Advanced Electrolytes for Metal Ion Batteries
A special issue of Batteries (ISSN 2313-0105). This special issue belongs to the section "Battery Materials and Interfaces: Anode, Cathode, Separators and Electrolytes or Others".
Deadline for manuscript submissions: closed (16 May 2023) | Viewed by 5450
Special Issue Editors
Interests: batteries; electrolytes; interface
Interests: ionic liquids; X-ray scattering; computational molecular modelling; (halogen-free) electrolytes; batteries; fuel-cells
Special Issue Information
Dear Colleagues,
This Special Issue “Advanced Electrolytes for Metal Ion Batteries” is focused on advanced electrolytes for batteries that employ a variety of metal-Ion charge carriers, e.g., Li+, Na+, K+, Zn+, Mg2+, Ca2+, Al3+. As a critical component of batteries, electrolytes play a significant role in the performance of batteries. Under tremendous efforts of researchers, electrolytes have achieved great development. In terms of existing morphology, liquid, quasi-solid and all-solid-state electrolytes have been explored. Meanwhile, some functionalized electrolytes are developed to meet different application scenarios, e.g., absolute security/reliability, low/high temperature, high operating voltage, bending and wearable, et al. Nevertheless, electrolytes have a lot of room for improvement to further release the performance of batteries. Thus more intensive efforts should be devoted to investigation of electrolytes from theoretical understandings to experimental characterization.
We are therefore organizing this Special Issue in Batteries (ISSN: 2313-0105). In this Special Issue, we are looking for original and innovative papers as well as reviews relevant to electrolytes for all kinds of metal Ion Batteries.
Potential topics include but are not limited to:
- Liquid, quasi-solid and all-solid-state electrolytes;
- Solid electrolytes interface;
- Interfacial design and evolution;
- Ion-conductive mechanisms;
- Safety evaluation for electrolytes ;
- Characterization techniques and theoretical computations/simulations of electrolytes;
- Materials Genome Initiative, artificial intelligence (AI) and machine learning (ML) of electrolytes.
In view of your international standing as a research scientist, we cordially invite you and your colleagues to contribute a manuscript.
Dr. Jin Han
Dr. Mariani Alessandro
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. Batteries is an international peer-reviewed open access monthly 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 2700 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
- liquid, quasi-solid and all-solid-state electrolytes
- solid electrolytes interface
- interfacial design and evolution
- ion-conductive mechanisms
- safety evaluation for electrolytes
- characterization techniques and theoretical computations/simulations of electrolytes
- materials genome initiative, artificial intelligence (AI) and machine learning (ML) of electrolytes