Smart Systems (SmaSys2021)

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Innovations in Materials Processing".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 16683

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Graduate School of Science and Engineering, Yamagata University, Yamagata 992-8510, Japan
Interests: polymer chemistry; organic–inorganic hybrid material; green chemistry
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Graduate School of Organic Materials Engineering, Yamagata University, Yamagata 992-8510, Japan
Interests: polymer physics; crystallization
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Guest Editor
Graduate School of Organic Materials Science, Yamagata University, Yamagata 992-8510, Japan
Interests: polymer chemistry; organic electronics; material science
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Guest Editor
Graduate School of Organic Materials Science, Yamagata University, Yamagata 992-8510, Japan
Interests: molecular simulations; soft matter dynamics and rheology
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Guest Editor
Institute for the Promotion of General Graduate Education, Yamagata University, Yamagata 992-8510, Japan
Interests: organic chemistry; biomaterial chemistry; polymer chemistry

Special Issue Information

Dear Colleagues,

We are planning to publish a Special Issue on “Smart Systems” related to the International Conference of Smart Systems Engineering (SmaSys2021, http://smasys.yz.yamagata-u.ac.jp/2021/) held on 7–8 October 2021 in Yonezawa, Japan. This Special Issue provides opportunities for collaboration across a wide range of fields and technologies related to emerging smart systems. Smart systems regard broad scientific and engineering fields. They include organic materials, organic electronics, organic devices, biomaterials, biomedical and biosystem engineering, electrical engineering and informatics, mechanical systems engineering, smart flexible structures and systems, green materials and their processing, tourism engineering with agriculture and foods, and new engineering education.

All the participants of SmaSys2021 and their colleagues—especially students—are encouraged to submit their works to this Special Issue. 

Prof. Dr. Bungo Ochiai
Prof. Dr. Go Matsuba
Prof. Dr. Tomoya Higashihara
Dr. Sathish K. Sukumaran
Dr. Kohei Osawa
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. Technologies 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 1600 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

  • organic materials, organic electronics, and organic devices
  • biomaterials, biomedical, and biosystems engineering
  • electrical engineering and informatics
  • mechanical systems engineering
  • smart flexible structures and systems
  • green materials and processing
  • tourism engineering with agriculture and foods
  • new engineering education

Related Special Issue

Published Papers (4 papers)

