Sensor For Detection Research Articles

Deep tomography for the three-dimensional atmospheric turbulence wavefront aberration

Multiconjugate adaptive optics (MCAO) can overcome atmospheric anisoplanatism to achieve high-resolution imaging with a large field of view (FOV). Atmospheric tomography is the key technology for MCAO. The commonly used modal tomography approach reconstructs the three-dimensional atmospheric turbulence wavefront aberration based on the wavefront sensor (WFS) detection information from multiple guide star (GS) directions. However, the atmospheric tomography problem is severely ill-posed. The incomplete GS coverage in the FOV coupled with the WFS detection error significantly affects the reconstruction accuracy of the three-dimensional atmospheric turbulence wavefront aberration, leading to a nonuniform aberration detection precision over the whole FOV. We propose an efficient approach for achieving accurate atmospheric tomography to overcome the limitations of the traditional modal tomography approach. We employed a deep-learning-based approach to the tomographic reconstruction of the three-dimensional atmospheric turbulence wavefront aberration. We propose an atmospheric tomography residual network (AT-ResNet) that is specifically designed for this task, which can directly generate wavefronts of multiple turbulence layers based on the Shack-Hartmann (SH) WFS detection images from multiple GS directions. The AT-ResNet was trained under different turbulence intensity conditions to improve its generalization ability. We verified the performance of the proposed approach under different conditions and compared it with the traditional modal tomography approach. The well-trained AT-ResNet demonstrates a superior performance compared to the traditional modal tomography approach under different atmospheric turbulence intensities, various turbulence layer distributions, higher-order turbulence aberrations, detection noise, and reduced GSs conditions. The proposed approach effectively addresses the limitations of the modal tomography approach, leading to a notable improvement in the accuracy of atmospheric tomography. It achieves a highly uniform and high-precision wavefront reconstruction over the whole FOV. This study holds great significance for the development and application of the MCAO technology.

The development of sensitive graphene-based surface acoustic wave sensors for NO2 detection at room temperature

Bilayer graphene (Bl-Gr) and sulphur-doped graphene (S-Gr) have been integrated with LiTaO3 surface acustic wave (SAW) sensors to enhance the performance of NO2 detection at room temperature. The sensitivity of the Bl-Gr SAW sensors toward NO2, measured at room temperature, was 0.29º/ppm, with a limit of detection of 0.068 ppm. The S-Gr SAW sensors showed 0.19º/ppm sensitivity and a limit of detection of 0.140 ppm. The origin of these high sensitivities was attributed to the mass loading and elastic effects of the graphene-based sensing materials, with surface changes caused by the absorption of the NO2 molecules on the sensing films. Although there are no significant differences regarding the sensitivity and detection limit of the two types of sensors, the measurements in the presence of interferent gases and various humidity conditions outlined much better selectivity and sensing performances towards NO2 gas for the Bl-Gr SAW sensors.Graphical

Marine Infrastructure Detection with Satellite Data—A Review

A rapid development of marine infrastructures can be observed along the global coasts. Offshore wind farms, oil and gas platforms, artificial islands, aquaculture, and more, are being constructed without a proper quantification of these human activities. Therefore, effective monitoring is required to maintain transparency towards environmental standards, marine resource management, inventorying objects, and global security. This study reviews remote sensing-based approaches to offshore infrastructure detection over the past 12 years. We analyzed 89 studies from over 30 scientific journals, highlighting spatial and temporal trends, methodological approaches, and regional and thematic research foci. Our results show a significant increase in research interest, especially since 2019. Asia, and especially China, is the predominant focus region in terms of first authorship, funding, and areas of investigation. Aquaculture is the most studied infrastructure, followed by platforms, offshore wind farms, and artificial islands. Gaofen, Sentinel, and Landsat are the most used satellite sensors for detection. The apparent shift towards automated detection methods, especially Deep Learning algorithms, reflects advances in computer vision. This study highlights the key role of earth observation in the field of off-shore infrastructure detection, which can contribute towards outlining effective monitoring practices for marine activities, as well as highlighting important knowledge gaps.

