Increase In Power Consumption Research Articles

Singular dielectricnanolaser with atomic-scale field localization.

Compressing the optical field to the atomic scale opens up possibilities for directly observing individual molecules, offering innovative imaging and research tools for both physical and life sciences. However, the diffraction limit imposes a fundamental constraint on how much the optical field can be compressed, based on the achievable photon momentum1,2. In contrast to dielectric structures, plasmonics offer superior field confinement by coupling the light field with the oscillations of free electrons in metals3-6. Nevertheless, plasmonics suffer from inherent ohmic loss, leading to heat generation, increased power consumption and limitations on the coherence time of plasmonic devices7,8. Here we propose and demonstrate singular dielectric nanolasers showing a mode volume that breaks the optical diffraction limit. Derived from Maxwell's equations, we discover that the electric-field singularity sustained in a dielectric bowtie nanoantenna originates from divergence of momentum. The singular dielectric nanolaser is constructed by integrating a dielectric bowtie nanoantenna into the centre of a twisted lattice nanocavity. The synergistic integration surpasses the diffraction limit, enabling the singular dielectricnanolaser to achieve an ultrasmall mode volume of about 0.0005 λ3 (λ, free-space wavelength), along with an exceptionally small feature size at the 1-nanometre scale. To fabricate the required dielectric bowtie nanoantenna with a single-nanometre gap, we develop a two-step process involving etching and atomic deposition. Our research showcases the ability to achieve atomic-scale field localization in laser devices, paving the way for ultra-precise measurements, super-resolution imaging, ultra-efficient computing and communication, and the exploration of light-matter interactions within the realm of extreme optical field localization.

Digital horizontal crosstalk compensation in 8K LCD displays for enhanced image quality

AbstractAs ultra‐high‐definition displays have gained popularity, mitigating the horizontal crosstalk effect in 8K LCD panels is crucial. High display resolution requires narrower signal line integration, intensifying the coupling effect. Traditional methods like Vcom feedback and increasing analog voltage drain‐drain (AVDD) load are slower and less accurate, leading to increased power consumption. In response, we propose an advanced digital signal compensation method. In this study, we developed a predictive model and investigated the intricate relationships among AVDD, Vcom, and storage capacity (Cst) ripples on horizontal crosstalk. Optimizing the ripple change (∆G) by varying the compensation coefficient (a) and decay ratio (τ) significantly reduces crosstalk effects. The digital compensation method allows rapid and precise compensation without delays, reducing horizontal crosstalk in 8K LCD panels from 4% to below 0.9%. This surpasses the requirement of minimizing crosstalk to less than 2%, substantially enhancing the image quality of high‐resolution displays.

Determining carrier frequency for implantable human body communication based on transmission efficiency–electromagnetic interference trade-off

This study proposes a novel methodology for determining the optimal carrier frequency for implantable human body communication (HBC) by focusing on the trade-off between transmission efficiency and electromagnetic interference (EMI) robustness. Traditional radio communication technologies use high-frequency bands, which pose some challenges, such as increased power consumption, EMI with other devices, and security risks. Implantable HBC is a promising alternative, wherein the human body is used as a transmission medium. Through phantom experiments simulating the human abdomen, we assess the transmission characteristics of implantable HBC between an abdominal neurostimulator and an external controller across a frequency range of 1–100 MHz. Results revealed that maximum transmission gain was achieved at 37.2 MHz. Moreover, rapid signal attenuation occurred beyond a minimal distance from the skin, indicating enhanced communication efficiency, reduced EMI, and enhanced information security. An evaluation index (EI) was also proposed to quantitatively assess the balance between transmission efficiency and EMI and determine an appropriate carrier frequency for specific implantable HBC applications. This study advances the field of implantable medical devices by optimizing communication performance while addressing key challenges in EMI and security.

