Additively manufactured nano-mechanical energy harvesting systems: advancements, potential applications, challenges and future perspectives

Nano Revolution: "Tiny tech, big impact: How nanotechnology is driving SDGs progress"

Nanotechnology has emerged as a powerful tool in addressing global challenges and advancing sustainable development. By manipulating materials at the nanoscale, researchers have unlocked new possibilities in various fields, including energy, healthcare, agriculture, construction, transportation, and environmental conservation. This paper explores the potential of nanotechnology and nanostructures in contributing to the achievement of the United Nations (UN) Sustainable Development Goals (SDGs) by improving energy efficiency and energy conversion, leading to a more sustainable and clean energy future, improving water purification processes, enabling access to clean drinking water for communities, enabling targeted drug delivery systems, early disease detection, and personalized medicine, thus revolutionizing healthcare, improving crop yields, efficient nutrient delivery systems, pest control mechanisms, and many other areas, therefore addressing food security issues. It also highlights the potential of nanomaterials in environmental remediation and pollution control. Therefore, by understanding and harnessing nanotechnology's potential, policymakers, researchers, and stakeholders can work together toward a more sustainable future by achieving the 17 UN SDGs.

A Review of Recent Advances in Human-Motion Energy Harvesting Nanogenerators, Self-Powering Smart Sensors and Self-Charging Electronics

In recent years, portable and wearable personal electronic devices have rapidly developed with increasing mass production and rising energy consumption, creating an energy crisis. Using batteries and supercapacitors with limited lifespans and environmental hazards drives the need to find new, environmentally friendly, and renewable sources. One idea is to harness the energy of human motion and convert it into electrical energy using energy harvesting devices-piezoelectric nanogenerators (PENGs), triboelectric nanogenerators (TENGs) and hybrids. They are characterized by a wide variety of features, such as lightness, flexibility, low cost, richness of materials, and many more. These devices offer the opportunity to use new technologies such as IoT, AI or HMI and create smart self-powered sensors, actuators, and self-powered implantable/wearable devices. This review focuses on recent examples of PENGs, TENGs and hybrid devices for wearable and implantable self-powered systems. The basic mechanisms of operation, micro/nano-scale material selection and manufacturing processes of selected examples are discussed. Current challenges and the outlook for the future of the nanogenerators are also discussed.

Monolithically Integrated Complementary Ferroelectric FET XNOR Synapse for the Binary Neural Network

Neuromorphic computing, which mimics the structure and principles of the human brain, has the potential to facilitate the hardware implementation of next-generation artificial intelligence systems and process large amounts of data with very low power consumption. Among them, the XNOR synapse-based Binary Neural Network (BNN) has been attracting attention due to its compact neural network parameter size and low hardware cost. The previous XNOR synapse has drawbacks, such as a trade-off between cell density and accuracy. In this work, we show nonvolatile XNOR synapses with high density and accuracy using a monolithically stacked complementary ferroelectric field-effect transistor (C-FeFET) composed of a p-type Si MFMIS-FeFET at the bottom and a 3D stackable n-type Al:IZTO MFS-FeTFT, achieving 60F2 per cell (2C-FeFET). For adjusting the threshold voltage and improving the switching speed (100 ns) of n-type ferroelectric TFT, we employed a dual-gate configuration and a unique operation scheme, making it comparable to those of Si-based FeFETs. We performed array-level simulation with a 512 × 512 subarray size and a 3-bit flash ADC, demonstrating that the image recognition accuracies using the MNIST and CIFAR-10 data sets were increased by 3.17 and 14.07%, respectively, in comparison to other nonvolatile XNOR synapses. In addition, we performed system-level analysis on a 512 × 512 XNOR C-FeFET, exhibiting an outstanding throughput of 717.37 GOPS and an energy efficiency of 196.7 TOPS/W. We expect that our approach would contribute to the high-density memory systems, logic-in-memory technology, and hardware implementation of neural networks.

