Temperature distribution along the direction of the line connecting two wellbores.</p> Full article ">Figure 4
<p>(<b>a</b>) Discrete fracture network in the thermal reservoir model; (<b>b</b>) tetrahedral mesh used for the thermal simulation.</p> Full article ">Figure 5
<p>Simulation results for a production period of 10 years: (<b>a</b>) average outlet temperatures; (<b>b</b>) average reservoir temperatures; (<b>c</b>) reservoir power generations; (<b>d</b>) average fracture apertures.</p> Full article ">Figure 6
<p>Injection well conditions: (<b>a</b>) Relationship between normal injection velocity and time; (<b>b</b>) Relationship between injection water temperature and time.</p> Full article ">Figure 7
<p>Improvement results for power generation efficiency optimization over the course of 80 days.</p> Full article ">Figure 8
<p>Temperature distribution of the reservoir: (<b>a</b>) position and pressure of each production well are not optimized; (<b>b</b>) position and pressure of each production well are optimized.</p> Full article ">Figure 9
<p>The evolution of fluid velocity in terms of time of each production well: (<b>a</b>) well 1; (<b>b</b>) well 2; (<b>c</b>) well 3; (<b>d</b>) well 4.</p> Full article ">Figure 10
<p>The evolution of temperature of fluid in terms of time of each production well: (<b>a</b>) well 1; (<b>b</b>) well 2; (<b>c</b>) well 3; (<b>d</b>) well 4.</p> Full article ">
16 pages, 3699 KiB  
Article
State Estimation of Membrane Water Content of PEMFC Based on GA-BP Neural Network
by Haibo Huo, Jiajie Chen, Ke Wang, Fang Wang, Guangzhe Jin and Fengxiang Chen
Sustainability 2023, 15(11), 9094; https://doi.org/10.3390/su15119094 - 5 Jun 2023
Cited by 2 | Viewed by 1932
Abstract
Too high or too low water content in the proton exchange membrane (PEM) will affect the output performance of the proton exchange membrane fuel cell (PEMFC) and shorten its service life. In this paper, the mathematical mechanisms of cathode mass flow, anode mass [...] Read more.
Too high or too low water content in the proton exchange membrane (PEM) will affect the output performance of the proton exchange membrane fuel cell (PEMFC) and shorten its service life. In this paper, the mathematical mechanisms of cathode mass flow, anode mass flow, water content in the PEM and stack voltage of the PEMFC are deeply studied. Furthermore, the dynamic output characteristics of the PEMFC under the conditions of flooding and drying membrane are reported, and the influence of water content in PEM on output performance of the PEMFC is analyzed. To effectively diagnose membrane drying and flooding faults, prolong their lifespan and thus to improve operation performance, this paper proposes the state assessment of water content in the PEM based on BP neural network optimized by genetic algorithm (GA). Simulation results show that compared with LS-SVM, GA-BP neural network has higher estimation accuracy, which lays a foundation for the fault diagnosis, life extension and control scheme design of the PEMFC. Full article
(This article belongs to the Special Issue Development Trends of New Energy Materials and Devices)
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Figure 1

Figure 1
<p>Cathode mass flow schematic diagram of the PEMFC stack.</p> Full article ">Figure 2
<p>Anode mass flow schematic diagram of the PEMFC stack.</p> Full article ">Figure 3
<p>Membrane water content schematic diagram of the PEMFC stack.</p> Full article ">Figure 4
<p>Stack voltage schematic diagram of the PEMFC.</p> Full article ">Figure 5
<p>Simulink dynamic model of the PEMFC power stack.</p> Full article ">Figure 6
<p>Output characteristic curves of the simulated model and existing model.</p> Full article ">Figure 7
<p>Step changes of PEMFC current density.</p> Full article ">Figure 8
<p>Dynamic characteristic curve of water content in the PEM.</p> Full article ">Figure 9
<p>Dynamic characteristic curve of the PEMFC output voltage.</p> Full article ">Figure 10
<p>Output voltage characteristics with drying membrane.</p> Full article ">Figure 11
<p>Output voltage characteristics with 100% humidified membrane.</p> Full article ">Figure 12
<p>State estimation of membrane water content by GA-BP neural network and LS-SVM.</p> Full article ">
20 pages, 9761 KiB  
Article
Study on the Influence of Solar Array Tube on Thermal Environment of Greenhouse
by Mingzhi Zhao, Yingjie Liu, Daorina Bao, Xiaoming Hu, Ningbo Wang and Lei Liu
Sustainability 2023, 15(4), 3127; https://doi.org/10.3390/su15043127 - 8 Feb 2023
Cited by 2 | Viewed by 1375
Abstract
The stratum and microenvironment temperatures in a greenhouse are important factors that affect crop yield. In order to solve the problem of temperature imbalance caused by solar radiation in greenhouses, this paper proposes the application of a solar radiation array tube in a [...] Read more.
The stratum and microenvironment temperatures in a greenhouse are important factors that affect crop yield. In order to solve the problem of temperature imbalance caused by solar radiation in greenhouses, this paper proposes the application of a solar radiation array tube in a greenhouse. By adding water or phase change materials to the array tube, the influence of the array tube on the formation and microenvironment temperature changes was studied, and a 10-day test was carried out. A test group and control group were set up to monitor test results, and the ground was divided into six areas. The depths of each area were 10 cm, 30 cm, and 50 cm, and the heights of the greenhouse centers were 0 cm, 30 cm, 60 cm, 90 cm, 120 cm, 150 cm, and 180 cm. Via an analysis of the test results obtained for the formation and microenvironment temperature, the arrangement of the array tube was found to exert a constant temperature regulation effect on the microenvironment of the greenhouse at a formation depth of 10 cm and was able to improve this formation depth to a certain extent. The temperature at 30 cm and 50 cm plays a positive role in building a good vegetation growth environment. Full article
(This article belongs to the Special Issue Development Trends of New Energy Materials and Devices)
►▼ Show Figures

