Consumer Electronics Research Articles

A review of graphene biopolymer composite in piezoelectric sensor applications

Abstract The amazing electrical, optical, mechanical and thermal properties combined with high specific surface area of graphene making it as an appealing integrant for stimuli responsive high performance smart materials. Typical graphene-based smart materials encompass mechanically exfoliated perfect graphene, chemical vapor deposited first-class graphene, chemically moded graphene including graphene oxide and reduced graphene oxide and their macroscopic assemblies or composites. The ability of these graphene-based materials ending up interacting with biopolymers to come up with quite fascinating electrical, mechanical, optical, thermal and sensing characteristics has have attracted a considerable number of attentions. The biggest advantage of using biopolymer-based materials is non-corrosiveness, ease in coloration, good tensile strength, and biodegradability but are abided by drawback of the poor mechanical strength, lack of response, and unstable environmental stability. However, graphene incorporated biopolymers provided beneficent attributes for example ability to detect various forms of stimuli such as gaseous molecules include biomolecules, pH value, mechanical flexibility, electrical and thermal conductivity to enable ongoing promising advancement of the piezoelectric sensor applications. This review explores the piezoelectric development based on several graphene fabricated biopolymer composite and it is use in healthcare monitoring, structural health monitoring, industrial process monitoring, consumer electronics applications. Furthermore, we enlighten the challenges and future perspectives of graphene biopolymer piezoelectric sensors.

Chitosan-Based Electronics: The Importance of Acid Strength and Plasticizing Additives on Device Performance.

A rise in demand for disposable consumer electronics such as smart packaging, wearable electronics, and single-use point-of-source sensors requires the development of eco-friendly and compostable electronic materials. Chitosan is derived from crustacean waste and offers high dielectric constant values without requiring rigorous purification, making it sustainable for large-scale electronic device manufacturing. When processed in acidic media, the protonated backbone of chitosan pairs with counterions from the acid dissociation to form chitosan thin films with electrical double layers (EDLs) and tunable capacitive properties. We report the importance of the choice of acid when processing chitosan by surveying a series of halogenated and biosourced acids with varying pKa values and solutions with different pH values. Oxalic acid outperforms other acids, with a maximum areal capacitance of 161 nF·mm-2. Tartaric acid and citric acid, despite lower capacitance values, showed promising results with a stable EDL capacitance and high reproducibility, making them optimal for large-area manufacturing. The incorporation of sorbitol as a plasticizer boosts the EDL formation onset of all chitosan-acid combinations to 1 × 103-105 Hz and improves reproducibility. High-performing single-walled carbon nanotube thin film transistors were made using chitosan-based dielectrics treated with different acids with and without sorbitol, leading to transconductance as high as ≈5.2 μS and Ion/Ioff of 105. The capacitors and transistors remain functional after one year of storage in ambient conditions. Overall, this study demonstrates durable high-performance electronics based on chitosan and stresses the importance of processing acid and the use of plasticizing additives, such as sorbitol.

HARNESSING BLOCKCHAIN WITH ENSEMBLE DEEP LEARNING-BASED DISTRIBUTED DOS ATTACK DETECTION IN IOT-ASSISTED SECURE CONSUMER ELECTRONICS SYSTEMS

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

Organic–inorganic covalent–ionic network enabled all–in–one multifunctional coating for flexible displays

Touch displays are ubiquitous in modern technologies. However, current protective methods for emerging flexible displays against static, scratches, bending, and smudge rely on multilayer materials that impede progress towards flexible, lightweight, and multifunctional designs. Developing a single coating layer integrating all these functions remains challenging yet highly anticipated. Herein, we introduce an organic–inorganic covalent–ionic hybrid network that leverages the reorganizing interaction between siloxanes (i.e., trifluoropropyl–funtionalized polyhedral oligomeric silsesquioxane and cyclotrisiloxane) and fluoride ions. This nanoscale organic–inorganic covalent–ionic hybridized crosslinked network, combined with a low surface energy trifluoropropyl group, offers a monolithic layer coating with excellent optical, antistatic, anti–smudge properties, flexibility, scratch resistance, and recyclability. Compared with existing protective materials, this all–in–one coating demonstrates comprehensive multifunctionality and closed–loop recyclability, making it ideal for future flexible displays and contributing to ecological sustainability in consumer electronics.

