Monitoring Applications Research Articles

Smart forest monitoring: A novel Internet of Things framework with shortest path routing for sustainable environmental management

AbstractForests play a pivotal role in protecting the environment, preserving vital natural resources, and ultimately sustaining human life. However, the escalating occurrences of forest fires, whether of human origin or due to climate change, poses a significant threat to this ecosystem. In recent decades, the emergence of the IoT has been characterised by the utilisation of smart sensors for real‐time data collection. IoT facilitates proactive decision‐making for forest monitoring, control, and protection through advanced data analysis techniques, including AI algorithms. This research study presents a comprehensive approach to deploying a dynamic and adaptable network topology in forest environments, aimed at optimising data transmission and enhancing system reliability. Three distinct topologies are proposed in this research study: direct transmission from nodes to gateways, cluster formation with multi‐step data transmission, and clustering with data relayed by cluster heads. A key innovation is the use of high‐powered telecommunication modules in cluster heads, enabling long‐range data transmission while considering energy efficiency through solar power. To enhance system reliability, this study incorporates a reserve routing mechanism to mitigate the impact of node or cluster head failures. Additionally, the placement of gateway nodes is optimised using meta‐heuristic algorithms, including particle swarm optimisation (PSO), harmony search algorithm (HSA), and ant colony optimisation for continuous domains (ACOR), with ACOR emerging as the most effective. The primary objective of this article is to reduce power consumption, alleviate network traffic, and decrease nodes' interdependence, while also considering reliability coefficients and error tolerance as additional considerations. As shown in the results, the proposed methods effectively reduce network traffic, optimise routing, and ensure robust performance across various environmental conditions, highlighting the importance of these tailored topologies in enhancing energy efficiency, data accuracy, and network reliability in forest monitoring applications.

Self‐Powered Wearable Pressure Sensors for Detection and Separation of Signals for Various Human Movements

A detailed study on the dynamic response of mountable pressure sensors is presented, with a focus on foot pressure sensors integrated with carbon nanotube (CNT)‐coated cotton fibers. The research explores the sensor's sensitivity to pressure changes, repeatability, hysteresis, and durability through rigorous modeling and experimental validation. Computational simulations using Python and experimental data demonstrate the sensor's nonlinear conductance response to applied force, attributed to the varying contact area and number of contact points among the fibers. Long‐term outdoor exposure tests confirm the material's resilience, maintaining electrical conductivity and structural integrity. The study also demonstrates the sensor's ability to monitor human activities like walking, running, stair climbing, and jumping, as well as detecting muscle movements during swallowing, coughing, and speech, highlighting potential applications in health monitoring and artificial voice synthesis. The MRMR algorithm is used for feature selection to distinguish activities, demonstrating the sensor's potential for activity recognition. Additionally, a piezoelectric sensor on the pressure sensors estimates harvested electric power to power wearable devices. This work contributes to the advancement of self‐powered wearable pressure sensors to monitor real‐time human activity, with implications for healthcare, sports performance, and assistive technologies.

Expanding the Toolbox of Oxidants: Controllable Etching of Ultrasmall Au Nanoparticles toward Tailorable NIR-II Luminescence and Ligand-Mediated Biodistribution and Clearance.

Oxidant-driven and controllable etching of small-sized nanoparticles (NPs, d < 3 nm) and tailorable modulation of their optical properties are challenging due to the high reactivity and complicated surface chemistry. Herein, we present a facile strategy for highly controllable oxidative etching of ultrasmall AuNPs and tailorable modulation of luminescence. The proper choice of a moderate oxidant, ClO-, could not only selectively etch the Au(I)-thiolate motifs from the nanoparticle surface at the subnanometer scale but also retained a stable metallic core structure without aggregation, which impressively prompted the wide-range luminescent switching from the visible to second near-infrared (NIR-II) region. The resultant oxidized AuNPs displayed highly luminescent NIR-II emission with a quantum yield of 3.0%, excellent monodispersed stability, ideal biocompatibility, and tunable shielding effects against protein adsorption. With those outstanding features, oxidized AuNPs could be utilized as nanoprobes for long-lasting and in vivo bioimaging of associated metabolic behaviors with distinguishable organ-specific targeting capabilities and ligand-mediated kinetics in nanoparticle clearance. These findings expand the toolbox of oxidants for the controllable synthesis of NIR-II nanoprobes and open up a path for exploring diverse ligand interactions on ultrasmall AuNPs with organs or tissues that might advance their monitoring applications for a wide range of clinically important diseases.

