Detection Mechanism Research Articles

Microwave-Assisted Synthesis of N, S Co-Doped Carbon Quantum Dots for Fluorescent Sensing of Fe(III) and Hydroquinone in Water and Cell Imaging

The detection of heavy metal ions and organic pollutants from water sources remains critical challenges due to their detrimental effects on human health and the environment. Herein, a nitrogen and sulfur co-doped carbon quantum dot (NS-CQDs) fluorescent sensor was developed using a microwave-assisted carbonization method for the detection of Fe3+ ions and hydroquinone (HQ) in aqueous solutions. NS-CQDs exhibit excellent optical properties, enabling sensitive detection of Fe3+ and HQ, with detection limits as low as 3.40 and 0.96 μM. Notably, with the alternating introduction of Fe3+ and HQ, NS-CQDs exhibit significant fluorescence (FL) quenching and recovery properties. Based on this property, a reliable “on-off-on” detection mechanism was established, enabling continuous and reversible detection of Fe3+ and HQ. Furthermore, the low cytotoxicity of NS-CQDs was confirmed through successful imaging of HeLa cells, indicating their potential for real-time intracellular detection of Fe3+ and HQ. This work not only provides a green and rapid synthesis strategy for CQDs but also highlights their versatility as fluorescent probes for environmental monitoring and bioimaging applications.

Low-Cost Laser Security System with Intrusion Detection and Alert Mechanism

Security becomes necessary factor nowadays. The crime gang improves their technology to perform their operation along with the development of technology. Hence, technology of security should be more improved to protect the crime works. We have decided to make a security project. In this project we have used laser light for security purpose. We know laser light goes through long distance. When any person or object passes through the laser line, the security alarm will start to ring. There are two parts of the system. One is transmitter and other is receiver. The transmitter side consists of a pair of dry cell batteries and an on-off switch. The receiver side, there is a focusing LDR (Light Dependent Resistor) sensor to sense the laser ray. It is connected with the main driver circuit which has two parts. One is signal of discontinuity ray and other is alarm circuit. When anybody passes through the ray, the main circuit sense the discontinuity and turn on the alarm circuit. The alarm will be ringing until the off button is pressed. There are two methods of ringing. One is the duration of ringing depends on preset timer and another is reset manually. Any option can be set by DPDT switch. We have used second method for our project. Mirrors are attached at every corner to reflect the ray coming from the laser. The reflected ray coming from the last mirror will hit the LDR directly. The system has built with low cost and high performance. The power consumption of the system is very low.

Activity of the Left Inferior Frontal Gyrus and the Error Detection Mechanism during Deception in Conditions of Different Monetary Benefit

Activity of the Left Inferior Frontal Gyrus and the Error Detection Mechanism during Deception in Conditions of Different Monetary Benefit

A Study to Investigate the Role and Challenges Associated with the Use of Deep Learning in Autonomous Vehicles

The application of deep learning in autonomous vehicles has surged over the years with advancements in technology. This research explores the integration of deep learning algorithms into autonomous vehicles (AVs), focusing on their role in perception, decision-making, localization, mapping, and navigation. It shows how deep learning, as a part of machine learning, mimics the human brain’s neural networks, enabling advancements in perception, decision-making, localization, mapping, and overall navigation. Techniques like convolutional neural networks are used for image detection and steering control, while deep learning is crucial for path planning, automated parking, and traffic maneuvering. Localization and mapping are essential for AVs’ navigation, with deep learning-based object detection mechanisms like Faster R-CNN and YOLO proving effective in real-time obstacle detection. Apart from the roles, this study also revealed that the integration of deep learning in AVs faces challenges such as dataset uncertainty, sensor challenges, and model training intricacies. However, these issues can be addressed through the increased standardization of sensors and real-life testing for model training, and advancements in model compression technologies can optimize the performance of deep learning in AVs. This study concludes that deep learning plays a crucial role in enhancing the safety and reliability of AV navigation. This study contributes to the ongoing discourse on the optimal integration of deep learning in AVs, aiming to foster their safety, reliability, and societal acceptance.

Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection

This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical–classical deep learning architectures within 6G environments.

