The introduction of two-dimensional (2D) nanofillers into thin-film (<1 μm thickness) mixed-matrix membranes (MMMs) remains challenging because of the interfacial issues that lead to Knudsen diffusion, thereby causing a loss of selectivity. Herein, we describe a surface modification technique that involves the application of an epoxy group ring opening process and free-radical polymerization to the graphene oxide (GO) surface to enable the grafting of CO2-philic poly(ethylene glycol) (PEG) chains. The use of functional groups to achieve the PEG modification of GO increases the interlayer spacing between the 2D GO layers. Specifically, the abundant PEG domain between the layers provides an effective CO2-philic pathway and minimizes the occurrence of polymer matrix/GO filler interfacial defects, resulting in excellent CO2 permeance and selectivity. The MMM consisting of GO-glycidyl methacrylate grafted with poly(oxyethylene methacrylate) achieved the highest performance, with a CO2 permeance of 3169 GPU, CO2/N2 selectivity of 37.4, and CO2/CH4 selectivity of 15.8. These results indicate the suitability of the as-prepared materials for commercial applications.
Metal-air batteries (MABs) and fuel cells (FCs) critically rely on electrocatalytic O2 activation, and O2 reduction reaction (ORR), with noble metal-free materials. However, the inception of their synergist reactivity is still unclear due to several electronic and structural limitations. Therefore, the correlation between their science and engineering and their experimental as well as theoretical activity descriptors can pave the way for the development of novel cheap, and efficient catalysts. Moreover, with this framework, several volcanic correlations were established, indicating that catalyst activity increases linearly with increasing binding energy of ORR intermediates up to a certain point, but after that, the activity decreases as binding energy increases. The motivation of this review is to highlight (i) recent designs and developments on non-noble-metal-containing electrocatalysts for ORR, (ii) correlations between science and engineering and existing activity descriptors to improve the electrocatalyst’s ORR performance, and (iii) prospects and challenges with non-noble-metal-based electrocatalysts. The “science and engineering” of the electrode materials discussed in this review will aid researchers in selecting and designing ORR electrocatalysts for energy conversion processes.
Along with the massive growth of the Internet from the 1990s until now, various innovative technologies have been created to bring users breathtaking experiences with more virtual interactions in cyberspace. Many virtual environments have been developed with immersive experience and digital transformation, but most are incoherent instead of being integrated into a platform. In this context, metaverse has been introduced as a shared virtual world that is fueled by many emerging technologies. Among such technologies, artificial intelligence (AI) has shown the great importance of enhancing immersive experience and enabling human-like intelligence of virtual agents. In this survey, we make a beneficial effort to explore the role of AI, including machine learning algorithms and deep learning architectures, in the foundation and development of the metaverse. As the main contributions, we convey a comprehensive investigation of AI-based methods concerning several technical aspects (e.g., natural language processing, machine vision, blockchain, networking, digital twin, and neural interface) that have potentials to build virtual worlds in the metaverse. Furthermore, several primary AI-aided applications, including healthcare, manufacturing, smart cities, and gaming, are studied to be promisingly deployed in the virtual worlds. Finally, we conclude the key contribution and open some future research directions of AI for the metaverse. Serving as a foundational survey, this work will help researchers, including experts and non-experts in related fields, in applying, developing, and optimizing AI techniques to polish the appearance of virtual worlds and improve the quality of applications built in the metaverse.
A conventional energy harvester usually has narrow operational bandwidth, which makes it difficult to harvest energy with varying frequencies in the actual field. Herein, a nonlinear piezoelectric energy harvester with a coupled beam array is designed to broaden bandwidth and improve energy harvesting performance. The proposed harvester consists of a base, two elastic supports, and four piezoelectric beams with different natural frequencies. Due to the coupling effect caused by the two elastic supports, the four piezoelectric beams have large output voltages not only at their own natural frequencies, but also at the natural frequencies of other beams. Meanwhile, the two elastic supports enable the four piezoelectric beams to become nonlinear beams, which also contributes to operational bandwidth broadening. Next, the equivalent mass-spring-damping model and governing equations of the harvester are obtained, based on the lumped-parameter method. A strong coupling is found to occur when the equivalent stiffness of the elastic support is small. Subsequently, a fabricated prototype and an experiment platform are utilized to measure the energy harvesting performance of the harvester. Under 1 g up-sweep excitation, the average output power of the harvester from 40 Hz to 80 Hz is 144.2 % higher and the bandwidth is 93.3 % wider than those of the non-coupled multi-resonance harvester, which houses four beams separately. Finally, actual applicability of the proposed energy harvester is evaluated by operating a Bluetooth location tracking Internet of Things (IoT) device without a battery. Besides, the fabricated prototype is applied to the vehicle engine where the frequencies of vibration sources change rapidly with time and velocity. The field test argues that the harvester can be used in unstable or varying conditions, where a typical vibration energy harvester may not work efficiently, due to its limited operational bandwidth.