Smart Wheel Chair using IOT

The microcontroller based wheelchair using android is a device designed to assist individuals with mobility impairments. The system utilizes an Android application installed on a smartphone to control the movement of the wheelchair. The smartphone communicates with a microcontroller via Bluetooth, which is responsible for processing the received commands and controlling the movement of the wheelchair's motors. The system includes obstacle detection sensors that detect any obstacles in the wheelchair's path and prevent collisions. The microcontroller based wheelchair using android is a device that combines modern technologies to help individuals with mobility impairments. It utilizes an Android application and a microcontroller to control the movement of the wheelchair. Here are some more details about this innovative device: Android Application: The Android application is installed on a smartphone and acts as a remote control for the wheelchair. The user can use the application to move the wheelchair forward, backward, left, and right. Additionally, the application provides the user with real-time feedback on the wheelchair's battery level, speed, and direction. Microcontroller: The microcontroller is responsible for processing the commands received from the smartphone and controlling the movement of the wheelchair's motors. The microcontroller is programmed to interface with the wheelchair's motors and control their speed and direction. Improving the quality of life for the elderly and disabled people and giving them the proper care at the right time is one the most important roles that are to be performed by us being a responsible member of the society. It's not easy for the disabled and elderly people to maneuver a mechanical wheelchair, which many of them normally use for locomotion. Hence there is a need for designing a wheelchair that is intelligent and provides easy maneuverability. In this context, an attempt has been made to propose a thought controlled wheelchair, which uses the captured signals from the brain and eyes and processes it to control the wheelchair. Electroencephalography (EEG) technique deploys an electrode cap that is placed on the user's scalp for the acquisition of the EEG signals which are captured and translated into movement commands by the arduino microcontroller which in turn move the wheelchair

Grooving and Absorption on Substrates to Reduce the Bulk Acoustic Wave for Surface Acoustic Wave Micro-Force Sensors

Improving measurement accuracy is the core issue with surface acoustic wave (SAW) micro-force sensors. An electrode transducer can stimulate not only the SAW but also the bulk acoustic wave (BAW). A portion of the BAW can be picked up by the receiving transducer, leading to an unwanted or spurious signal. This can harm the device’s frequency response characteristics, thereby potentially reducing the precision of the micro-force sensor’s measurements. This paper examines the influence of anisotropy on wave propagation, and it also performs a phase-matching analysis between interdigital transducers (IDTs) and bulk waves. Two solutions are shown to reduce the influence of BAW for SAW micro sensors, which are arranged with acoustic absorbers at the ends of the substrate and in grooving in the piezoelectric substrate. Three different types of sensors were manufactured, and the test results showed that the sidelobes of the SAW micro-force sensor could be effectively inhibited (3.32 dB), thereby enhancing the sensitivity and performance of sensor detection. The SAW micro-force sensor manufactured using the new process was tested and the following results were obtained: the center frequency was 59.83 MHz, the fractional bandwidth was 1.33%, the range was 0–1000 mN, the linearity was 1.02%, the hysteresis was 0.59%, the repeatability was 1.11%, and the accuracy was 1.34%.

A novel split PEC sensor based on magneto–optic nanostructure and photocurrent polarity switching strategy

BackgroundPhotoelectrochemical (PEC) sensors have attracted much attention due to their low cost, simple instrumentation and high sensitivity. However, conventional PEC sensors require layer–by–layer modification of the photoelectrode surface, which has the disadvantages of being time–consuming and unstable. In addition, complex interfering substances in real samples may lead to false–positive or false–negative detection results. It was thought that the above drawbacks could be eliminated by the construction of a polarity inversion PEC sensor. In this work, a magnetically separated PEC sensor was constructed for the detection of Carcinoembryonic antigen (CEA). ResultsDuring the experiment, the construction of the sensor was used for sensitive detection of CEA. In the experimental process, Fe3O4@SiO2@CdS, a semiconductor material with magnetic properties, was chosen as the substrate material, and ZnO/CuO was used as the marker on the DNA2 molecule, and a split magnetic separation PEC sensor was constructed, which was used to realize the sensitive detection of CEA. Eventually, the detection range of the sensor for CEA detection is 1–10000 pg/mL, with the detection limit of 0.34 pg/mL. Additionally, the PEC sensor has the advantages of high speed, high efficiency, high sensitivity, good specificity, and high stability. The sensing platform constructed in this work can also be extended to detect other targets, which provides a new idea for PEC sensing platforms. SignificanceIn this experiment, we developed a split PEC immunosensor based on magneto–optic nanostructure and photocurrent polarity switching strategy. Specifically, the proposed magnetic nanostructure Fe3O4@SiO2@CdS–DNA1 exhibits good paramagnetism and dispersion ability. By magnetic separation process, the PEC signals of opposite polarity can be obtained.