Automatic assessment of nailfold capillaroscopy software: a pilot study

IntroductionCapillaroscopy is a simple method of nailfold capillary imaging, used to diagnose diseases from the systemic sclerosis spectrum. However, the assessment of the capillary image is time-consuming and subjective. This makes it difficult to use for a detailed comparison of studies assessed by various physicians. This pilot study aimed to validate software used for automatic capil­lary counting and image classification as normal or pathological.Material and methodsThe study was based on the assessment of 200 capillaroscopic images obtained from patients suffering from systemic sclerosis or scleroderma spectrum diseases and healthy people. Dinolite MEDL4N Pro was used to perform capillaroscopy. Each image was analysed manually and described using working software. The neural network was trained using the fast.ai library (based on PyTorch). The ResNet-34 deep residual neural network was chosen; 10-fold cross-validation with the validation and test set was performed, using the Darknet-YoloV3 state of the art neural network in a GPU-optimized (P5000 GPU) environment. For the calculation of 1 mm capillaries, an additional detection mechanism was designed.ResultsThe results obtained under neural network training were compared to the results obtained in manual analysis. The sensitivity of the automatic tool relative to manual assessment in classification of correct vs. pathological images was 89.0%, specificity 89.4% for the training group, in validation 89.0% and 86.9% respectively. For the average number of capillaries in 1 mm the precision of real images detected within the region of interest was 96.48%.ConclusionsThe pilot software for fully automatic capillaroscopic image assessment can be a useful tool for the rapid classification of a normal and altered capillaroscopy pattern. In addition, it allows one to quickly calculate the number of capillaries. In the future, the tool will be developed and will make it possible to obtain full imaging characteristics independent of the experience of the examiner.

Leveraging Machine Learning for Cybersecurity: Techniques, Challenges, and Future Directions

This study aims to explore the application of machine learning (ML) in enhancing cybersecurity measures, focusing specifically on intrusion detection, malware detection, fraud detection, and anomaly detection systems. A systematic review of 743 scholarly papers was conducted, from which 115 were selected for in-depth analysis. The review process involved evaluating advancements in ML techniques within the context of cybersecurity. The findings reveal that ML-driven systems significantly enhance the automation of security processes, improve the recognition of novel threats, and reduce human error in cyber threat management. However, challenges such as adversarial attacks and the need for high-quality model training pose significant barriers to the broader adoption of ML in cybersecurity. The study discusses the implications of these findings, emphasizing the necessity for developing robust and adaptive ML models that can withstand adversarial threats while improving integration across various cybersecurity applications. The insights gained from this research provide a comprehensive overview of the potential benefits and challenges of implementing ML in cybersecurity, highlighting the need for continuous innovation in threat detection mechanisms. This review acknowledges limitations such as the predominance of literature from Western contexts, potentially overlooking insights from other regions. Furthermore, the complexity of implementing ML systems in dynamic cyber environments remains a critical challenge. Future research should concentrate on refining ML algorithms to enhance resilience against adversarial threats, exploring the integration of emerging technologies for improved cybersecurity, and addressing gaps in the existing literature regarding the life cycle of ML models in real-world applications.

The development of carbon nanostructured biosensors for glucose detection to enhance healthcare services: a review

In this review, the forefront of biosensor development has been marked by a profound exploration of carbon nanostructured materials for the specific application of glucose detection. Moreover, this progressive line of inquiry capitalizes on the distinctive attributes of carbon nanostructured materials such as carbon nanotubes, carbon quantum dots, and graphene which exhibit unique characteristics in the development of biosensor engineering design. It also enhanced analytical performances regarding the limit of detection, selectivity, sensitivity, and reproducibility towards glucose detection in biological samples. Most importantly, the strategic integration of carbon nanostructured-based biosensor architectures has played a significant role in advancements, characterized by heightened sensitivity, exquisite selectivity, and augmented stability in glucose detection processes. Furthermore, utilizing these advanced materials has engendered a transformative impact on electrochemical properties, propelling the biosensors to achieve rapid and precise glucose-sensing capabilities. The confluence of carbon nanostructures with biosensor technology has not only elevated the scientific understanding of glucose detection mechanisms. Still, it has also paved the way for miniaturized and portable biosensors. This transformative shift holds great promise for the realization of point-of-care diagnostics, representing a pivotal step towards durability and efficient glucose monitoring in health/medical care. These advancements emphasize the crucial role of carbon nanostructured-based biosensors in opening the way to a new avenue of superiority and effectiveness in diabetes management. Conclusively, the challenges and, in a forward-looking stance, the prospective futures of glucose biosensors anchored on carbon nanostructured frameworks were considered.