This study proposes an advanced algorithm for predicting the optimal orientation in human manikin 3D printing. We can print the manikin mesh data on any scale depending on the user’s needs. Once the 3D printing scale was determined, the manikin data were dissected based on the 3D printer’s maximal printing volume using our previous work. Then, we applied the newly proposed algorithm, designated as “per-pixel signed-shadow casting,” to each dissected manikin part to calculate the volumes of the object and the support structure. Our method classified the original mesh triangles into three groups—alpha, beta, and top-covering—to eliminate the need for special hardware such as graphic cards. The result is shown as a two-dimensional bitmap file, designated as “tomograph”. This tomograph represents the local support structure distribution information on a visual and quantitative basis. Repeating this tomography method for the three rotational axes resulted in a four-dimensional (4D) box-shaped graph. The optimal orientation of any arbitrary object is easily determined from the lowest-valued pixel in the 4D box graph. We applied this proposed method to several basic primitive shapes with different degrees of symmetry and complex shapes, such as the famous “Stanford Bunny”. Finally, the algorithm was applied to human manikins using several printing scales. The theoretical values were compared with those obtained from analytical or g-code-based experimental volumes.
This paper aims to design a robust observer-based dissipative controller for discrete-time Takagi–Sugeno (T-S) fuzzy systems with nonhomogeneous Markov jumps through a non-parallel distributed compensation (non-PDC) scheme. Based on a mode-dependent nonquadratic Lyapunov function, the final form of the stabilization conditions is expressed as linear matrix inequalities in a less conservative manner. To be specific, this paper proposes a decoupling technique that can address the inherent nonconvex terms by extracting them from the stabilization conditions, where all slack variables are set to be fuzzy-basis-dependent for less conservative performance. Furthermore, the proposed stabilization method adopts a one-step design strategy that simultaneously designs the fuzzy observer and control gains without any iteration procedures by employing a positive tuning parameter. In particular, the time-varying transition probabilities included in the stabilization conditions are effectively removed using a modified relaxation technique that can avoid excessive use of free weighting matrices. Finally, based on four examples, the validity of the proposed method is verified through comparison with other studies.
Cold-formed steel (CFS) is in the spotlight as a structural material for buildings and civil infrastructures because it is easy to produce and is cost effective. Recent studies have advanced the popularity and usability of CFS even more by showing its potential as a structural member suitable for special moment-resisting structures. Within the framework of those studies, CFS bolted moment-resisting connections allow the through-plate to protrude beyond the beam depth in both the upper and lower directions, like wings, which could hamper placing decks on the beams. This study proposes a modification to such through-plates, in which one protruding portion is eliminated and the depth of the other is extended instead in order to enhance the practicality in field applications. Finite element analysis is used to investigate and verify the proposed design candidates and finally, suggest an optimum design configuration. Since the bolted connection has a significant impact on the structural response of the CFS beam, an improved bolt modeling approach is suggested based on the bolt modeling approaches proposed by past researchers. The suggested bolt modeling approach is shown to provide better accuracy in simulating plastic deformations in the bolted connection area and the consequent failure behavior when compared to the existing approach. The numerical analysis results show that the through-plate with one wing-side cut should have the depth of the other wing-side such that the diagonal edge could make an angle larger than 35 degrees. To prevent the possibility of buckling in the changed through-plate, attaching the flange plate perpendicular to the diagonal edge is highly recommended. The findings from this study are expected to provide a guidance for the through-plate design in practical structure design.