A Multimode Optical Sensor for Selective and Sensitive Detection of Harmful Heavy Metal Cr(VI) in Fresh Water and Sea Water.

Water pollution originating from heavy metals has shown great impacts on the ecological environment and human health due to their extremely low biodegradability. Hexavalent chromium Cr(VI), as one harmful heavy metal with strong oxidation, high biological permeability, and high carcinogenicity, is becoming an increasingly serious threat to human health. Therefore, conveniently but accurately, monitoring the Cr(VI) level in water to maintain its normal level and ensuring the stability of the ecosystem and human health become very valuable. However, most of these heavy metal sensors reported are turn-off type single-emission sensors. In this work, a ratiometric fluorescence/colorimetry/smartphone triple-mode turn-on optical sensor for Cr(VI) was developed based on a multifunctional metal-organic framework platform. The detection limits for these three mutual verification modes were only 1.28, 4.89, and 68.4 nM, respectively. Additionally, the color changes of the detection system under sunlight can also be observed directly by the naked eye. The accuracy and practicability of this multimode sensor were further proved by the detection of Cr(VI) in actual water and seawater samples, and the recovery rate ranged from 97.308 to 104.041%.

Raman spectroscopy‐based molecularly imprinted polymer sensor for sensitive detection of lysophosphatidic acid in serum

AbstractLysophosphatidic acid (LPA) has significant potential as a biomarker for diagnosis of ovarian cancer in female patients because it is present throughout all phases of the illness. Regrettably, there is not a clinically useful test for this biomarker yet. In this work, an ultra‐detection of LPA molecule by Raman spectroscopy‐based molecularly imprinted polymer (MIP) in serum samples is reported. β‐CD was synthesized to facilitate the collection of Raman spectroscopy and combined with MIP to denote it as β‐CD‐MIP for the adsorption of LPA with high sensitivity and selectivity. Linear regression model in serum samples shows peaks at 1100 and 1116 cm−1, respectively. A satisfactory linear fit was seen with LPA concentration log I = 3.7 log C + 1255.74 and with spiked serum log I = 4.6 log C + 327.22, respectively. The literature survey suggests the threshold LPA level in healthy person is 1.50 μM/L whereas the LPA level rises in patients with benign ovarian cancer tumor as 7.90 μM/L. This LPA level rises to 16.99 μM/L in ovarian cancer patients. On the basis of the band intensity, the limit of detection of the β‐CD‐MIP sensor for LPA was recorded as 0.003 μM/L. The binding constant of β‐CD‐MIP for LPA in serum was 0.031–0.003 μM/L, respectively. β‐CD‐MIP gel was optimized, characterized, and successfully applied on spiked serum samples. The findings of this research show that this substrate has a lot of potential for ultradetection of trace amounts of LPA molecule from the serum samples. The MIP sensor is robust, recyclable, efficient, and selective.

Design of a highly sensitive and precise long-range surface plasmon resonance sensor for early detection of malaria

This manuscript presents an innovative Kretschmann configured for Long Range Surface Plasmon Resonance (LRSPR) sensor for malaria detection. It utilizes tungsten diselenide (WSe2) as a bio+molecular recognition element (BRE) layer to attach target analyte. The optimized sensor design demonstrates exceptional performance in detecting different malaria stages (Schizont with refractive index (RI) = 1.371, Trophozoite with RI = 1.381, and Ring with RI = 1.396) at a 633 nm wavelength. In comparison to conventional surface plasmon resonance (CSPR), the LRSPR biosensor exhibits a substantial improvement, a 6.67 to 10 times increase in detection accuracy (DA), an impressive 40 to 86.55 times enhancement in imaging figure of merit (IFOM), and a 6.0 to 8.66 times boost in imaging sensitivity (Simg.) for the sequential detection of different stages of malaria. COMSOL Multiphysics simulations highlight a deeper penetration depth (PD) of 258.71 nm, 276.85 nm, and 373.04 nm for the detection of Schizont, Trophozoite, and Ring stages, respectively.

One-step growth of Cu-doped carbon dots in amino-modified carbon nanotube-modified electrodes for sensitive electrochemical detection of BPA.