Research on the design and application of Analog-to-Digital converters

Abstract. In the modern digital era, the conversion of analog signals to digital data is a crucial process for a wide range of applications in industries such as consumer electronics, medical devices, and telecommunications. The accelerated evolution of digital technology is driving a surge in demand for high-performance analog-to-digital converters (ADCs). The paper presents a comprehensive analysis of analog-to-digital converters, emphasizing their types, applications, performance measurements, and most recent developments. In the paper, emphasis is placed on the mechanisms of different ADC architectures, such as Successive Approximation Register (SAR), Sigma-Delta (-), and Flash ADCs, and their respective strengths and weaknesses are evaluated. In terms of technique, it synthesizes and summarizes research results from multiple academic sources and technical papers. In addition to underscoring the pivotal concerns of ADC design, including power consumption, resolution, and sample rate, the research delves into novel approaches that leverage the use of advanced materials and techniques. The research results demonstrate the continued development of ADC technology and its critical function in improving the effectiveness and performance of digital systems.

Electrophoretic video display based on image semantic segmentation

AbstractElectrophoretic displays (EPDs) are popular in consumer electronics, like e‐readers and tags, for their paper‐like visuals and minimal power consumption. However, EPDs struggle to rapidly switch to target grayscales due to the inherent limitations in the response speed of their display particles. Video halftoning technology achieves a smoother video grayscale display, but it also inevitably reduces the image quality and affects the viewing experience. Therefore, this paper proposes an EPD driving method based on image semantic segmentation. The method could distinguish the dynamic region and the static region accurately. For static areas, high‐definition driving waveforms were used to ensure the image display accuracy. For the dynamic area, dynamic target and dynamic text were processed by different algorithms, respectively, and the speed‐driving waveforms were used to maintain the fluency and clarity of the video display. With the method we proposed, the display quality for EPD in complex video playback was optimized significantly. Compared to DBS refresh techniques, the SSIM had increased from 4% to 82% and the PSNR had increased from 2.56 dB to 10.89 dB with our method, significantly enhancing the video playback performance of EPDs.

Innovations in Energy Through Nanomaterials Enhancing Solid-State Battery Safety and Efficacy

The development of solid-state batteries (SSBs) represents a significant advancement in energy storage technology, addressing the limitations of traditional liquid electrolyte batteries, such as safety concerns and efficiency issues. However, SSBs face challenges including rigid solid-to-solid contacts, poor interfacial stability, and suboptimal performance across temperature ranges. This paper explores the role of nanotechnology in enhancing the performance and safety of SSBs. It highlights the applications of nanomaterials, such as nano-composites, nano-coatings, and embedded nanoparticles, which improve ionic conductivity, mechanical strength, and thermal management. The paper also discusses the challenges of commercializing nanotechnology in SSBs, including high production costs and regulatory hurdles. Despite these challenges, nanotechnology offers promising solutions for increasing the energy density and cycle life of SSBs, making them viable for applications in electric vehicles and consumer electronics. The paper concludes with recommendations for future research directions, emphasizing the need for continued innovation to fully realize the potential of SSBs in various industries.

Towards Hybrid Deep Learning-based Threat Detection with Blockchain Technology for Secure IoT-based Consumer Electronics

Towards Hybrid Deep Learning-based Threat Detection with Blockchain Technology for Secure IoT-based Consumer Electronics

Improving Energy Density in Lithium-Ion Batteries Using Nanomaterials

Lithium-ion batteries are pivotal in modern energy storage, powering everything from consumer electronics to electric vehicles. Despite their advantages, current materials limit their energy density, particularly for electric vehicles. This paper explores the potential of nanomaterials to enhance lithium-ion battery performance, focusing on anodes, cathodes, and electrolytes. The study discusses the limitations of traditional graphite anodes and highlights nanostructured silicon and metal oxides as alternatives to significantly increase capacity and reduce volume expansion. For cathodes, it examines how nanomaterials like S-TiO2 yolk-shell structures and high-voltage cathode materials can improve energy density and stability. The role of nanostructured solid electrolytes in enhancing ionic conductivity and overall battery performance is also analyzed. The paper addresses the challenges of using nanomaterials, such as higher costs and the need for scalable production methods. It suggests future research directions, including optimizing nanostructures and developing cost-effective manufacturing processes. The findings underscore the potential of nanotechnology to overcome current limitations and significantly boost the energy density of lithium-ion batteries.