Ultra-compact dual-channel integrated CO2 infrared gas sensor

Expiratory CO2 concentrations can directly reflect human physiological conditions, and their detection is highly important in the treatment and rehabilitation of critically ill patients. Existing respiratory gas analyzers suffer from large sizes and high power consumption due to the limitations of the internal CO2 sensors, which prevent them from being wearable to track active people. The internal and external interference and sensitivity limitations must be overcome to realize wearable respiratory monitoring applications for CO2 sensors. In this work, an ultra-compact CO2 sensor was developed by integrating a microelectromechanical system emitter and thermopile detectors with an optical gas chamber; the power consumption of the light source and ambient temperature of the thermally sensitive devices were reduced by heat transfer control; the time to reach stabilization of the sensor was shortened; the humidity resistance of the sensor was improved by a dual-channel design; the light loss of the sensor was compensated by improving the optical coupling efficiency, which was combined with the amplitude trimming network to equivalently improve the sensitivity of the sensor. The minimum size of the developed sensor was 12 mm × 6 mm × 4 mm, and the reading error was <4% of the reading from −20 °C to 50 °C. The minimum power consumption of the sensor was ~33 mW, and the response time and recovery time were 10 s (@1 Hz), and the sensor had good humidity resistance, stability, and repeatability. These results indicate that the CO2 sensor developed using this strategy has great potential for wearable respiratory monitoring applications.

Background-Free Imaging of Food Freshness Using Curcumin-Functionalized Upconversion Reversible Hydrogel Patch.

Functionalized upconversion nanomaterials can overcome the drawbacks faced of strong background interference, photodamage, and spectral overlap by conventional optical labeling. Here, curcumin-functionalized upconversion hydrogel patch is designed with background-free and reversible for food freshness monitoring by ultra-sensitive response to biogenic amines. By loading the probes onto hydrogel patch, utilizing the good ductility to solve the problem of non-smooth surface coverage, thus accurately capturing biogenic amines. The presence of biogenic amines leads to the conversion of the diketone group on the probe to enolate ions, which triggers fluorescence resonance energy transfer (FRET) and ultimately causes the upconverted fluorescence to gradually change from green to red. The probe exhibits good detection capability for biogenic amines with a low limit of detection (LOD) of 2.73 µm. Interestingly, the patch can be restored to its initial state after water rinsing, realizing reversible detection of biogenic amines. Additionally, combining the color recognition system of smartphone can convert the imaging signal into a data signal to achieve quantitative analysis and show a reliable assessment comparable to the results of high performance liquid chromatography (HPLC). This study demonstrates the practical applicability in real-time monitoring of freshness, suggests great potential in developing optical nano-sensing strategy to ensure food safety.

Wearable Biosensor Smart Glasses Based on Augmented Reality and Eye Tracking.

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user's intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system's real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system's ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses' algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

A review on early-age cracking of concrete: Causes and control

The early-age cracking of concrete is one of the most critical parameters affecting the performance of concrete structures. It is attributed to both the internal and external influences such as early age restrained shrinkage, thermal deformation as well as the applied load. In this paper, a detailed review of the causes of early-age cracking is first presented, followed by the factors affecting the concrete cracking including raw materials quality problems, improper construction measures, external environmental influences and structural design flaws. Then the preventive measures for concrete cracking are summarized. The potential for the application and development of structural health monitoring and artificial intelligence techniques for early-age cracking of concrete is also discussed. Through the comprehensive review of the early-age cracking of concrete, the research direction for the subsequent research on early-age cracking of concrete is pointed out. The current problems and concerns of concrete cracking are also prospected.