The synthesis of a cost-effective and efficient catalyst is vital for accelerating the rate of photoelectrochemical hydrogen production. Here, titanium dioxide nanorods sensitized by nickel sulfide, cadmium sulfide, and zinc sulfide nanoparticles (ZnS/CdS/[email protected]2) were developed as a photoanode material via a hydrothermal process followed by the successive ionic layer adsorption and reaction. The morphological analysis of ZnS/CdS/[email protected]2 revealed the nanorods (TiO2) phase in which NiS, CdS, and ZnS nanoparticles are distributed homogeneously. The photoelectrochemical performance analysis of ZnS/CdS/[email protected]2 furnished a maximum photocurrent density of 6.1 mA/cm², which is 7.6 times higher than that of pure TiO2 (0.8 mA/cm²). Additionally, the sensitized TiO2 nanorod arrays as a photoanode show high photoelectrochemical hydrogen production of 491.52 μmol/cm², which is 4.9 times higher than that of pristine TiO2 (99.61 μmol/cm²) over 6 h under simulated solar irradiation. These results suggested the potential for the synthesis and usage of novel hybrids of TiO2 nanorods decorated with transition metal chalcogenides for efficient photoelectrochemical hydrogen production.
Carfilzomib (CFZ) is a second-generation proteasome inhibitor effective in blood cancer therapy. However, CFZ has shown limited efficacy in solid tumor therapy due to the short half-life and poor tumor distribution. Albumin-coated nanocrystal (NC) formulation was shown to improve the circulation stability of CFZ, but its antitumor efficacy remained suboptimal. We hypothesize that NC size reduction is critical to the formulation safety and efficacy as the small size would decrease the distribution in the reticuloendothelial system (RES) and selectively increase the uptake by tumor cells. We controlled the size of CFZ-NCs by varying the production parameters in the crystallization-in-medium method and compared the size-reduced CFZ-NCs (z-average of 168 nm, NC168) with a larger counterpart (z-average of 325 nm, NC325) as well as the commercial CFZ formulation (CFZ-CD). Both CFZ-NCs showed similar or higher cytotoxicity than CFZ-CD against breast cancer cells. NC168 showed greater uptake by cancer cells, less uptake by macrophages and lower immune cell toxicity than NC325 or CFZ-CD. NC168, but not NC325, showed a similar safety profile to CFZ-CD in vivo. The biodistribution and antitumor efficacy of CFZ-NCs in mice were also size-dependent. NC168 showed greater antitumor efficacy and tumor accumulation but lower RES accumulation than NC325 in 4T1 breast cancer model. These results support that NC formulation with an optimal particle size can improve the therapeutic efficacy of CFZ in solid tumors.
A photocatalytic system of Cu(0) nanocluster-bound graphitic carbon nitride (Cu(0)-g-C3N4) was developed for three-component arylsulfonylation reactions. The Cu(0)-g-C3N4 nanocomposite has a structure in which 2–5 nm metallic Cu nanoclusters are anchored on the surface of g-C3N4 nanosheets. The Cu(0) clusters were synthesized by reducing Cu²⁺ ions with ascorbic acid in the presence of g-C3N4 nanosheets. Cu(0)-g-C3N4 was designed to exhibit dual catalytic activity involving the transfer of photo-induced electrons from g-C3N4 to Cu(0) for the reduction of arylhalides, while the holes in g-C3N4 oxidize the sulfur dioxide anion. The structure of the catalyst was confirmed by electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The well-defined, air-stable Cu(0)-g-C3N4 catalyst effectively promotes the sulfonylation of aryl iodides, thiourea dioxide, and electrophiles to afford the corresponding arylsulfonyl compounds in moderate to good yields. The reusability of the catalyst was also evaluated, and it could be reused up to ten times in arylsulfonylation without the loss of its activity.
To accurately diagnose breast cancer, pathologists take biopsy and perform microscopic examination. However, this procedure is inconvenient, time-consuming, and requires high cost. To overcome these challenges, a deep neural network is used to offer a computer-based observation to categorize breast cancer as benign or malignant. In this study, the binary classification of breast cancer from histopathological images is proposed using multistage transfer learning incorporating domain and resolution transformations. We trained our models with a publicly available BreaKHis dataset. We modified and fine-tuned the well-known architectures, VGG19, ResNet (50 and 101), InceptionV3, Xception, Inception-ResNetV2, and NASNet-Large to classify 40 × magnification histopathology images as benign or malignant, then transferred the pre-trained weights for 100 × , 200 × , and 400 × magnification classifications. Translation of the proposed CNN based multistage transfer learning to vision transformer was also performed. Our approach achieved a remarkable performance by demonstrating a test accuracy of 93.97%100% and an F1-score of 0.911.00. The results from our study depict the use of multistage transfer learning improves the breast histopathological images classification performance. Therefore, the proposed approach might be applied in a clinical setting to offer improved breast cancer diagnosis.