Evaluating Signal Quality and System Performance in NB-IoT Communications

The expansion of the Internet of Things (IoT) has created a need for reliable and fault-tolerant communication networks. However, ensuring consistent signal quality and power efficiency has proven to be challenging. This study evaluated the performance of Narrowband IoT (NB-IoT) communications using the SIM7020 module connected to a Raspberry Pi 4 Model B, focusing on signal quality across indoor, outdoor, urban and rural areas. Supervised machine learning for indoor localisation based on Received Signal Strength Indicator (RSSI) has been introduced, for example, to enhance NB-IoT performance. However, this and other approaches have encountered difficulties in mobile and obstructed environments, including signal attenuation, connectivity variability and increased power consumption. The objective of this study was to analyse NB-IoT signal strength and power consumption, providing guidance for deploying real-time communication IoT applications. Empirical data was analysed to understand the RSSI and Cellular Signal Quality (CSQ) in different locations. Signal quality in urban and outdoor environments was prone to fluctuations due to mobility and interference, whereas rural areas had weaker but more consistent signals. Indoor environments suffered from significant signal attenuation. The results emphasise the importance of improved handover mechanisms and adaptive deployment strategies to ensure reliable connectivity across various IoT applications.

Optimal propulsion efficiency for NACA0012 foils with asymmetries in motion: A hybrid approach using the Taguchi method and artificial neural networks

This study uses the Taguchi method and artificial neural networks to optimize the thrust and propulsion efficiency of a NACA0012 foil, examining variables like downstroke duration (S = 0.3–0.5), angle of attack during downstroke and upstroke (αmd, αmu = 20°–50°), and Strouhal number (St = 0.2–0.5). Analysis shows that the St greatly affects thrust, while the αmu is pivotal for propulsion efficiency. Thrust is optimized by using a shorter S of 0.3, a greater αmd of 50° and a larger St of 0.45–0.5 and efficiency is optimized by using symmetric S of 0.5, a smaller αmd, αmu of 20° and a lower St of 0.35–0.39. Force and flow field analysis shows that certain combinations optimize thrust by mitigating negative thrust in the early downstroke, though they increase power consumption, lowering overall efficiency. Configurations balancing stroke duration and minimizing the angle of attack enhance efficiency by decreasing power demand for steady thrust. Wake structure analysis reveals that optimal efficiency involves generating reverse Kármán vortex streets, while optimizing thrust leads to a more chaotic wake. This study advances fluid dynamics in propulsion systems and aids in designing more efficient, adaptable underwater vehicles by identifying the key balance between thrust optimization and propulsion efficiency.

Enhanced PtRu by CeO2 hollow nanofibers: Hydrogen gas sensing with CO-resistant in fuel cell

The broad flammability range of hydrogen (4–75 %) underscores the critical need for precise and efficient detection methods. Traditional catalytic combustion sensors can detect combustible gases rapidly; however, they have disadvantages such as high operating temperatures, increased power consumption, and dependence on platinum, with vulnerability to CO poisoning. This study introduced a groundbreaking approach that utilizes CeO₂ hollow nanofibers supported by PtRu nanoparticles to sense hydrogen under extreme conditions, including environments with CO concentrations up to 500 ppm. The unique structure of CeO₂, derived from the Pt/Ce3+ redox pair, provides a high density of oxygen vacancies, which enhances hydrogen detection and enables reactions at room temperature. Our sensor can detect hydrogen concentrations ranging from as low as 100 ppm to as high as 50 %, showcasing its wide detection range and superior selectivity. Our findings confirmed the superior performance of the sensor in realizing consistent and rapid detection in a CO-rich atmosphere. This study presented a significant step toward robust wide-range hydrogen detection, paving the way for safer and more sustainable environmental monitoring.

