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Nanomaterials
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Intelligent Nanomaterials and Nanosystems
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Intelligent Nanomaterials and Nanosystems

A special issue of Nanomaterials (ISSN 2079-4991). This special issue belongs to the section "Nanofabrication and Nanomanufacturing".

Deadline for manuscript submissions: closed (22 February 2023) | Viewed by 14275

Special Issue Editors

Prof. Dr. Mircea Dragoman

E-Mail Website
Guest Editor
National Institute for Research and Development in Microtechnology (IMT), Str. Erou Iancu Nicolae 126A, 077190 Voluntari, Romania
Interests: graphene; nanoelectronics; microwaves; optoelectronics; microelectronics and semiconductor engineering; electronic engineering; ferroelectric
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Abdul Wahab

E-Mail Website
Guest Editor
School of Mechanical, Medical and Process Engineering, Science & Engineering Faculty, Queensland University of Technology, 2 George St, GPO Box 2434, Brisbane, QLD 4001, Australia
Interests: H2 storage and energy storage, CO2 capture and conversion; nanomaterials-based catalysts and metal-doped nanomaterials; sensor and biomedical; composite and thin film; water treatment

Special Issue Information

Dear Colleagues,

Huge effort is being expended to mimic brain attributes such as learning, computing, or signal processing. This Special Issue is dedicated to nanomaterials that are used to sense any external stimulus (mechanical, optical, thermal, biological, etc.) or that are able to self-organize, actuate, adapt their physical properties to external stimuli, or even compute. Learning and computing are the pillars of intelligent nanosystems and are the key issues that confer intelligence to a nanosystem. Here, neuromorphic computing based on artificial neurons and synapses and arrays based on them are the main vectors for the development of intelligent nanosystems. Artificial synapses and neurons can be implemented with a series of nanomaterials and used for learning purposes such as pattern recognition. Moreover, artificial retinas and other integrated circuits benefit from neuromorphic devices.

Prof. Dr. Mircea Dragoman
Dr. Mohammad Abdul Wahab
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. Nanomaterials 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 2900 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

  • sensors (mechanical, optical, thermal, biological, etc.)
  • actuators -adaptive, self-organized materials
  • materials with memory and computing abilities
  • neuromorphic materials (2D materials, phase change materials, etc.)
  • memristors and memtransistors
  • artificial neurons and synapses
  • crossbar arrays
  • artificial learning
  • artificial vision
  • neuroelectronics, implantable nanodevices, wireless transmitters and receivers
  • Intelligent electronic devices such as reconfigurable transistors for artificial intelligence

Published Papers (7 papers)