Low-Cost Manufacturing of Monolithic Resonant Piezoelectric Devices for Energy Harvesting Using 3D Printing

The rapid increase of the Internet of Things (IoT) has led to significant growth in the development of low-power sensors. However; the biggest challenge in the expansion of the IoT is the energy dependency of the sensors. A promising solution that provides power autonomy to the IoT sensor nodes is energy harvesting (EH) from ambient sources and its conversion into electricity. Through 3D printing, it is possible to create monolithic harvesters. This reduces costs as it eliminates the need for subsequent assembly tools. Thanks to computer-aided design (CAD), the harvester can be specifically adapted to the environmental conditions of the application. In this work, a piezoelectric resonant energy harvester has been designed, fabricated, and electrically characterized. Physical characterization of the piezoelectric material and the final resonator was also performed. In addition, a study and optimization of the device was carried out using finite element modeling. In terms of electrical characterization, it was determined that the device can achieve a maximum output power of 1.46 mW when operated with an optimal load impedance of 4 MΩ and subjected to an acceleration of 1 G. Finally, a proof-of-concept device was designed and fabricated with the goal of measuring the current passing through a wire.

Dynamic Pluronic F127 Crosslinking Enhancement of Biopolymeric Nanocomposites for Piezo-Triboelectric Single-Hybrid Nanogenerators and Self-Powered Sensors

Biomechanical and nanomechanical energy harvesting systems have gained a wealth of interest, resulting in a plethora of research into the development of biopolymeric-based devices as sustainable alternatives. Piezoelectric, triboelectric, and hybrid nanogenerator devices for electrical applications are engineered and fabricated using innovative, sustainable, facile-approach flexible composite films with high performance based on bacterial cellulose and BaTiO₃ , intrinsically and structurally enhanced by Pluronic F127, a micellar cross-linker. The voltage and current outputs of the modified versions with multiwalled carbon nanotube as a conductivity enhancer and post-poling effect are 38 V and 2.8 µA cm^-2 , respectively. The multiconnective devices' power density can approach 10 µW cm^-2 . The rectified output power is capable of charging capacitors, driving light-emitting diode lights, powering a digital watch and interfacing with a commercial microcontroller board to operate as a piezoresistive force sensor switch as a proof of concept. Magnetoelectric studies show that the composites have the potential to be incorporated into magnetoelectric systems. The biopolymeric composites prove to be desirable candidates for multifunctional energy harvesters and electronic devices.

Emerging technologies for COVID (ET-CoV) detection and diagnosis: Recent advancements, applications, challenges, and future perspectives

In light of the constantly changing terrain of the COVID outbreak, medical specialists have implemented proactive schemes for vaccine production. Despite the remarkable COVID-19 vaccine development, the virus has mutated into new variants, including delta and omicron. Currently, the situation is critical in many parts of the world, and precautions are being taken to stop the virus from spreading and mutating. Early identification and diagnosis of COVID-19 are the main challenges faced by emerging technologies during the outbreak. In these circumstances, emerging technologies to tackle Coronavirus have proven magnificent. Artificial intelligence (AI), big data, the internet of medical things (IoMT), robotics, blockchain technology, telemedicine, smart applications, and additive manufacturing are suspicious for detecting, classifying, monitoring, and locating COVID-19. Henceforth, this research aims to glance at these COVID-19 defeating technologies by focusing on their strengths and limitations. A CiteSpace-based bibliometric analysis of the emerging technology was established. The most impactful keywords and the ongoing research frontiers were compiled. Emerging technologies were unstable due to data inconsistency, redundant and noisy datasets, and the inability to aggregate the data due to disparate data formats. Moreover, the privacy and confidentiality of patient medical records are not guaranteed. Hence, Significant data analysis is required to develop an intelligent computational model for effective and quick clinical diagnosis of COVID-19. Remarkably, this article outlines how emerging technology has been used to counteract the virus disaster and offers ongoing research frontiers, directing readers to concentrate on the real challenges and thus facilitating additional explorations to amplify emerging technologies.

High-Resolution In Situ High-Content Imaging of 3D-Bioprinted Single Breast Cancer Spheroids for Advanced Quantification of Benzo(a)pyrene Carcinogen-Induced Breast Cancer Stem Cells