Insights into Sub‐Thermionic Transport in Schottky Barrier FETs

AbstractBoltzmann carrier statistics restrict the sub‐threshold swing (SS ≥ 60 mVdec–1) and, consequently, the supply voltage (≈ 1 V) of the thermionic transistor. This primary bottleneck results in rapidly increasing power consumption, heating, and associated reliability/scalability challenges as more devices are packed into a small footprint. Despite significant efforts from the device community, a suitable sub‐thermionic alternative is yet to emerge. In this work, an attempt is made to understand the physics of Schottky barrier FETs demonstrating SS < 60 mVdec–1, as evident in some experimental results. Employing the in‐house TCAD tool, the electrostatic potential in the device is accurately modeled with the sweeping gate bias. Next, the Tsu–Esaki model is used to compute the tunneling current. The analysis shows that the steep SS results from an abrupt change in the tunneling barrier profile, from a step‐like to a triangular‐like. However, in the ON regime, the drift component begins to dominate the transfer characteristics. Thus, the subtle interplay between the drift and tunneling components of transport leads to SS < 60 mVdec–1 in OFF‐state with improved in ON‐state. Finally, using the calibrated model, the performance metrics of an all‐2D material‐based CMOS inverter operating at a low VDD ≈ 0.6 V are projected.

Topologically Defective Lattice Potential‐Based Gain‐Dissipative Ising Annealer with Large Noise Margin

AbstractGain‐dissipative Ising machines (GIMs) are annealers inspired by physical systems such as Ising spin glasses to solve combinatorial optimization problems. Compared to traditional quantum annealers, GIM is relatively easier to scale and can save on additional power consumption caused by low‐temperature cooling. However, traditional GIMs have a limited noise margin. Specifically, their normal operation requires ensuring that the noise intensity is lower than their saturation fixed point amplitude, which may result in increased power consumption to suppress noise‐induced spin state switching. To enhance the noise robustness of GIM, in this study a GIM based on a topologically defective lattice potential (TDLP) is proposed. Numerical simulations demonstrate that the TDLP‐based GIM can accurately simulate the bifurcation spin evolution in the Ising model. Furthermore, through the MAXCUT benchmark based on G‐set graphs, the optimal performance of TDLP‐based GIM is shown to surpass that of traditional GIMs. Additionally, the proposed TDLP‐based GIM successfully solves the MAXCUT benchmark and domain clustering dynamics benchmark based on G‐set graphs when the noise intensity exceeds its saturation fixed‐point amplitude. This indicates that the proposed system provides a promising architecture for breaking the small noise constraints required by traditional GIMs.

Feasibility Study of Solar-Powered Electric Vehicle Charging Infrastructure at Selected Petrol Stations in Malaysia

The transition from the conventional internal combustion engine (ICE) to electric vehicles (EV) are expected to result in higher total electricity demand in the country. With the introduction of more EVs into the market, the need for charging stations will grow, subsequently increasing power consumption. This research study aims to assess the potential of solar energy production at petrol stations in central region of Peninsular Malaysia and assess the feasibility of solar-powered petrol stations in meeting the energy demands of electric vehicles. 10 samples of existing petrol stations are selected to install a solar-powered DC charging station as a case study. To study their energy consumption, 90kW DC charger was evaluated. The design of the PV systems for the petrol stations was done by using the PVsyst simulation software. As a result, it is found that 90% of the designed PV systems are able to generate enough energy to meet the estimated annual energy consumption of the 90kW DC charging system. The results also showed that the cost to install the solar-powered charging system for both power ratings can be recouped in less than 2 years. Overall, this research study offers valuable insights for addressing the rising energy consumption resulting from the increased penetration of electric vehicles in the future. Additionally, the implementation of solar-powered charging infrastructure aligns with the country’s renewable energy objectives and efforts to reduce greenhouse gas emissions. However, the assessment of EV penetration and the adoption of charging infrastructure was conducted based on government targets rather than relying on current data regarding the actual numbers and trends of EVs and chargers in Malaysia. The availability of accurate and up-to-date data on the number of EVs, their battery characteristics, chargers, and user charging behaviors in Malaysia would contribute to further enhancing this research work.