AI and Machine Learning Solutions for Real-Time Fault-Tolerant System Design in Critical Applications

This study presents an innovative framework for implementing real-time fault-tolerant systems using artificial intelligence (AI) and machine learning (ML) to enhance reliability and resilience in critical applications. Addressing the needs of sectors such as aerospace, healthcare, automotive, and industrial automation, the proposed system integrates fault detection, isolation, and recovery mechanisms into a multi-layered architecture. Through the use of deep learning for accurate anomaly detection and reinforcement learning for rapid fault isolation, the system achieves high fault tolerance with minimal latency. The framework leverages edge computing for real-time data processing, ensuring timely responses to faults without excessive computational demands. Results from multiple case studies demonstrate significant improvements in fault detection accuracy, isolation speed, and recovery rates, affirming the framework’s adaptability and effectiveness in high-stakes environments. These findings highlight the potential of AI-driven fault-tolerant systems to elevate operational safety and reliability standards across diverse critical industries.

Fake News Detection Revisited: An Extensive Review of Theoretical Frameworks, Dataset Assessments, Model Constraints, and Forward-Looking Research Agendas

The emergence and acceptance of digital technology have caused information pollution and an infodemic on Online Social Networks (OSNs), blogs, and online websites. The malicious broadcast of illegal, objectionable and misleading content causes behavioural changes and social unrest, impacts economic growth and national security, and threatens users’ safety. The proliferation of AI-generated misleading content has further intensified the current situation. In the previous literature, state-of-the-art (SOTA) methods have been implemented for Fake News Detection (FND). However, the existing research lacks multidisciplinary considerations for FND based on theories on FN and OSN users. Theories’ analysis provides insights into effective and automated detection mechanisms for FN, and the intentions and causes behind wide-scale FN propagation. This review evaluates the available datasets, FND techniques, and approaches and their limitations. The novel contribution of this review is the analysis of the FND in linguistics, healthcare, communication, and other related fields. It also summarises the explicable methods for FN dissemination, identification and mitigation. The research identifies that the prediction performance of pre-trained transformer models provides fresh impetus for multilingual (even for resource-constrained languages), multidomain, and multimodal FND. Their limits and prediction capabilities must be harnessed further to combat FN. It is possible by large-sized, multidomain, multimodal, cross-lingual, multilingual, labelled and unlabelled dataset curation and implementation. SOTA Large Language Models (LLMs) are the innovation, and their strengths should be focused on and researched to combat FN, deepfakes, and AI-generated content on OSNs and online sources. The study highlights the significance of human cognitive abilities and the potential of AI in the domain of FND. Finally, we suggest promising future research directions for FND and mitigation.

Luminescent Multifunctional Nanomaterials: Capacitive Removal and Enhanced Detection Efficiency of Heavy Metals Ions for Advanced Water and Wastewater Treatment Application

AbstractIn recent years, heavy metal ions pollution in the industrialized environment of the societies threaten human health that flaunt ill‐sorted blueprints in freshwater resources obviously. The paradigm of designing luminescent multifunctional nanomaterials finds directions to the strategies of synthesizing cost‐effective, green, and versatile nanomaterials not only for detection, but also removal process of heavy metal ions in large scale applications. Among them, discovering the advances of luminescent multifunctional nanomaterials provides broad types of biomaterials, polymers and porous nanoparticles that grabs focal of investigations over the past several years due to their unique advantages such as enhanced detection efficiency with lowest limit of detection (LOD), minimum ions interference in versatile removal process, fast responsivity and selectivity as outstanding as unique physicochemical properties. This review paper tries to highlight the paradigm of principles for design, development, and utilization of luminescence nanomaterials for considering fundamental detection and removal mechanisms of heavy metal ions. In particular, these nanomaterials increase the remediation quality that are tackled in detail by focusing on opportunities and challenges in the field. Finally, design methods of these nanomaterials and concentrating on empowered detection and removal efficiency for heavy metals ions highlights novel prospective and strategies for largescale applications.