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Research

10 pages, 1428 KiB  
Article
Electronic Structure Calculation of Cr3+ and Fe3+ in Phosphor Host Materials Based on Relaxed Structures by Molecular Dynamics Simulation
by Joichiro Ichikawa, Hiroko Kominami, Kazuhiko Hara, Masato Kakihana and Yuta Matsushima
Technologies 2022, 10(3), 56; https://doi.org/10.3390/technologies10030056 - 27 Apr 2022
Cited by 3 | Viewed by 2404
Abstract
The electronic structures of the luminescent center ions Cr3+ and Fe3+ in the deep red phosphors LiAl5O8:Cr3+, α-Al2O3:Cr3+, and γ-LiAlO2:Fe3+ were calculated by the DV-Xα method, [...] Read more.
The electronic structures of the luminescent center ions Cr3+ and Fe3+ in the deep red phosphors LiAl5O8:Cr3+, α-Al2O3:Cr3+, and γ-LiAlO2:Fe3+ were calculated by the DV-Xα method, in which the local distortion induced by the replacement of Al3+ sites in the host crystals by the luminescent center ions was reproduced by classical molecular dynamics (MD) simulation. The MD simulations based on classical dynamics allowed for the handling of more than 1000 atoms for the lattice relaxation calculations, which was advantageous to simulate situations in which a small number of foreign atoms (ions) were dispersed in the host lattice as in phosphors, even when typical periodic boundary conditions were applied. The relaxed lattices obtained after MD indicated that the coordination polyhedra around Cr3+ and Fe3+ expanded in accordance with the size difference between the luminescent center ions and Al3+ in the host crystals. The overall profiles of the partial density of states (p-DOSs) of the isolated Cr3+ and Fe3+ 3d orbitals were not significantly affected by the lattice relaxation, whereas the widths of the energy splitting of the 3d orbitals were reduced. The electronic structure calculations for Fe–Fe pairs in γ-LiAlO2 showed that the antiferromagnetic interactions with antiparallel electron spins between the Fe3+ ions were preferred, especially when the Fe–Fe pair was on the first-nearest neighboring cation sites. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2021))
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9 pages, 2280 KiB  
Article
Water-Assisted Perovskite Quantum Dots with High Optical Properties
by Masaaki Yokoyama, Ryota Sato, Junya Enomoto, Naoaki Oshita, Taisei Kimura, Keisuke Kikuchi, Satoshi Asakura, Kazuki Umemoto and Akito Masuhara
Technologies 2022, 10(1), 11; https://doi.org/10.3390/technologies10010011 - 17 Jan 2022
Cited by 4 | Viewed by 2690
Abstract
Lead halide perovskite quantum dots (PeQDs) have excellent optical properties, such as narrow emission spectra (FWHM: 18–30 nm), a tunable bandgap (λPL: 420–780 nm), and excellent photoluminescence quantum yields (PLQYs: >90%). PeQDs are known as a material that is easily decomposed [...] Read more.
Lead halide perovskite quantum dots (PeQDs) have excellent optical properties, such as narrow emission spectra (FWHM: 18–30 nm), a tunable bandgap (λPL: 420–780 nm), and excellent photoluminescence quantum yields (PLQYs: >90%). PeQDs are known as a material that is easily decomposed when exposed to water in the atmosphere, resulting in causing PeQDs to lower performance. On the other hand, according to the recent reports, adding water after preparing the PeQD dispersion decomposed the PeQD surface defects, resulting in improving their PLQY. Namely, controlling the amount of assisting water during the preparation of the PeQDs is a significantly critical factor to determining their optical properties and device applications. In this paper, our research group discovered the novel effects of the small amount of water to their optical properties when preparing the PeQDs. According to the TEM Images, the PeQDs particle size was clearly increased after water-assisting. In addition, XPS measurement showed that the ratio of Br/Pb achieved to be close to three. Namely, by passivating the surface defect using Ostwald ripening, the prepared PeQDs achieved a high PLQY of over 95%. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2021))
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8 pages, 6443 KiB  
Article
Enhanced Air Stability of Perovskite Quantum Dots by Manganese Passivation
by Ryota Sato, Kazuki Umemoto, Satoshi Asakura and Akito Masuhara
Technologies 2022, 10(1), 10; https://doi.org/10.3390/technologies10010010 - 16 Jan 2022
Cited by 2 | Viewed by 2789
Abstract
Organic-inorganic perovskite quantum dots (PeQDs) have attracted attention due to their excellent optical properties, e.g., high photoluminescence quantum yields (PLQYs; >70%), a narrow full width at half maximum (FWHM; 25 nm or less), and color tunability adjusted by the halide components in an [...] Read more.
Organic-inorganic perovskite quantum dots (PeQDs) have attracted attention due to their excellent optical properties, e.g., high photoluminescence quantum yields (PLQYs; >70%), a narrow full width at half maximum (FWHM; 25 nm or less), and color tunability adjusted by the halide components in an entire tunability (from 450 nm to 730 nm). On the other hand, PeQD stability against air, humidity, and thermal conditions has still not been enough, which disturbs their application. To overcome these issues, with just a focus on the air stability, Mn2+ ion passivated perovskite quantum dots (Mn/MAPbBr3 QDs) were prepared. Mn2+ could be expected to contract the passivating layer against the air condition because the Mn2+ ion was changed to the oxidized Mn on PeQDs under the air conditions. In this research, Mn/MAPbBr3 QDs were successfully prepared by ligand-assisted reprecipitation (LARP) methods. Surprisingly, Mn/MAPbBr3 QD films showed more than double PLQY stability over 4 months compared with pure MAPbBr3 ones against the air, which suggested that oxidized Mn worked as a passivating layer. Improving the PeQD stability is significantly critical for their application. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2021))
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15 pages, 29424 KiB  
Article
A Deep Learning-Based Dirt Detection Computer Vision System for Floor-Cleaning Robots with Improved Data Collection
by Daniel Canedo, Pedro Fonseca, Petia Georgieva and António J. R. Neves
Technologies 2021, 9(4), 94; https://doi.org/10.3390/technologies9040094 - 1 Dec 2021
Cited by 8 | Viewed by 7835
Abstract
Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. [...] Read more.
Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application. Full article
(This article belongs to the Special Issue Smart Systems (SmaSys2021))
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