Improving Maraging Steel 350 Machinability via Wiper Insert-Enhanced Face Milling

Despite the prevalent application of 18% Ni maraging steel in critical sectors such as aerospace and automotive due to its unique characteristics, including high ductility, yield strength, and hardenability, its machining presents enormous challenges, categorizing it as a difficult-to-machine material. The cutting tool’s geometry is crucial in machining, significantly affecting chip formation, cutting forces, power consumption, and obtainable surface quality. In particular, wiper insert technology, characterized by its multi-radius design, offers an increased contact area compared to conventional inserts, potentially enhancing the quality of the machined surface. This study explores the effectiveness of wiper inserts in the face-milling of maraging steel 350, conducting a comparative analysis across three distinct machining setups. These setups vary by alternating the number of wiper and conventional inserts within the same cutter, thereby examining the influence of insert configuration on machining outcomes. The research employs a reliable and well-established statistical approach to evaluate how different variables, such as cutting speed and feed rate, affect surface quality, power consumption, and material removal rate (MRR). It also sheds light on the material removal mechanisms facilitated by each type of insert. The findings reveal that incorporating a higher number of wiper inserts significantly enhances the surface finish but concurrently increases power consumption. Thus, the study successfully identifies an optimal set of process parameters that attain a balance between achieving superior surface quality and maintaining energy efficiency in the machining of maraging steel 350. This balance is crucial for optimizing manufacturing processes while adhering to the stringent quality and sustainability standards required in aerospace and automotive manufacturing.

A novel pulse-jet cleaning method to effectively extend the lifetime of pleated filter and reduce dust emission

To solve the problem of poor cleaning effect of pleated filter, this study explored in detail the impact of various cleaning method on the filtration and cleaning performance of the filter media in clean-on-demand mode. Furthermore, the clogging mechanism of the filter and the deposition structure of dust on the filter media were analyzed based on the effective filtration area ratio and true resistance coefficient calculated by the proposed model. The results demonstrated that for the constant pulse-jet cleaning pressure (PJCP), increasing PJCP was more conducive to removing dust deposited on the filter media and improving regeneration efficiency, although higher PJCP increased power consumption. Different from the thick primary dust layer formed on the filter surface under the constant PJCP of 0.3 MPa, the dust was mainly deposited inside the filter media under the constant PJCP of 0.5 and 0.7 MPa. In contrast, stepwise increasing PJCP was more effective in cleaning and regenerating the filter media, which was reflected in further extending the filter lifetime, increasing the processing air volume and reducing the dust emission concentration with acceptable energy consumption. Moreover, the filter media mainly clogged itself and formed a medium thickness primary dust layer on the filter surface.

Design of Low Power Ternary Logic Encoder and ADC using CNTFET

Ternary logic has received substantial attention over the past decade due to its compensations of smaller chip area and interconnection compared to outmoded binary logic. Carbon Nanotube Field Effect Transistor (CNTFET) technology is widely used for ternary logic implementation due to its versatile threshold voltages. The increased power consumption in current designs utilizing CNFETs, as linked to binary logic, is attributed to elevated static power dissipation within the design. This work recommends alternative triple encoder designs with a focus on reducing the consumption of power. The model uses an additional Vdd/2 supply voltage to decrease static power dissipation and encoder power delay. Cadence virtuoso-based circuit simulations are implemented for the proposed encoder design.

The influence of wall effects on self-propelled performance of brown trout swimming

When benthic fish engage in predation, they often swim near the riverbank or close to hard rocks, where they are subjected to the combined effects of side and benthic walls. This study focuses on the brown trout and employs a three-dimensional numerical model to simulate the process of brown trout accelerating from a stationary state to a cruising state under the influence of wall effects. A self-developed subroutine algorithm is applied to solve the various hydrodynamic parameters of brown trout swimming. By varying the distance between the fish's center of gravity and the wall, this study explores the self-propelled performance and efficiency of fish swimming affected by the sidewall as well as the combined influence of the side and benthic walls. This study also reveals the hydrodynamic mechanism of wall effects that impact the performance of fish swimming in the body/caudal fin (BCF) mode. The results demonstrate that when the distance is less than 0.5 times the body length of the fish, swimming near the sidewall can enhance speed and thrust, but the swimming efficiency will reduce. Closer proximity to the benthic wall leads to increased power consumption and decreased efficiency, which is disadvantageous for fish swimming. The findings of this study reveal the unstable wall effects experienced by fish and offer insights for designing biomimetic underwater vehicles that leverage wall effects as well as for creating habitats that support fish swimming in the BCF mode.