Copper-doped carbon dots and aminated carbon nanotubes (Cu-CDs/NH2-CNTs) nanocomposites were synthesized by a one-step growth method, and the composites were characterized for their performance. An electrochemical sensor for sensitive detection of bisphenol A (BPA) was developed for using Cu-CDs/NH2-CNTs nanocomposites modified with glassy carbon electrodes (GCE). The sensor exhibited an excellent electrochemical response to BPA in 0.2M PBS (pH 7.0) under optimally selected conditions. The linear range of the sensor for BPA detection was 0.5-160μM, and the detection limit (S/N = 3) was 0.13μM. Moreover, the sensor has good interference immunity, stability and reproducibility. In addition, the feasibility of the practical application of the sensor was demonstrated by the detection of BPA in bottled drinking water and Liu Yang River water.

MIM waveguide based Nano refractive Index Sensor for Hemoglobin and Glucose Concentration Detection in Human Body

Abstract In this study, a nanoscale refractive index sensor structure is proposed, which is realised using a metal-insulator-metal (MIM) waveguide and a grooved circular ring with double disk cavity (GRDD) structure coupled to each other. The finite element method (FEM) was used to analyze and investigate the effects of the variations of each structural parameter on the transmittance spectra and the comprehensive performance of the sensor. Based on the simulation results, the optimum sensitivity parameter of the sensor structure is 2800 nm/RIU with a figure of merit (FOM) value of 51.9 RIU-1. The sensor structure is capable of being used in biomedical field with sensitivities of 3.895 nm/gL^(-1), 4.870 nm/gL^(-1), and 4.955 nm/gL^(-1), respectively, for detecting hemoglobin of blood types A, B, and O, and 4.48 nm/gL^(-1), for detecting glucose concentration.

Research of flexible sensor based on nanomaterial

A flexible sensor is a sensor made of flexible materials, generally composed of three layers: base layer, sensing layer, and packaging layer, can be widely used in medical electronics, biological monitoring, wearable electronics and other fields. Compared with traditional electronic devices, flexible electronics have cheap substrates and can be attached to curved surfaces to achieve sensing functions, which attract researchers in different fields. In recent years, the development of nanotechnology and polymer materials has greatly promoted the innovation of flexible electronics. This paper focuses on the application of nanotechnology in flexible sensing. Through horizontal induction and analysis of nanotechnologies in polymer -based sensors, hydrogel sensors, self-powered sensors and other sensors, suggestions for continuous detection and clinical treatment of sensors are put forward vertically. Achieving the structure and intrinsic flexibility of the flexible sensor substrate, deposition of the micro-circuit onto the substrate, encapsulation without breaking the ductility condition, maximizing its conductive, thermal and mechanical response, self-powered, degradability and other properties that meet human needs are all the focus of the flexible electronics discussion. In this paper, the structure, response and drug delivery of the flexible sensor will be analyzed and discussed from the Angle of material.

A polylactic acid-carbon nanofiber-based electro-conductive sensing material and paper-based colorimetric sensor for detection of nitrates.

Plastics are ubiquitous in today's lifestyle, and their indiscriminate use has led to the accumulation of plastic waste in landfills and oceans. The waste accumulates and breaks into micro-particles that enter the food chain, causing severe threats to human health, wildlife, and the ecosystem. Environment-friendly and bio-based degradable materials offer a sustainable alternative to the vastly used synthetic materials. Here, a polylactic acid and carbon nanofiber-based membrane and a paper-based colorimetric sensor have been developed. The membrane had a surface area of 3.02 m2 g-1 and a pore size of 18.77 nm. The pores were evenly distributed with a pore volume of 0.0137 cm3 g-1. The membrane was evaluated in accordance with OECD guidelines and was found to be safe for tested aquatic and terrestrial models. The activated PLA-CNF membrane was further used as a bio-based electrode for the electrochemical detection of nitrates (NO3-) in water samples with a detection limit of 0.046 ppm and sensitivity of 1.69 × 10-4 A ppm-1 mm-2, whereas the developed paper-based colorimetric sensor had a detection limit of 156 ppm for NO3-. This study presents an environment-friendly, low-carbon footprint disposable material for sensing applications as a sustainable alternative to plastics.