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Research

14 pages, 2291 KiB  
Article
Effects of Top and Bottom Electrodes Materials and Operating Ambiance on the Characteristics of MgFx Based Bipolar RRAMs
by Nayan C. Das, Yong-Pyo Kim, Sung-Min Hong and Jae-Hyung Jang
Nanomaterials 2023, 13(6), 1127; https://doi.org/10.3390/nano13061127 - 22 Mar 2023
Cited by 3 | Viewed by 1699
Abstract
The effects of electrode materials (top and bottom) and the operating ambiances (open-air and vacuum) on the MgFx-based resistive random-access memory (RRAM) devices are studied. Experiment results show that the device’s performance and stability depend on the difference between the top [...] Read more.
The effects of electrode materials (top and bottom) and the operating ambiances (open-air and vacuum) on the MgFx-based resistive random-access memory (RRAM) devices are studied. Experiment results show that the device’s performance and stability depend on the difference between the top and bottom electrodes’ work functions. Devices are robust in both environments if the work function difference between the bottom and top electrodes is greater than or equal to 0.70 eV. The operating environment-independent device performance depends on the surface roughness of the bottom electrode materials. Reducing the bottom electrodes’ surface roughness will reduce moisture absorption, minimizing the impact of the operating environment. Ti/MgFx/p+-Si memory devices with the minimum surface roughness of the p+-Si bottom electrode show operating environment-independent electroforming-free stable resistive switching properties. The stable memory devices show promising data retentions of >104 s in both environments with DC endurance properties of more than 100 cycles. Full article
(This article belongs to the Special Issue Intelligent Nanomaterials and Nanosystems)
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24 pages, 5249 KiB  
Article
Sensitive Detection of Rosmarinic Acid Using Peptide-Modified Graphene Oxide Screen-Printed Carbon Electrode
by Irina Georgiana Munteanu, Vasile Robert Grădinaru and Constantin Apetrei
Nanomaterials 2022, 12(19), 3292; https://doi.org/10.3390/nano12193292 - 22 Sep 2022
Cited by 7 | Viewed by 1891
Abstract
Peptides have been used as components in biological analysis and fabrication of novel sensors due to several reasons, including well-known synthesis protocols, diverse structures, and acting as highly selective substrates for enzymes. Bio-conjugation strategies can provide a simple and efficient way to convert [...] Read more.
Peptides have been used as components in biological analysis and fabrication of novel sensors due to several reasons, including well-known synthesis protocols, diverse structures, and acting as highly selective substrates for enzymes. Bio-conjugation strategies can provide a simple and efficient way to convert peptide-analyte interaction information into a measurable signal, which can be further used for the manufacture of new peptide-based biosensors. This paper describes the sensitive properties of a peptide-modified graphene oxide screen-printed carbon electrode for accurate and sensitive detection of a natural polyphenol antioxidant compound, namely rosmarinic acid. Glutaraldehyde was chosen as the cross-linking agent because it is able to bind nonspecifically to the peptide. We demonstrated that the strong interaction between the immobilized peptide on the surface of the sensor and rosmarinic acid favors the addition of rosmarinic acid on the surface of the electrode, leading to an efficient preconcentration that determines a high sensitivity of the sensor for the detection of rosmarinic acid. The experimental conditions were optimized using different pH values and different amounts of peptide to modify the sensor surface, so that its analytical performances were optimal for rosmarinic acid detection. By using cyclic voltammetry (CV) as a detection method, a very low detection limit (0.0966 μM) and a vast linearity domain, ranging from 0.1 µM to 3.20 µM, were obtained. The novelty of this work is the development of a novel peptide-based sensor with improved performance characteristics for the quantification of rosmarinic acid in cosmetic products of complex composition. The FTIR method was used to validate the voltammetric method results. Full article
(This article belongs to the Special Issue Intelligent Nanomaterials and Nanosystems)
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13 pages, 3694 KiB  
Article
Binary-Synaptic Plasticity in Ambipolar Ni-Silicide Schottky Barrier Poly-Si Thin Film Transistors Using Chitosan Electric Double Layer
by Ki-Woong Park and Won-Ju Cho
Nanomaterials 2022, 12(17), 3063; https://doi.org/10.3390/nano12173063 - 03 Sep 2022
Cited by 1 | Viewed by 1741
Abstract
We propose an ambipolar chitosan synaptic transistor that effectively responds to binary neuroplasticity. We fabricated the synaptic transistors by applying a chitosan electric double layer (EDL) to the gate insulator of the excimer laser annealed polycrystalline silicon (poly-Si) thin-film transistor (TFT) with Ni-silicide [...] Read more.
We propose an ambipolar chitosan synaptic transistor that effectively responds to binary neuroplasticity. We fabricated the synaptic transistors by applying a chitosan electric double layer (EDL) to the gate insulator of the excimer laser annealed polycrystalline silicon (poly-Si) thin-film transistor (TFT) with Ni-silicide (NiSi) Schottky-barrier source/drain (S/D) junction. The undoped poly-Si channel and the NiSi S/D contact allowed conduction by electrons and holes, resulting in artificial synaptic behavior in both p-type and n-type regions. A slow polarization reaction by the mobile ions such as anions (CH3COO− and OH−) and cations (H+) in the chitosan EDL induced hysteresis window in the transfer characteristics of the ambipolar TFTs. We demonstrated the excitatory post-synaptic current modulations and stable conductance modulation through repetitive potentiation and depression pulse. We expect the proposed ambipolar chitosan synaptic transistor that responds effectively to both positive and negative stimulation signals to provide more complex information process versatility for bio-inspired neuromorphic computing systems. Full article
(This article belongs to the Special Issue Intelligent Nanomaterials and Nanosystems)
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12 pages, 3434 KiB  
Article
Biocompatible Casein Electrolyte-Based Electric-Double-Layer for Artificial Synaptic Transistors
by Hwi-Su Kim, Hamin Park and Won-Ju Cho
Nanomaterials 2022, 12(15), 2596; https://doi.org/10.3390/nano12152596 - 28 Jul 2022
Cited by 11 | Viewed by 1902
Abstract
In this study, we proposed a synaptic transistor using an emerging biocompatible organic material, namely, the casein electrolyte as an electric-double-layer (EDL) in the transistor. The frequency-dependent capacitance of the indium-tin-oxide (ITO)/casein electrolyte-based EDL/ITO capacitor was assessed. As a result, the casein electrolyte [...] Read more.
In this study, we proposed a synaptic transistor using an emerging biocompatible organic material, namely, the casein electrolyte as an electric-double-layer (EDL) in the transistor. The frequency-dependent capacitance of the indium-tin-oxide (ITO)/casein electrolyte-based EDL/ITO capacitor was assessed. As a result, the casein electrolyte was identified to exhibit a large capacitance of ~1.74 μF/cm2 at 10 Hz and operate as an EDL owing to the internal proton charge. Subsequently, the implementation of synaptic functions was verified by fabricating the synaptic transistors using biocompatible casein electrolyte-based EDL. The excitatory post-synaptic current, paired-pulse facilitation, and signal-filtering functions of the transistors demonstrated significant synaptic behavior. Additionally, the spike-timing-dependent plasticity was emulated by applying the pre- and post-synaptic spikes to the gate and drain, respectively. Furthermore, the potentiation and depression characteristics modulating the synaptic weight operated stably in repeated cycle tests. Finally, the learning simulation was conducted using the Modified National Institute of Standards and Technology datasets to verify the neuromorphic computing capability; the results indicate a high recognition rate of 90%. Therefore, our results indicate that the casein electrolyte is a promising new EDL material that implements artificial synapses for building environmental and biologically friendly neuromorphic systems. Full article
(This article belongs to the Special Issue Intelligent Nanomaterials and Nanosystems)
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18 pages, 19536 KiB  
Article
Classification of Amino Acids Using Hybrid Terahertz Spectrum and an Efficient Channel Attention Convolutional Neural Network
by Bo Wang, Xiaoling Qin, Kun Meng, Liguo Zhu and Zeren Li
Nanomaterials 2022, 12(12), 2114; https://doi.org/10.3390/nano12122114 - 20 Jun 2022
Cited by 5 | Viewed by 1595
Abstract
Terahertz (THz) spectroscopy is the de facto method to study the vibration modes and rotational energy levels of molecules and is a widely used molecular sensor for non-destructive inspection. Here, based on the THz spectra of 20 amino acids, a method that extracts [...] Read more.
Terahertz (THz) spectroscopy is the de facto method to study the vibration modes and rotational energy levels of molecules and is a widely used molecular sensor for non-destructive inspection. Here, based on the THz spectra of 20 amino acids, a method that extracts high-dimensional features from a hybrid spectrum combined with absorption rate and refractive index is proposed. A convolutional neural network (CNN) calibrated by efficient channel attention (ECA) is designed to learn from the high-dimensional features and make classifications. The proposed method achieves an accuracy of 99.9% and 99.2% on two testing datasets, which are 12.5% and 23% higher than the method solely classifying the absorption spectrum. The proposed method also realizes a processing speed of 3782.46 frames per second (fps), which is the highest among all the methods in comparison. Due to the compact size, high accuracy, and high speed, the proposed method is viable for future applications in THz chemical sensors. Full article
(This article belongs to the Special Issue Intelligent Nanomaterials and Nanosystems)
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12 pages, 3800 KiB  
Article
An AI-Assisted and Self-Powered Smart Robotic Gripper Based on Eco-EGaIn Nanocomposite for Pick-and-Place Operation
by Qi-Lun Goh, Pei-Song Chee, Eng-Hock Lim and Danny Wee-Kiat Ng
Nanomaterials 2022, 12(8), 1317; https://doi.org/10.3390/nano12081317 - 12 Apr 2022
Cited by 12 | Viewed by 2622
Abstract
High compliance and muscle-alike soft robotic grippers have shown promising performance in addressing the challenges in traditional rigid grippers. Nevertheless, a lack of control feedback (gasping speed and contact force) in a grasping operation can result in undetectable slipping and false positioning. In [...] Read more.
High compliance and muscle-alike soft robotic grippers have shown promising performance in addressing the challenges in traditional rigid grippers. Nevertheless, a lack of control feedback (gasping speed and contact force) in a grasping operation can result in undetectable slipping and false positioning. In this study, a pneumatically driven and self-powered soft robotic gripper that can recognize the grabbed object is reported. We integrated pressure (P-TENG) and bend (B-TENG) triboelectric sensors into a soft robotic gripper to transduce the features of gripped objects in a pick-and-place operation. Both the P-TENG and B-TENG sensors are fabricated using a porous structure made of soft Ecoflex and Euthethic Gallium-Indium nanocomposite (Eco-EGaIn). The output voltage of this porous setup has been improved by 63%, as compared to the non-porous structure. The developed soft gripper successfully recognizes three different objects, cylinder, cuboid, and pyramid prism, with a good accuracy of 91.67% and has shown its potential to be beneficial in the assembly lines, sorting, VR/AR application, and education training. Full article
(This article belongs to the Special Issue Intelligent Nanomaterials and Nanosystems)
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Graphical abstract