DASR-Net: Land Cover Classification Methods for Hybrid Multiattention Multispectral High Spectral Resolution Remote Sensing Imagery

The prompt acquisition of precise land cover categorization data is indispensable for the strategic development of contemporary farming practices, especially within the realm of forestry oversight and preservation. Forests are complex ecosystems that require precise monitoring to assess their health, biodiversity, and response to environmental changes. The existing methods for classifying remotely sensed imagery often encounter challenges due to the intricate spacing of feature classes, intraclass diversity, and interclass similarity, which can lead to weak perceptual ability, insufficient feature expression, and a lack of distinction when classifying forested areas at various scales. In this study, we introduce the DASR-Net algorithm, which integrates a dual attention network (DAN) in parallel with the Residual Network (ResNet) to enhance land cover classification, specifically focusing on improving the classification of forested regions. The dual attention mechanism within DASR-Net is designed to address the complexities inherent in forested landscapes by effectively capturing multiscale semantic information. This is achieved through multiscale null attention, which allows for the detailed examination of forest structures across different scales, and channel attention, which assigns weights to each channel to enhance feature expression using an improved BSE-ResNet bilinear approach. The two-channel parallel architecture of DASR-Net is particularly adept at resolving structural differences within forested areas, thereby avoiding information loss and the excessive fusion of features that can occur with traditional methods. This results in a more discriminative classification of remote sensing imagery, which is essential for accurate forest monitoring and management. To assess the efficacy of DASR-Net, we carried out tests with 10m Sentinel-2 multispectral remote sensing images over the Heshan District, which is renowned for its varied forestry. The findings reveal that the DASR-Net algorithm attains an accuracy rate of 96.36%, outperforming classical neural network models and the transformer (ViT) model. This demonstrates the scientific robustness and promise of the DASR-Net model in assisting with automatic object recognition for precise forest classification. Furthermore, we emphasize the relevance of our proposed model to hyperspectral datasets, which are frequently utilized in agricultural and forest classification tasks. DASR-Net’s enhanced feature extraction and classification capabilities are particularly advantageous for hyperspectral data, where the rich spectral information can be effectively harnessed to differentiate between various forest types and conditions. By doing so, DASR-Net contributes to advancing remote sensing applications in forest monitoring, supporting sustainable forestry practices and environmental conservation efforts. The findings of this study have significant practical implications for urban forestry management. The DASR-Net algorithm can enhance the accuracy of forest cover classification, aiding urban planners in better understanding and monitoring the status of urban forests. This, in turn, facilitates the development of effective forest conservation and restoration strategies, promoting the sustainable development of the urban ecological environment.

State of continental discharge estimation and modelling: challenges and opportunities for Africa

ABSTRACT Africa’s diverse climates and sparse hydro-meteorological networks create significant challenges in accurately estimating river discharge. Discharge data are crucial for managing water resources and predicting extremes. Our review assesses the data gap, existing methods, and technologies for river discharge estimation in Africa. Limited gauging networks on rivers, including in 63 transboundary basins, hinder accurate discharge modelling, affecting resource management and disaster response. Despite the potential of remote sensing, Geographic Information System (GIS), satellite imagery, and machine learning, their large-scale application for river discharge monitoring in Africa is limited. We propose the use of a monitoring system involving local communities in data collection and decision making, supported by global data centres, enhanced regional data sharing, and strengthened transboundary cooperation. For example, incorporating water data products, including discharge data, in data cubes, such as Digital Earth Africa, could improve monitoring. Strategic investments in hydro-meteorological instrumentation are crucial for strengthening climate resilience.

Unveiling the silent information of wastewater-based epidemiology of SARS-CoV-2 at community and sanitary zone levels: experience in Córdoba City, Argentina

ABSTRACT The emergence of COVID-19 in 2020 significantly enhanced the application of wastewater monitoring for detecting SARS-CoV-2 circulation within communities. From October 2021 to October 2022, we collected 406 wastewater samples weekly from the Córdoba Central Pipeline Network (BG-WWTP) and six specific sewer manholes from sanitary zones (SZs). Following WHO guidelines, we processed samples and detected SARS-CoV-2 RNA and variants using real-time PCR. Monitoring at the SZ level allowed for the development of a viral activity flow map, pinpointing key areas of SARS-CoV-2 circulation and tracking its temporal spread and variant evolution. Our findings demonstrate that wastewater-based surveillance acts as a sensitive indicator of viral activity, detecting imminent increases in COVID-19 cases before they become evident in clinical data. This study highlights the effectiveness of targeted wastewater monitoring at both municipal and SZ levels in identifying viral hotspots and assessing community-wide circulation. Importantly, the data shows that environmental wastewater studies provide valuable insights into virus presence, independent of clinical COVID-19 case records, and offer a robust tool for adapting to future public health challenges.