Multiple hot spot cooling with flow boiling of HFE-7000 in a multichannel pin fin heat sink

Electronics cooling applications require a high cooling performance due to increasing power consumption. Phase change heat transfer is a popular approach for offering high heat flux cooling. Controlling temperature distribution and mitigating boiling instabilities under high heat flux conditions present a significant challenge. While numerous studies investigated the effect of pin fins in a minichannel with single hotspots on boiling enhancement, few research studies are available for minichannels with multiple segmented hotspots. This study's primary objective is to dissipate high heat (1.4 kW) using flow boiling of a dielectric fluid, namely HFE-7000, which has lower thermophysical properties than water, to achieve a uniform temperature distribution, and to suppress boiling instabilities. The boiling performance of a pin fin multichannel heat sink with dimensions of 3 × 14 × 300 mm (H × W × L) subjected to 8 hotspots with uniform heating was investigated in this regard. Flow boiling experiments were performed over a wide range of input heat power from ∼0.1 to ∼1.4 kW with a coolant mass flux of 500 kg/m2 s in the three parallel minichannels having staggered elliptical pin fins with no tip clearance on each hotspot and distribution pins at the inlet. High-speed visualization was used to relate the heat transfer coefficient variations along the channel to flow patterns. The heat transfer performance and flow regimes at different locations were obtained and discussed. Thanks to the mixing effect of elliptical micro pin fins with optimum geometrical parameters along the heat sink, the presence of distribution pin fins at the inlet for suppressing boiling instabilities and early transition to stable annular flow, the temperature uniformity (∼2.3 °C, temperature difference between the inlet and outlet hotspots) could be achieved at the highest heat input. Heat transfer coefficient reduction upto 27 % was reported along the channel when comparing inlet (1st) and outlet (8th) hotspots.

UGV에서 효율적인 노면 모니터링을 위한 퓨전 센서 시스템

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of =10, =10.

Extended EDAS Analysis for Multi-Criteria Decision-Making Based on Distributed Generation (DG) Technologies System

Recently, there has been a growing interest in distributed generation (DG) technologies, driven by various factors such as fuel price uncertainties, environmental constraints, and increasing power consumption along with transmission capacity shortages, in modern power systems. DG, which involves utilizing clean and renewable energy sources for power generation within the distribution system, has gained significant attention globally. Many developing countries, including Libya, are considering the adoption of DG technologies as part of their energy system expansion plans. Libya, located in North Africa and characterized by vast desert lands, has abundant solar radiation, making solar energy a promising and sustainable source of power. However, despite this energy potential, the southern part of Libya faces frequent power outages. In order to effectively maintain service quality, it is essential to conduct quantitative evaluation of wireless sensor networks. the evaluation of wireless sensor networks involves addressing the multiple attribute group decision-making (MAGDM) problem. To tackle the challenges of MAGDM, an extension of the classical EDAS (Evaluation based on Distance from Average Solution) method is proposed in this paper. The proposed method incorporates interval-valued intuitionistic fuzzy sets (IVIFSs), which provide a more flexible and comprehensive representation of uncertainty, to handle the complexities of MAGDM. The paper begins with a brief review of essential concepts related to IVIFSs. Then, the weights of attributes are determined using the CRITIC method. Subsequently, the IVIF-EDAS method is established by integrating the EDAS method with IVIFSs, and all the calculation procedures are described.

Energy efficient cluster routing protocol for wireless sensor networks using hybrid metaheuristic approache’s

