Micro and Nano Systems Letters最新文献

Identifying the phononic crystal (PnC) with bandgap is a problematic process because all phononic crystals don’t have bandgap. Predicting the Phononic bandgaps (PnBGs) is a computationally expensive task. Here we explore the potential of machine learning (ML) tools to expedite the prediction and maximize the resonator based PnBG. The Gaussian process regression (GPR) model is trained to learn the relationship between complicated shape and band structure of cavity. Bayesian optimization (BO) derives a new shape by leveraging the fast inference of the trained model, which is updated with the augmentation of newly explored structures to escalate the prediction power over performance expansion through active learning. Artificial intelligence (AI) assisted optimization requires a small number of generations to achieve convergence. The obtained results are validated via experimental measurements.

Novel sensing and actuation technologies have notably advanced haptic interfaces, paving the way for more immersive user experiences. We introduce a haptic system that transcends traditional pressure-based interfaces by delivering more comprehensive tactile sensations. This system provides an interactive combination of a robotic hand and haptic glove to operate devices within the wireless communication range. Each component is equipped with independent sensors and actuators, enabling real-time mirroring of user’s hand movements and the effective transmission of tactile information. Remarkably, the proposed system has a multimodal feedback mechanism based on both vibration motors and Peltier elements. This mechanism ensures a varied tactile experience encompassing pressure and temperature sensations. The accuracy of tactile feedback is meticulously calibrated according to experimental data, thereby enhancing the reliability of the system and user experience. The Peltier element for temperature feedback allows users to safely experience temperatures similar to those detected by the robotic hand. Potential applications of this system are wide ranging and include operations in hazardous environments and medical interventions. By providing realistic tactile sensations, our haptic system aims to improve both the performance and safety of workers in such critical sectors, thereby highlighting the great potential of advanced haptic technologies.

This study deals with the process of developing and optimizing the spray coating method for large-area deposition of carbon nanotubes. Carbon nanotubes have excellent electrical and thermal properties and strength, so they are used in various fields of application. However, existing deposition methods have limitations. In this study, the possibility of the spray coating method for large-area deposition of carbon nanotubes is presented, and additional conditions for this are introduced. A spray coating solution was prepared using dichlorobenzene as a solvent for 3 mg carbon nanotubes. By controlling the spray coating speed, the spray coating conditions were optimized by analyzing the surface shape, structure, and resistance of the deposited carbon nanotubes. As a result, we confirmed the possibility of depositing carbon nanotubes on a large area through the spray coating method, and it is expected to contribute to increasing the application possibilities in industrial and scientific fields.

Laser-induced graphene (LIG) is a three-dimensional graphene structure fabricated through the irradiation of a polymer substrate with laser energy (or fluence, equivalently). This methodology offers a cost-effective and facile means of producing 3D nanostructures, yielding graphene materials characterized by extremely high surface area and superior electrical properties, rendering them advantageous for various electrochemical applications. Nonetheless, it is imperative to acknowledge that the structures and material properties of LIG are subject to substantial variations contingent upon processing parameters, thereby underscoring the necessity for systematic inquiry and systematic comprehension of processing conditions, such as fluence and multi-passing, and resultant outcomes. Herein, we explored the impact of different laser fluence levels on the structural and material properties of LIG. We, especially, focused on how laser fluence affected substrate temperature and found that it caused polyimide (PI) substrate pyrolysis, resulting in changes in 3D structures and material density to LIG properties. We also investigated the effects of a multi-passing process on 3D LIG structures and material qualities, varying fluences, and temperature fluctuations. Lastly, we assessed electrochemical properties using LIGs produced under different conditions as working electrodes, leading to distinct impedance profiles and cyclic voltammetry (CV) curves. These variations were linked to the unique structural and material characteristics of the LIG samples.

A quasi-static (QS) MEMS mirror scanner with concentric vertical combs (CVC) is presented. The increase rate of overlapped area of the CVC, tends to show larger values and more uniform than that of conventional vertical combs, resulting in improved linearity and scanning angle, respectively. In this paper, the performance of the QS scanner with CVC, whose equivalent mirror diameter is 3.9 mm, was theoretically analyzed and compared with the fabricated one and also other types of vertical combs such as staggered vertical combs (SVC) and angular vertical combs (AVC). The linearity was less than 0.1%, and the average value of the experimental OSA (optical scanning angle) was up to 13.5 degrees, which is only 1/3 and 39% larger than other scanners, respectively, under the condition that the configuration and dimension of each MEMS scanner is similar each other.

This investigation aimed to evaluate the thermal conductivity ratio (TCR) of SWCNT-CuO/Water nanofluid (NF) using experimental data in the T range of 28–50 ℃ and solid volume fraction range of SVF = 0.03 to 1.15% by an artificial neural network (ANN). MLP network with Lundberg-Marquardt algorithm (LMA) was utilized to predict data (TCR) by ANN. In the best case, from the set of various structures of ANN for this nanofluid, the optimal structure was chosen, which consists of 2 hidden layers, the first layer with the optimal structure consisting of 5 neurons and the second layer containing 7 neurons. Eventually, for the optimal structure, the R² coefficient and MSE are 0.9999029 and 6.33377E-06, respectively. Based on all ANN information, MOD is in a limited area of − 3% < MOD <  + 3%. Comparison of test, correlation yield, and ANN yield display that ANN evaluates laboratory information more exactly.