Visual signal transduction for environmental stewardship: A novel biosensing approach to identify and quantify chlorpyrifos-related residues in aquatic environments

The pervasive presence of organophosphate pesticides (OPs), such as chlorpyrifos (CPF), in aquatic ecosystems underscores the urgent need for sensitive and reliable detection methods to safeguard environmental and public health. This study addressed the critical need for a novel biosensor capable of detecting CPF and its toxic metabolite, 3,5,6-trichloro-2-pyridinol (TCP), with high sensitivity and selectivity, suitable for field applications in environmental monitoring. The study engineered a whole-cell biosensor based on E. coli strains that utilize the ChpR transcriptional regulator and the vioABCE gene cluster, providing a distinct visual and colorimetric response to CPF and TCP. The biosensor's performance was optimized and evaluated across various water matrices, including freshwater, seawater, and soil leachate. The biosensor demonstrated high sensitivity with a broad linear detection range, achieving limits of detection (LODs) at 0.8 μM for CPF and 7.813 nM for TCP. The linear regression concentration ranges were 1.6–12.5 μM for CPF and 15.6–125 nM for TCP, aligning with environmental standard limits and ensuring the biosensor's effectiveness in real-world scenarios. This innovative biosensing approach offers a robust, user-friendly tool for on-site environmental monitoring, significantly mitigating OPs contamination and advancing current detection technologies to meet environmental protection standards.

Improving the Science of Adolescent Social Media and Mental Health: Challenges and Opportunities of Smartphone-Based Mobile Sensing and Digital Phenotyping

AbstractThe impact of social media (SM) use (‘screentime’) on adolescent mental health has been the focus of increasing concern, despite mixed findings from empirical research. Current methodological approaches rely on self-reported SM use, which has limited accuracy and obscure the dynamic interplay of SM use and mental health. Smartphone-based mobile sensing offers new opportunities to gain insights into adolescents’ SM use patterns and behaviors, particularly at an idiographic level. Considerations and challenges of smartphone sensing methods for capturing adolescents’ SM use patterns and behaviors in clinical psychological science are discussed in the context of a pilot study using smartphone-based sensing with adolescents. The pilot study included 19 adolescents (Mean age = 15.84; 68% boys; 79% White) who installed a passive monitoring application (AWARE) on their phones for 31 (SD = 5.6) days. Descriptive data of sensing acceptability and feasibility are presented based on participant ratings and data yield ratio of usable data (74.18%). Sensing yielded 10,038 hourly observations collected from the ‘application foreground’ sensor across all participants from social media apps, and a total of 645 applications used. Categorization of SM apps were coded (kappa &gt;.90) into ‘social networking’ (N = 20 apps) and ‘broader SM’ (N = 41) and compared to both Play Store-defined SM apps (N = 26) and popular SM apps based on Common Sense Media Survey (N = 9). Descriptive data on extracted behavioral features (duration, checking) from SM use categories (binned hourly and daily) are presented. Challenges, opportunities, and future directions of sensing methods for SM use are discussed to inform our understanding of its impacts on mental health and to improve the rigor of SM research in clinical psychological science.

SiO2-capped ZnO quantum dot based highly sensitive optical fiber humidity sensor with potential applications in human breath monitoring and voice print recognition

This article describes the development and characterization of an optical fiber humidity sensor employing intensity modulation via the evanescent wave (EW) absorption technique. For the development of the sensor, SiO2-capped ZnO quantum dot (QDs) thin film is synthesized over the decladded portion of plastic cladding silica (PCS) fiber via the sol-gel method. A thorough experimental investigation was conducted by varying the thickness of the sensing film to optimize the sensor’s response. The sensing probe with optimized film thickness of 891 nm demonstrates a linear response over 30.5%–92.5%RH with an enhanced sensitivity of 46.2 mV/%RH (0.0138RH−1). Very fast response and recovery times of 2 s and 2.5 s are observed during humidification and dehumidification for the optimized sensing probe. The maximum resolution recorded during the short stability test is ±0.12%RH. Additionally, the proposed sensor demonstrates a very high degree of repeatability, reversibility, and stability. The proposed sensor has also been tested for human breath monitoring and voice print recognition. The result shows the sensor is able to detect minute humidity fluctuations in exhaled air during breathing and speaking.