Influence of Sustainability Concern in the Purchase Decisions of Customers: A Study on Electronic Goods

Understanding how sustainability issues influence customer choices has become critical for sectors like electronics that have a significant environmental impact. This study looks at how customer choices in the electronic goods market are impacted by sustainability condition such energy efficiency, recyclability, product lifespan, and corporate social responsibility (CSR). This study investigates which sustainability features customers value most and how they react to sustainable product offerings utilizing a questionnaire. The results emphasise how critical environmentally friendly practices are in consumer electronics and provide producers data on how to match their goods to the growing demand from consumers for viable solutions

Ultracompact optical microscopes made of liquid crystal Pancharatnam–Berry optical elements

Ultracompact optical systems are increasingly sought after for applications such as consumer electronics and medical imaging. Here, we present a design and manufacturing approach for ultracompact bright- and dark-field optical microscopes entirely made of flat liquid crystal optical elements. Both systems utilize liquid crystal PB lenses as objective and tube lenses, while the dark-field optical microscope incorporates an additional Q-plate with a +1 topological defect to filter zero-order light. We demonstrate two optical microscopic systems with a numerical aperture of 0.25 and overall thickness of just 5 mm. The system can achieve high imaging performance with a resolution better than 7 µm. We further demonstrate an exemplary application in biological imaging to effectively enhance edge contrast in imaging onion epidermal cells. This work presents an example in exploiting the flatness and high quality of liquid crystal optical elements to achieve compactness and high-quality imaging promising for various applications.

A Text Generation Method Based on a Multimodal Knowledge Graph for Fault Diagnosis of Consumer Electronics

As consumer electronics evolve towards greater intelligence, their automation and complexity also increase, making it difficult for users to diagnose faults when they occur. To address the problem where users, relying solely on their own knowledge, struggle to diagnose faults in consumer electronics promptly and accurately, we propose a multimodal knowledge graph-based text generation method. Our method begins by using deep learning models like the Residual Network (ResNet) and Bidirectional Encoder Representations from Transformers (BERT) to extract features from user-provided fault information, which can include images, text, audio, and even olfactory data. These multimodal features are then combined to form a comprehensive representation. The fused features are fed into a graph convolutional network (GCN) for fault inference, identifying potential fault nodes in the electronics. These fault nodes are subsequently fed into a pre-constructed knowledge graph to determine the final diagnosis. Finally, this information is processed through the Bias-term Fine-tuning (BitFit) enhanced Chinese Pre-trained Transformer (CPT) model, which generates the final fault diagnosis text for the user. The experimental results show that our proposed method achieves a 4.4% improvement over baseline methods, reaching a fault diagnosis accuracy of 98.4%. Our approach effectively leverages multimodal fault information, addressing the challenges users face in diagnosing faults through the integration of graph convolutional network and knowledge graph technologies.

Metasurface folded lens system for ultrathin cameras.

Slim cameras are essential in state-of-the-art consumer electronics such as smartphones or augmented/virtual reality devices. However, reducing the camera thickness faces challenges primarily due to the thick lens systems. Current lens systems, composed of stacked refractive lenses, are fundamentally constrained from becoming thinner due to the presence of empty spaces between lenses and the excessive volume of each lens. Here, we present a lens system using metasurface folded optics to overcome these pervasive issues. In our design, metasurfaces are arranged horizontally on a glass wafer and direct light along multifolded paths inside the substrate. This approach achieves an ultra-slim lens system with a thickness of 0.7 millimeters and 2× thinner relative to the EFL, thereby overcoming the inherent limitations of conventional optical platforms. It delivers quasi-diffraction-limited imaging quality with a 10° field of view and an f number of 4 at an operational wavelength of 852 nanometers. Our findings provide a compelling platform for compact cameras using folded nano-optics.