Cancer stem cells (CSCs), also known as tumor-initiating cells, are critically correlated with carcinogenesis and are strongly affected by the environmental factors. Environmental carcinogens, such as benzo(a)pyrene (BaP), are associated with the overproduction of CSCs in various types of cancers, including breast cancer. In this report, we present a sophisticated 3D breast cancer spheroid model for the direct identification and quantitative determination of CSCs induced by carcinogens within intact 3D spheroids. To this end, hydrogel microconstructs containing MCF-7 breast cancer cells were bioprinted within direct-made diminutive multi-well chambers, which were utilized for the mass cultivation of spheroids and in situ detection of CSCs. We found that the breast CSCs caused by BaP-induced mutations were higher in the biomimetic MCF-7 breast cancer spheroids than that in standard 2D monolayer cultures. Precisely controlled MCF-7 cancer spheroids could be generated by serially cultivating MCF-7 cells within the printed hydrogel microconstructs, which could be further utilized for high-resolution in situ high-content 3D imaging analysis to spatially identify the emergence of CSCs at the single spheroid level. Additionally, potential therapeutic agents specific to breast CSCs were successfully evaluated to verify the effectiveness of this model. This bioengineered 3D cancer spheroid system provides a novel approach to investigating the emergence of CSC induced by a carcinogen for environmental hazard assessment in a reproducible and scalable format.

Exploring the Potential of Big Data Analytics in Urban Epidemiology Control: A Comprehensive Study Using CiteSpace

In recent years, there has been a growing amount of discussion on the use of big data to prevent and treat pandemics. The current research aimed to use CiteSpace (CS) visual analysis to uncover research and development trends, to help academics decide on future research and to create a framework for enterprises and organizations in order to plan for the growth of big data-based epidemic control. First, a total of 202 original papers were retrieved from Web of Science (WOS) using a complete list and analyzed using CS scientometric software. The CS parameters included the date range (from 2011 to 2022, a 1-year slice for co-authorship as well as for the co-accordance assessment), visualization (to show the fully integrated networks), specific selection criteria (the top 20 percent), node form (author, institution, region, reference cited, referred author, journal, and keywords), and pruning (pathfinder, slicing network). Lastly, the correlation of data was explored and the findings of the visualization analysis of big data pandemic control research were presented. According to the findings, "COVID-19 infection" was the hottest cluster with 31 references in 2020, while "Internet of things (IoT) platform and unified health algorithm" was the emerging research topic with 15 citations. "Influenza, internet, China, human mobility, and province" were the emerging keywords in the year 2021-2022 with strength of 1.61 to 1.2. The Chinese Academy of Sciences was the top institution, which collaborated with 15 other organizations. Qadri and Wilson were the top authors in this field. The Lancet journal accepted the most papers in this field, while the United States, China, and Europe accounted for the bulk of articles in this research. The research showed how big data may help us to better understand and control pandemics.

Additive manufacturing of Al2O3 ceramics with MgO/SiC contents by laser powder bed fusion process

Laser powder bed fusion is a laser-based additive manufacturing technique that uses a high-energy laser beam to interact directly with powder feedstock. LPBF of oxide ceramics is highly desirable for aerospace, biomedical and high-tech industries. However, the LPBF of ceramics remains a challenging area to address. In this work, a new slurry-based approach for LPBF of ceramic was studied, which has some significant advantages compared to indirect selective laser sintering of ceramic powders. LPBF of Al2O3 was fabricated at different MgO loads up to 80 wt%. Several specimens on different laser powers (70 W-120 W) were printed. The addition of magnesia influenced the microstructure of the alumina ceramic significantly. The findings show that when the laser power is high and the magnesia load is low, the surface quality of the printing parts improves. It is feasible to produce slurry ceramic parts without binders through LPBF. Furthermore, the effects of SiC and MgO loads on the microstructure and surface morphology of alumina are compared and analysed.

Power-Delay Area-Efficient Processing-In-Memory Based on Nanocrystalline Hafnia Ferroelectric Field-Effect Transistors

Ferroelectric field-effect transistors (FeFETs) have attracted enormous attention for low-power and high-density nonvolatile memory devices in processing-in-memory (PIM). However, their small memory window (MW) and limited endurance severely degrade the area efficiency and reliability of PIM devices. Herein, we overcome such challenges using key approaches covering from the material to the device and array architecture. High ferroelectricity was successfully demonstrated considering the thermodynamics and kinetics, even in a relatively thick (≥30 nm) ferroelectric material that was unexplored so far. Moreover, we employed a metal-ferroelectric-metal-insulator-semiconductor architecture that enabled desirable voltage division between the ferroelectric and the metal-oxide-semiconductor FET, leading to a large MW (∼11 V), fast operation speed (<20 ns), and high endurance (∼10¹¹ cycles) characteristics. Subsequently, reliable and energy-efficient multiply-and-accumulation (MAC) operations were verified using a fabricated FeFET-PIM array. Furthermore, a system-level simulation demonstrated the high energy efficiency of the FeFET-PIM array, which was attributed to charge-domain computing. Finally, the proposed signed weight MAC computation achieved high accuracy on the CIFAR-10 dataset using the VGG-8 network.