Advances of Triboelectric and Piezoelectric Nanogenerators toward Continuous Monitoring and Multimodal Applications in the New Era

Abstract Benefiting from the widespread potential applications in the era of Internet of Thing (IoT) and metaverse, triboelectric and piezoelectric nanogenerators have attracted considerably increasing attention. Their outstanding characteristics, such as self-powered ability, high output performance, integration compatibility, cost effectiveness, simple configurations and versatile operation modes, could effectively expand the lifetime of vastly distributed wearable, implantable and environmental devices, eventually achieving self-sustainable, maintenance-free and reliable systems. However, current triboelectric/piezoelectric based active (i.e., self-powered) sensors still encounter serious bottlenecks in continuous monitoring and multimodal applications due to their intrinsic limitations of monomodal kinetic response and discontinuous transient output. This work systematically summarizes and evaluates the recent research endeavours to addressing the above challenges, with detailed discussions on the challenge origins, designing strategies, device performance and corresponding diverse applications. Finally, conclusions and outlook regarding the research gap in self-powered continuous multimodal monitoring systems are provided, proposing the necessity of future research development in this field.

Use of Digital Smartphone Applications for Blood Pressure Monitoring: An Integrative Review

With the purpose of identifying in the literature and describing the scientific evidence for the use of smartphone applications for blood pressure monitoring, this study is an integrative literature review, employing a qualitative approach conducted with studies from 2014 to 2024, accessed via the Virtual Health Library (VHL) portal. The research resulted in 10 articles addressing the use of digital technology in smartphones and answering the following guiding question: What is the effectiveness of this technology for diagnostic purposes and blood pressure control? This study showed that digital smartphone applications are effective for blood pressure monitoring, providing important information for both patients and healthcare professionals. The evidence found indicates that digital applications are effective in monitoring blood pressure variations; however, they are not effective for diagnostic purposes but can be used to complement existing data.

Maximum Fourier spectrum cyclostationarity blind deconvolution and its application in structural health monitoring of power transformers

Abstract Power transformer is the most important equipment that affects whether the electric power system can be operated safely and normally, whose condition assessment problem has attracted considerable attention. Background noise frequently affects the effectiveness of nonintrusive techniques based on vibro-acoustic signals for structural health monitoring in power transformers. It is challenging to effectively eliminate background noise interference from time-frequency domain transformer defect detection techniques such as wavelet transformations, modal decomposition, etc. In this scenario, the Fourier spectrum cyclostationarity index (FSC) is designed based on the cyclostationarity index used for rotating machinery fault diagnosis to construct a maximum Fourier spectrum cyclostationarity blind deconvolution method (MFSCBD) for transformer fault detection in this paper. Firstly, the limitations of the traditional blind deconvolution (BD) in transformer fault detection are discussed in the mathematical principle. Then, a new BD framework based on Kepler optimization algorithm is proposed according to the principle of convex optimization to address the problems of difficulty in solving the complex blind objective function in the traditional differential BD framework and the ill-condition problem in the Rayleigh quotient BD framework. Subsequently, a synthetic nonstationary and nonlinear simulation signal is constructed for numerical verification, and a six-microphone array is designed to obtain the practical signals from the operating transformer to verify the performance of MFSCBD. Finally, the applications on the simulated and experimental signals of power transformers demonstrate that MFSCBD outperforms complete ensemble empirical mode decomposition with adaptive noise and successive variational mode decomposition to some extent for structural health monitoring.

The Development of a Flexible Humidity Sensor Using MWCNT/PVA Thin Films.