Polyaniline (PANI) nanostructures were synthesized via an acid-free green route using redox-active, water-soluble 9,10-Anthraquinone-2-sulfonic acid sodium salt (AQSA). As a counter anion for the PANI backbone, the AQSA plays an important role in polyaniline (PANI) chain growth and as a morphology regulator. The AQSA doped PANI shows pronounced specific capacitance with effective cycling stability. As-synthesized nanotubular structures of PANI_AQSA (0.5, 1.0, and 1.5) with various concentration ratios of aniline to AQSA were achieved in appreciable yield. The intrinsic physico-chemical properties were analyzed using various physico-analytical techniques such as Field Emission Scanning Electron Microscopy (FESEM), Energy Dispersive Spectroscopy (EDS), powder X-ray diffraction analysis (XRD), Fourier Transmission Infrared spectroscopy (FT-IR), X-ray Photo Electron Spectroscopy (XPS), and Raman analysis. Nanotubular structures of PANI_AQSA were analyzed using FESEM studies. The electrochemical studies, galvanostatic charge-discharge (GCD), and Electrochemical impedance spectroscopy (EIS) were methodically investigated. PANI_AQSA_1.5 portrays enhanced specific capacitance of 440 Fg⁻¹ at 1 Ag⁻¹ (three-electrode system) in 1 M sulphuric acid electrolyte than its regular Cl⁻ counter ion (276 Fg⁻¹ at 1 Ag⁻¹). The fabricated symmetric PANI_AQSA//PVA_H2SO4//PANI_AQSA supercapacitor showed a specific capacitance of 391 Fg⁻¹ at 1 Ag⁻¹ with 93 % capacitance retention for ~1000 cycles.
An innovative autonomous resonance‐tuning (ART) energy harvester is reported that utilizes adaptive clamping systems driven by intrinsic mechanical mechanisms without outsourcing additional energy. The adaptive clamping system modulates the natural frequency of the harvester's main beam (MB) by adjusting the clamping position of the MB. The pulling force induced by the resonance vibration of the tuning beam (TB) provides the driving force for operating the adaptive clamp. The ART mechanism is possible by matching the natural frequencies of the TB and clamped MB. Detailed evaluations are conducted on the optimization of the adaptive clamp tolerance and TB design to increase the pulling force. The energy harvester exhibits an ultrawide resonance bandwidth of over 30 Hz in the commonly accessible low vibration frequency range (<100 Hz) owing to the ART function. The practical feasibility is demonstrated by evaluating the ART performance under both frequency and acceleration‐variant conditions and powering a location tracking sensor.
Solid‐state lithium metal batteries (LMBs) are garnering attention as a next‐generation battery technology that can surpass conventional lithium‐ion batteries in terms of energy density and operational safety under the condition that the issue of uncontrolled Li dendrite is resolved. In this study, we investigate various plastic crystal‐embedded elastomer electrolytes (PCEEs) with different phase‐separated structures, prepared by systematically adjusting the volume ratio of the phases, to elucidate the structure‐property‐electrochemical performance relationship of the PCEE in the LMBs. At an optimal volume ratio of elastomer phase to plastic‐crystal phase (i.e., 1:1), bicontinuous‐structured PCEE, consisting of efficient ion‐conducting, plastic‐crystal pathways with long‐range connectivity within a crosslinked elastomer matrix, exhibits exceptionally high ionic conductivity (∼10−3 S cm−1) at 25°C and excellent mechanical resilience (elongation at break ≈ 300%). A full cell featuring this optimized PCEE, a 35 μm‐thick Li anode, and a high loading LiNi0.83Mn0.06Co0.11O2 (NMC‐83) cathode delivers a high energy density of 437 Wh kganode+cathode+electrolyte−1. The established structure‐property‐electrochemical performance relationship of the PCEE for solid‐state LMBs is expected to inform the development of the elastomeric electrolytes for various electrochemical energy systems. This article is protected by copyright. All rights reserved
In South Korea, existing diesel generators are being replaced with photovoltaic (PV) generators in several standalone microgrids. However, their reliability and stability are still not guaranteed owing to PV fluctuations arising from unpredictable environmental changes. To reduce the effects of PV fluctuations, an energy storage system (ESS) with a similar capacity is needed to increase renewable energy sources (RES). A large-capacity ESS can be used as a main power source without a diesel generator when the state of charge is sufficient. In this study, a new method is proposed to mitigate the transients caused by the transition of an ESS to constant voltage constant frequency (CVCF) mode as a diesel generator is disconnected from a standalone microgrid. Existing PI controllers cannot completely suppress transients; hence, an adaptive sliding mode control (ASMC) method is applied to achieve a seamless transition. The transient effects of changing modes are analyzed, and the influencing factors are derived. Case studies on a practical standalone microgrid in South Korea are conducted through a time-domain simulation using DIgSILENT PowerFactory® software.
