Recent Advances in Nanomaterial Synthesis
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Materials Chemistry".
Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 33890
Special Issue Editors
Interests: machine learning; deep learning; advanced manufacturing; metal-organic frameworks; joule heating
Interests: high-quality microcavity lasers; metamaterial for electromagnetic wave modulation
Interests: machine learning; laser processing; metamaterials
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Nanomaterials have emerged as a promising class of materials with a size of less than 100 nm in at least one dimension. Nanomaterials possess remarkable and interesting mechanical, electrical, optical, electrochemical, and magnetic properties due to their high aspect ratio, unique surface chemistry, and quantum-size effect. The development of nanomaterials is intimately linked to the development of synthesis methods. Hence, it is always an attractive research topic for academic and industry communities to update conventional methods or develop new synthesis methods with the aim to enhance the performance and property, increase yields, lower the energy consumption, reduce the amounts of raw resources used, etc. Over the past decade, many novel synthetic methods including carbothermal shock, Joule heating, and microwave heating have been widely investigated and achieved exciting research progress. Meanwhile, green synthesis such as ball milling and extrusion synthesis has also been developed to make the nanomaterial synthesis safer, energy efficient, and less toxic. In addition, the rapid development of robotics and machine learning has greatly powered the material synthesis method, making it more efficient and automatic.
This Special Issue is devoted to presenting the recent advances in various synthetic methods in nanomaterials. We welcome researchers to submit their findings in the following forms: original research articles, reviews, perspectives, short summaries, and communications.
Topics of this Special Issue include, but are not limited to, the following:
- Green synthesis including ball milling and extruding synthesis;
- Synthesis automation with the aid of robotics;
- Machine-learning-guided synthesis optimization;
- Conventional synthesis methods including soft/hard template, solvo-/hydro-thermal, sol-gel, micro-emulsion, etc.
Dr. Yunchao Xie
Dr. Hongxing Dong
Dr. Chi Zhang
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. Molecules 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 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
- far-from equilibrium synthesis
- green chemistry
- synthesis automation
- machine learning
- synthesis optimization
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