The exponential demand for real-time monitoring applications has altered the course of sensor development, from sensor electronics miniaturization, e.g., resorting to printing techniques, to low-cost, flexible and functional wearable materials. Humidity sensing has been used in the prevention and diagnosis of medical conditions, as well as in the assessment of physical comfort. This paper presents a resistive flexible humidity sensor composed of silver interdigitated electrodes (IDTs) screen printed onto polyimide film and an active layer of multiwall carbon nanotubes (MWCNT) dispersed in a water-soluble polymer, polyvinyl alcohol (PVA). Different MWCNT/PVA sensor sizes and MWCNT percentages are tested to study their effect on the initial electrical resistance (Ri) values and sensor response at different humidity percentages. The results show that the Ri values decrease with the increase in % MWCNT. The sensor size did not influence the sensor response, while the % MWCNT affected the sensor behavior upon relative humidity (RH) increments. The 1% MWCNT/PVA sensor showed the best response, reaching a relative electrical resistance, ΔR/R0, of 509% at 99% RH. Comparable with other reported sensors, the produced MWCNT/PVA flexible sensor is simpler, greener and shows a good sensitivity to humidity, being easily incorporated in wearable monitoring applications, from sports to medical fields.

The Role of Machine Learning in Enhancing Particulate Matter Estimation: A Systematic Literature Review

As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM2.5 and PM10. This review paper presents a comprehensive systematic assessment of machine learning (ML) techniques for estimating PM concentrations, drawing on studies published from 2018 to 2024. Traditional statistical methods often fail to account for the complex dynamics of air pollution, leading to inaccurate predictions, especially during peak pollution events. In contrast, ML approaches have emerged as powerful tools that leverage large datasets to capture nonlinear, intricate relationships among various environmental, meteorological, and anthropogenic factors. This review synthesizes findings from 32 studies, demonstrating that ML techniques, particularly ensemble learning models, significantly enhance estimation accuracy. However, challenges remain, including data quality, the need for diverse and balanced datasets, issues related to feature selection, and spatial discontinuity. This paper identifies critical research gaps and proposes future directions to improve model robustness and applicability. By advancing the understanding of ML applications in air quality monitoring, this review seeks to contribute to developing effective strategies for mitigating air pollution and protecting public health.

Responsive Magnetic Particle Imaging Tracer: Overcoming "Always-On" Limitation, Eliminating Interference, and Ensuring Safety in Adaptive Therapy.

Magnetic particle imaging (MPI) has emerged as a novel technology utilizing superparamagnetic nanoparticles as tracers, essential for disease diagnosis and treatment guidance in preclinical animal models. Unlike other modalities, MPI provides high sensitivity, deep tissue penetration, and no signal attenuation. However, existing MPI tracers suffer from "always-on" signals, which complicate organ-specific imaging and hinder accuracy. To overcome these challenges, we have developed a responsive MPI tracer using pH-responsive PdFe alloy particles coated with a gatekeeper polymer. This tracer exhibits pH-sensitive Fe release and modulation of the MPI signal, enabling selective imaging with a higher signal-to-noise ratio and intratumoral pH quantification. Notably, this responsive tracer facilitates subtraction-enhanced MPI imaging, effectively eliminating interference from liver uptake and expanding the scope of abdominal imaging. Additionally, the tracer employs a dual-function mechanism for adaptive cancer therapy, combining pH-switchable enzyme-like catalysis with dual-key co-activation of ROS generation, and Pd skeleton that scavenges free radicals to minimize Fe-related toxicity. This advancement promises to significantly expand MPI's applicability in diagnostics and therapeutic monitoring, marking a leap forward in imaging technology.

Ammonia gas sensors based on multi-wall carbon nanofiber field effect transistors by using gate modulation

Ammonia gas sensors play a critical role in environmental monitoring and healthcare applications due to the harmful effects of ammonia exposure. In this study, I present a novel approach to ammonia detection using field-effect transistors (FETs) based on Multi-Wall Carbon Nanofibers (MWCNFs), known for their outstanding electrical properties. I fabricate and characterize MWCNF-FET sensors, utilizing interdigitated Source-Drain electrodes directly deposited onto aligned semiconducting MWCNFs. By systematically investigating the effect of gate bias on sensor performance, I demonstrate that applying a positive gate voltage enhances the sensor's gas response by up to 55 % across varying concentrations of ammonia. Additionally, I show that appropriate gate modulation significantly improves the device's reversibility, ensuring reliable, repeatable measurements. These findings highlight the potential of MWCNF-FETs in developing high-performance, gate-tunable ammonia gas sensors for real-world applications.