Numerical Simulation of Electromagnetic Nondestructive Testing Technology for Elasto–Plastic Deformation of Ferromagnetic Materials Based on Magneto–Mechanical Coupling Effect

A numerical tool for simulating the detection signals of electromagnetic nondestructive testing technology (ENDT) is of great significance for studying detection mechanisms and improving detection efficiency. However, the quantitative analysis methods for ENDT have not yet been sufficiently studied due to the absence of an effective constitutive model. This paper proposed a new magneto–mechanical model that can reflect the dependence of relative permeability on elasto–plastic deformation and proposed a finite element–infinite element coupling method that can replace the traditional finite element truncation boundary. The validity of the finite element–infinite element coupling method is verified by the experimental result of testing electromagnetic analysis methods using TEAM Problem 7. Then, the reliability and accuracy of the proposed model are verified by comparing the simulation results under elasto–plastic deformation with experimental results. This paper also investigates the effect of elasto–plastic deformation on the transient magnetic flux signal, a quantitative hyperbolic tangent model between Bzpp (peak–peak value of the normal component of magnetic flux signal) and elastic stress, and the exponential function relationship between Bzpp and plastic deformation is established. In addition, the difference and mechanism of a magnetic flux signal under elasto–plastic deformations are analyzed. The results reveal that the variation of the transient magnetic flux signal is mainly due to domain wall pinning, which is significantly affected by elasto–plastic deformation. The results of this paper are important for improving the accuracy of quantitative ENDT for elasto–plastic deformation.

Modified Metaheuristics Optimization for Cyberbullying Detection on Online Data Science Platform

Online harassment detection faces significant challenges due to its expansive reach and anonymity. Addressing this issue demands effective detection mechanisms capable of processing vast data streams and adapting to evolving online language. Leveraging advancements in artificial intelligence, we propose a novel approach grounded in natural language processing (NLP) and metaheuristic algorithms. Our methodology integrates term frequency-inverse document frequency (TF-IDF) encoding and the AdaBoost algorithm for classification. To tackle the NP-hard problem of hyperparameter selection, we introduce a modified crayfish optimization algorithm (COA), termed GI-COA. This paper represents a pioneering effort in utilizing metaheuristic algorithms for hyperparameter selection in harassment detection models. Through experimentation, we demonstrate the efficacy of our approach in fostering a safer online environment. The best performing optimize models demonstrate and accuracy exceeding 77%.

Design of a Duplex-to-Complex Structure-Switching Approach for the Homogeneous Determination of Marine Biotoxins in Water

In this report, we describe a fluorescent assay for the detection of six marine toxins in water. The mechanism of detection is based on a duplex-to-complex structure-switching approach. The six aptamers specific to the targeted cyanotoxins were conjugated to a fluorescent dye, carboxyfluorescein (FAM). In parallel, complementary DNA (cDNA) sequences specific to each aptamer were conjugated to a fluorescence quencher BHQ1. In the absence of the target, an aptamer–cDNA duplex structure is formed, and the fluorescence is quenched. By adding the toxin, the aptamer tends to bind to its target and releases the cDNA. The fluorescence intensity is consequently restored after the formation of the complex aptamer–toxin, where the fluorescence recovery is directly correlated with the analyte concentration. Based on this principle, a highly sensitive detection of the six marine toxins was achieved, with the limits of detection of 0.15, 0.06, 0.075, 0.027, 0.041, and 0.026 nM for microcystin-LR, anatoxin-α, saxitoxin, cylindrospermopsin, okadaic acid, and brevetoxin, respectively. Moreover, each aptameric assay showed a very good selectivity towards the other five marine toxins. Finally, the developed technique was applied for the detection of the six toxins in spiked water samples with excellent recoveries.

Fluorescence Detection and Inhibition Mechanisms of DNTPH on Aβ42 Oligomers Characterized as Products in the Four Stages of Aggregation.