Liquid-Phase Exfoliated Graphene and Polytetrafluoroethylene for Highly Durable and Reusable Chemical Leak Detection Sensors

Graphene-based chemical sensors hold promise across diverse applications owing to their exceptional sensitivity and selectivity. However, achieving their long-term durability and reusability while preserving high sensitivity remains a significant challenge, particularly in harsh environments where exposure to strong chemicals is inevitable. This paper presents a novel approach to address this challenge by synergistically integrating liquid-phase exfoliated graphene (LPEG) with polytetrafluoroethylene (PTFE) within a single sensing strip. Through a comprehensive experimental investigation, we demonstrate the fabrication of highly durable and reusable chemical leak detection sensors by combining LPEG and PTFE. Furthermore, we explore the sensing mechanism, highlighting the roles of LPEG and PTFE in enhancing sensitivity and selectivity, along with durability and reusability. Performance evaluation reveals the sensors’ robustness against mechanical and chemical degradation, coupled with excellent recyclability. This innovative approach holds promise for applications in environmental monitoring, industrial safety, and healthcare, thus advancing the field of graphene-based chemical leak detection sensors.

Fault Detection and Interactive Multiple Models Optimization Algorithm Based on Factor Graph Navigation System

Accurate and stable positioning is significant for vehicle navigation systems, especially in complex urban environments. However, urban canyons and dynamic interference make vehicle sensors prone to disturbance, leading to vehicle positioning errors and even failures. To address these issues, an adaptive loosely coupled IMU/GNSS/LiDAR integrated navigation system based on factor graph optimization with sensor weight optimization and fault detection is proposed. First, the factor nodes and system framework are constructed based on error models of sensors, and the optimization method principle is derived. Second, the interactive multiple-model algorithm based on factor graph optimization (IMMFGO) is utilized to calculate and adjust sensor weights for global optimization, which will reduce the impact of disturbed sensors. Finally, a multi-stage fault detection, isolation, and recovery (MSFDIR) strategy is implemented based on the IMMFGO results and IMU pre-integration measurements, which can detect significant sensor faults and optimize the system structure. Vehicle experiments show that our IMMFGO method generally obtains better performance in positioning accuracy by 23.7% compared to adaptive factor graph optimization (AFGO) methods, and the MSFDIR strategy possesses the capability of fault sensor detection, which provides an essential reference for multi-source vehicle navigation systems in urban canyons.

An R-type hexagonal ferrite nanoparticles-based electrochemical sensor for Levofloxacin detection in pharmaceutical and clinical specimens

The usage of Levofloxacin (LEV) has increased in recent years for the treatment of bacterial infections in both human and veterinary fields. In this context, there has been a significant demand for the development of a highly sensitive and cost-effective approach to LEV quantification. In this study, R-type hexagonal ferrite nanoparticles (SrSn2Fe4O11-NPs) were prepared by an auto-ignition methodology and various analytical techniques were used for the materiaĺs characterization, including X-ray diffraction (XRD), Field emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS), Brunauer Emmett and Teller (BET) analysis, dynamic light scattering (DLS), and vibrating sample magnetometer (VSM) analysis. The characterization confirmed that the prepared material has a crystalline structure single-phase with a crystalline size of 35.02 nm. The R-type hexagonal ferrite nanoparticles were immobilized on a glassy carbon electrode (GCE) by a simple drop-casting approach to developing an efficient electrochemical sensor (SrSn2Fe4O11-NPs/GCE) for sensitive and selective LEV detection through an extended concentration range (0.06 × 10−6 to 170 × 10−6 M) and a low detection limit of (41.5nM). The developed sensor was applied successfully to quantitatively determine LEV in clinical samples and pharmaceutical preparations with excellent recoveries from 95.2 to 102.5 %.

A new electrochemical biosensor based on graphene oxide for rapid detection of synthetic testosterone as performance-enhancing drugs in athletes

This study showcases the successful development of a hybrid reduced graphene oxide-metal organic framework modified glassy carbon electrode (rGO-MOFs/GCE) electrochemical biosensor for the detection of synthetic testosterone (TST) as a performance-enhancing drug in athletes. The results demonstrate a linear relationship between the voltametric responses and the testosterone concentration, indicating the sensor's effectiveness in measuring TST. Furthermore, the sensor's performance in detecting TST in real human serum samples from athletes was also validated. The sensor's linear range and detection limit are comparable, if not superior, to those of other recently developed electrochemical sensors, positioning it as a promising tool for TST detection. Additionally, the method's selectivity was assessed and found to be unaffected by substances typically present in biological fluids and food samples, further affirming the sensor's reliability. The application of the rGO/MOF/GCE sensor to detecting and quantifying TST in actual human serum samples yielded favorable results. The synergistic effects of rGO and MOF, which improve the electrical conductivity and biocompatibility of MOF and the porosity and catalytic activity of rGO, are responsible for the sensor's enhanced performance in terms of sensitivity, selectivity, stability, and response time. These findings underscore the potential of the rGO/MOF/GCE sensor platform for various biomedical applications.

