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Advances in Machine-Learning-Assisted Nanomaterials: Applications in Simulation, Detection, Classification, and Imaging

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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Nanomaterials and Nanotechnology".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 4384

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Special Issue Editors

Prof. Dr. Shekhar Bhansali

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Guest Editor

College of Engineering and Computing, Florida International University, Miami, FL, USA
Interests: sensors; electrochemical; nanomateriais; applications; agriculture; machine learning

Dr. Fernando Gomes de Souza Junior

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Guest Editor

Biopolymers & Sensors Lab., Macromolecules Institute, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil
Interests: natural resources; polymerization; nanocomposites; characterization; imaging; environmental recovery; nanomedicine; sensors; machine learning; data mining
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Dear Colleagues,

Nanotechnology, defined as atomically precise engineering, comprises a broad spectrum of areas, from medicine to all engineering branches. Nanomaterials have recently brought substantial scientific, technological, and social development, driving science through intriguing and challenging routes. The most remarkable impacts are in medicine, electronics, sensors, energy production, and storage, besides environmental applications.

New technologies have allowed the synthesis of unique nanomaterials with increasingly varied and versatile properties. However, optimizing the preparation and characterization of these nanomaterials and their composites demands new tooling. In particular, machine learning (ML) models can bring unprecedented advantages to the classification process and even quantification of nanomaterials, even allowing the fine-tuning of their properties. Thus, this Special Issue of Materials is focused on nanomaterials, nanocomposites, their applications, and ML tools developed to assist in obtaining, classifying, quantifying, and understanding the properties of these materials.

Authoritative review articles and original research papers describing recent findings in advanced nanomaterials are expected to cover various topics. Potential topics include but are not limited to:

Adsorption of contaminants;
Amphiphilic surfaces;
Cancer;
Catalysts;
Characterization techniques;
Controlled drug release;
Ferrites;
Medicine;
Neglected diseases;
Pandemics, including COVID-19 and variants;
Sensors;
Silver and gold nanoparticles;
Soil fertility;
Top-down and bottom-up preparation of nanomaterials;
Toxicity;
Water treatment;
Zeolites.

We hope that new ideas will promote the fast development of the exciting area of nanomaterials. We invite you to contribute to this Special Issue by submitting papers on your best research activities.

Prof. Dr. Shekhar Bhansali
Dr. Fernando Gomes de Souza Junior
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. Materials 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 2600 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

nanomaterials
nanoparticles
synthesis
characterization
new applications
machine learning
modeling

Published Papers (3 papers)

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Research

15 pages, 3618 KiB

Open AccessArticle

Leveraging Deep Neural Networks for Estimating Vickers Hardness from Nanoindentation Hardness

by Junbo Niu, Bin Miao, Jiaxu Guo, Zhifeng Ding, Yin He, Zhiyu Chi, Feilong Wang and Xinxin Ma

Materials 2024, 17(1), 148; https://doi.org/10.3390/ma17010148 - 27 Dec 2023

Viewed by 632

Abstract

This research presents a comprehensive analysis of deep neural network models (DNNs) for the precise prediction of Vickers hardness (HV) in nitrided and carburized M50NiL steel samples, with hardness values spanning from 400 to 1000 HV. By conducting rigorous experimentation and obtaining corresponding nanoindentation data, we evaluated the performance of four distinct neural network architectures: Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and Transformer. Our findings reveal that MLP and LSTM models excel in predictive accuracy and efficiency, with MLP showing exceptional iteration efficiency and predictive precision. The study validates models for broad application in various steel types and confirms nanoindentation as an effective direct measure for HV hardness in thin films and gradient-variable regions. This work contributes a validated and versatile approach to the hardness assessment of thin-film materials and those with intricate microstructures, enhancing material characterization and potential application in advanced material engineering. Full article

(This article belongs to the Special Issue Advances in Machine-Learning-Assisted Nanomaterials: Applications in Simulation, Detection, Classification, and Imaging)

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20 pages, 4167 KiB

Open AccessArticle

Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization

by Dmitry S. Polyanichenko, Bogdan O. Protsenko, Nikita V. Egil and Oleg O. Kartashov

Materials 2023, 16(15), 5321; https://doi.org/10.3390/ma16155321 - 28 Jul 2023

Viewed by 691

Abstract

The most in-demand instrumental methods for new functional nanomaterial diagnostics employ synchrotron radiation, which is used to determine a material’s electronic and local atomic structure. The high time and resource costs of researching at international synchrotron radiation centers and the problems involved in developing an optimal strategy and in planning the control of the experiments are acute. One possible approach to solving these problems involves the use of deep reinforcement learning agents. However, this approach requires the creation of a special environment that provides a reliable level of response to the agent’s actions. As the physical experimental environment of nanocatalyst diagnostics is potentially a complex multiscale system, there are no unified comprehensive representations that formalize the structure and states as a single digital model. This study proposes an approach based on the decomposition of the experimental system into the original physically plausible nodes, with subsequent merging and optimization as a metagraphic representation with which to model the complex multiscale physicochemical environments. The advantage of this approach is the possibility to directly use the numerical model to predict the system states and to optimize the experimental conditions and parameters. Additionally, the obtained model can form the basic planning principles and allow for the optimization of the search for the optimal strategy with which to control the experiment when it is used as a training environment to provide different abstraction levels of system state reactions. Full article

(This article belongs to the Special Issue Advances in Machine-Learning-Assisted Nanomaterials: Applications in Simulation, Detection, Classification, and Imaging)

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29 pages, 5030 KiB

Open AccessArticle

Biofuels and Nanocatalysts: Python Boosting Visualization of Similarities

by Fernando Gomes Souza, Jr., Kaushik Pal, Jeffrey Dankwa Ampah, Maria Clara Dantas, Aruzza Araújo, Fabíola Maranhão and Priscila Domingues

Materials 2023, 16(3), 1175; https://doi.org/10.3390/ma16031175 - 30 Jan 2023

Cited by 3 | Viewed by 2477

Abstract

Among the most relevant themes of modernity, using renewable resources to produce biofuels attracts several countries’ attention, constituting a vital part of the global geopolitical chessboard since humanity’s energy needs will grow faster and faster. Fortunately, advances in personal computing associated with free and open-source software production facilitate this work of prospecting and understanding complex scenarios. Thus, for the development of this work, the keywords “biofuel” and “nanocatalyst” were delivered to the Scopus database, which returned 1071 scientific articles. The titles and abstracts of these papers were saved in Research Information Systems (RIS) format and submitted to automatic analysis via the Visualization of Similarities Method implemented in VOSviewer 1.6.18 software. Then, the data extracted from the VOSviewer were processed by software written in Python, which allowed the use of the network data generated by the Visualization of Similarities Method. Thus, it was possible to establish the relationships for the pair between the nodes of all clusters classified by Link Strength Between Items or Terms (LSBI) or by year. Indeed, other associations should arouse particular interest in the readers. However, here, the option was for a numerical criterion. However, all data are freely available, and stakeholders can infer other specific connections directly. Therefore, this innovative approach allowed inferring that the most recent pairs of terms associate the need to produce biofuels from microorganisms’ oils besides cerium oxide nanoparticles to improve the performance of fuel mixtures by reducing the emission of hydrocarbons (HC) and oxides of nitrogen (NOx). Full article

(This article belongs to the Special Issue Advances in Machine-Learning-Assisted Nanomaterials: Applications in Simulation, Detection, Classification, and Imaging)

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