Aβ42 aggregation was implicated in the pathogenesis of Alzheimer's disease (AD) without effective treatment available currently. Future efforts in clinical trials should instead focus on applying those antiamyloid treatment strategies to the preclinical stage and "the earlier, the better". How to identify and inhibit Aβ42 oligomers in the different stages of aggregation is therefore becoming the key to controlling primary aggregation and consequent AD development. Aggregation-induced emission probe DNTPH was demonstrated recently, enabling detection of amyloid at wavelengths up to 710 nm and exhibiting strong inhibitory effects on Aβ fibrosis at low dose. However, the detection and inhibition mechanisms of Aβ oligomers at various early stages of aggregation remain unknown. To this end, we built four different morphologies of Aβ42 pentamers characterized by products in monomeric aggregate (PM), primary nucleation (PP), secondary nucleation (PS), and fibril stages (PF) to explore the distinguishable ability and inhibition mechanisms of DNTPH with different concentrations upon binding. The results showcased that DNTPH does detect the four different Aβ42 oligomers with conspicuous fluorescence (λPM = 657 nm, λPP = 639 nm, λPS = 630 nm, and λPF = 648 nm) but fails to distinguish them, indicating that additional improvements are required further for the probe to achieve it. The inhibition mechanisms of DNTPH on the four Aβ42 aggregation are however of amazing differences. For PM and PP, aggregation was inhibited by altering the secondary structural composition, i.e., by decreasing the β-sheet and toxic turn (residues 22-23) probabilities, respectively. For PS, inhibition was achieved by segregating and keeping the two disordered monomeric species (PSM) away from the ordered secondary seed species (PSF) and consequently blocking further growth of the PSF seed. The inhibition mechanism for PS is first probed and proposed so far, as far as we know, and the corresponding aggregation stage of PS is the most important one among the four stages. The inhibition of PF was triggered by distorting the fibril chains, disrupting the ordered fibril surface for the contact of monomers. In addition, the optimal inhibitory concentrations of DNTPH for PM, PP, and PF were determined to be 1:3, while for PS, it was 1:5. This outcome offers a novel perspective for designing drugs targeting Aβ42 oligomers at different aggregation stages.

A Comprehensive Survey on Cyber-Physical System Security in the Internet of Things (IoT): Addressing and Solutions

Background: Cyber-Physical Systems (CPSs) integrate computing, control, and communication technologies, bridging cyberspace and the physical world to enhance critical infrastructure and safety-critical systems. Existing surveys often address CPS security from a single perspective, necessitating a more comprehensive approach. Methods: This paper presents a thorough review of CPS security from three perspectives: the physical domain, the cyber domain, and the cyber-physical domain. We examine attacks on physical components like sensors, cyber-attacks targeting CPSs, and integrated cyber-physical attacks. For each domain, we analyse corresponding detection and defence mechanisms. Results: Our review reveals that CPSs face significant security threats across all domains. In the physical domain, attacks on sensors can disrupt system operations, but various defences are available. In the cyber domain, CPSs are vulnerable to malware, hacking, and denial-of-service attacks, with several detection and defence strategies in place. The cyber-physical domain highlights complex threats where cyber-attacks cause physical damage, requiring comprehensive security approaches. Conclusion: By examining CPS security from multiple perspectives, this review provides a holistic understanding of current threats and defence mechanisms. It identifies future research directions to enhance CPS security, aiming to better protect critical infrastructure against evolving cyber threats.

Detecting and Analyzing Network Attacks: A Time-Series Analysis Using the Kitsune Dataset

Network security is a critical concern in today’s digital world, requiring efficient methods for the automatic detection and analysis of cyber attacks. This study uses the Kitsune Network Attack Dataset to explore network traffic behavior for IoT devices under various attack scenarios, including ARP MitM, SYN DoS, and Mirai Botnet. Utilizing Python-based data analysis tools, we preprocess and analyze millions of network packets to uncover patterns indicative of malicious activities. The study employs packet-level time-series analysis to visualize traffic patterns and detect anomalies specific to each attack type. Key findings include high packet volumes in attacks such as SSDP Flood and Mirai Botnet, with the Mirai Botnet attack involving multiple IP addresses and lasting over 2 hours. Notable attack-specific behaviors include high traffic on port -1 and targeted traffic on specific ports like 53195. The SYN DoS and Mirai Botnet attacks are characterized by their prolonged durations, suggesting significant disruption. Overall, the study highlights distinctive attack patterns and underscores the importance of understanding these characteristics to enhance detection and response mechanisms.