Figure 1

Figure 1
<p>Model layout diagram.</p> Full article ">Figure 2
<p>CSG shadow interferogram.</p> Full article ">Figure 3
<p>CSG pipe wall temperature diagram of different sections.</p> Full article ">Figure 4
<p>Test site layout diagram.</p> Full article ">Figure 5
<p>Performance test curve.</p> Full article ">Figure 6
<p>Measuring point position.</p> Full article ">Figure 7
<p>Temperature comparison of measuring points in different regions of strata. (<b>a</b>) Measuring point of area 1, (<b>b</b>) Measuring point of area 2, (<b>c</b>) Measuring point of area 3, (<b>d</b>) Measuring point of area 4, (<b>e</b>) Measuring point of area 5, (<b>f</b>) Measuring point of area 6.</p> Full article ">Figure 7 Cont.
<p>Temperature comparison of measuring points in different regions of strata. (<b>a</b>) Measuring point of area 1, (<b>b</b>) Measuring point of area 2, (<b>c</b>) Measuring point of area 3, (<b>d</b>) Measuring point of area 4, (<b>e</b>) Measuring point of area 5, (<b>f</b>) Measuring point of area 6.</p> Full article ">Figure 8
<p>Temperature comparison of microenvironment measuring points.</p> Full article ">Figure 9
<p>Temperature of measuring points in different regions of strata when the heat storage medium is water. (<b>a</b>) Measuring point of area 1, (<b>b</b>) Measuring point of area 2, (<b>c</b>) Measuring point of area 3, (<b>d</b>) Measuring point of area 4, (<b>e</b>) Measuring point of area 5, (<b>f</b>) Measuring point of area 6.</p> Full article ">Figure 10
<p>Temperature of measuring points in different regions of strata when the heat storage medium is PCM. (<b>a</b>) Measuring point of area 1, (<b>b</b>) Measuring point of area 2, (<b>c</b>) Measuring point of area 3, (<b>d</b>) Measuring point of area 4, (<b>e</b>) Measuring point of area 5, (<b>f</b>) Measuring point of area 6.</p> Full article ">Figure 11
<p>Temperature of measuring points at different heights in the microenvironment. (<b>a</b>) 0 cm measuring point, (<b>b</b>) 30 cm measuring point, (<b>c</b>) 60 cm measuring point, (<b>d</b>) 90 cm measuring point, (<b>e</b>) 120 cm measuring point, (<b>f</b>) 150 cm measuring point, (<b>g</b>) 180 cm measuring point.</p> Full article ">Figure 11 Cont.
<p>Temperature of measuring points at different heights in the microenvironment. (<b>a</b>) 0 cm measuring point, (<b>b</b>) 30 cm measuring point, (<b>c</b>) 60 cm measuring point, (<b>d</b>) 90 cm measuring point, (<b>e</b>) 120 cm measuring point, (<b>f</b>) 150 cm measuring point, (<b>g</b>) 180 cm measuring point.</p> Full article ">Figure 12
<p>Temperature of measuring points at different heights of microenvironment. (<b>a</b>) 0 cm measuring point, (<b>b</b>) 30 cm measuring point, (<b>c</b>) 60 cm measuring point, (<b>d</b>) 90 cm measuring point, (<b>e</b>) 120 cm measuring point, (<b>f</b>) 150 cm measuring point, (<b>g</b>) 180 cm measuring point.</p> Full article ">
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