LabEmbryoCam: An opensource phenotyping system for developing aquatic animals

Phenomics is the acquisition of high-dimensional data on an individual-wide scale and is proving transformational in areas of biological research related to human health including medicine and the crop sciences. However, more broadly, a lack of accessible transferrable technologies and research approaches is significantly hindering the uptake of phenomics, in contrast to molecular-omics for which transferrable technologies have been a significant enabler. Aquatic embryos are natural models for phenomics, due to their small size, taxonomic diversity, ecological relevance, and high levels of temporal, spatial and functional change. Here, we present LabEmbryoCam, an autonomous phenotyping platform for timelapse imaging of developing aquatic embryos cultured in a multiwell plate format, and while optimised for embryos, the instrument is extremely versatile. The LabEmbryoCam capitalises on 3D printing, single board computers, consumer electronics and stepper motor enabled motion. These provide autonomous X, Y and Z motion, a web application streamlined for rapid setup of experiments, user email notifications and a humidification chamber to reduce evaporation over prolonged acquisitions. Downstream analyses are provided, enabling automated embryo segmentation, heartbeat detection, motion tracking, and energy proxy trait (EPT) measurement. LabEmbryoCam is a scalable, and flexible laboratory instrument, that leverages embryonic and early life stage organisms to tackle key global challenges including biological sensitivity assessment, toxicological screening, but also to support broader engagement with the earliest stages of life.

IEEE Consumer Electronics Magazine: Call for Research & Review Articles

IEEE Consumer Electronics Magazine: Call for Research & Review Articles

Polymer electrolytes based on PEO and lithium tetraalkoxyborate salts with ionic liquid properties for lithium-based batteries

The development of lithium-ion batteries (LIBs) with improved safety features is crucial due to the inherent risks associated with liquid electrolytes, such as fires, explosions, and leakage. This study explores the potential of solid polymer electrolytes (SPEs) based on poly(ethylene oxide) (PEO) and lithium tetraalkoxyborate salts (LiTAB) with ionic liquid properties as a safer alternative. Ionic liquids (ILs) are examined for their high ionic conductivity, wide electrochemical window, and excellent thermal stability, despite their high synthesis costs and viscosity challenges. The study proposes the use of a novel LiTAB salt with an oligomeric star-shaped anion structure that reduces mobility, enhancing lithium ion transference numbers. This IL serves as both a lithium salt and an active plasticizer in PEO-based SPEs, offering potential for 3D printing applications. Experimental results demonstrate that these electrolytes exhibit favorable rheological properties, high ionic conductivities, and significant lithium ion transference numbers, addressing the key limitations of conventional PEO-based electrolytes. The findings suggest that incorporating these LiTAB salts in SPEs could significantly enhance the safety and performance of LIBs, particularly for applications in miniaturized consumer electronics and electronic implants.

Design and performance investigation of tunnel-FET based energy efficient approximate and accurate adders targeted towards low power IoT nodes

Abstract The proliferation of IoT enabled SoCs in state-of-the-art consumer electronics has necessitated the use of beyond CMOS devices for the energy efficient computations and sensing design space. In the low supply voltage (VDD) regime, Tunnel FETs (TFETs) have shown serious potential at VDD less than 0.5 V. However, the inherent structure, characteristic interband tunneling and enhance Miller capacitance in TFETs do not allow the conventional CMOS logic design styles to be universally used for TFET based logic circuits. In this paper, we propose three energy efficient and novel TFET based accurate adder circuits namely, Static Energy Recovery Full adder (SERF), Complementary and Level Restoring Carry Logic Full adder (CLRCL) and Balanced Restoring Carry Logic full adder (BRCL) based on constituent TFET based functional units which do not use the CMOS design style. Further, novel approximate adders have been developed for energy efficient implementation in IoT nodes. The BRCL adder consumes approximately 60% less area and 85.36% and 89.6% less energy in comparison to SERF and CLRCL respectively. The approximate adders in the best and worst case facilitate approximately 92% and 75% savings in area and consumes 71% less power than the BRCL adder. When compared to the standard UMC 45 nm bulk MOSFET based designs, the TFET based accurate adders operate at 25% less energy while the approximate adders consume 40% less. All the circuit simulations have been performed using Spectre Simulator of Cadence and device characterization using 2D-TCAD tool.