Design and experiment of a deviation information detection mechanism for sugar beet harvesters based on agricultural machinery and agronomy integration

To address large geometric shape differences, poor ridge distribution straightness and the uncertain spatial distribution of beet roots during the harvesting period, as well as damage caused by the inaccurate row correction detection mechanisms of combine harvesters, this study conducts a design analysis and performance tests of deviation information detection mechanisms based on the integration of agricultural machinery and agronomy. The agronomic process of mechanized sugar beet production was analyzed, the geometric dimensions of root tubers under typical planting patterns of typical varieties in main sugar beet production areas were measured, and a three-dimensional geometric model of beet root tubers was established. A device for measuring ridge shape and root tuber distribution was designed, and agronomic parameters during the harvesting period, such as plant spacing, ridge height, unearthed height, and deviation distance of beet root tubers, were measured and analyzed. A spatial model of ridge shape and beet root tuber growth distribution on the ridge during harvesting was established. The overall structure of the deviation information detection mechanism was analyzed and designed, and the structural forms and key parameters of key mechanisms, such as left‒right swing detection and up‒down floating profiling, were designed on the basis of agronomic parameter analysis. Using the missed detection rate as the evaluation index, field performance tests of the deviation information detection mechanism were conducted. The results revealed that when the average forward speed of the harvester was 0.84 m/s, the average missed detection rate of the deviation information detection mechanism was 0.56%. This method has high adaptability and detection accuracy and achieves a high level of integration for agricultural machinery (detection spatial region of the detection mechanism) and agronomy (beet root distribution spatial region). This study provides a technical basis and methodological reference for improving the quality and efficiency of automatic row correction combined with harvesting operations for crops such as sugar beets.

Riding Feeling Recognition Based on Multi-Head Self-Attention LSTM for Driverless Automobile

With the emergence of driverless technology, passenger ride comfort has become an issue of concern. In recent years, driving fatigue detection and braking sensation evaluation based on EEG signals have received more attention, and analyzing ride comfort using EEG signals is also a more intuitive method. However, it is still a challenge to find an effective method or model to evaluate passenger comfort. In this paper, we propose a long- and short-term memory network model based on a multiple self-attention mechanism for passenger comfort detection. By applying the multiple attention mechanism to the feature extraction process, more efficient classification results are obtained. The results show that the long- and short-term memory network using the multi-head self-attention mechanism is efficient in decision making along with higher classification accuracy. In conclusion, the classifier based on the multi-head attention mechanism proposed in this paper has excellent performance in EEG classification of different emotional states, and has a broad development prospect in brain-computer interaction.

Design and synthesis of naphthalimide-based CO fluorescent probes realizing tricolor detection and turn-on/off response

Carbon monoxide (CO) fluorescent probes have attracted tremendous attention attributed to their low detection limit, high specificity, simple operation and good biocompatibility. However, the relationship between the molecular structure and the fluorescence response signal still needs to be systematically elucidated for different applications. Herein, three different CO fluorescent probes based on naphthalimide derivative with blue emission (B-NIA), green emission (G-NIA), and near-infrared emission (R-NIA) were designed and synthesized. Three-primary colors and opposite turn-on or turn-off fluorescent responses to CO were achieved by regulating the conjugation system of fluorophores and the position of fluorescent recognition group. The change of fluorescence response and sensing mechanism for CO detection was studied by theory calculation and mass spectrometry analysis. All the B/G/R-NIA possessed high sensitivity for CO detection. Notably, the R-NIA emerged with a noticeable NIR fluorescence response to CO with a low detection limit (0.61 µM) and high selectivity and relative pH stability. In addition, the R-NIA showed particularly low cytotoxicity and has been successfully used to detect CO in living cells. These studies provided the theoretical reference and technical route to synthesize fluorescent probes with different emission wavelengths and opposite fluorescent responses to CO for various application scenarios