The development of wireless sensor networks (WSNs) has been particularly notable in the context of smart computing, where various application areas have been identified. These networks consist of self-configured, small sensor nodes with battery power. However, sensors have limited energy and resources at their disposal. When unbalanced nodes exist within the network, it adversely affects the network’s lifetime due to increased power consumption. Designing efficient routing for wireless sensor networks remains a challenging task, particularly in terms of energy efficiency. Optimal solutions to address these challenges involve reducing node energy consumption through the implementation of clustering techniques. The current clustering schemes do not take into account node energy balancing, node density, and scalability when selecting low-energy nodes as cluster heads. The existing system is limited to exploring global opportunities within specified search zones. To enhance the performance of WSNs, a hybrid approach combining artificial bee colony (ABC) and ant colony optimization (ACO) has been developed. This approach helps in selecting an ideal cluster head from a group of terminals. Several factors are considered in the cluster head election, including residual energy at the nodes, distance to neighbors, distance to the base station, node degree, and node centrality. ACO determines the path between the cluster leader and the base station (BS) by selecting the most efficient route in terms of distance, remaining power, and node degrees. The performance of this proposed methodology has been analyzed in terms of energy consumption, network lifetime, and data packets at the base station. A comparison has been made between the outputs of the proposed methods and traditional benchmarking methods. The results reveal a substantial improvement in the average network lifetime, showcasing an increase of 40.50%, 33.17%, 25.00%, and 15.49% when compared to LEACH, Beecluster, iABC, and BeeSensor, respectively. In terms of alive nodes, our solution surpasses LEACH, Beecluster, iABC, and BeeSensor by 28.7%, 22.51%, 20.95%, and 12.47%, respectively. Additionally, the energy consumption of our approach proves to be significantly lower than LEACH, Beecluster, iABC, and BeeSensor, recording reductions of 47.25%, 32.40%, 27.38%, and 22.20%, respectively.

Impact of Ambient Temperature and Humidity on the Performance of Vapour Compression Air Conditioning System - Experimental and Numerical Investigation

Due to global warming, population growth, and urbanisation, demands for space cooling in buildings have significantly increased. Mechanical vapour compression cooling technology is relatively mature, well-understood and most commonly used globally. However, they suffer from high energy consumption and adverse environmental impact due to the refrigerants used. As ambient temperature and humidity have significant effects on the performance of vapour compression systems, this paper experimentally and numerically investigates their impact on the performance of vapour compression air conditioning system in terms of power consumption and cooling capacity. It was concluded that at ambient humidity of 55%, as the ambient temperature increases from 22 °C to 40 °C, the compressor temperature has increased from 46 °C to 59 °C which increased its power consumption from 751.2 to 935.52 Watts, giving an increase of 10.24 Watts for every one degree of ambient temperature increase. Also, as ambient humidity in-creased from 32% to 88%, the power consumption increased from 739.2 Watts to 853.2 Watts, an increase of 114 Watts. A validated CFD (Computational Fluid Dynamics) model was developed and predicted a loss in the enthalpy change from 11.7kJ/kg.d.a to 3.6kJ/kg.d.a. due to ambient temperature increasing from 22 °C to 40 °C and a loss of enthalpy change from 12.857kJ/kg.d.a to 5.524kJ/kg.d.a. due to increasing the ambient air humidity from 32% to 88%. This work high-lighted the impacts of ambient conditions on the performance of vapour compression cooling system in terms of increased power consumption and loss of cooling capacity.

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

Microheater Topology for Advanced Gas Sensor Applications with Carbyne-Enriched Nanomaterials

The response characteristics of carbyne-enriched surface-acoustic-wave (SAW)-based gas sensors utilizing meander and rectangular microheater topologies were investigated to assess their desorption and recovery properties. Comparative analysis of contact resistance and interface capacitance before and after heating revealed minimal deviation in contact resistance, signifying strong thermal stability in the carbyne-enriched layer. However, the interface capacitance varied with the microheater size. Our analysis reveals that a small meander microheater configuration (line width: 300 µm) facilitates efficient sensor recovery at ethanol concentration measurements in the range of 180–680 ppm, maintaining a low deviation in time delay across different concentrations (~2.3%), resulting in a narrow hysteresis and linear sensor response. Conversely, the large meander microheater (line width: 450 µm) and rectangular dense microheater induce irreversible changes in the sensing structure, leading to a widened hysteresis at higher concentrations and increased power consumption. Recovery patterns display substantial deviations from initial values at different concentration levels. Higher concentrations exhibit broader hysteresis, while lower concentrations show narrower hysteresis loops, compared to the small meander microheater. The study offers insights into desorption rates, power consumption variations, and recovery behaviors related to different microheater configurations. It demonstrates the importance of microheater topology selection in tailoring recovery properties and response characteristics, contributing to the advancement of carbyne-based sensor technology.