An antifouling electrochemical biosensor using self-signal for Salmonella typhimurium direct detection in food sample

Eating food contaminated by foodborne pathogens can lead to illness. The development of electrochemical sensors for pathogen detection has received widespread attention. However, the analytical performance of electrochemical sensors is inevitably affected by the non-specific adsorption of molecules in the sample. Moreover, the external signal probes might be affected by the complex components in the sample accompanied with signal suppression. This work presents an electrochemical aptasensor for Salmonella typhimurium detection based on the self-signal of poly-xanthurenic acid and the antifouling ability of chondroitin sulfate. The detection time was 60 min. The linear range was from 101 to 107 CFU/mL, and the detection limit was 3 CFU/mL. The biosensors presented good repeatability and storage stability. And the biosensors has been successfully applied in milk and orange juice. This strategy is expected to be applied in the design of other antifouling biosensors, to achieve rapid detection of pathogens and ensure food safety.

Prototyping a wearable and stretchable graphene-on-PDMS sensor for strain detection on human body physiological and joint movements.

In the era of wearable electronic devices, which are quite popular nowadays, our research is focused on flexible as well as stretchable strain sensors, which are gaining humongous popularity because of recent advances in nanocomposites and their microstructures. Sensors that are stretchable and flexible based on graphene can be a prospective 'gateway' over the considerable biomedical speciality. The scientific community still faces a great problem in developing versatile and user-friendly graphene-based wearable strain sensors that satisfy the prerequisites of susceptible, ample range of sensing, and recoverable structural deformations. In this paper, we report the fabrication, development, detailed experimental analysis and electronic interfacing of a robust but simple PDMS/graphene/PDMS (PGP) multilayer strain sensor by drop casting conductive graphene ink as the sensing material onto a PDMS substrate. Electrochemical exfoliation of graphite leads to the production of abundant, fast and economical graphene. The PGP sensor selective to strain has a broad strain range of ⁓60%, with a maximum gauge factor of 850, detection of human physiological motion and personalized health monitoring, and the versatility to detect stretching with great sensitivity, recovery and repeatability. Additionally, recoverable structural deformation is demonstrated by the PGP strain sensors, and the sensor response is quite rapid for various ranges of frequency disturbances. The structural designation of graphene's overlap and crack structure is responsible for the resistance variations that give rise to the remarkable strain detection properties of this sensor. The comprehensive detection of resistance change resulting from different human body joints and physiological movements demonstrates that the PGP strain sensor is an effective choice for advanced biomedical and therapeutic electronic device utility.

Hydrothermal Synthesis of Tartaric Acid Functionalized Amino Acid CQD for Sensing of Hg2+ and Fe3+ Ions in Aqueous Medium

AbstractCarbon quantum dots (CQDs) are a type of carbon‐based nanoparticles that are typically smaller than 10 nanometres in size. In this study, L‐tryptophan‐mediated CQDs were synthesized using the hydrothermal carbonization (HTC) technique, which is known for its non‐toxic, environmentally friendly, and cost‐effective nature. The synthesized carbon quntum dot was successfully characterized. The HR‐TEM analysis clearly revealed the formation of specific aggregates with a size distribution ranging from 3.0 to 5.1 nm. These L‐tryptophan‐mediated CQDs exhibited remarkable emission behaviour, displaying strong and stable blue fluorescence. Moreover, they were utilized as a sensor for the sensitive detection of Fe3+ and Hg2+ ions in an aqueous solution, employing a fluorescence quenching mechanism. The limit of detection for Fe3+ sensing was determined to be 1.2×10−5 M, while for Hg2+ sensing, it was found to be 1.9×10−5 M. The sensing of Fe3+ and Hg2+ ions was also confirmed through visual eye detection, as a significant color change of the CQDs was observed. Additionally, a competitive study was conducted to verify the selectivity of the L‐tryptophan‐mediated CQDs towards Fe3+ and Hg2+ metal ions.