Hypertension is a severe public health issue worldwide that significantly increases the risk of cardiac vascular disease, stroke, brain hemorrhage, and renal dysfunction. Early screening of blood pressure (BP) levels is essential to prevent the dangerous complication associated with hypertension as the leading cause of death. Recent studies have focused on employing photoplethysmograms (PPG) with machine learning to classify BP levels. However, several studies claimed that electrocardiograms (ECG) also strongly correlate with blood pressure. Therefore, we proposed a concatenated convolutional neural network which integrated the features extracted from PPG and ECG signals. This study used the MIMIC III dataset, which provided PPG, ECG, and arterial blood pressure (ABP) signals. A total of 14,298 signal segments were obtained from 221 patients, which were divided into 9150 signals of train data, 2288 signals of validation data, and 2860 signals of test data. In the training process, five-fold cross-validation was applied to select the best model with the highest classification performance. The proposed concatenated CNN architecture using PPG and ECG obtained the highest test accuracy of 94.56–95.15% with a 95% confidence interval in classifying BP levels into hypotension, normotension, prehypertension, hypertension stage 1, and hypertension stage 2. The result shows that the proposed method is a promising solution to categorize BP levels effectively, assisting medical personnel in making a clinical diagnosis.
In this work, machine learning approach based on polynomial regression was explored to analyze the optimal processing parameters and predict the target particle sizes for ball milling of alumina ceramics. Data points were experimentally collected by measuring the particle sizes. Prediction interval (PI)-based optimization methods using polynomial regression analysis are proposed. As a first step, functional relations between processing parameters (inputs) and quality responses (outputs) are derived by applying the regression analysis. Later, based on these relations, objective functions to be maximized are defined by desirability approach. Finally, the proposed PI-based methods optimize both parameter points and intervals of the target mill for accomplishing user-specified target responses. The optimization results show that the PI-based point optimization methods can select and recommend statistically reliable optimized parameter points even though unique solutions for the objective functions do not exist. From the results of confirmation experiments, it is established that the optimized parameter points can produce desired final powders with quality responses quite similar to the target responses.
This paper addresses the problem of nonlinear and dotted defect detection for multi-vision-based mask inspection systems in mask manufacturing lines. As the mask production amounts increased due to the spread of COVID-19 around the world, the mask inspection systems require more efficient defect detection algorithms. However, the traditional computer vision detection algorithms suffer from various types and very small sizes of the nonlinear and dotted defects on masks. This paper proposes a deep learning-based mask defect detection method, which includes a convolutional neural network (CNN) and efficient preprocessing. The proposed method was developed to be applied to real manufacturing systems, and thus all the training and inference processes were conducted with real data produced by real mask manufacturing systems. Experimental results show that the nonlinear and dotted defects were successfully detected by the proposed method, and its performance was higher than the previous method.
Two Sn(IV)porphyrin-based supramolecular squares ( 1 and 2 ) were synthesized via the reaction of Re(CO) 5 Cl with two [ trans -bis(4-pyridyl)(porphyrinato)]Sn(IV) complexes ( SnP ¹ and SnP ² ). Although the structural framework of these architectures was fixed...
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