This study is focusing on the durability of fluoropolymer hydrophobic coatings against falling droplets. Devices such as smart self-cleaning lens or droplet-based energy generators are open-air electrowetting-on-dielectric (EWOD) devices, which are applications that utilize falling droplets. Therefore, the hydrophobic coatings of these devices are exposed to environment factors such as raindrop, and it is necessary to examine the durability of hydrophobic coatings in similar environments and the effectiveness of recovery. Thus, in this study, we simulate raindrops to damage samples with various thicknesses of Cytop (CTX-809SP2). Subsequently, damaged samples are heated to recover their hydrophobicity, and we repeat this damage-recovery cycle several times to evaluate the long-term durability of hydrophobic coating. The EWOD samples of three different hydrophobic coating thicknesses (0.1 μm, 0.5 μm, and 1.0 μm) are damaged by falling droplets from a certain height for 10 days. The damaged samples are then recovered by heating them on a hot plate at 200 ℃ for 24 h and evaluate their EWOD performance. In addition, the hydrophobic coatings are repeatedly damaged and recovered several times to examine the number of recovery limitations of the coatings. After the second damage-recovery cycle, the thickest hydrophobic coating sample shows 7 % better EWOD performance than others. Additionally, after the third damage-recovery cycle, the EWOD performance of all samples significantly degrade, experimentally verifying the number of recovery limitations of the hydrophobic coating. The results of this study are expected to provide useful information for open-air EWOD devices on the methods for evaluating their durability and the thickness selection of hydrophobic coating.

The World Health Organization reports that metabolic disorders are responsible for a significant proportion of global mortality. Considering this, breath sensors have gained prominence as effective tools for monitoring and diagnosing metabolic disorders, thanks to recent advancements in science and technology. In human exhaled breath, over 870 distinct volatile organic components (VOCs) have been identified. Among several VOCs, the detection of acetone in exhaled breath has received considerable attention in biomedical applications. Research indicates a strong correlation between high acetone levels in human breath and several diseases, such as asthma, halitosis, lung cancer, and diabetes mellitus. For instance, acetone is particularly noteworthy as a biomarker in diabetes, where its concentration in exhaled breath often surpasses 1.76 parts per million (ppm), compared to less than 0.8 ppm in healthy individuals. Early diagnosis and intervention in diseases associated with elevated acetone levels, aided by such non-invasive techniques, have the potential to markedly reduce both mortality and the financial burden of healthcare. Over time, various nanostructured gas sensing technologies have been developed for detecting acetone in both ambient air and exhaled breath. This article presents a mini review of cutting-edge research on acetone gas sensing, focusing specifically on nanostructured metal oxides. It discusses critical factors influencing the performance of acetone gas sensors, including acetone concentration levels and operational temperature, which affect their sensitivity, selectivity, and response times. The aim of this review is to encourage further advancements in the development of high-performance acetone gas sensors utilizing nanostructured materials, contributing to more effective management of metabolic disorders.

The utilization of high-resolution printed flexible electronic devices is prevalent in various fields, including energy storage, intelligent healthcare monitoring, soft robotics, and intelligent human–machine interaction, owing to its compact nature and mechanical flexibility. The EHD jet printing technology has the potential to develop the field of printing industry through its ability to fabricate high-resolution, flexible, stretchable, and 3D structures for electronic applications such as displays, sensors, and transistors. The EHD jet printing technology involves the use of solution-based inks made of diverse functional materials to print a wide range of structures. Consequently, it is imperative to have a comprehensive understanding of nanomaterial composites that are printed using EHD jet printing technology. This review provides a thorough overview of nanomaterial composite inks printed for electronic devices using EHD jet printing technology. In particular, a comprehensive overview has been provided about the utilization of EHD jet printing for nanomaterial composites in several domains, including flexible electrodes, flexible displays, transistors, energy harvesting, sensors, and biomedical applications. Moreover, this analysis presents a concise overview of the limitations and prospective future directions for nanomaterial composites fabricated by EHD jet printing.

This paper presents a digital microfluidic (DMF) platform based on a printed circuit board (PCB) for droplet mixing. Mixing droplets without a top cover plate is important for bio-chemical analysis. For this reason, a more efficient mixing method is required especially for mixing a viscous liquid droplet in an open surface. Here, to improve the performance of droplet mixing, we propose the integration of an acoustically oscillating bubble to an electrowetting-on-dielectric (EWOD) chip, which can generate microstreaming inside the droplet. Firstly, an EWOD chip integrated with through-holes for bubble trapping was designed and fabricated through PCB fabrication. This PCB manufacturing technology helps to place more electrodes in the limited chip size. Secondly, we developed the custom-made circuit and interface to individually control multiple actuators (including EWOD actuation and acoustic excitation). Finally, an operation test was conducted to evaluate the capability of not only droplet transportation but also mixing on an open surface. The proposed PCB-based DMF platform for bubble-induced droplet mixing was experimentally verified and expected to make DMF chips more efficient when used for clinical point-of-care diagnostic applications.