10 pages, 2684 KiB  
Article
Graphene/Ferroelectric (Ge-Doped HfO2) Adaptable Transistors Acting as Reconfigurable Logic Gates
by Mircea Dragoman, Adrian Dinescu, Daniela Dragoman, Cătălin Palade, Valentin Şerban Teodorescu and Magdalena Lidia Ciurea
Nanomaterials 2022, 12(2), 279; https://doi.org/10.3390/nano12020279 - 17 Jan 2022
Cited by 6 | Viewed by 2143
Abstract
We present an array of 225 field-effect transistors (FETs), where each of them has a graphene monolayer channel grown on a 3-layer deposited stack of 22 nm control HfO2/5 nm Ge-HfO2 intermediate layer/8 nm tunnel HfO2/p-Si [...] Read more.
We present an array of 225 field-effect transistors (FETs), where each of them has a graphene monolayer channel grown on a 3-layer deposited stack of 22 nm control HfO2/5 nm Ge-HfO2 intermediate layer/8 nm tunnel HfO2/p-Si substrate. The intermediate layer is ferroelectric and acts as a floating gate. All transistors have two top gates, while the p-Si substrate is acting as a back gate. We show that these FETs are acting memtransistors, working as two-input reconfigurable logic gates with memory, the type of the logic gate depending only on the values of the applied gate voltages and the choice of a threshold current. Full article
(This article belongs to the Special Issue Intelligent Nanomaterials and Nanosystems)
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