A deep learning approach to optimize remaining useful life prediction for Li-ion batteries

Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency. The model’s name reflects its precision (“AccuCell”) and predictive strength (“Prodigy”). The proposed methodology involves preparing a dataset of battery operational features, split using an 80–20 ratio for training and testing. Leveraging 22 variations of current (critical parameter) across three Li-ion cells, AccuCell Prodigy significantly reduces prediction errors, achieving a mean square error of 0.1305%, mean absolute error of 2.484%, and root mean square error of 3.613%, with a high R-squared value of 0.9849. These results highlight its robustness and potential for advancing battery health management.

Electrochemical Sacrificial Alchemy: Crafting Hollow Hydroxide Hierarchy for Practical Aqueous Energy Storage Solution

AbstractEffective utilization of internal active sites in high‐mass loading electrode materials is essential for advancing practical energy storage. Herein, a novel electrochemical sacrificial alchemy for the first time to directly transform the solid Co‐Ni‐Zn carbonate hydroxide (CH) into its hollow structure with a strategic hierarchical architecture is introduced. In contrast to the complex and often cumbersome procedures of traditional methods, the approach is built on the simple electrochemically induced in situ etching and remodeling process. The experimental and theoretical analyses highlight the significant role of electric field force‐induced Zn sacrificial etching and hydroxide redeposition in creating internal cavities and regenerating external active sites, leading to the final hollow hierarchical structure with primary 1D hollow nanorod arrays and secondary reconstructed 2D nanoflakes, effectively exposing abundant energy storage sites. Benefiting from the synergistic effect, the resulting Co‐Ni‐Zn CH exhibited exceptional electrochemical performance. As proof of concept, a modular pouch‐type hybrid supercapacitor (HSC) composed of Co‐Ni‐Zn CH cathode achieved an ultrahigh energy density of 46.9 Wh kg−1. This device can efficiently power consumer electronics with a faster charging speed compared to commercial shared power banks. This research unveils a facile electrochemical approach for structuring electrode materials in energy storage solutions.

Solid‐State Revolution: Assessing the Potential of Solid Polymer Electrolytes in Lithium‐Ion Batteries

AbstractLithium‐ion batteries (LIBs) are crucial for achieving sustainable energy goals due to their high energy density and long cycle life. They dominate markets like consumer electronics, electric vehicles, and stationary energy storage systems. However, current LIBs use liquid electrolytes, which are toxic, flammable, and their liquid state does not resist dendrite growth, causing battery capacity decline and failure. Additionally, the limited availability of lithium and other metals makes liquid‐based LIBs less sustainable. On the other hand, solid polymer electrolytes (SPEs) offer a safer alternative as they are non‐volatile and can resist dendrite growth. However, ion transport in solids is much more restricted than in liquids, while imperfect solid‐solid interfaces contribute to interfacial resistance leading to lower ionic conductivity and increasing Ohmic losses or requiring battery operation at elevated temperatures. Chemical and mechanical degradation of these interfaces can also result in battery capacity fade, and poorer cyclic performance compared to liquid electrolytes. Understanding the ionic transport mechanisms in SPEs is critical for designing and optimizing the nanostructure of polymers and polymer/electrode interfaces to overcome these limitations. In this review, the fundamental mechanisms of ion transport in SPEs will first be explored. Various state‐of‐the‐art approaches for addressing the key challenges in SPEs and their solutions are then discussed. Furthermore, the current status of SPEs is analyzed to determine their potential for replacing liquid electrolytes in the future.