A review on morphotropic phase boundary in fluorite-structure hafnia towards DRAM technology

In the present hyper-scaling era, memory technology is advancing owing to the demand for high-performance computing and storage devices. As a result, continuous work on conventional semiconductor-process-compatible ferroelectric memory devices such as ferroelectric field-effect transistors, ferroelectric random-access memory, and dynamic random-access memory (DRAM) cell capacitors is ongoing. To operate high-performance computing devices, high-density, high-speed, and reliable memory devices such as DRAMs are required. Consequently, considerable attention has been devoted to the enhanced high dielectric constant and reduced equivalent oxide thickness (EOT) of DRAM cell capacitors. The advancement of ferroelectric hafnia has enabled the development of various devices, such as ferroelectric memories, piezoelectric sensors, and energy harvesters. Therefore, in this review, we focus the morphotropic phase boundary (MPB) between ferroelectric orthorhombic and tetragonal phases, where we can achieve a high dielectric constant and thereby reduce the EOT. We also present the role of the MPB in perovskite and fluorite structures as well as the history of the MPB phase. We also address the different approaches for achieving the MPB phase in a hafnia material system. Subsequently, we review the critical issues in DRAM technology using hafnia materials. Finally, we present various applications of the hafnia material system near the MPB, such as memory, sensors, and energy harvesters.

Parametric Effects of Fused Filament Fabrication Approach on Surface Roughness of Acrylonitrile Butadiene Styrene and Nylon-6 Polymer

This research objective is to optimize the surface roughness of Nylon-6 (PA-6) and Acrylonitrile Butadiene Styrene (ABS) by analyzing the parametric effects of the Fused Filament Fabrication (FFF) technique of Three-Dimensional Printing (3DP) parameters. This article discusses how to optimize the surface roughness using Taguchi analysis by the S/N ratio, ANOVA, and modeling methods. The effects of ABS parameters (initial line thickness, raster width, bed temperature, build pattern, extrusion temperature, print speed, and layer thickness) and PA-6 parameters (layer thickness, print speed, extrusion temperature, and build pattern) were investigated with the average surface roughness (Ra) and root-mean-square average surface roughness (Rq) as response parameters. Validation tests revealed that Ra and Rq decreased significantly. After the optimization, the Ra-ABS and Rq-PA-6 for the fabricated optimized values were 1.75 µm and 21.37 µm, respectively. Taguchi optimization of Ra-ABS, Rq-ABS, Ra-PA-6, and Rq-PA-6 was performed to make one step forward to use them in further research and prototypes.

L-shape triple defects in a phononic crystal for broadband piezoelectric energy harvesting

This study proposes a phononic crystal (PnC) with triple defects in an L-shape arrangement for broadband piezoelectric energy harvesting (PEH). The incorporation of defects in PnCs has attracted significant attention in PEH fields owing to properties such as energy localization and amplification near the defect. Several studies have been conducted to enhance output electric power of PnC-based PEH systems with single defects. However, it is susceptible to the limitations of narrow bandwidth. Recently, double-defect-incorporated systems have been proposed to widen the PEH bandwidth via defect-band splitting. Nevertheless, the PEH performance rapidly decreases in the frequency range between the split defect bands. The limitations of single- and double-defect-incorporated systems can be resolved by the incorporation of the proposed design concept, called the L-shape triple defects in a PnC. The isolated single defect at the top vertex of the letter 'L' compensates for the limitations of double-defect-incorporated systems, whereas the double defects at the bottom vertices compensate for the limitations of the single-defect-incorporated systems. Hence, the proposed design can effectively confine and harvest elastic-wave energy over broadband frequencies while enhancing the application of single and double defects. The effectiveness of the proposed design concept is numerically validated using the finite element method. In the case of a circular hole-type PnC, it is verified that the PnC with L-shape triple defects broadens the bandwidth, and improves the output voltage and electric power compared with those of single- and double-defect-incorporated systems. This study expands the design space of defect-incorporated PnCs and might shed light on other engineering applications of the frequency detector and elastic wave power transfer.